Digital Pricing: A Guide to Strategic Pricing for the Digital Economy 3031245903, 9783031245909

Table of contents :
Aim of this book
Contents
About the Author
List of Figures
1: Basics of Price Management
1.1 Digitization and Pricing
The scope of the pricing logic in this book
1.2 Price as a Profit Driver
Case study 1: Price effect
1.3 Specific Characteristics of the Price
1.4 The Price-Volume Function as an Indicator of Customer Response
Practical tip
1.5 Market Dynamics and Price Elasticity
Practical tip
1.6 Price Actions, Elasticities, and Profit Effects
As an interim conclusion
Case study 2: Price effect
Practical example: Price effect
1.7 Practical Examples: Market Dynamics in Modern Industries
Case study 1: High demand effect of price cuts
Case study 2: Positive profit effect of price premia
Case study 3: Increasing price elasticity through digitization and price transparency
Case Study 4: Increasing price acceptance through technical advantage and innovation
References
2: Characteristics of Digital Pricing
2.1 Characteristics of Digital Offerings in Terms of Pricing
2.2 General Conditions and Price-Related Characteristics of the Internet
2.3 Stages of Development of Digitization
2.4 Digitization and Competitive Dynamics
References
3: Business Models
3.1 Business Models as the Starting Point for Digital Pricing
Practical Example: Disney Theme Parks
3.2 Digital Business Models
Case Study Music Streaming: Spotify
Practical Examples: Marketplaces and Platforms
Example: Cell Phones
3.3 Value Creation Through Data and Data-Driven Business Models
Practical Example 1: Data-Driven Business Models
Practical Example 2: Data-Driven Business Models
Practical Example 3: Data-Driven Business Models
3.4 The Three-Level Model of Digital Pricing
Practical Example: Modern Monetization Models
Practical example 1: Hitachi (B2B)
Practical example 2: AVE (B2C)
Practical example 3: Amazon (B2C)
Practical example 4: Google (B2C)
Practical Example: Amazon (B2C)
3.5 Method Tip: Business Model Map
Case Study: Resources as a Starting Point for the Business Model Definition
Project Outline for the Business Model Map
References
4: Revenue Models
4.1 Delineation: Revenue Models
Practical Example 1: Google Waymo
Practical Example 2: Automobile Manufacturer
Practical Example 3: Amazon
Practical Example 4: Google
Case Study Music Streaming: Spotify
4.2 Services and Revenue Sources on the Internet
Case Study: Revenue Sources for Smartphones
Case Study: Revenue Sources for Games
Case Study: Revenue Sources for Films
Case Study: Apple Revenue Sources
4.3 Overview of Selected Revenue Models
Case Study Music Streaming: Spotify
References
5: Pricing Process Part 1: Analysis
5.1 Introduction to the Pricing Process
5.2 Determinants of Pricing
5.3 Costs
Case Study MediaMarktSaturn
5.4 Competition
5.5 Customers
References
6: Pricing Process Part 2: Strategy
6.1 From Corporate and Competitive Strategy to Pricing Strategy
6.2 Dimensions of the Pricing Strategy
6.3 Pricing Targets
Method Tip: Target Prioritization
Case Studies: Amazon ebooks and Apple iPhone
6.4 Competitive Strategies
Case Studies
Apple Case Study
6.5 Strategic Segmentation and Positioning
Case Studies
Implementation Tip
6.6 Competitive Advantage Matrix
Case Study Praktiker
Case Study Loewe
6.7 Strategic Behavior in Competition
Case Study Retail Germany
References
7: Pricing Process Part 3: Structure (3a: Price Differentiation)
7.1 Basics of Price Differentiation
7.2 Variants of Price Differentiation
7.2.1 Price Differentiation According to Market Segments
Project Outline Pedelecs
Case Study: Price Differentiation for Advertising Customers on the Internet
Case Study Media-Saturn Retail Group
Case Study Boss
7.2.2 Quantity-Based Price Differentiation
Case Study Quantity-Based Price Differentiation
7.2.3 Price Differentiation According to Products
7.2.3.1 Price Bundling
Case Study: Microsoft Office Package
Case Study ``Customized Bundling´´
Case Studies Product Type
Method Tip
7.2.3.2 Performance-Related Price Differentiation
Case Study Netflix
7.3 Prerequisites for Price Differentiation Concepts
References
8: Pricing Process Part 3: Structure (3b: Price Models)
8.1 Delimitation and Definition: Price Models
8.2 The Six Pillars of a Price Model
Case Studies
Case Study Aladoo
8.3 Reference Bases in Detail
8.3.1 Usage-Independent Reference Bases
Case Study Telecommunications
8.3.2 Usage-Dependent Reference Bases
8.3.3 Value-Dependent Reference Bases
Case Studies: Outcome-Based Price Models (Cf. Figure 8.5)
8.4 Price Metrics in Detail
8.5 Competitive Strategies and Price Models
8.6 Methodical Innovation: Concept for Optimizing Price Models
8.7 Success Criteria of Price Models
Example 1: Software (SAP)
Example 2: DVD Rental (Netflix)
Example 3: Occupational Disability Insurance (WWK)
References
9: Pricing Process Part 3: Structure (3c: Price Optimization)
9.1 Methods for Determining the Optimum Price
9.1.1 Observation
9.1.1.1 Price Experiments
Case Study Offer Configurator
9.1.1.2 Econometric Analysis of Market Data
9.1.1.3 Voice of Consumer Analytics (``Social Listening´´)
9.1.1.4 A/B Testing
9.1.1.5 Online Auctions
9.1.2 Survey
9.1.2.1 Direct Price Query
9.1.2.2 Open Line Pricing
9.1.2.3 Gabor-Granger Method
9.1.2.4 Price-Sensitivity-Meter
9.1.2.5 Conjoint Measurement
Excursus: Comparison of Methods (Conjoint Measurement Vs. Price Experiments)
9.1.3 Workshops
9.1.3.1 Focus Group Interviews
9.1.3.2 Expert Estimation
9.1.4 Excursus: Impact of Artificial Intelligence on Price Optimization Methods
9.2 Calculation of the Profit-Optimal Price
Case Study: Automatic Price Optimization Via a Repricing Tool
9.3 Simulation Analyses for Product and Price Optimization
9.4 Method Innovation: Value-Price Optimization
9.4.1 Value-Price Optimization: Philosophy and Fundamentals
9.4.2 Methodological Steps of Value-Price Optimization at a Glance
Practical Tip
Method Tip: Analysis of Value Drivers
9.4.3 Summary of the Value Driver Analysis
9.5 Pricing Strategies for New Products
Case Studies Penetration Strategy
Case Studies Skimming Strategy
References
10: Pricing Process Part 3: Structure (3d: Portfolio Pricing)
10.1 Challenges of Portfolio Pricing
Apple Case Study
Practical Example: Long-tail Business Model and Price Structure
10.2 Methodical Derivation of Price Structures
10.3 Project Outline: Product Line Pricing for Information Goods
10.4 Method Innovation: Analysis and Steering of Price Elasticity
10.5 Implementation Tip: Price-Related Measures to Influence Price Elasticity
10.6 Dynamic Pricing
10.6.1 Definition of Dynamic Pricing
10.6.2 History and Development
Case Study Gas Stations
10.6.3 Delineation: Dynamic Pricing Versus Revenue Management
Case Study Air Traffic
10.6.4 Requirements and Fields of Application
10.6.5 Objective of Dynamic Pricing
10.6.6 Technical Variants and Forms of Dynamic Pricing
10.6.7 Personalized Dynamic Pricing
10.6.8 Dynamic Pricing Case Studies
10.6.9 Risk Factors in Dynamic Pricing
References
11: Pricing Process Part 4: Implementation
11.1 Introduction: Condition System and Sales Management
11.2 Basics of the Condition System
11.3 Performance-Oriented Condition Systems
11.4 Target Price Systems and Peer Pricing
11.5 Price Enforcement
11.5.1 Value Selling
11.5.2 Total Cost of Ownership Approach
11.5.3 Total Value of Ownership Bonus System
11.6 E-Bidding
11.7 Incentive System
11.8 Tactical Pricing
References
12: Pricing Process Part 5: Monitoring
12.1 Price Monitoring: Challenges
12.2 Pricing Cockpit
12.2.1 Financial Monitoring
12.2.2 Monitoring Market Effects
Case Study of User-oriented Monitoring: Amazon and Netflix
Case Studies: Target Prioritization in Selected Industries
Method Tip: Integration of Financial and Market Objectives
Case Study Pricing Power: Sea Freight
12.2.3 Monitoring Pricing Excellence
References
13: Pricing Process and Pricing Psychology
13.1 Focus Topic Pricing Process and Pricing Psychology: Introduction
13.2 Price Psychology and Structure (1): Mental Accounting
13.3 Price Psychology and Structure (2): Price Level Effect
13.4 Price Psychology and Structure (3): Anchoring
13.5 Price Psychology and Structure (4): Value Effect
13.6 Price Psychology and Structure (5): Price Threshold Effect
13.7 Price Psychology and Structure (6): Compromise Effect
13.8 Price Psychology and Structure (7): Decoy Effect
13.9 Price Psychology and Structure (8): Price Figure Communication
13.10 Price Psychology and Implementation (1): Tiered Discounts
13.11 Price Psychology and Implementation (2): Endowment Effect
13.12 Price Psychology and Implementation (3): Loss Aversion
References

Citation preview

Management for Professionals

Frank Frohmann

Digital Pricing

A Guide to Strategic Pricing for the Digital Economy

Management for Professionals

The Springer series Management for Professionals comprises high-level business and management books for executives, MBA students, and practice-oriented business researchers. The topics span all themes of relevance for businesses and the business ecosystem. The authors are experienced business professionals and renowned professors who combine scientific backgrounds, best practices, and entrepreneurial vision to provide powerful insights into how to achieve business excellence.

Frank Frohmann

Digital Pricing A Guide to Strategic Pricing for the Digital Economy

Frank Frohmann Rüdesheim on the Rhine, Germany

ISSN 2192-810X (electronic) ISSN 2192-8096 Management for Professionals ISBN 978-3-031-24590-9 ISBN 978-3-031-24591-6 (eBook) https://doi.org/10.1007/978-3-031-24591-6 Original German edition published by Springer Fachmedien Wiesbaden GmbH, Wiesbaden, Germany, 2018 # Springer Nature Switzerland AG 2023 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Aim of this book

The digitalization of business life has an enormous impact on price management in all industries and for all company offerings. However, important entrepreneurial decisions precede the setting of prices: first, the definition of revenue sources (the revenue model) and, second, the definition of the value to customer as the central pillar of the overarching business model. Professional price management must go beyond the pure optimization of the pricing process and also reflect the higher-level decisions on the business model and the revenue model. I was the first author to demonstrate this with the German edition of the book Digitales Pricing (2018). The work has achieved an outstanding resonance in science and corporate practice. Digital business and revenue models have evolved in all sectors in recent years as a result of technological changes. This has resulted in an expansion of the challenges in the pricing process (including price model design and dynamic pricing). This required further development of the entire work, including the methodological innovations of my book. The methodological innovations include: • • • •

Methodical linking of business model, revenue model, and pricing process Definition of “revenue model” and logical link with pricing models 11 C approach to describe the determinants of pricing Methodical integration of “price optimization” and “price-performance positioning” • Comprehensive definition of “pricing model” (6 pillars) and decision-supportlogic to derive price models • Integration of pricing psychology and pricing process. This book is based on almost 30 years of practical experience in strategy, marketing, sales, and price management across all industries. It addresses executives and entrepreneurs, pricing experts in companies, and academics and students.

v

Contents

1

2

3

4

Basics of Price Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1 Digitization and Pricing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Price as a Profit Driver . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3 Specific Characteristics of the Price . . . . . . . . . . . . . . . . . . . . 1.4 The Price–Volume Function as an Indicator of Customer Response . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5 Market Dynamics and Price Elasticity . . . . . . . . . . . . . . . . . . . 1.6 Price Actions, Elasticities, and Profit Effects . . . . . . . . . . . . . . 1.7 Practical Examples: Market Dynamics in Modern Industries . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Characteristics of Digital Pricing . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Characteristics of Digital Offerings in Terms of Pricing . . . . . . 2.2 General Conditions and Price-Related Characteristics of the Internet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 Stages of Development of Digitization . . . . . . . . . . . . . . . . . . 2.4 Digitization and Competitive Dynamics . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1 1 4 6 10 12 15 20 22 27 27 29 35 41 44

Business Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Business Models as the Starting Point for Digital Pricing . . . . . 3.2 Digital Business Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 Value Creation Through Data and Data-Driven Business Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4 The Three-Level Model of Digital Pricing . . . . . . . . . . . . . . . . 3.5 Method Tip: Business Model Map . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

49 49 52

Revenue Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1 Delineation: Revenue Models . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Services and Revenue Sources on the Internet . . . . . . . . . . . . . 4.3 Overview of Selected Revenue Models . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

83 83 86 93 98

62 68 73 79

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Contents

5

Pricing Process Part 1: Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1 Introduction to the Pricing Process . . . . . . . . . . . . . . . . . . . . . 5.2 Determinants of Pricing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3 Costs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4 Competition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.5 Customers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

103 103 104 107 110 113 114

6

Pricing Process Part 2: Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1 From Corporate and Competitive Strategy to Pricing Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2 Dimensions of the Pricing Strategy . . . . . . . . . . . . . . . . . . . . . 6.3 Pricing Targets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4 Competitive Strategies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.5 Strategic Segmentation and Positioning . . . . . . . . . . . . . . . . . 6.6 Competitive Advantage Matrix . . . . . . . . . . . . . . . . . . . . . . . 6.7 Strategic Behavior in Competition . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

117 117 118 120 123 129 136 139 144

7

Pricing Process Part 3: Structure (3a: Price Differentiation) . . . . . . 7.1 Basics of Price Differentiation . . . . . . . . . . . . . . . . . . . . . . . . 7.2 Variants of Price Differentiation . . . . . . . . . . . . . . . . . . . . . . . 7.2.1 Price Differentiation According to Market Segments . . 7.2.2 Quantity-Based Price Differentiation . . . . . . . . . . . . . 7.2.3 Price Differentiation According to Products . . . . . . . . 7.3 Prerequisites for Price Differentiation Concepts . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

149 149 151 151 158 161 172 175

8

Pricing Process Part 3: Structure (3b: Price Models) . . . . . . . . . . . . 8.1 Delimitation and Definition: Price Models . . . . . . . . . . . . . . . 8.2 The Six Pillars of a Price Model . . . . . . . . . . . . . . . . . . . . . . . 8.3 Reference Bases in Detail . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.3.1 Usage-Independent Reference Bases . . . . . . . . . . . . . 8.3.2 Usage-Dependent Reference Bases . . . . . . . . . . . . . . 8.3.3 Value-Dependent Reference Bases . . . . . . . . . . . . . . . 8.4 Price Metrics in Detail . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.5 Competitive Strategies and Price Models . . . . . . . . . . . . . . . . 8.6 Methodical Innovation: Concept for Optimizing Price Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.7 Success Criteria of Price Models . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

179 179 182 189 189 195 197 203 206

Pricing Process Part 3: Structure (3c: Price Optimization) . . . . . . . 9.1 Methods for Determining the Optimum Price . . . . . . . . . . . . . 9.1.1 Observation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.1.2 Survey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

217 217 218 222

9

208 210 213

Contents

ix

9.1.3 9.1.4

Workshops . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Excursus: Impact of Artificial Intelligence on Price Optimization Methods . . . . . . . . . . . . . . . . . . . . . . . 9.2 Calculation of the Profit-Optimal Price . . . . . . . . . . . . . . . . . . 9.3 Simulation Analyses for Product and Price Optimization . . . . . 9.4 Method Innovation: Value-Price Optimization . . . . . . . . . . . . . 9.4.1 Value-Price Optimization: Philosophy and Fundamentals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.4.2 Methodological Steps of Value-Price Optimization at a Glance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.4.3 Summary of the Value Driver Analysis . . . . . . . . . . . 9.5 Pricing Strategies for New Products . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

11

229 232 233 237 243 243 245 251 253 258

Pricing Process Part 3: Structure (3d: Portfolio Pricing) . . . . . . . . . 10.1 Challenges of Portfolio Pricing . . . . . . . . . . . . . . . . . . . . . . . . 10.2 Methodical Derivation of Price Structures . . . . . . . . . . . . . . . . 10.3 Project Outline: Product Line Pricing for Information Goods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.4 Method Innovation: Analysis and Steering of Price Elasticity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.5 Implementation Tip: Price-Related Measures to Influence Price Elasticity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.6 Dynamic Pricing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.6.1 Definition of Dynamic Pricing . . . . . . . . . . . . . . . . . . 10.6.2 History and Development . . . . . . . . . . . . . . . . . . . . . 10.6.3 Delineation: Dynamic Pricing Versus Revenue Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.6.4 Requirements and Fields of Application . . . . . . . . . . . 10.6.5 Objective of Dynamic Pricing . . . . . . . . . . . . . . . . . . 10.6.6 Technical Variants and Forms of Dynamic Pricing . . . 10.6.7 Personalized Dynamic Pricing . . . . . . . . . . . . . . . . . . 10.6.8 Dynamic Pricing Case Studies . . . . . . . . . . . . . . . . . . 10.6.9 Risk Factors in Dynamic Pricing . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

263 263 269

279 280 280 281 282 283 283 285

Pricing Process Part 4: Implementation . . . . . . . . . . . . . . . . . . . . . 11.1 Introduction: Condition System and Sales Management . . . . . . 11.2 Basics of the Condition System . . . . . . . . . . . . . . . . . . . . . . . 11.3 Performance-Oriented Condition Systems . . . . . . . . . . . . . . . . 11.4 Target Price Systems and Peer Pricing . . . . . . . . . . . . . . . . . . 11.5 Price Enforcement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.5.1 Value Selling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.5.2 Total Cost of Ownership Approach . . . . . . . . . . . . . . 11.5.3 Total Value of Ownership Bonus System . . . . . . . . . .

287 287 288 291 293 294 294 295 296

272 274 276 277 277 278

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11.6 E-Bidding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.7 Incentive System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.8 Tactical Pricing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

. . . .

298 299 300 302

12

Pricing Process Part 5: Monitoring . . . . . . . . . . . . . . . . . . . . . . . . . 12.1 Price Monitoring: Challenges . . . . . . . . . . . . . . . . . . . . . . . . . 12.2 Pricing Cockpit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.2.1 Financial Monitoring . . . . . . . . . . . . . . . . . . . . . . . . 12.2.2 Monitoring Market Effects . . . . . . . . . . . . . . . . . . . . 12.2.3 Monitoring Pricing Excellence . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

305 305 306 307 309 314 315

13

Pricing Process and Pricing Psychology . . . . . . . . . . . . . . . . . . . . . 13.1 Focus Topic Pricing Process and Pricing Psychology: Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.2 Price Psychology and Structure (1): Mental Accounting . . . . . . 13.3 Price Psychology and Structure (2): Price Level Effect . . . . . . . 13.4 Price Psychology and Structure (3): Anchoring . . . . . . . . . . . . 13.5 Price Psychology and Structure (4): Value Effect . . . . . . . . . . . 13.6 Price Psychology and Structure (5): Price Threshold Effect . . . 13.7 Price Psychology and Structure (6): Compromise Effect . . . . . 13.8 Price Psychology and Structure (7): Decoy Effect . . . . . . . . . . 13.9 Price Psychology and Structure (8): Price Figure Communication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.10 Price Psychology and Implementation (1): Tiered Discounts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.11 Price Psychology and Implementation (2): Endowment Effect . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.12 Price Psychology and Implementation (3): Loss Aversion . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

319 319 320 322 323 325 325 329 331 332 333 335 336 337

About the Author

Frank Frohmann looks back on many years of experience in the development of business models and pricing strategies for numerous companies in a wide range of industries. His extensive experience in strategy, marketing, and sales is based on three main fields of activity: management consulting (Simon-Kucher & Partners), operational price management (Lufthansa), and in-house consulting (Bosch, Evonik), among others. After studying business administration at the University of Mainz, Frohmann worked for Simon-Kucher & Partners in Bonn as of 1996. From 2003 to 2007, Frank Frohmann worked in central pricing at the Lufthansa Group. At the headquarters of Robert Bosch GmbH, he advised all business units on pricing and product development issues as an in-house consultant. From 2013 to 2018, he worked for an international chemical group. This book is based on almost 30 years of practical experience in price management across all industries. Frank Frohmann was already dealing with digitalization issues in business model projects for business-to-customer (B2C) and business-tobusiness (B2B) companies at the end of the 1990s.

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List of Figures

Fig. 1.1 Fig. 1.2 Fig. 1.3 Fig. 1.4 Fig. 1.5 Fig. 3.1 Fig. 3.2 Fig. 3.3 Fig. 3.4 Fig. 3.5 Fig. 3.6 Fig. 4.1 Fig. 4.2 Fig. 5.1 Fig. 5.2 Fig. 6.1 Fig. 6.2 Fig. 6.3 Fig. 6.4 Fig. 7.1 Fig. 7.2 Fig. 7.3 Fig. 8.1 Fig. 8.2

The three-level approach “digital pricing” (Source: Own representation) . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The six dimensions of price (Source: Own representation) . . . . . . . . Individual price–volume function (Source: Simon & Fassnacht, 2019, p. 94) . . . .. . . . . . . . . . . .. . . . . . . . . . . .. . . . . . . . . . . .. . . . . . . . . . . .. . . . . . . . Aggregated price–volume function (Source: Simon & Fassnacht, 2019, p. 95) . . . .. . . . . . . . . . . .. . . . . . . . . . . .. . . . . . . . . . . .. . . . . . . . . . . .. . . . . . . . Logic of price optimization (Source: Own representation) . . . . . . . . Four major dimensions of a business model . . . . . . . . . . . . . . . . . . . . . . . . The three-level approach “digital pricing” . . . . . . . . . . . . . . . . . . . . . . . . . . The three-level approach “digital pricing”: example Amazon . . . . The three-level approach “digital pricing” in detail . . . . . . . . . . . . . . . . Business model map, overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Competitive advantage matrix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Seven selected revenue sources at a glance . . . . . . . . . . . . . . . . . . . . . . . . . Five selected revenue models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Pricing process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The 11 determinants of pricing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Segmentation and positioning (based on Simon & Fassnacht, 2019, p. 42) . . . .. . . . . . . . . . . .. . . . . . . . . . . .. . . . . . . . . . . .. . . . . . . . . . . .. . . . . . . . Overview of multivariate analysis methods . . . . . . . . . . . . . . . . . . . . . . . . . Segmentation and positioning based on willingness to pay . . . . . . . Competitive advantage matrix (Simon, 1992) . . . . . . . . . . . . . . . . . . . . . . Price differentiation (based on Simon & Fassnacht, 2019, page 224) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Non-linear pricing (based on Simon, 1992) . .. . .. .. . .. . .. .. . .. .. . .. . Three effects of price differentiation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Revenue sources and price models; example: passenger car supplier. (Source: Own representation) .. . .. . .. . .. . .. . .. . .. . .. . .. . .. . Revenue sources and price models; for example: smart mobility (Source: Own representation) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

2 9 10 11 19 50 69 70 72 74 78 84 93 104 104 132 134 135 137 154 159 175 180 180

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xiv

Fig. 8.3 Fig. 8.4 Fig. 8.5 Fig. 8.6 Fig. 9.1 Fig. 9.2 Fig. 9.3 Fig. 9.4 Fig. 9.5

Fig. 9.6 Fig. 9.7 Fig. 9.8 Fig. 9.9 Fig. 9.10 Fig. 9.11 Fig. 9.12

Fig. 10.1 Fig. 10.2 Fig. 11.1 Fig. 11.2 Fig. 12.1 Fig. 12.2

List of Figures

Pricing process and price model optimization (Source: Own representation) . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The six pillars of a price model (Source: Own representation) .. . . Outcome-based price models: selected examples (Source: Own representation) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Outcome-based price model: Hitachi (Source: Own representation) . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Methods to determine the price optimum (1) (Source: Own representation) . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Methods to determine the price optimum (2) (Source: Own representation) . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Methods to determine the price optimum (3) (Source: Own representation) . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Expert estimate to determine the price optimum (Source: Own representation) . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Calculation of the profit-optimal price (based on Simon & Fassnacht, 2019, page 189) (Source: Own representation) . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Decision support model (based on Simon & Fassnacht, 2019, page 183) (Source: Own representation) . . . . . . . . . . . . . . . . . . . . . . . . . . . . Value-price portfolio (Source: Own representation) . . . . . . . . . . . . . . . Competitive advantage matrix (example): product, supplier A (illustrative) (Source: Own representation) . . . . . . . . . . . . . . . . . . . . . . . . . Derivation of a value-price portfolio from the competitive advantage matrix (Source: Own representation) . . . . . . . . . . . . . . . . . . . . Optimization of the value-price positioning; integration of methodologies (Source: Own representation) . . . . . . . . . . . . . . . . . . . . . . . Process flow of the integrated methodology (Source: Own representation) . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Derivation of the target positioning for a new product (Source: Own representation; price target matrix adapted from Simon & Dolan, 1997) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Challenges of portfolio pricing (Source: Own representation) . . . . Price structure Apple iPad in the USA 2010 (Source: Own illustration) . . .. . .. . . .. . . .. . . .. . .. . . .. . . .. . .. . . .. . . .. . . .. . .. . . .. . . .. . .. . . Condition system: from list price to transaction price (Source: Own representation) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Value selling and benefit quantification (“price walk”) (Source: Own representation) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Three pillars of price monitoring (Source: Own representation) . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Pricing power and pricing strategy (Source: Own representation) . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

181 182 200 201 218 223 229 231

234 241 244 246 247 249 252

253 266 270 290 298 307 310

List of Figures

Fig. 12.3 Fig. 13.1 Fig. 13.2

xv

Pricing target matrix, extended (Source: Own representation; Simon & Dolan, 1997) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 312 Pricing process and psychology (structure) (Source: Own representation) . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 321 Pricing process and psychology (Source: Own representation) . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 334

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Basics of Price Management

1.1

Digitization and Pricing

Data is the core driver of digitization: And the amount of information for price optimization is growing progressively. Price points are by definition data, are optimized on the basis of a large amount of information, and are subject to success monitoring via central metrics (key performance indicators; KPIs). The potential for price optimization has increased exponentially in recent years due to digitization. Real-time data on purchasing behavior allows faster estimations of the effects of price changes on key performance indicators. Price effects on sales volumes, market shares, and profits can be forecast more accurately as technology advances. However, the enormous opportunities of digitization for price management have not yet been comprehensively covered in corporate practice. This is very surprising against the background of the following contexts: 1. The characteristics of digital offerings lead to specific pricing challenges. Information goods offer outstanding potential for price optimization. 2. The enormous dynamics in digital business models, revenue sources, and the resulting price models are significantly broadening the spectrum of pricing. 3. Digitization offers a wide range of opportunities, especially in terms of dynamic pricing, price differentiation as well as revenue and price models. 4. Generating new revenue opportunities is critical to success in order to amortize the extensive investments in digitization. 5. The global coronavirus pandemic has accelerated the move toward digital business models. 6. Pricing must play a much more important role in corporate processes. This is because price optimization is preceded by important business decisions: firstly, the definition of revenue sources (revenue model). On the other hand, the definition of customer value as the central pillar of the business model. Both of these overriding decisions are an elementary component of price management for digital offerings. # Springer Nature Switzerland AG 2023 F. Frohmann, Digital Pricing, Management for Professionals, https://doi.org/10.1007/978-3-031-24591-6_1

1

2

1

Fig. 1.1 The three-level approach “digital pricing” (Source: Own representation)

Basics of Price Management

Revenue model

Business model 3 levels „Digital pricing"

PricingProcess

Increasing digitization and the internet are fueling innovation at all three connected levels: in business models (level 1), in revenue models (level 2), and across the pricing process (level 3) (Fig. 1.1). The scope of the pricing logic in this book This book provides an overview of processes and methods of price optimization for digital offerings. Products and services whose business model is changing as a result of digitization are also covered. A special focus is on the pricing process. This central management process ranges from analysis and target prioritization to setting introductory prices and price structures to monitoring. Deficits in value extraction do not necessarily affect basic pricing decisions. Quantitative optimization of price levels is only one of many building blocks in the overall “value extraction” process. The horizontal perspective (pricing process; level 3) must be extended to include the vertical dimension (business and revenue model; levels 1 and 2). Pricing processes must go beyond pure optimization and also reflect the higher-level decisions on the business model and the revenue model. These vertical processes and interactions are becoming increasingly important with the increasing digitization of business life. The starting point for the three-dimensional optimization is the customer benefit (“value to customer”) which is one of the major pillars of a business model. The immediate consequence of this is that professional pricing must incorporate the latest findings in behavioral psychology. Since customer behavior is the most important factor influencing profits, especially in the digital age, the perception of benefits and prices is of paramount importance. Controlling perception is critical to success. To put it another way: The provider’s perceived benefits and the price image of a

1.1 Digitization and Pricing

3

company are more significant than its actual positioning. The latest findings from brain research are presented as the focus chapter “Pricing Psychology and the Pricing Process”. With respect to the concrete challenges of the pricing process, it is shown how the perception of customers can be controlled with the help of pricepsychological levers. Numerous examples from different industries, short project outlines as well as innovative pricing approaches are elementary components of this book. Methodological innovations include: • • • • •

Methodical linking of business model, revenue model, and pricing process. Definition of “revenue model” and logical link with price models. “11 C Approach” to describe the major determinants of pricing. Methodical integration of “price optimization” and “price-value positioning”. Comprehensive definition of “price model” (six pillars) and decision support logic to derive price models. • Integration of pricing psychology and pricing process.

Customer needs and willingness to pay must be at the center of price strategy considerations (Simon, 2015a). Those who only inadequately capture the customer benefits of their products cannot fully capture the values created. This is fatal for several reasons (Simon & Fassnacht, 2009): 1. Prices are the most important driver of corporate value and profit. Even minimal price changes can have a comparatively strong impact on profitability. 2. In highly competitive markets, it is often not possible to significantly expand sales volumes or further reduce costs. The only instrument left for increasing profits is therefore price. 3. The profit lever price is eroded by a lack of professionalism and implementation discipline. 4. It is often not consumers’ lacking willingness to pay that is responsible for sales and profit problems, but incorrect pricing and offering design. Missteps in price management have fatal consequences for the profitability of a product or a company. The list of examples of pricing failures is long: Praktiker, Loewe, Nokia as well as the numerous failures in the implementation of dynamic pricing (see Chap. 10) and price models (see Chap. 8). 5. Customer behavior and user expectations have changed enormously in the last few years of increasing digitization. 6. Lack of customer focus is the main cause of failure in business model transformation. These insights are not anchored in all companies. Typical mistakes include (cf. Simon & Fassnacht, 2016): • Ignorance of structural relationships and processes of pricing. • Misjudgment of competitors. • Insufficient use of professional methods of optimization.

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Basics of Price Management

• Neglect of customer benefits and willingness to pay in product development and pricing. • Too much focus on setting price points, neglecting price models. • Ignorance of the interactions of business model, revenue model, and pricing process. • Reducing the opportunities of digitization to automation of pricing processes. • Insufficient involvement of sales in the pricing process. • Neglecting the impact of pricing decisions on perceived price fairness.

1.2

Price as a Profit Driver

There are only three profit drivers: price, volume, and cost (Simon & Fassnacht, 2016, 2019; Simon, 2012). Price decisions are associated with the greatest profit leverage for companies. Price variations often have a stronger impact on profit than cost or sales volume variations. This is well known but still described in an undifferentiated way. This is because this basic economic interrelation applies in both directions: Pricing offers the greatest profit opportunities, but is also associated with the highest risk. Price increases can drive revenues and profits sharply higher if a company has a strong “pricing power”. Take Netflix in 2019 (a 10% price increase boosted operating profit by 71%). However, things can also go quite differently: A price change can also lead to very negative consequences. A particularly striking case at the industry level: in 2009, all IATA airlines generated a loss of more than USD 11 billion worldwide (Anonymous, 2009). And this despite the fact that airlines—earlier than other industries—implemented highly professional pricing processes. Revenue management was already implemented by airlines at the end of the 1960s, while today it is sometimes presented as “new” under the buzzword “dynamic pricing”. One of numerous examples at the corporate level: Microsoft lost USD 4 billion with its X-Box in the period between 2002 (market launch) and 2006. The flaw at the core: too high a starting price compared to Sony. All subsequent price reductions could not compensate for the misplacement (Simon, 2015b). The accurate assessment of the sales effects of pricing decisions is the core of professional profit optimization (Simon, 2015a, p. 40 ff.). A simple calculation example serves to illustrate the interrelationships (cf. Simon & Fassnacht, 2016, 2019): Case study 1: Price effect A company sells its product for 10 EUR per unit. The annual sales volume is 100,000 units. The variable unit costs are 6 EUR. The margin (unit contribution margin) is therefore 4 EUR. The total contribution margin in the initial situation is 400,000 EUR. Furthermore, fixed costs of 300,000 EUR are assumed. The company generates a profit of 100,000 EUR. In the following, (continued)

1.2 Price as a Profit Driver

5

we will examine the leverage effect of the profit drivers (price, sales volume, and costs) on the profit. In the calculation example, fixed and variable costs are considered separately. The calculation is based on the premise that all parameters improve by 10% each (Simon & Fassnacht, 2016, p. 2). The other factors remain constant (ceteris paribus assumption). In the case of price, an improvement means that the company’s margins increase in the wake of a price increase. The parameter changes lead to the following effects on profit: 1. A 10% increase in price (from 10 to 11 EUR) leads to an increase in profit from 100,000 to 200,000 EUR, and consequently to a 100% improvement in profit. 2. A 10% improvement in sales volume results in a 40% increase in profit. 3. A 10% reduction in variable unit costs also has a relatively strong impact of 60% on profit. 4. The fixed cost reduction has a much weaker effect with a 30% increase in profit. Taking into account the ceteris paribus assumption, the summary of the case study is: Price has the greatest power as a profit driver. The simple calculation proves: Investing management resources in pricing measures can achieve a significantly higher profit effect than increasing sales volumes or reducing costs (Simon, 2012). However, the enormous leverage of price works in both directions. Pricing involves an asymmetry as a profit driver. Price measures offer the greatest potential compared to other instruments (Simon & Fassnacht, 2019; Simon, 2015a). However, turning the price screw in the wrong direction can quickly destroy a large part of the profit. This becomes immediately clear if one runs through the calculation outlined above for a price reduction, a volume reduction, and a cost increase of 10% each (Simon & Fassnacht, 2016, p. 3). The conclusion is that a drop in sales volumes is associated with less severe profit reductions than a price reduction. This is because the variable unit costs fall as a consequence of the reduction in volume. If the customer does not respond to a price reduction, on the other hand, this has a fully negative impact on profits. The ceteris paribus assumption (no reaction; isolated variation of parameters) is very helpful for understanding profit effects. However, it very rarely corresponds to reality. This is because sales volumes typically change when prices vary. This leads to the key question of what information is relevant for assessing opportunities and risks. The answer is: the effect of the price depends on the customer’s reaction. The decisionmaking process of customers is reflected in a key performance indicator that is critical for success—price elasticity (Simon & Fassnacht, 2016, p. 7; Simon, 2015a, p. 12). Price elasticity plays a decisive role in determining the profit impact of price

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Basics of Price Management

measures (Roll & Schreiner, 2011). The simply structured initial example has deliberately excluded price elasticity. In the further course, the effects of the demand response on the profit calculation are specifically elaborated.

1.3

Specific Characteristics of the Price

The high-profit relevance results, among other things, from the specifics of price within the marketing mix and from fundamental economic trends (Simon & Fassnacht, 2019; Simon, 2015b; Roll et al., 2012): 1. Price changes by companies often have very strong effects on sales volumes. In many supply segments (products, services, software), price elasticity is many times higher than the advertising impact or the sales leverage. Demand is more strongly influenced by price changes than by advertising measures or the size of the sales force. 2. In many markets, consumers react particularly quickly to price measures. Advertising and product changes often have a delayed effect. The high speed of the price effect is particularly evident in online retailing, but also in many services such as air travel. Here, market shares can change very quickly when competitors take price action. 3. Pricing measures can be implemented without much delay. Increasing digitization is enabling ever more dynamic pricing. However, competitors can also respond quickly with price. And they are increasingly doing so, driven by the potential of information technology. 4. In competitive industries in particular, price is a key success factor. This can be explained by developments on the demand and supply side as follows. Increasing importance of pricing: customer perspective 1. Digitization has made it much easier to compare prices and offers. Price comparison portals and search engines as well as electronic sales channels massively increase transparency. Digital marketplaces such as Amazon Marketplace and platforms like Alibaba make product searches and price comparisons fast and convenient for users. This sustainably strengthens price awareness and increases the market power of customers. Social networks lead to a multiplication of transparency and market power. 2. The use of mobile devices for shopping is standard. This applies especially to the younger generation of “digital natives”. Customers make decisions much more spontaneously. And they systematically exploit price differences in terms of time or sales channels. 3. For many customers, price has become one of the most important selection criteria when making a purchase decision. In e-commerce, price (even ahead of product availability and assortment) is the most significant factor for customer decisions. Against the backdrop of increasing digitization, the “smart shopper” segment is becoming increasingly important (Salden et al., 2017, p. 14).

1.3 Specific Characteristics of the Price

7

4. The flexible use of services is gaining in importance in numerous sectors. In comparison, owning a product is losing relevance. Using instead of buying is a trend in numerous end-customer sectors. One example is the movie market, where more and more users are streaming content instead of buying DVDs (Mohammed, 2019). The same applies to the music streaming sector. Many young people do not own a car but use mobility services such as carsharing or ridehailing via app. Shared services are also becoming more important in the accommodation and taxi sectors. This explains the demand for ride-sharing services (Uber) and accommodation offers (Airbnb). 5. Many customers decide on an offer at short notice and depending on the context. Small differences in the price level often determine the selection decision. Take ridehailing, for example. Many customers use several ride service apps at the same time, always looking for the best offer. Price and availability are the core selection criteria. Customer loyalty is relatively low. Price focus and low brand loyalty are challenges that also affect many other online business models (e.g., digital food delivery services, bikesharing, and e-scooters) (Ermisch, 2022; Kyriasoglou & Rest, 2022; Heiny, 2021). 6. Products and services are offered globally at a high level of quality and are often perceived as interchangeable. One of numerous examples is the smartphone industry. Hardware producers are finding it increasingly difficult to stand out from the competition. Devices are becoming more and more similar. However, the increasing convergence of quality is also affecting numerous online industries, such as online retailing. Customers’ price sensitivity has increased enormously, particularly as a result of improved choice and intensified competition. 7. New digital technologies are shaping people’s needs. Modern customers have higher expectations of products, services, software, and digital services: “Simple”, “convenient”, “easy to use”, “available anywhere”, and “at the touch of a button”. In its core business, Amazon has permanently shifted the bar for the convenience of online shopping with these attributes (Eisenlauer, 2012). Speed and convenience are key requirements in all sectors, not least in B2B industries. The less effort consumers and business customers have to make, the higher their loyalty to the provider. 8. Economic concentration on the demand side has increased significantly in many industries. This no longer affects only business-to-business (B2B) markets. Digital technologies also support the bundling of potential buyers in businessto-consumer (B2C) sectors. Procurement models such as co-shopping are creating virtual purchasing communities that use their buying power to negotiate price reductions. One example is the C2M platform Pinduduo from China. This consumer-to-manufacturer (C2M) business model brings together bargain hunters who enforce lower final prices via collective orders. Mobility services are a second example: these can be booked jointly and billed digitally via costsharing models. 9. In B2B business, cost pressure on the procurement side is increasing. Sixty percent of purchasing decisions are consistently made online, without any

8

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Basics of Price Management

personal contact with potential suppliers. Buyers in industry and retail are showing ever greater professionalism in price negotiations, driven not least by technological trends. Quantifying the price-performance ratio of different suppliers is possible much more efficiently than before with latest IT tools. Increasing importance of pricing: supplier perspective 1. Many industries are characterized by increasing competitive dynamics. One keyword here is “business migration”, i.e., the entry of companies into foreign business areas. The resulting convergence of industries tends to lead to a more active use of price. For example, Alibaba, Alphabet (Google), Amazon, and Microsoft dominate the growth market of cloud computing (Lüder, 2021). All four companies started out in different industries, but have entered direct cut-throat competition as a result of the huge market shifts in the software sector. The large technology groups are waging the battle for market leadership on the basis of price, among other factors. 2. Oligopoly structures prevail in numerous industries. A few large companies generate the majority of sales. Strong brands, enormous development budgets and the economies of scale of the strong market players intensify competition for smaller providers. The market position of the dominant technology groups— such as Amazon, Meta (Facebook), Alphabet, Alibaba, and Tencent—is based not least on the competitive instrument of price. 3. New competitors are using price measures in a targeted manner to gain market entry or establish dominant positions (Roll, 2009). Aggressive pricing strategies increasingly include information goods and digital services. Example: The Estonian low-cost provider Bolt not only offers electric scooters for use on the German market. A smartphone app is also used to arrange rides in cabs and rental cars from cooperating companies. In the ridehailing business, Bolt attacked its competitors with competitive prices at the beginning of 2022 (Lücke, 2022). 4. Especially in markets with interchangeable products, services offer great potential for differentiation from the competition. For more and more customers, the service they receive is an important purchasing criterion. This strategy, which has long been valid, will become even more important against the backdrop of digitization and the potential for digital services. This results in particular potentials in the development of new sources of revenue (see Chap. 4) and the creation of innovative price models (see Chap. 8). 5. Tenders for industrial goods are increasingly being placed via the Internet. In reverse auctions, price plays a central role in the awarding of contracts. 6. The methods for measuring customer value and willingness to pay have been significantly improved by advances in information technology. Modern approaches such as voice-of-consumer analytics (social listening) enable the scaled collection of unstructured user information (see Chap. 9). 7. Companies like Amazon have been using analytical customer relationship management (CRM) systems for many years. The usage and purchasing behavior of customers is recorded in real time with CRM tools. The data collected provides answers to a number of essential questions in product and price development.

1.3 Specific Characteristics of the Price Fig. 1.2 The six dimensions of price (Source: Own representation)

9

Customer

Product

Quantity

Time

Region

Channel

8. Prices are not one-dimensional data. Simon defines price as “the number of monetary units that a buyer must pay for a unit of volume of the product or service” (Simon & Fassnacht, 2016, p. 6; Simon, 2015b, p. 10). I supplement this three-dimensional definition (customer, product, volume) with three additional parameters: Region, time, and distribution channel. A price consists of at least six dimensions (Fig. 1.2). The number of price points has increased significantly due to technology-driven differentiation of distribution channels alone. In the case of services (e.g., air travel), additional price criteria (e.g., destination, booking class, and transport class) are added. In dynamic pricing, prices—based on complementary factors—are automatically adjusted over time (Fisher et al., 2017). The list of factors that determine the temporal variation of prices is endless (see Chap. 10). The combination of the individual dimensions results in an enormous complexity. 9. The multidimensional definition of price means that pricing must be an integral part of a company’s strategy process. Strategies deal with the long-term orientation to target customers with different products—in diverse regions and sales channels. Consequently, price is a central lever within the framework of corporate strategy. 10. The price level often results from the interaction of several components. In many cases, the decision on the price level does not concern only one parameter. In practice, a wide variety of price models can be found. The most diverse variants of price differentiation are used in parallel (see Chap. 7). Price is a very effective instrument for steering sales volumes, revenue, and profit. On the one hand, the price pressure of companies is increasing. This is primarily due to the increased buying power in the increasingly technology-driven competition. However, advancing digitization is also increasing the potential for value exploitation. Rules of thumb and heuristics are still used (Simon & Fassnacht, 2019; Simon, 2015a; Roll et al., 2012). However, simple methods such as cost-plus pricing and adjustment to competition—used in isolation—are unsuitable. If the customer is ignored, pricing failures are inevitable. The greatest risk is misjudging the customer’s reaction.

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1.4

Basics of Price Management

The Price–Volume Function as an Indicator of Customer Response

In order to estimate the effectiveness of planned pricing measures, one must know the relationship between price, sales volume, and profit. The crucial question is how different price points will affect the buying behavior of professional buyers and consumers. Only with the knowledge of the customer’s willingness to pay can professional pricing decisions be made (Simon & Fassnacht, 2019; Simon, 2015a, 2015b; Roll & Achterberg, 2010). The individual price–response function can be used particularly well to explain pricing relationships. It describes the situation of a buyer. The individual price effect ultimately reflects the outcome of a “zero-one” decision. This relates to a unit of a product, a service, or an information good. The customer’s trade-off results in the maximum price. This is the highest price a customer is willing to pay for a product. The maximum price corresponds to the perceived benefit (cf. Simon & Fassnacht, 2019; Simon, 2015b). It reflects the customer’s perception of the company’s product, communication, and services. In addition, brand image can also have an influence on willingness to pay. When considering a single buyer, two cases can be distinguished on the supply side (Fig. 1.3). The way in which price and benefit are weighed up differs fundamentally for products used over the long term and offerings (including services) consumed over the short term (Simon & Fassnacht, 2009, 2019): 1. Durable consumer goods: In this case, the consumer buys one unit of the product. This “yes-no” case applies, for example, to passenger cars, complex machine tools, as well as refrigerators and washing machines. An increasingly important subcategory of long-term offers are digital consumer goods such as smartphones and tablets, reading devices for electronic books, and consoles for video games. In the case of these, usage is not subject to any time restrictions. The potential

"Yes-no" case

"Variable-quantity" case

Sales volume

Sales volume 4 3

1 2 1

5

willingness to pay

Price

3

5

7

willingness to pay

Fig. 1.3 Individual price–volume function (Source: Simon & Fassnacht, 2019, p. 94)

9

Price

1.4 The Price–Volume Function as an Indicator of Customer Response

11

Sales volume a q =a-b p

b (slope)

Price a/b (maximum price) Fig. 1.4 Aggregated price–volume function (Source: Simon & Fassnacht, 2019, p. 95)

customer decides to buy if the price does not exceed the perceived value of the product. 2. Consumer goods (including services and information goods): The consumer buys a larger or smaller quantity depending on the price. This “variable quantity” case applies, for example, to food and beverages and numerous services. Digital services—such as streaming movies or downloading music from the Internet— are also part of the “variable quantity” case. The potential customer decides for each unit whether or not to buy it at a certain price. The higher the price, the lower the quantity purchased by the customer. In some cases, the customer is only allowed to use the online services for a limited period of time. This applies, for example, to Spotify, the world’s largest music streaming service. The more than 195 million paying premium customers are entitled to download music tracks for as long as they pay for the subscription (Anonymous, 2021a, 2022). The aggregated price–volume function applies to segments, i.e., to groupings of individual customers. To determine it, one adds up the number of customers at different price points. It is a matter of aggregating the buying customers (“yes-no” case) or the sales volumes in the “variable-volume” case. As a rule, the more expensive the product, the less is sold and vice versa (see Simon & Fassnacht, 2019; Simon, 2015b; Roll & Achterberg, 2010). The aggregated price–volume function has a negative slope (Fig. 1.4).

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Practical tip What should be considered if the price is set on an individual basis? In the yes– no case, one should explore the tolerance level of each individual customer and demand exactly this price. It is the key challenge in industries where prices are negotiated individually with customers (e.g., software in B2B business with large customers). In the variable quantity case, there are different possibilities: 1. A uniform price may be charged regardless of the volume purchased. 2. The price is differentiated according to the quantity purchased by customers.

1.5

Market Dynamics and Price Elasticity

The influence of price on sales volumes can be represented by a single indicator, price elasticity. In this section, I will show: 1. How the price response can be determined 2. By which supply and demand trends the level of price elasticity is influenced 3. How this important indicator is developing in modern industries Price elasticity is the most important measure of the influence of price on sales volumes. It indicates how much demand changes in percentage terms when the price is varied (see Simon & Fassnacht, 2019; Simon, 2015b; Roll & Achterberg, 2010). Let us assume a price increase of 5%: A price elasticity of -2 means that the quantity purchased decreases by 10%. The price response corresponds to the percentage change in sales volume divided by the percentage price variation. It is an indicator of whether a market tends to be price sensitive (high price elasticity) or price insensitive (low price response). Price elasticity is thus an initial indication of whether there is a leeway for price changes. Price responsiveness is the result of measuring price effects on demand. It can be realized values from the past or estimated data. In the course of digitization, the technical prerequisites for measuring price–quantity relationships have improved significantly. Numerous survey techniques and observation methods can be used to determine or forecast the price response of customers (see Chap. 9). The price response is negative because price and sales volume change in the opposite direction. Only in exceptional cases is the elasticity positive. In the case of price variations, there is then a change in sales volume in the same direction. This is the case when the market equates a higher price with better quality. For the price–quality indication, there are examples from the B2C and B2B business. If the price is seen as a prestige symbol by the customer, a similar phenomenon can occur (Veblen effect). Another cause for a rectified

1.5 Market Dynamics and Price Elasticity

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variation of price (cause) and demand (effect) are macroeconomic influencing factors (geopolitical risks, pandemics, etc.). An example of this: After the outbreak of the global pandemic in 2020, price increases in numerous digital sectors (including video streaming) did not damage the increase in demand. With a few exceptions, the sale of a product is not only dependent on its own price. The prices of other products also have a more or less significant influence. Cross-price elasticity describes this relationship. It is a measure of how the quantities of a product vary when the price of another good changes (see Simon & Fassnacht, 2009, 2019; Simon, 2015a; Roll & Achterberg, 2010). This can be a competitor product or an offering within the company’s own product line. In this case, a distinction must be made between complementary products and substitute goods. For substitutive (i.e., competing) products, the cross-price elasticity is positive. The leading sign is positive because both changes are in the same direction. For example, a competitor price reduction leads to a decrease in own sales volumes. In the case of competitive pricing and product line pricing, knowledge of cross-price elasticity is critical to success. Practical tip Segment your most important product–customer combinations according to price elasticity of demand and cross–price elasticity. These findings can be used to derive key implications for pricing strategy (especially your behavior vis-à-vis the competition). Commodity markets are characterized by high cross-price elasticity and high price elasticity of demand. A balanced price strategy is particularly necessary in the following market constellations: 1. High cross-price elasticity (buyers switch suppliers easily). 2. Low price elasticity of demand (volume does not react to the price level). The market dynamics here resemble the prisoner’s dilemma from game theory. In a meta-study, price elasticities were analyzed in numerous industries, countries, and product categories. The core result of the empirical measurement of price elasticities is that the mean value of the price response is -2.62 (Simon & Fassnacht, 2009, p. 104). The price response can be read graphically from the slope of the price–volume function. High elasticities correspond to a steep function, while lower price effects are reflected in a flatter curve. Price elasticities tend to be high in the following competitive constellations (see Simon & Fassnacht, 2009, p. 108 f.; Wübker, 2004; Roll et al., 2012; Jensen & Henrich, 2011): 1. High interchangeability of offerings: Commodity structures can be found in some industries, in rare cases also in digital sectors such as telecommunications, media, and in sub-segments of online retailing (e.g., e-commerce). Commodities include homogeneous mass products such as raw materials (crude oil, cement, steel, iron

14

2.

3.

4.

5. 6.

7.

8.

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Basics of Price Management

ore), electricity, and basic chemicals. The more interchangeable the offers are, the more likely it is that even small price deviations will lead to a change of suppliers. Price elasticity is higher for commodity goods than for premium products or “specialties”. For customers, specialties are associated with differences in brand image as well as product and service differentiation. These can be skimmed through price premiums compared with competitors. “Specialties” tend to have a low price elasticity. High price transparency: Market transparency makes many customers more price conscious. A good market overview and easy comparability of prices from the customer’s point of view can be due to various factors. Frequent special offers, strong price advertising, and the increasing importance of digitization lead to a higher impact of price on demand. Good product knowledge and high product type involvement: The greater the customer’s experience and familiarity with the industry and competitive offerings, the more attention will be paid to price measures. High purchase frequency: Customers perceive price changes the stronger the more frequently they buy a product. Products with low purchase frequency tend to have lower price elasticities and offer the potential for price increases. This category includes, for example, niche products, slow-moving items, and variants. Low brand loyalty: The lower the customer loyalty to a provider, the more likely it is that even small price changes will lead to a switch of suppliers. Purchasing concentration and increasing buying power of professional customers: The negotiation power of a buyer vis-à-vis its suppliers depends on the market structure. A case in point: In food retail, a few dominant retail enterprises face a large number of consumer goods manufacturers. The large chains of retailers negotiate much more confidently with their suppliers because of the increase in power. Many manufacturers are struggling with sharp price fluctuations of raw materials, but often cannot pass on higher costs. Discontinuations from product assortments have occurred frequently in the recent past: Kaufland and Unilever (2019), Edeka and Nestlé (2018). During 2021—and even more so at the beginning of 2022—conflicts between food manufacturers and the major supermarket chains increased. This was caused by the increased costs for energy, logistics, and raw materials (Reiche, 2018; Hielscher, 2021; Heidenreich, 2022). Low importance of image and prestige of the supplier in the context of the purchase decision: A poorly developed brand awareness also contributes to higher elasticity. Examples of this are no-name brands, me-too products, or generic retail brands (see Simon & Fassnacht, 2016, p. 76). Simple price models: The introduction of transparent price models by companies influences the price sensitivity of customers. If price models refer to the same unit of measure (e.g., product, weight, or a unit of time), the price level becomes more important in a competitive comparison. Simple subscription models (e.g., in music streaming: Spotify, Apple Music, and Amazon) have disadvantages compared to innovative reference bases of price models. Success-based or

1.6 Price Actions, Elasticities, and Profit Effects

15

performance-based price models significantly reduce the comparability of prices among competitors. Customers’ price elasticity results from their perception of value. In contrast to analog products or traditional services, the determinants of the value of digital products are much more varied. The value of a digital product for the user depends on the following criteria (Simon & Fassnacht, 2009, p. 517; Roll, 2003): • • • • • • • •

Scope of the information asset Number of units used Topicality Compression quality Duration of use Number of co-users Network effects Price model.

Consequently, it is more difficult to quantify the perception of value and the price acceptance for digital products than for consumer goods.

1.6

Price Actions, Elasticities, and Profit Effects

In most industries and companies, price increases cause at least some customers to reduce the volume purchased or to switch to the competition. Conversely, price reductions often do not lead to the desired improvements in sales and profits. There is a massive risk of misjudgment in both directions. With a planned price variation, it is always a question of both the right direction and the level of the price change. The decisive factor is the correct assessment of which demand reactions are to be expected in the case of planned price changes. Simple calculations and heuristics can be used to reduce the complexity of the decision. These deliberately avoid the complex collection of all data. Instead, however, they provide initial useful indications without much effort (Simon & Fassnacht, 2016, p. 202). Contribution margin calculation determines the extent to which a product contributes to covering the company’s fixed costs through its revenue. It is based on a strict separation between fixed and variable cost items. Fixed costs are not dependent on changes in quantity—and therefore not on price. Consequently, fixed costs must not influence short-term price decisions. The price that generates the highest contribution margin is also optimal from a profit perspective (Simon & Fassnacht, 2009, 2019; Simon, 2015a, 2015b). A particularly efficient decision support for price changes is a contribution margin simulation in the form of the “volume hurdle” calculation (Smith, 2011). This formula determines the volume change required to justify a price change in terms of profitability. The following questions describe the essence of price management:

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• How much sales volume in percent must our product gain with a price reduction? • How much sales volume can we lose in the event of a price increase without changing the contribution margin? With the help of the volume hurdle formula, these core questions can be answered in just a few seconds. The calculation of the “break-even quantity change” can also be used efficiently by the sales department when preparing for negotiations. For this purpose, two data have to be processed: – The margin (the unit contribution margin, as a profitability measure). – The planned percentage change in price (Smith, 2011). %ΔQ =

- %ΔP %CM þ %ΔP

A significant advantage of the volume hurdle calculation is the reference to the price elasticity. By dividing the percentage change in sales (as a result) by the price change (as a planned measure), an indication of price elasticity (arc elasticity) is obtained. The decision rule resulting from the contribution margin calculation is: • Price reductions should only be made if the increase in sales volume is at least as large as the calculated percentage. • Price increases only make sense if the drop in sales volume is at most as large as the calculated value. The profit effect of price changes is affected by the cost structure. This relationship is reflected by the denominator of the volume hurdle formula. In addition to the price variation, the denominator also contains the margin. For example, in the case of a price reduction, the higher the variable unit costs, the greater the increase in volume must be for profit to improve. Conversely, offers with high margins have more scope for a profitable price reduction. Put another way: With a high margin, even a small increase in sales volume leads to a jump in profits. The reason for the enormous profit leverage of pricing is the high share of fixed costs in total expenses. Graphically, sales increase sharply as volume increases. However, the cost curve changes only slightly because marginal costs are very low. This interrelation is characteristic of digital offerings. The relationship is measured with the elasticity of profit with respect to capacity utilization (Simon, 1992). The higher this elasticity measure, the more important a large sales volume (or high capacity utilization) is. The relationship shows: High profits and losses are very close to each other— depending on the sales volume. A one percentage point increase in sales volume can lead to millions of euros in additional profit for many digital offerings.

1.6 Price Actions, Elasticities, and Profit Effects

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As an interim conclusion The volume hurdle formula makes it clear that products with different contribution margins require different levels of volume changes. The profit leverage of price-driven volume changes is enormously high in fixed cost-intensive industries. Put another way: The lower the margin, the more quantity change is necessary. The following calculation demonstrates this for two different cost structures. The calculation is based on a planned price reduction of 10%: 1. With a variable unit cost share of 50%, a 10% price reduction is only worthwhile if an increase in sales of more than 25% is expected (this corresponds to a price elasticity of absolutely >2.5). 2. With a margin of 30%, sales must increase by at least 50% for a price reduction of 10% to make sense. In this case, the price elasticity is -5. The example calculations demonstrate an important relationship in price management: the lower the margins, the more dangerous price cuts are. Low unit contribution margins, in turn, are one of the core characteristics of the price-competitive growth markets of online retailing, mobile communications, media, food retailing, mobility services, etc.

Case study 2: Price effect The following example shows how important it is to understand the effects of price on sales and profits. An estimation of price elasticity is imperative. The initial situation is the same as in the first case study. A company sells its product for 10 EUR. The annual sales volume is 100,000 units. The variable unit costs are 6 EUR. Scenario A: The company plans a price reduction of 10%. Relevant for the final decision is the knowledge of the contribution margin effect. It is a question of the necessary extent of the sales volume increase in order to achieve at least the same contribution margin as before the price change. With a margin of 40% and a price reduction of 10%, a sales increase of 33% is necessary! In other words, the price elasticity would have to be -3.3 to ensure at least the status quo in the contribution margin. In very few markets and customer segments are such volume increases realistic. Especially in highly competitive industries, the following determinants of pricing speak against this: (continued)

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1. Some competitors will respond to the price cuts with similar markdowns. 2. The lock-in of customers to their previous provider does not allow such a strong switch. 3. Lack of capacity on the part of the price initiator prevents the additional demand from being served on the market. Scenario B: The company plans a price increase of 20%. Raising the price by 2–12 EUR corresponds to a 50% increase in the unit contribution margin. Here, the question arises as to how much sales decline one could cope with before the positive margin effect is fully compensated. The result of the contribution margin calculation is: If the drop in sales volume does not exceed 100%, the price measure would be worthwhile. Only if the volume loss exceeds 100% will the profit be reduced compared to the initial situation. 100% loss of sales volume corresponds to a relatively high price elasticity of 5 or an absolute decrease to 50,000 units. The price increase scenario shows that price increases only reduce profits if sales volumes drop significantly. In the case of companies with a strong brand image, high customer loyalty, and high-quality products, such a massive drop is not to be expected. Consequently, price increases are often associated with positive profit effects here. This constellation applies to numerous companies in various industries. Gillette, Starbucks, Miele, Boss, and Apple are some of numerous examples (Simon, 2015b). Scenario C: The company tests a variation in both directions as part of dynamic pricing. In this example, we assume a change of 4% and a margin of 20%. Using the volume hurdle formula, the asymmetry of price effects can be proven. With a margin of 20% and a price change of 4%, the following rule applies to achieve the same profit: 1. With a price reduction of 4%, sales volume must increase by 11%. 2. With a price increase of 4%, on the other hand, one can afford a 15% decline in volume. The asymmetric effect of price changes can be explained by the influence of the margin level on the profit change. Mathematically, the margin is in the denominator of the volume hurdle formula. As a simple decision rule, the following statements can be derived (Fig. 1.5): 1. Price reductions are particularly useful in case of high elasticities and, above all, for high-margin products. 2. Price increases have a positive effect on profits, especially at low elasticities and low margins.

1.6 Price Actions, Elasticities, and Profit Effects

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Logic of price optimization

Price reduction

P r ic e e la s tic ity

high

medium

low

Price increase low

Competitive response

medium

high

Margin Fig. 1.5 Logic of price optimization (Source: Own representation)

The crucial point here is that elasticities are always relative; they depend in particular on the extent to which prices are adjusted upward or downward. Demand effects can also be influenced by the timing of price changes (see Chap. 13). Practical example: Price effect The German supermarket chain Real launched the discount program RealPro in November 2019. The core was a flat discount on all products in the amount of 20%. At that time, the discount card cost 69 EUR per year (Schader, 2020). Against the background of the volume hurdle formula, it is debatable why Real granted such a high discount. In food retail, average margins in many offer categories are even below the discount level granted. The DYI store chain Praktiker failed a few years ago with a similar flat discount strategy.

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Practical Examples: Market Dynamics in Modern Industries

Case study 1: High demand effect of price cuts In the growth market of cloud computing, Amazon was able to multiply sales volumes within a few years with its Web Services (AWS). Amazon leases IT services to business customers via the Internet (Lindner, 2018). Computing capacities are provided on external servers. Customers use the Amazon platform to handle their IT services. The business model is based on economies of scale. The more companies use the cloud offering, the cheaper it can be offered. The world’s largest cloud computing provider has implemented 20 price cuts in 4 years in its highly profitable AWS business. The demand response was so high that the price reductions for storage services led to disproportionate sales expansions (Anonymous, 2013, 2018a; Eisenlauer, 2012; Schütte, 2017). Amazon Web Services (AWS) has regularly managed to combine high profits with strong sales growth with its database services in recent years (Anonymous, 2013, 2018a; Eisenlauer, 2012; Schütte, 2017; Lindner, 2018). As a result, Amazon’s subsidiary AWS now generates a large share of the group’s net profit: more than 70% of the company’s profit and about 13% of Amazon’s revenue is generated by Web Services. This is how AWS finances its core online commerce business. The leasing of computing services via the Internet (cloud computing) has an even greater significance for Amazon than the iPhone has for Apple. The same correlation—a positive profit effect from price cuts—applied to T-Mobile a few years ago in the US mobile communications market. In 2017, it was able to generate higher revenue and profits. The basis for this was, among other things, price-driven customer churn from the market leaders Verizon and AT&T to T-Mobile (Anonymous, 2017).

Case study 2: Positive profit effect of price premia In the telecommunications industry, premium service providers such as Deutsche Telekom can successfully differentiate themselves from low-cost providers. The segment of quality-conscious “value buyers” prefers additional services to the core Internet access offering, such as security software or higher bandwidths. These add-on services are associated with a high willingness to pay and correspondingly positive profit effects (Anonymous, 2018b). In the smartphone market, Apple is very successful with a premium strategy. The pioneer, which launched its first iPhone in June 2007, is able to enforce a significant price premium compared with its competitors. The premium prices of the Apple iPhone are based on the benefits and image advantages perceived by numerous cell phone users. This greatly reduces price (continued)

1.7 Practical Examples: Market Dynamics in Modern Industries

elasticity. Against the backdrop of this outstanding “value generation”, price premiums over competitors have led to positive revenue effects and recordbreaking margins for 15 years. The iPhone dominates the high-priced smartphone segment. It is the most profitable and successful product of all time. At times, Apple generated up to 70% of its corporate revenue with the iPhone. At the end of 2022, this relative share is around 50%. The price leader accounts for a large share of profits in the global smartphone market. In 2016, the global profit share averaged more than 79% (Anonymous, 2018c; Fröhlich, 2018; Eisenlauer, 2017; Mansholt, 2018). At the peak, it was even 91% (Kharpal, 2016). Evidence of Apple’s impressive consistency: in the second quarter of 2021, the company still generated 75% of global smartphone profits (Anonymous, 2021b; Sokolow, 2022). Netflix, the market leader in video streaming, saw its profits increase tenfold between 2010 and 2019. Both revenue levers contributed to profit growth: Sales volumes (new customers) and Price (six price increases from 2011 to 2019). In particular, the price increase implemented in January 2019 for all three subscription versions (Basic, Standard, Premium) proved to be a great success (Gürtler & Rauffmann, 2022; Anonymous, 2019a, 2019b). Subscriber growth was not affected by the price increase. In fact, 2 million new streaming subscribers were acquired in the United States. Netflix’s revenue increased by 35% in 2019 (Anonymous, 2019c). Operating income rose by 59% (from USD 154 million in the previous year to USD 245 million). The example impressively proves: price increases can have a major impact on the profitability of a company. Amazon also has a high pricing power with its Prime loyalty program. After several years without price adjustments, the price for the Prime subscription in Germany was significantly increased from 49 to 69 EUR in 2017. Cancellations were absent except for a few cases. The explanation: Price sensitivity is affected by the Prime membership. With over 200 million customers worldwide, the Prime service is the most successful customer loyalty program in the world. In the USA, prices for the Prime service were increased at the beginning of 2022 for the first time since 2018 (USD 14.99 instead of USD 12.99 per month; USD 139 instead of USD 119 per year). A similar market impact as in the aforementioned examples is to be expected.

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Case study 3: Increasing price elasticity through digitization and price transparency Legally required publications of prices lead to complete transparency in the German gasoline market. Since 2013, service stations have been required to report their price changes promptly to the Market Transparency Unit for Fuels (MTS-K). This official data from over 14,000 service stations is made available by the MTS-K to providers of Internet portals and smartphone apps. With the help of this platform, motorists can compare the current prices of all gas stations. In the course of technological support, a clear change in user behavior is obvious: customers systematically exploit price cycles and increasingly refuel in times of price reductions. In other words, price elasticity for fuels has increased significantly. The enormous increase in market transparency has intensified competition among gas stations (Anonymous, 2018d, 2018e).

Case Study 4: Increasing price acceptance through technical advantage and innovation In early 2022, the Chinese market for electric cars was dominated by domestic brands such as Xpen and Nio, as well as the global market leader Tesla. Early adopters had a high willingness to pay for vehicles with futuristic designs and innovative features (Seiwert, 2022). Among the particularly popular purchasing criteria in the mobility sector are driver assistance systems for autonomous driving. Electromobility pioneer Tesla is able to enforce high premium prices for its software update feature FSD (Heiny & Rest, 2022; Freitag & Rest, 2022). Adjustments have been made continuously in the recent past. Most recently, Tesla was able to raise the price of its driver assistance system from USD 10,000 to USD 12,000 in mid-January 2022.

References Anonymous. (2009). IATA erwartet noch höhere Verluste für 2009. DVZ online. Accessed May 2, 2018, from https://www.dvz.de/rubriken/markt-unternehmen/detail/news/iata-erwartet-nochhoehere-verluste-fuer-2009.html Anonymous. (2013). Fakten-Check Amazon. So tickt der Online-Gigant. Bild online. Accessed May 2, 2018, from https://www.bild.de/geld/wirtschaft/amazon/amazon-fakten-check-so-ticktder-online-gigant-29196630.bild.html Anonymous. (2017). Steigender Umsatz und Gewinn; T-Mobile US prescht an den Prognosen vorbei. Wirtschaftswoche Online. Accessed May 2, 2018, from https://www.wiwo.de/ unternehmen/it/steigender-umsatz-und-gewinn-t-mobile-us-prescht-an-den-prognosen-vorbei/1 9712610.html

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Anonymous. (2018a). Google, Facebook, Amazon – Der Gegenwind nimmt zu. Manager Magazin Online. Accessed May 2, 2018, from https://www.manager-magazin.de/unternehmen/artikel/ datenschutz-widerstand-gegen-facebook-google-und-amazon-waechst-a-1185889.html Anonymous. (2018b). Telekom-Kunden zahlen bei DSL drauf. Bild Online. Accessed May 2, 2018, from https://www.bild.de/geld/mein-geld/dsl-tarife/telekom-kunden-zahlen-drauf-55057242. bild.html Anonymous. (2018c). iPhone X ist ein Flop – Und trotzdem wird Apples nächstes Modell wohl noch teurer. Focus Online. Accessed May 2, 2018, from https://www.focus.de/digital/handy/ iphone/analysten-prognose-iphone-x-ist-ein-flop-und-trotzdem-wird-apples-naechstes-modellwohl-noch-teurer_id_8772182.html Anonymous. (2018d). Preiskampf an der Zapfsäule. So sparen Sie bis zu 30 Cent pro Liter. Bild Online. Accessed May 2, 2018, from https://www.bild.de/geld/wirtschaft/benzinpreis/30-centpro-liter-sparen-55115990.bild.html Anonymous. (2018e, March 17). Der Spritpreis schwankt immer öfter. Frankfurter Allgemeine Zeitung, p. 29. Anonymous. (2019a). Netflix hebt Preise deutlich an – zunächst nur in den USA. https://www. wiwo.de/unternehmen/dienstleister/aktie-legt-zu-netflix-hebt-preise-deutlich-an-zunaechst-nurin-den-usa/23870042.html. Accessed 20 April 2022, from Anonymous. (2019b). Preiserhöhung bei Netflix droht: So fies spielt man jetzt mit neuen Kunden. https://www.focus.de/digital/internet/preiserhoehung-bei-netflix-droht-so-fies-spielt-man-jetztmit-neuen-kunden_id_10254509.html. Accessed 20 April 2022, from Anonymous. (2019c). Drittes Quartal. Netflix überrascht dank “Stranger Things” mit Umsatz- und Gewinnsprung. Accessed April 20, 2022, from https://www.manager-magaziAnoynymouse/ unternehmen/artikel/netflix-kann-im-dritten-quartal-gewinn-umsatz-und-kundenzahl-steigerna-1291951.html Anonymous. (2021a). Spotify zieht neue Abonnenten an. Accessed April 20, 2022, from https:// www.handelsblatt.com/technik/it-internet/musikstreaming-spotify-zieht-neue-abonnenten-anumsatz-legt-deutlich-zu/27742888.html?ticket=ST-1610388-pe65eU52fJleNBfaibfz-ap1 Anonymous. (2021b). Unangefochten: Apple erwirtschaftet 75 Prozent der weltweiten Smartphone-Gewinne. Accessed April 20, 2022, from https://www.finanzen.net/nachricht/ aktien/marktfuehrer-unangefochten-apple-erwirtschaftet-75-prozent-der-weltweitensmartphone-gewinne-10648997 Anonymous. (2022). Spotify generiert weniger Kunden als erwartet – Aktie bricht zweistellig ein. Accessed April 20, 2022, from https://www.wiwo.de/prognose-spotify-generiert-wenigerkunden-als-erwartet-aktie-bricht-zweistellig-ein/28034260.html Eisenlauer, M. (2012). Der Tech-Freak. Hier empfängt der König von Amazon unseren TechFreak. Accessed May 2, 2018, from https://www.bild.de/digital/multimedia/amazon/chef-jeffbezos-interview-tech-freak-26681524.bild.html Eisenlauer, M. (2017). Kein Supercycle. Schadet das iPhone X Apple? Accessed May 2, 2018, from https://www.bild.de/digital/smartphone-und-tablet/apple/iphone-x-hype-54276620.bild.html Ermisch, S. (2022). Langsamer und ohne Lager: So will Bringoo andere Lieferdienste angreifen. Accessed April 20, 2022, from https://www.wiwo.de/erfolg/gruender/den-meisten-geht-es-umkomfort-und-auswahl-langsamer-und-ohne-lager-so-will-bringoo-andere-lieferdiensteangreifen/27976384.html Fisher, M., Gallino, S., & Li, J. (2017). Competition-based dynamic pricing in online retailing: A methodology. Science, 64(6), 2496–2514. Freitag, M., & Rest, J. (2022). Wie Elon Musk Apple übertrumpfen will. Accessed April 20, 2022, from https://www.manager-magazin.de/unternehmen/autoindustrie/tesla-wie-elon-musk-sogarapple-ueberholen-will-a-7872dbee-0002-0001-0000-000189635030 Fröhlich, C. (2018). Phil Schiller – Dieser Mann soll den Apfel glänzen lassen. Accessed May 2, 2018, from https://www.sterAnoynymouse/digital/smartphones/phil-schiller-interview%2D %2Des-gibt-keine-preis-obergrenze-fuer-dasiphone-7776804.html

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Gürtler, T., & Rauffmann, T. (2022). Hat Netflix doch noch ein entscheidendes Ass im Ärmel? Accessed April 20, 2022, from https://www.wiwo.de/unternehmen/it/streaming-wars-hatnetflix-doch-noch-ein-entscheidendes-ass-im-aermel/27998172.html Heidenreich, R. (2022, March 17). Konzerne nutzen Kriegssituation aus. Wiesbadener Kurier, p. 22. Heiny, L. (2021). Investoren rüsten Voi für die Scooter-Schlacht. Accessed April 20, 2022, from https://www.manager-magazin.de/unternehmen/voi-was-fredrik-hjelm-mit-115-millionen-dol lar-frischem-kapital-vorhat-a-77141f0b-b1a5-4d00-b2ce-aa3e845570e2 Heiny, L., & Rest, J. (2022). Wie Tesla 2022 das Billionen-Spiel eröffnen will. Accessed April 20, 2022, from https://www.manager-magazin.de/unternehmen/autoindustrie/tesla-gruenheideaustin-fsd-und-weitere-schluesselfragen-2022-a-92b34fdc-b8dd-410b-991e-c7f899698154 Hielscher, H. (2021). Ist der Fahrrad-Boom vorbei? Accessed April 20, 2022, from https://www. wiwo.de/unternehmen/handel/e-bikes-und-co-ist-der-fahrrad-boom-vorbei/27764694.html Jensen, O., & Henrich, M. (2011). Grundlegende preisstrategische Optionen auf B2B-Märkten. In C. Homburg & C. Totzek (Eds.), Preismanagement auf B2B-Märkten (pp. 75–104). Gabler. Kharpal, A. (2016). Apple captures record 91 percent of global smartphone profits: research. Accessed May 2, 2018, from https://www.cnbc.com/2016/11/23/apple-captures-record-91percent-of-global-smartphone-profits-research.html Kyriasoglou, C., & Rest, J. (2022). Fahrerflucht bei Gorillas. Accessed April 20, 2022, from https://www.manager-magaziAnoynymouse/unternehmen/lieferdienst-gorillas-fahrerfluchtbei-gorillas-ceo-kagan-suemer-sucht-neue-geldgeber-a-cecb8459-0002-0001-0000-00018122 9025 Lindner, R. (2018, April 28). Amazon ist Apple auf den Fersen. Frankfurter Allgemeine Zeitung, No. 99, p. 24. Lücke, H. (2022). Bolt E-Scooter: Alle Details zu Preisen und Städten in Deutschland. Accessed April 20, 2022, from https://www.inside-digital.de/ratgeber/bolt-e-scooter-in-deutschland-allestaedte-und-preise Lüder, C. (2021, December 22). Cloud – das steckt dahinter. Wiesbadener Kurier, p. 15. Mansholt, M. (2018). Smartphone-Konkurrenz. iPhone X verkauft sich schlechter als gedacht – das stellt Samsung vor Probleme. Accessed May 2, 2018, from https://www.stern.de/digital/ smartphones/iphone-x-verkauft-sich-schlechter-als-gedacht%2D%2D-und-stellt-samsung-vorprobleme-7869276.html Mohammed, R. (2019, November 12). Why is every streaming service using the same price model? Harvard Business Review. Accessed April 22, 2022, from https://hbr.org/2019/11/why-isevery-streaming-service-using-the-same-pricing-model Reiche, L. (2018). Kein Frieden im Preiskampf. Edeka weitet Boykott gegen Nestlé aus. Manager Magazin Online. Accessed May 2, 2018, from https://www.manager-magazin.de/unternehmen/ handel/edeka-nestle-boykott-ausgeweitet-und-trifft-jetzt-30-prozent-der-umsaetze-a-1201491. html Roll, O. (2003). Internetnetnutzung von Konsumenten. Eine qualitativ-empirische Studie auf handlungstheoretischer Basis. Gabler, Edition Wissenschaft. Roll, O. (2009). Pricing trends from a management perspective. Journal of Revenue and Pricing Management, 8(4), 396–398. Roll, O., & Achterberg, L. H. (2010). Potenziale und Elemente eines integrierten Preismanagements. Theorie und praktische Anwendung. In M. Bernecker (Ed.), Jahrbuch Marketing 2010/2011 (pp. 95–110). Johanna-Verlag. Roll, O., Pastuch, K., & Buchwald, G. (Eds.). (2012). Praxishandbuch Preismanagement. Strategien – Management – Lösungen. Wiley. Roll, O., & Schreiner, J. (2011). Ertragssteigerung durch professionelles Preismanagement. Performance, 2, 67–75. Salden, S., Schaefer, A., & Zand, B. (2017). Der Kunde als Gott. Der Spiegel, 50(2017), 12–19. Schader, P. (2020). Real Pro endet 2021, aber Walmart+ und Tesco Clubcard Plus setzen den Trend: Werden Supermärkte zu Mitglieds-Clubs? Accessed April 20, 2022, from https://www.

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supermarktblog.com/2020/09/29/real-pro-endet-2021-aber-walmart-und-tesco-clubcard-plussetzen-den-trend-werden-supermaerkte-zu-mitglieds-clubs/ Schütte, C. (2017). Kampf gegen Monopole: Geht es Amazon und Google an den Kragen? Manager Magazin Online. Accessed May 2, 2018, from http://www.manager-magaziAnoynymouse/ magazin/artikel/monopole-trustbusters-ii-a 1178562.html Seiwert, M. (2022). Volkswagen in China. “Bei E-Autos sind einige chinesische Anbieter mindestens gleichauf”. Accessed April 20, 2022, from https://www.wiwo.de/unternehmen/ auto/volkswagen-in-china-bei-e-autos-sind-einige-chinesische-anbieter-mindestens-gleichauf/2 8024540.html Simon, H. (1992). Preismanagement: Analyse – Strategie – Umsetzung (2nd ed.). Gabler. Simon, H. (2012). Simon-Kucher expert talk: Price management. Accessed May 2, 2018, from https://www.youtube.com/watch?v=fkWkJNXXV7k Simon, H. (2015a). Preisheiten. Campus. Simon, H. (2015b). Confessions of the pricing man. Copernicus. Simon, H., & Fassnacht, M. (2009). Strategie – Analyse – Entscheidung – Umsetzung (3rd ed.). Gabler. Simon, H., & Fassnacht, M. (2016). Strategie – Analyse – Entscheidung – Umsetzung (4th ed.). Gabler. Simon, H., & Fassnacht, M. (2019). Strategy, analysis, decision, implementation. Springer Nature. Smith, T. (2011). Pricing strategy: Setting price levels, managing price discounts, establishing price structures. Nelson Education. Sokolow, A. (2022, January 29). Apple trotzt Chip-Knappheit. Wiesbadener Kurier, p. 8. Wübker, G. (2004). Professionelle Preisfindung: Wege aus der Ertragskrise. BusinessVillage GmbH.

2

Characteristics of Digital Pricing

2.1

Characteristics of Digital Offerings in Terms of Pricing

Traditional pricing concepts cannot be easily applied to digital offerings (Bontis & Chung, 2000, p. 246; Buxmann et al., 2008). This is because information goods (software, online content, digital services, etc.) are subject to different economic rules than products and personal services. Information goods are products and services whose production and distribution can be digitized. In the following, the focus is on two categories of offerings: 1. Digital products (such as electronic books and newspapers, software, online music, and video games). 2. Digitized services as an extension of traditional offerings and services (e.g., digital consulting services, financial services, mobility services, and online maintenance of machines). Digital offerings are services that consist entirely of data and are therefore distributed at low cost. The basic properties of digital goods can be described in key points as follows: 1. Indestructibility, reproducibility, and convertibility. • Indestructibility: A difference between new and used digital goods is not detectable. Use does not lead to an objective loss of quality. Nevertheless, there may be a loss of value over time as perceived by the customer. • Reproducibility: Digital services can be duplicated without loss of quality and at low cost (Simon & Fassnacht, 2009, p. 517; Skiera & Spann, 2002, p. 272). • Convertibility: Modifications to digital offerings can be made with little effort. Versioning is simple and inexpensive (Buxmann & Lehmann, 2009). 2. Network effects. The perceived value of an online offering for the customer depends not only on the features of the solution. The number of users—and thus the degree of dissemination—plays a decisive role in determining the value # Springer Nature Switzerland AG 2023 F. Frohmann, Digital Pricing, Management for Professionals, https://doi.org/10.1007/978-3-031-24591-6_2

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of an information offering as perceived by the customer (Katz & Shapiro, 1985, p. 424). As a rule, the larger a network is, the more valuable it is to the customer. Interaction between users leads to an increased perception of value for all users. Direct network effects arise when users can communicate more efficiently with each other by sharing a digital service (e.g., an online platform). The value basis for economic transactions increases as the number of users increases. Put simply, the more consumers trade on eBay’s C2C platform, the greater the probability that interested parties willing to pay for an offer will emerge. The more prospective buyers team up on Pinduoduo’s C2M platform, the greater the likelihood of obtaining an attractive discount from the manufacturer. The same coherence applies to social networks such as Facebook, career portals (Xing, LinkedIn, etc.) or even dating services such as Tinder and Parship. Indirect network effects result from the interaction between the use of basic digital good and the use of complementary offerings. The market penetration of a digital product (e.g., standard software) leads to an increase in the availability of complementary offerings (e.g., consulting services). Bundling with services in turn increases the attractiveness of the software solution for customers. Many products in network industries only become attractive for the user through the parallel offer of complementary products. The immediate consequence of this is that the total profit of software (and other digital offerings) over the product lifecycle is strongly based on subsequent sales with accompanying services. 3. Interaction of digital offerings with services. In the case of services, too, an increasingly high proportion of value creation is determined by information and can therefore be digitized. One example of this is remote maintenance for industrial goods. Personal services such as consulting services—but also artistic services such as music—can be standardized through refinement (Corsten, 1988; Meyer, 1992). Here, the services are stored on media and the storage media are subsequently multiplied. In streaming, titles are not stored on a device but replayed directly from the network. In addition to music, movies/videos and games are also significant use cases for streaming. 4. Lock-in effects. Customers are often tied to a provider for the long term by their initial investment. This “tie-in” effect applies in particular to B2B sectors. The adaptation of corporate processes to one supplier leads to reduced flexibility to switch vendors at short notice. Potential switching of suppliers incurs costs for the customer (“switching costs”). New competitors have to overcome a significant hurdle when entering the market (Shapiro & Varian, 1999, p. 103). “Lock-in” effects also arise in the case of digital services used for consumption (such as electronic books). Using the market for electronic books as an example, this effect can be described succinctly. Reading e-books is based on a digital service. Use of the service requires investment in a hardware device and integrated reading software. Once a customer has committed to a platform (e.g., Amazon) and created a library, switching is unattractive. The same coherence applies to online games and streaming services in the movie and music sectors. Apple, with its ecosystem of diverse offerings (revenue sources), is a perfect example of the “lock-in” effect. The digital ecosystem of various services (music, video),

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software, and digital content ties customers to the relatively expensive hardware devices. Lock-in effects explain the high importance of market share (or a critical size) in digital offerings. 5. Relatively high fixed costs. High one-off value creation costs are typical for digitized products. The marginal costs of producing each additional unit of a digital offering (e.g., an additional Internet call, a digital bank transaction, or a software unit) tend toward zero (Simon, 2016). For music used online, variable costs are limited to download and payment processing. A music CD, on the other hand, is associated with variable costs for production as well as logistics and distribution (Buxmann et al., 2008, p. 111). In book production, the largest costs consist of the initial creation of the original. The “first copy costs” are independent of how many users will read the book. The creation and delivery of all further digital copies are practically free of cost. 6. Uncertain quality assessment before purchase. Digital offerings are experience goods. Users can judge their value only after purchase (Buxmann et al., 2008, p. 137; Simon & Fassnacht, 2009, p. 517). In the course of the assessment process prior to the purchase decision, the customer resorts to substitutional indicators of quality. Price plays an exposed role as a quality indicator. The perception of value, which is often unclear prior to purchase, has an influence on price elasticity. Users’ willingness to pay tends to be reinforced due to the quality indication of the price level. If, in addition, the prices of suppliers are not comparable due to intransparencies, there is potential for price increases. To put it another way: “Pricing power” is comparatively high in this case. Pricing power is the ability to increase the price level without losing demand. The characteristics addressed are of great importance for the pricing of digital offerings. They must be taken into account when defining the pricing strategy and designing price structures. These specifics also have a major influence on possible approaches to price differentiation and the selection of price models.

2.2

General Conditions and Price-Related Characteristics of the Internet

The strategic characteristics of the Internet are outlined below in key points (Simon & Fassnacht, 2009; Buxmann & Lehmann, 2009): 1. 2. 3. 4. 5.

Medium for the distribution (online distribution) of digital content. Unique distribution channel for information goods. Additional distribution channel for physical goods. Very low transaction costs for distribution of digital content. Interconnectivity of numerous market participants (interactivity).

The Internet offers unlimited opportunities for market participants to interact. This specific characteristic affects three different dimensions:

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(a) Direct or indirect exchange among consumers (b) Interaction between different suppliers (c) Interaction of suppliers and consumers Network effects lead to a significant increase in the potential customer base for companies. The efficient coordination of supply and demand results in new types of revenue and price models. 6. Increased transparency about offers and prices The strongest overall effect of digitization on pricing is a significant increase in price and value transparency (Simon, 2016; Tacke, 2018). Price comparison portals (e.g., Idealo, Billiger, DealDoktor, Check24, and Verivox) determine the lowest price for a product. The most frequent comparisons are made in B2C sectors—for vacations, flights, and hotels. Electricity and gas as well as insurance and cell phone contracts are also the subject of frequent comparison queries. Online services and price search engines such as pricewatch.com offer cross-sector price comparisons. In almost every industry or sector, specialized services exist—in addition to these— that track prices continuously. Pricetracker websites actively inform users when price conditions they have set have been reached: for example, when the price of a product falls below an amount the user has predefined. Pricegrabber.com or onlinepricealert.com offer their users the option of a price alert; they help customers identify sellers who can undercut a predefined price limit for a product (Simon & Fassnacht, 2009, p. 520). With the help of the iPhone app “ShopSavvy”, potential shoppers scan the barcode of a product in a store—they immediately receive information about what the same offer costs in the surrounding competitor stores. The increased price transparency and enhanced choices result in a shift of negotiation power in favor of consumers. Price comparison engines are also playing an increasing role in B2B sectors. Crowdfox Professional is the world’s first comparison platform for business customers based on artificial intelligence. Crowdfox is a B2B portal that enables purchasing a significant three-digit million amount of C-items of different sizes and industries. Transparency affects the price response function in all sectors (B2C and B2B) (Simon, 2016; Tacke, 2018). Increased price transparency can lead to an increase or decrease in sales volume, even in the absence of a price change. Price reductions that undercut competitors’ prices lead to a larger increase in volume compared to situations when price transparency is lower. In the case of price increases (or when the gap to competitors’ prices increases), an opposite effect results: the sales volume tends to decline more sharply. The increased transparency has a drastic effect, particularly in the case of pricing failures by companies. Media Markt came under severe pressure in the course of a “Black Friday” discount campaign—the background to this was repeated error messages about the payment function in the web store. Rating portals such as Trustpilot quickly made the displeasure of individual customers transparent to the general public (Hucko, 2022). Legal framework

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conditions can also lead to increased pressure on companies. Chinese digital corporations such as Tencent, Baidu, Alibaba, or ByteDance will have to become more transparent in the application of their recommendation algorithms in the future (Hecking, 2022a). The basic principles, goals, and mechanisms of their algorithms must be disclosed to the user more clearly than before. 7. Rather low willingness to pay for digital offers By making it easier to compare offers and thus increasing market transparency, the Internet has a massive impact on price elasticities. Internet users are often more price sensitive. There is a pronounced free-of-charge mentality among web users that has grown over the years. Many large Internet companies started exclusively with free services. Some still offer users services largely free of charge today. These include Facebook, Alphabet (YouTube), and some online newspapers, for example. A massive amount of content on the Internet is provided free of charge by countless users. Digital platforms such as YouTube base their business model on users uploading content themselves (such as music videos or films). Money is earned, in particular, from advertising revenues. The historically shaped habituation to free offers reduces the willingness to pay for innovative digital offers. The music market offers a particularly striking example of this coincidence. The key challenge in the music sector and in many other industries over the years has been to successively build and maintain a monetization fundament on the Internet (Dunkel & Steinmann, 2018). Online media portals (such as wiwo.de and manager-magazin.de in Germany) are following this path and increasingly supplement their free content offerings with paid premium services. Creative revenue and price models are one of the main levers for the migration from free to paid content. 8. Easy imitability of content on the Internet For business models based on online products, copyright protection is often very difficult to enforce. The protection of intellectual property rights is becoming more and more important with increasing digitization. Digital rights management is becoming a key challenge with direct consequences for price enforcement. One example is the compensation of music rights holders by streaming portals. Providers such as Apple Music and Spotify have to pay a high amount per download to the music labels as rights holders. This explains the still unstable profit structure of Spotify, the global market leader in music streaming (Kaiser, 2018). 9. Oligopolistic structures Due to the structural characteristics of digital services (network effects, switching costs, economies of scale, etc.), there is a high concentration of providers on the Internet. Online sectors are characterized by a natural tendency toward concentration. The “winner takes all” principle applies to many digital platforms. For example, e-commerce in Germany is reduced to a few large companies. The ten largest

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retailers (in particular the market leaders Amazon, Otto, and Zalando) account for two-thirds of the online market. The global market leader Amazon alone dominates more than 50% of Internet commerce in Germany. Due to Amazon’s high reach, a smaller online retailer can hardly do without sales via the platform of the global market leader. In China, the concentration in E-commerce is even significantly higher: the three most important companies (Alibaba, JD.com, and Pinduoduo) account for more than 90% of all digital goods sales (Hirn, 2018). Online advertising could be described as a duopoly for a long time. Even though Amazon has caught up significantly: Alphabet (Google) and Meta (Facebook) still control 60% of the global market for “digital advertising” (Hohensee, 2021). In mobile operating systems, competition is even more limited: the two dominant mobile operating systems (Android and iOS) form a duopoly. This also applies to the app stores of the two digital corporations Apple and Alphabet. Other duopoly structures can be found in the following digital industries and regions: Online food delivery services in the USA (DoorDash, Uber) and China (Meituan-Dianping, Ele.me); bikesharing in China (Ofo, Meituanbike); digital payment services in China (Alipay, TenPay). 10. Improved analysis of customer-specific transactions Digitization enables the analysis of individual transactions and realized prices. The focus here is on improved knowledge of user requirements, behaviors, and willingness to pay. The global market leader for music streaming is a particularly convincing example of a comprehensive analysis of user behavior (Dunkel & Steinmann, 2018). Spotify collects data in the triple-digit billion range from its more than 400 million active users every day. The use, appreciation, or rejection of tracks can be registered in detail via special software. The behavior of different users is systematically tracked for matches and patterns. Netflix (market leader in video streaming) uses algorithms to systematically record the behavior of its more than 220 million customers (Lange & Osterholt, 2022). Among other things, clickstreams, IP addresses used, and end devices as well as preferred formats are recorded. The technical basis for this: ever more computing power, large amounts of data, and powerful algorithms. 11. Individualization of offers The systematic evaluation of information about customer requirements (smart data) enables a more tailored product offering. The aim is to learn as much as possible about customers and to tailor the service to them individually (Lange, 2018). Companies with digital business models personalize their products and services based on detailed customer knowledge. At the core is a tailored design around what individual customers prefer and are willing to pay for. Spotify offers personalized listening recommendations for all subscribers of its service under the label “your selection of the week” (Dunkel & Steinmann, 2018).

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12. More flexible and efficient pricing Price implementation costs used to be so high—relative to profit growth—that price variations were sometimes not worthwhile (Skiera et al., 2005, p. 286). In web stores today, prices can be adjusted as often as desired without incurring additional costs. Personalized products/services lead to a much more effective monetization at the level of the individual customer. New opportunities for price differentiation arise: versioning can be technically represented at the individual customer level. The Internet enables an efficient time-based variation of prices up to real-time pricing. Dynamic pricing is becoming established in more and more product categories and industries. Dynamic pricing is a form of intertemporal price differentiation. A core element is the definition of suitable factors that determine the “temporal variation of prices”. The most intelligent form of dynamic pricing is based on a differentiation down to the individual level (personalized dynamic pricing; PDP). PDP is the dynamic determination of individual consumer prices according to personal characteristics (“identity-based pricing”; “one-to-one pricing”). 13. Fundament for pricing innovation The growing importance of the Internet is fueling pricing innovations. Simon defines pricing innovation as new “ideas, systems and methods for finding out about prices and shape them” (Simon, 2015, p. 236). Business models such as online auctions (eBay) or co-shopping (Letsbuyit; Powershopping) only emerged with the advent of digitization. The same applies to new price models such as “customer driven pricing” (CDP), “name your own price”, or “pay what you want”. Creative price models allow companies to stand out from the competition without having to adjust the price level. They are a discrete contribution to “value generation” for the customer. Innovative price models are described in detail in Chap. 8. 14. Increasing professionalization of the decision-making processes of customers and suppliers The potential for data analysis and decision preparation have increased significantly for both companies and customers. An enhanced professionalization can be observed on both sides. The interrelations can be explained as follows: The Internet is characterized by price transparency and comparability. Content providers, retailers, and manufacturing companies are trying to cope with this challenge by dynamic price adjustments. Customers in turn react by increasingly using price agents and price comparison portals. Countless mobile applications are available to users. This leads to an increased price pressure for online retailers, manufacturers of products as well as service providers. In the travel industry, there are rebooking portals that analyze and automatically exploit price fluctuations for services like hotel accommodations and flights on behalf of customers. The initiative to buy is digitally supported at the most favorable time. Rebookers (DreamCheaper, Rooms

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Ninja, Fairfly) thus exploit the dynamic pricing of airlines and hotels. Grinchbots represent the latest evolutionary stage of automated purchase bots. Bot software in the field of “denial of inventory” implements automated orders in online stores. In consumer goods segments with high demand and comparatively low supply, scalping software is used to exploit resales at higher prices (Gürtler, 2021). “Denial of inventory”—as the core goal of Grinchbots—is neither in the interests of product manufacturers nor customers. 15. Business and revenue model innovation Beyond the operative pricing process, increasing digitization and the Internet are fueling innovations in services, processes, and revenue models (Sauberschwarz & Weiß, 2018). Chapters 3 and 4 are devoted to these interrelationships. 16. Opposing effects on profit The increase in price transparency (and thus also price elasticity) leads to a decline of the price ceiling. This tends to put pressure on sales prices. On the other hand, new digital services and upgraded products lead to an increased willingness to pay. Reduced distribution costs also have a positive margin effect and pull the lower price limit (price floor) downward. Which of the opposing individual effects predominates—and how margins develop as a result—depends on the online business model, the industry, and the structural characteristics of the digital offering. The interrelationships outlined above affect a large number of offerings from a wide range of industries: (a) Digital services where automated distribution via the Internet took place at a very early stage (software, music, books, newspapers, banking services, consulting services, and media content). (b) Physical products that are digitally augmented (cars, houses, household appliances such as refrigerators and washing machines, etc.). (c) Digital service elements of physical products (e.g., preventive maintenance using sensors in mechanical engineering). (d) Products that appear to be less favored for electronic transactions; these include fresh products such as food, designer clothing, and products that require an explanation (such as complex insurance policies). In the recent past, it has become apparent that digitization also offers great potential for products in the fourth category (Amazon Fresh, Amazon Pharmacy). In principle, all products can be sold via the Internet. As an interim conclusion, it can be stated: Customization is at the heart of the digital transformation—increased access to customer data is the key driver.

2.3 Stages of Development of Digitization

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Stages of Development of Digitization

The aspects of digitization described in this book relate to pricing-relevant business processes. Digitization involves shifting an ever greater proportion of price management data and processes to the digital sphere: – Information flows that were previously organized in an analog form (e.g., the creation of paper documents such as price lists) can now be handled digitally (e.g., the communication of prices on web stores). – Processes (such as measuring willingness to pay, accounting for complex price models, or negotiating prices) are partially or fully automated. Digitization is an ongoing process that has had a massive impact on economic development for over 35 years (Ochsenkühn, 2017). The various phases of the Internet evolution are of particular importance for price management. The development can be summarized in short keywords as follows: • Phase 1—Emergence of the Internet: Networking of people (e.g., via mail systems). In 1995, around 250,000 people in Germany owned stationary Internet connections. • Phase 2—E-commerce: Distribution of digital content and physical products via the Internet. In September 1995, the “Auction Web” website was launched (later renamed eBay). From this point on, the auctioning of items via the Internet became suitable for consumers. By 1995, it was already possible to purchase the Otto Group’s entire product portfolio on the Internet (Knieps, 2022). Amazon’s online bookstore had already been founded the year before. • Phase 3—Interactive Internet: Creation of web content by users. User-generated content includes videos on portals such as YouTube, contributions to blogs, information on wikis (e.g., Wikipedia), and user comments in web stores. The core characteristic of this evolutionary stage is a stronger integration of consumers and users into the value creation processes of companies. The result: a shift of market power in the direction of users, who increasingly share their experiences in consumer forums. • Phase 4—Mobile Internet (m-commerce): With the introduction of the iPhone by Apple in 2007, mobile data connections became suitable for mass use. Internet transactions increasingly shifted from stationary (e-commerce) to mobile (m-commerce) in the years that followed. Online rental services such as Airbnb and Uber have since been able to serve customers without having to invest in assets. Overnight accommodations with private individuals or driving services can be easily arranged online via a smartphone app. B2C platforms have established themselves in numerous sectors (bikesharing, e-scooters, online food delivery, car sharing, and re-commerce). • Phase 5—Internet of Things (IoT): The networking and communication of machines with each other (“Machine to Machine Communication”) describes the technical development of the recent past. In 2017, more than 8 billion Internet-

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enabled devices were already connected to the worldwide network (Jansen, 2017). Business processes as well as interactions with market partners (suppliers, customers, sales agents) are based on a global system of computer networks, sensors, drive elements, and devices that use the Internet protocol. There is direct communication of the interconnected devices via the Internet. The adaptive machines need information to make intelligent decisions. This is where the dichotomy of sensors and actuators comes into play: sensors record data (e.g., temperature, brightness, motion, and location). Actuators trigger actions of other devices. Devices networked via IoT autonomously trigger services and payments. A networked refrigerator can autonomously reorder food and pay for it (“machine to machine payment”). Self-driving cars are able to find a free parking space— they pay within the given budget. The development is highly dynamic. One of the pioneers of this stage of development is the technology and service company, Bosch. As early as 2017, numerous Bosch products were Internet-enabled. Digitized products ranged from drills to washing machines and refrigerators up to car parts for the after-sales sector. The technology company is one of the leading suppliers in the field of connected cars, smart building technology, and household appliances that interact online. Since 2020, Bosch has been able to connect every newly developed electronic product digitally (Flaig, 2017). In many industries, new business models are emerging around the Internet of Things. Networked products are the basis for value-added services for customers. In particular, traditional hardware-driven companies are making the transition from product to service provider with this holistic approach (Sauberschwarz & Weiß, 2018). One example of this is preventive maintenance in mechanical engineering. A machine equipped with sensors uses algorithms to analyze its status. Based on this, it can initiate its own maintenance and servicing (Lietzmann, 2018). Bosch, for example, uses software solutions that allow customers to recognize when their machines need maintenance at an early stage (Giersberg, 2018). BMW is one of the pioneers in the digital market of connected cars. The premium manufacturer’s car models have been equipped with sensors for years. The volume of data as well as the number of globally linked end devices continues to increase dramatically. The number of devices that are connected online will rise to around 60 billion by 2025 (Lietzmann, 2018; Lindinger, 2018; Jansen, 2017). • Phase 6—Artificial intelligence (AI): While the evolutionary phase of the Internet of Things is primarily about the interconnection of hardware, companies are working on intelligent, self-learning systems as part of the next technological leap. At the core of artificial intelligence are machines that can solve problems independently (Wirminghaus et al., 2018; Anonymous, 2018b; Marx, 2018). Computer systems generate knowledge from large amounts of data and make predictions based on these learnings. Machines capable of learning can evaluate an almost unlimited amount of data fed in via the Internet of Things. According to Otto Group, AI enables it to make predictions with more than 90% accuracy based on four price dimensions: product, time, region, and channel (Knieps, 2022). Example: What will be the sales volume for a newly launched garment

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in the first 2 weeks in certain markets? “Machine learning” is a branch of artificial intelligence—it has been used in marketing since the late 2000s. Machine learning can be used for pricing and sales processes in terms of increasing efficiency and better market exploitation. As a result, patterns in consumer behavior and willingness to pay can be identified, for example. However, the automatic recognition of patterns and statistical correlations is only a small part of AI. Deep learning first emerged in the 2010s. It is a special form of machine learning in which layers of data are linked together to solve problems. Computers are increasingly achieving learning processes; algorithms are able to recognize recurring patterns in data on their own. The four main categories of artificial intelligence can be summarized in brief keywords as follows (Joho, 2018; Anonymous, 2018b): – Perceptual AI—Cognitive AI specializes in reproducing and imitating the higher cognitive functions of humans (e.g., speech and vision). The main application of perceptual AI is human–computer interaction (HCI). Speech recognition systems such as Apple Siri, Google Now, Amazon Alexa, and Microsoft Cortana employ natural user interfaces. Digital assistants recognize the spoken word, interpret its meaning, and act accordingly. The benefit of this is that users interact with machines via simple voice commands without having to write any computer code. In marketing, chatbots (voice dialog systems) and automatic image recognition are the main applications. Google Lens is an example of computer recognition. The app can be used to recognize restaurant signs and get instant information about the menu (including the price structure). – Internet AI—Perceptual AI uses the digital traces that users leave behind when they surf the Internet, shop online, and consume digital content. Digital pioneers such as Netflix, Spotify, and Amazon use this type of AI to recommend personalized offers (content, products, and services) to their users in an automated way. The basis of the recommendation engines is the comprehensive analysis of preferences as well as past customer behavior. – Autonomous AI is behind technologies like self-driving cars. These are machine learning (ML) models that control machines, robots, drones, and other autonomous systems. – Business AI—Decision AI is the most diverse category. It assists in automating business decisions. In all standardized processes, self-learning hardware can support value creation (Joho, 2018). Selected examples include: 1. Price analysis and monitoring: Simple analyses that are helpful for price optimization (such as scenario analyses) can be performed by computer programs. In particular, the analysis and monitoring area in pricing will continue to become highly automated. Self-learning machines will assist the pricing experts.

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2. Dynamic Pricing: AI can automate difficult decisions that would require analyses of huge amounts of data. Determining the optimal price for a particular product or service is a major challenge: A multitude of factors play a role and these in turn depend on each other (see Chap. 10). 3. Price implementation: Intelligent machines execute rule-based price adjustments. Cognitive assistance systems are the technological basis for this. The creation of price lists can be increasingly controlled by artificial intelligence algorithms. 4. Peer pricing: This machine learning method supports the process of price implementation. Optimized price proposals are determined automatically during the bidding process. The core of this artificial intelligence method is a classification algorithm (see Chap. 11). Artificial intelligence also brings about changes in business models—beyond the pricing process (see Hecking, 2019): – Machine learning is the basis for the development of new products that support consumers’ everyday lives. One example of this is digital voice assistants. These are integrated in smartphones (such as Siri from Apple) or in digital speakers (such as Alexa/Echo from Amazon). – The increasing market penetration of voice-controlled digital assistants such as Alexa or Siri is the technological basis for new digital services. Customer-specific offers can be generated automatically via the possibilities of “voice commerce”. These are based on the voice-controlled search queries of the customer (Rottwilm, 2018b). – Voice assistants are increasingly finding their way into hardware sectors. The Mercedes-Benz User Experience (MBUX) works via voice control. Smart voice assistants are becoming more and more standard in cars (Pander, 2018). – Machine learning systems also enable innovative offerings. One example is the personalized recommendation of music. Individually tailored music recommendations can be derived on the basis of the user’s previous choices. Proactive, digitized offers are based on the self-learning system’s assessment that the customer would likely prefer certain tracks, even if they have never heard them before (Albert & Schultz, 2018). The outlined business model of a personal music channel leads to new revenue sources and Price models based on them. Different variants are possible for price model design—from subscription to “pay per hit” to bundling. – In retail, a massive expansion of operational processes via new technologies leads to a significant increase in value to customer. “Smart retail” works in simplified terms as follows: Cameras (video recordings) and sensors in parking lots record the number of arriving vehicles as well as the number of people. This environmental data is combined with internal store data, e.g., from sensors on shopping carts. An analysis of in-store movement patterns rounds out the findings. The number of checkout personnel required can be forecasted in real time. Waiting times at checkouts are avoided. In addition, detailed insights for optimal store design results (Reimann, 2021; Scheppe, 2022).

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– Augmented reality (AR) services are catching on in a number of industries. In the B2C sector, Ikea was one of the first companies to build on AR. With the help of its app called “places”, users can virtually drape pieces of furniture in their living room or bedroom before making a purchase. In the furniture retail sector, Otto uses a similar app with augmented reality technology (Knieps, 2022). – Artificial intelligence is also increasingly being used in the B2B segment to derive proactive offers. One example of this is Amazon Web Services (AWS), the global market leader in cloud computing. With the help of intelligent algorithms, Amazon derives from usage behavior patterns of the past how the requirements of its business customers will develop in the future. AWS can already adapt its computing systems to the wishes that its users are likely to have tomorrow (Rottwilm, 2018b). • Phase 7—Blockchain and tokenization: Blockchain is a decentralized register for recording information. The technology enables digital storage and decentralized management of assets. The database is publicly accessible—the decentralized system creates a direct link between consumers and companies (Hülsbömer, 2022). Peer-to-peer sharing is enabled by this: the direct exchange of assets and information without the involvement of intermediaries. The blockchain allows consumers to earn money with their personal data. Consumer activity tokens or digital coins are used for this purpose. Tokens play a major role in the context of the Blockchain as official financial instruments. The term tokenization describes the digital mapping of ownership rights to assets. A token is an intangible representation of an asset. Assets are digital products, physical objects (e.g., buildings), and rights (including licensing rights). NFTs (non-fungible tokens) are of particular importance in the context of digitization. An NFT as a token has two functions: It reflects intangible digital items (pieces of music, videos, or items in online games); it represents tangible assets (such as products). The two main implications of non-fungible tokens in the context of digital transformation are: (a) Interactions will be made much more transparent in the future through NFT. (b) Payment mechanisms in platform business models are changing. Even if the technologies outlined have developed differently (objective, timing, etc.): Only when IoT, blockchain, and AI are applied in an integrated manner do companies exploit the full potential of digital transformation. The connection between the technologies is to be explained as follows: IoT provides data— Blockchain defines the rules of interaction of the networked devices—AI optimizes business processes and rules via pattern recognition. • Phase 8—Metaverse: The next-generation Internet is a 3-D virtual habitat. Metaverse represents an artificial world in which users can actively participate. The gaming industry is pioneering the technological evolution at the business model level. Microsoft, Meta (Facebook), and Tencent (Epic Games) are the

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driving digital corporations (Sokolow & Dembach, 2021). Tapping into the metaverse leads to new sources of revenue—because high-resolution screens, virtual reality glasses, 3-D cameras, and headphones are required to use the virtual universe. For the users, corresponding earning opportunities result from the new technology—similar to the blockchain (Hohensee, 2021, 2022; Hecking, 2022b). Parallel to the technical evolutionary stages of digitization outlined above, clear patterns can also be discerned at the company and industry level. Large hardware corporations like IBM and software companies like Microsoft were the drivers of digitization until the 1990s. The period from 1985 to 1995—shortly before the widespread establishment of the Internet—was characterized by an enormous dominance of the software market leader Microsoft. With the penetration of the stationary Internet and mobile communications at the end of the 1990s, cell phone companies (Nokia), network operators (Deutsche Telekom, Vodafone), and Internet providers (AOL, Yahoo) became the pioneers of technological development. At the turn of the millennium, the three first-generation technology platforms—Baidu, Alibaba Group, and Tencent—emerged in China. The market potential of the networked household had already been systematically examined before the turn of the millennium by two companies for which I worked as a management consultant. However, there was a lack of suitable hardware and software to enable the digital services of the “smart home” to be implemented in a marketable form. With the introduction of the iPhone by Apple in June 2007 and the rapid penetration of apps, a gradual shift of value creation from hardware to software began. Mobile commerce made its breakthrough. The smartphone was the most important driver of digital transformation in the past decade. The business model of the platforms developed in parallel. Both developments went hand in hand: the platforms of the digital market leaders Apple, Amazon, Alphabet (Google), and Facebook were increasingly used on mobile devices over the course of the last few years (Armbruster, 2018). New business models of the sharing economy such as Airbnb and Uber have achieved initial success in terms of user acceptance in recent years. However, “value generation” for the customer and “value capture” for the company are in disproportion. A stable profit situation does not exist at Uber since the start in 2008 until today. Artificial intelligence is currently driving a large number of companies across a wide range of industries. At its core, it is about automated solutions for users. From purchase planning, financial investments (credit conditions), partner search to transportation (autonomous driving), and AI is changing all industries as a cross-sectional technology. Car manufacturers such as Tesla are pursuing the most sustainable project in the context of artificial intelligence with their vision of the self-driving car (Reintjes, 2018; Eckl-Dorna, 2018a).

2.4 Digitization and Competitive Dynamics

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Digitization and Competitive Dynamics

No industry remains unaffected by the innovation dynamics of the digital age. Established companies find themselves confronted with new digital solutions in selected parts of their value chain from companies outside the industry. Selected examples of “business migration” are: 1. Digital corporations from outside the car industry, such as Apple, Amazon, and Tencent, are using their enormous power and cash flow to invest in the huge business potentials of “autonomous driving”. In doing so, they are directly attacking Tesla, Volkswagen, and other car companies in the most important growth segment of their core business (Eckl-Dorna, 2018b). 2. The dominant competitors in e-commerce have long since ceased to be mere online retailers. Amazon, Alibaba, and Tencent are data corporations that are creating new digitized services with the help of artificial intelligence. Amazon— which started as an online retailer of consumer products—is expanding horizontally (new product segments) and vertically (new value creation processes). As part of its horizontal business expansion, Amazon Business offers products for business customers in excess of 100 million items—an immediate attack on wholesale. The vertical expansion is transforming Amazon into a logistics company that is continuously digitizing its business processes and revolutionizing delivery processes. In 2016, it launched its own cargo airline under the PrimeAir brand. With around 100 aircraft, Amazon Air is the fourth-largest cargo airline in the world. The core effect of Amazon’s own air freight fleet: Full control over the central part of its value creation in logistics. 3. Due to technological and legal changes, online groups such as Alibaba, Tencent, and Amazon are increasingly migrating into the financial services market. They are thus becoming new competitors for established banks and credit card providers (Rottwilm, 2018a). The duopolists in the area of “mobile operating systems” are intensifying the competitive situation with Apple Pay and Google Pay. By entering the financial services business, the online market leaders in the western (Amazon) and eastern hemispheres (Alibaba) are gaining further data on the spending behavior and income of their customers. This data can be used to create value in the core business (Hirn, 2018; Rottwilm, 2018b). 4. Autonomous driving, mobility services, and electric mobility are leading to a radical disruption of the automotive industry. The focus of offerings (revenue models) is shifting from hardware to software and digital services. Revenues from software and digital services promise double-digit profit margins. Carsharing providers are an alternative to traditional car rental companies (Heckel & Ermisch, 2018; Anonymous, 2018a). Ridehailing providers compete with traditional cab operators, but also with bus companies and railroads. However: innovative sharing models (such as Uber in the area of ride services) will be attacked by the future business model of self-driving cars and may themselves quickly become obsolete. Tesla is focusing on an autonomously driving fleet of robotic electric cars, which is also expected to dominate the cab and ridehailing

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industry in the future. Digital business models in the mobility sector are leading to increased competition from carmakers, transportation companies (such as car rental firms), insurance companies, and digital corporations such as Apple, Baidu, and Alphabet (Fritz et al., 2018). 5. Software vendors are confronted with entirely new competitors. In the past, they competed exclusively at the product level. New business models (software as a service) and the resulting revenue models (e.g., revenue from the sale of contacts) are expanding the competitive radius. Today, there is competition at the contact level from many different providers—these include Internet search engines and media companies, among others. In the rental of software in the B2B segment, the market leader Amazon—which started out as a pure online retailer—has left all the technology companies behind. Much of Amazon’s profits come from its cloud computing division, Web Services (AWS). Amazon is increasingly becoming a software company. From a profit point of view, the cloud division is an internal cross-subsidy for the core business “e-commerce”. Four findings demonstrate the enormous competitive dynamics in the wake of digitization: (a) In 2007, the five largest companies in the world in terms of market capitalization were still exclusively energy companies (exception: Microsoft). Ten years later, the top five were made up of corporations whose business model is based on digitization: Apple, Alphabet (Google), Microsoft, Amazon, and Meta (Rottwilm, 2018b). (b) Increasing digitization is fuelling the speed of market developments. The following examples illustrate the dynamics: The classic telephone took a total of 75 years to reach a penetration of 100 million users. In the case of Facebook and WhatsApp, the time span for acquiring the same number of users was reduced to 4 and 2 years, respectively (Kroker, 2017). Disney’s streaming service has gained 95 million subscribers worldwide since its launch (in November 2019 in the USA) until the end of 2021. Disney+ reached this number almost ten times faster than Netflix (Lange & Osterholt, 2022). (c) The market capitalization of digital companies follows the speed at which new business models are developed. Apple was worth more than USD 3 trillion on the US stock exchange on the first trading day of 2022 (Schwerdtfeger, 2022). It took the corporation 38 years to cross the first trillion threshold. A further USD 1000 billion in market capitalization was achieved in just 2 years. The third trillion was generated after only 14 months. (d) Ninety percent of all globally available data in 2017 were no more than 2 years old (Jauernig, 2017). Data is the foundation of companies’ business models in the age of cloud computing, the internet of things, artificial intelligence, blockchain, and metaverse. The decisive factors for large technology companies are size and market share. The overriding goal is to establish and protect dominant market positions. The resulting

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market power leads to an outstanding “pricing power”. For example, Apple has transformed the distribution of music in a short period of time and has taken a strong position in the music industry (Hajek, 2018). The digital transformation took place in two stages: first with the music service iTunes as well as the iPod (as of 2001), then by means of the streaming service Apple Music (as of 2015). Apple pursues the technique of “lock-in”—retaining users by offering an integrated hardware, software, and service standard—like no other technology group. The technology group’s influence 15 years ago was already so great that network operators such as T-Mobile had their prices dictated by them at the beginning of the iPhone’s market penetration. In addition, they had to cede a significant share of their revenues to Apple. Even Google—the quasi-monopolist in the field of search engines—is yielding a double-digit billion USD amount to Apple to remain the standard search engine for its core product, the iPhone (Beuth, 2016). Taken together across all market partners, Apple’s business model offers a particularly striking case study in the sustainable generation of pricing power. In the media industry, Facebook has recently gained more and more advertising revenue shares, although the platform provider does not even offer its own content. For a long time, growth of the number of users was more important than revenue for the company founded in 2004 (Hackhausen, 2013; Hauck, 2014; Simon & Fassnacht, 2009, p. 519). The result: over 2 billion users worldwide; the profit margin is even higher than Apple’s. In the case of Meta (Facebook), network effects are the core prerequisite for driving advertising revenues. The same applies to Google’s parent company Alphabet. Both have been able to keep their cumulative share (60%) stable for years in the world’s most important advertising market—the USA (Schmidt, 2016). All other advertising companies lost ground to the oligopolists, with the exception of Amazon. With a relative share of 7%, Amazon is a serious competitor. Eighty-six percent of Alphabet’s revenue and 98% (in the case of Facebook) resulted from online ads in 2020. Increased market power very often leads to greater opportunities on the sales side, but the procurement side is also strongly affected in many industries. Amazon exploited its market power in online book retailing to a great extent over many years: not as a monopoly that demands comparatively high prices from customers, rather as a company that tries to put pressure on purchasing prices vis-à-vis its suppliers with the corresponding procurement power. Smaller publishers in particular felt the effects of this in discount negotiations. More than 50% of total sales in the German retail sector were generated by Amazon in 2022. Amazon generated around three times as much revenue (13.9 billion EUR) as runner-up otto.de (Rabe, 2022). As the largest single market player, Amazon earned higher revenues than its nine top-selling competitors combined. The development toward a platform economy has led to a considerable concentration in numerous industries. Not since the end of the nineteenth century has economic concentration been as great as it was at the beginning of 2022. The trend toward market concentration has been accelerated by the global pandemic. It will intensify—on the development path toward the data economy and AI. Digital corporations can invest billions of dollars in AI projects by means of their financial

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power. And they benefit most from economies of scale. That is because, against a backdrop of a customer base in the hundreds of millions or billions, technology platforms can diversify easily and cost-effectively. Pricing power, negotiating better terms with suppliers, and economies of scale in production explain the profitability impact of high market shares.

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Knieps, S. (2022). Die Digitalisierung ist der größte Umbruch der Menschheit. Accessed April 22, 2022, from https://www.wiwo.de/unternehmen/handel/michael-otto-beim-gipfeltreffen-derweltmarktfuehrer-die-digitalisierung-ist-der-groesste-umbruch-der-menschheit/28032822.html Kroker, M. (2017). Digital ist schneller: Telefon benötigt 75 Jahre für 100 Millionen Nutzer – Candy Crush nur 1,3 Jahre. Accessed May 2, 2018, from https://blog.wiwo.de/look-at-it/201 7/02/01/digital-ist-schneller-telefon-benoetigt-75-jahre-fuer-100-millionen-nutzer-candycrush-nur-13-jahre/ Lange, K. (2018). Coba-Vorstand Michael Reuther zur Digitalisierung. “Mit Smart Data zu expandieren, ist die Königsdisziplin”. Accessed May 2, 2018, from https://www.managermagazin.de/unternehmen/industrie/digitalisierung-im-mittelstand-michael-reuther-ueber-smartdata-nutzung-a-1203288.html Lange, K., & Osterholt, S. (2022). Warum Netflix vor schwierigen Zeiten steht. Accessed April 22, 2022, from https://www.manager-magazin.de/unternehmen/netflix-aktie-mit-kurssturzwarum-der-streamingdienst-vor-schwierigen-zeiten-steht-a-ce771ab4-dea2-4cab-b689-d3e774 aaf902 Lietzmann, P. (2018). Digital-Chef Klaus Helmrich. Siemens testet Fabriken vorher am Computer – und verzehnfacht dadurch die Produktion. Accessed April 23, 2018, from https://www.focus. de/finanzen/news/unternehmen/mindsphere-in-der-cloud-siemens-testet-fabriken-am-com puter-und-verzehnfacht-dadurch-produktion_id_8786168.html Lindinger, M. (2018). Digitale Flut. Frankfurter Allgemeine Woche, 6, 60. Marx, U. (2018, February 28). Künstliche Intelligenz macht der Industrie Beine. Frankfurter Allgemeine Zeitung, 99, 20. Meyer, A. (1992). Dienstleistungs-Marketing: Erkenntnisse und praktische Beispiele. FGM. Ochsenkühn, A. (2017). Das Internet frisst seine Kinder. Chancen und Risiken der Digitalisierung. Amac-Buch Verlag. Pander, J. (2018). Wir wollen das Apple der Autos werden. Accessed May 2, 2018, from http:// www.spiegel.de/auto/aktuell/auto-start-up-byton-schneller-als-alle-anderen-a-1203947.html Rabe, L. (2022). E-Commerce. Statistiken zum E-Commerce weltweit. Accessed April 22, 2022, from https://de.statista.com/themen/2604/E-commerce-weltweit/#topicHeader__wrapper Reimann, E. (2021, December 30). Deutsche bleiben Supermärkten treu. Wiesbadener Kurier, p. 21. Reintjes, D. (2018). Google dringt ins Auto vor und ist kaum aufzuhalten. Accessed April 17, 2018, from https://www.wiwo.de/unternehmen/auto/android-auto-google-dringt-ins-auto-vor-und-istkaum-aufzuhalten/23187632.html Rottwilm, C. (2018a). Online-Riese auf Expansionskurs Banken aufgepasst - Amazon will eigene Konten anbieten. Accessed May 2, 2018, from https://www.manager-magazin.de/unternehmen/ handel/banken-aufgepasst-amazon-will-eigene-konten-anbieten-a-1196672.html Rottwilm, C. (2018b). 3 Gründe, warum Amazon bald mehr wert ist als Apple. Accessed May 2, 2018, from https://www.manager-magazin.de/finanzen/boerse/boersenwert-warum-amazonapple-bald-ueberholen-wird-a-1205108.html Sauberschwarz, L., & Weiß, L. (2018). Schluß mit dem Digitalisierungstheater. Accessed May 2, 2018, from https://www.capital.de/wirtschaft-politik/schluss-mit-dem-digitalisierungstheater Scheppe, M. (2022). Eigene Onlineshops rentieren sich selten – warum die Kosmetikriesen trotzdem darauf setzen. Accessed April 22, 2022, from https://www.handelsblatt.com/ unternehmen/handel-konsumgueter/henkel-loreal-beiersdorf-eigene-onlineshops-rentierensich-selten-warum-die-kosmetikriesen-trotzdem-darauf-setzen/27968136.html#:~:text=Henkel %2C%20L'Or%C3%A9al%2C%20Beiersdorf,lohnt%20sich%20das%20oft%20nicht Schmidt, H. (2016). Wirtschaft 4.0. Accessed May 2, 2018, from https://www.youtube.com/watch? v=53VGXX4_Pvo Schwerdtfeger, H. (2022). Irrer Börsenwert. Der Apple ist faul. Accessed April 22, 2022, from https://www.wiwo.de/finanzen/boerse/irrer-boersenwert-der-apple-ist-faul/27946248.html Shapiro, C., & Varian, H. (1999). Information rules: A strategic guide to the network economy. Harvard Business School.

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Simon, H. (2015). Preisheiten. Campus. Simon, H. (2016). Pricing in the new era of digitization. Warsaw Conference. Accessed May 2, 2018, from https://www.youtube.com/watch?v=hHT3bI3UkV8&t=1373s Simon, H., & Fassnacht, M. (2009). Preismanagement: Strategie – Analyse – Entscheidung – Umsetzung (3rd ed.). Gabler. Skiera, B., & Spann, M. (2002). Preisdifferenzierung im Internet. In M. Schlögel, T. Tomczak, & C. Belz (Eds.), Roadmap to e-business (pp. 270–284). Thexis. Skiera, B., Spann, M., & Walz, U. (2005). Erlösquellen und Preismodelle für den Business-toConsumer-Bereich im Internet. Wirtschaftsinformatik, 47(4), 285–294. Sokolow, A., & Dembach, C. (2021, October 30). Neuer Name für die Plattform der Zukunft. Wiesbadener Kurier, p. 7. Tacke, G. (2018, April 19). Digitization: “Think big, start smart”. Presentation European Sales Conference SKP 2018. Wirminghaus, N., Buttlar, H. von, & Kreimeier, N. (2018). Künstliche Intelligenz – Hype oder Hoffnung? Accessed May 2, 2018, from https://www.capital.de/wirtschaft-politik/kuenstlicheintelligenz-hype-oder-hoffnung

3

Business Models

3.1

Business Models as the Starting Point for Digital Pricing

Digitization is often reduced to a means of process optimization or seen as an IT initiative. These misinterpretations also apply to pricing. In terms of price management, digitization is often conceptually limited to topics such as automated pricing or pricing for online channels. In the following, I will show that digital transformation is less about an IT phenomenon or a tool for increasing productivity. Digitization is not a project, but a holistic process. At its core it is about: 1. Serving unresolved customer needs or those that have not yet been technologically feasible. 2. The rapid identification and agile implementation of new business opportunities. 3. A technologically supported innovation in the broadest sense. Digitization enables new business models, additional revenue streams, greater integration of customers into business processes, innovative price models, etc. It influences all aspects of price management and enables innovation across the individual stages of the pricing process. To understand new business opportunities and their impact on price management, a definitional delineation is very important. The starting point for digital pricing is the business model. A business model is a structured visualization of how a company creates and extracts value (Wirtz, 2011; Bieger & Reinhold, 2011). It visualizes the logical relationships of how a company creates value for target customers and generates profits by monetizing the value. A business model answers four questions (Osterwalder & Pigneur, 2010) with respect to “value generation” and “value capturing”: • Who are our customers? • What values relevant to the customer do we want to create? # Springer Nature Switzerland AG 2023 F. Frohmann, Digital Pricing, Management for Professionals, https://doi.org/10.1007/978-3-031-24591-6_3

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Fig. 3.1 Four major dimensions of a business model

Target customers

Business Models

Value to customer

Business model

Operating model

Profit model

• How do we create the services within the framework of the value creation processes? • How do we extract the value created? A business model is based on four major components (Fig. 3.1). 1. Target customers 2. Benefit (value to customer) An innovation of the business model can result from the creation of new values for the customer (value innovation). Digitized products and services are tailored to the individual needs of users. They serve previously unsolved customer problems. Added value for the customer often goes far beyond the core service offered. Online retailers such as Amazon have been able to offer customers various additional benefits that were not available on the market in this form before the introduction of web stores. The ease of use and operation of the online store for customers is one of Amazon’s key success factors. Reviews from users on the platform, fast deliveries, and personalized content are three other key value drivers that keep customers loyal. By offering personalized recommendations, Amazon went from being a pure retailer to a service provider. The technology enterprise is increasingly transferring this service idea to previously unserved industries (Rottwilm, 2018; Hielscher, 2021; Hielscher, 2022). The value to customer of streaming services in the field of music is essential as follows: “Full choice” in combination with the “possibility of small-scale consumption”.

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3. Value Creation Architecture (Operating Model) In the course of designing the value proposition, the following questions need to be answered: • On the basis of which competencies and resources do the benefit for our customers arise? • Which value creation partners do we work with? • Who takes over which processes in which parts of the value chain (development, production, application technology, logistics, etc.)? • How do we design the interfaces to our customers and suppliers? Numerous internal and external processes (sales management, offer presentation, customer service, etc.) are directly relevant to price management. New ways in the architecture of value creation are one of the drivers of innovative business models (operational innovation). On the third level of the business model, digitized processes can be created, for example, to generate additional customer value. The acceleration of deliveries through automated processes is an example of innovative value creation processes. A variant of this is the delivery by drones (e.g., Amazon) or self-driving robots (e.g., Google Waymo). Creating value for the customer is often based on fundamental innovations in value architecture. Disney offers a concise example with its theme parks. Practical Example: Disney Theme Parks – Initial situation: Long waiting times for users as the main reason for dissatisfaction. – Objective: Significant improvement in customer benefit through reduction of waiting times. – Implementation via the architecture of value creation: “Disney Land” guests receive a wristband with an RFID chip before visiting the theme park. The “MagicBand” enables the individual assignment and position tracking of each visitor. – Effect on customer benefits: Manual check-in processes are eliminated; in the restaurant, dishes ordered (via smartphone) can be delivered directly to the seat. – Result: Significant enhancement of net visitor time; significant increase in revenue (merchandise sales, catering, etc.). 4. Profit Model This relates to the two essential cornerstones of sales and costs. The relevant questions in the design of the profit model are:

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• • • • •

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How do we earn money? How do we extract the value created for the customer? Which benefits (products or services) have profit potential? Will new revenue streams result from our digital offerings? What potential does the revenue structure offer for our price model?

Innovation with regard to the fourth pillar of the business model means creating new ways of converting the value created into profits (profit model innovation). The monetization of value is the core of value extraction. With the help of new revenue sources and price models, the additional value created can be better monetized—i.e., converted into profits (Osterwalder & Pigneur, 2010). At the level of the business model, the rough framework is outlined—at the resulting levels (revenue model and pricing process), concrete decisions can be derived. This integrative approach is shown below.

3.2

Digital Business Models

The central business concept of the digital economy is the platform model. At its core, a platform business model consists of at least three market participants: 1. Providers. 2. Consumers. 3. The platform operator, who acts as an intermediary between supplier and customer with the help of digital technologies. The four essential characteristics of a digital platform are: 1. Digital infrastructure that can be continuously optimized with the help of software updates. 2. Neutral intermediary as intermediate instance (focus: mediation). 3. Autonomous suppliers and consumers. 4. Growth through a dual effect on the supply and demand side (scale and network effects). Network effect: The value of the offering increases with the number of users in both groups. Scale effect: Due to high fixed costs, an increase in volume leads to disproportionate profit growth. Companies that reach a relevant mass of customers achieve economies of scale. The advantages arise on two sides—in purchasing and in sales. The resulting market power has a double positive effect on margins. Platforms can be divided into the following categories according to the supplier– demand structures: B2C, B2B, C2C, C2M. eBay is the best-known example of a Consumer2Consumer (C2C) platform. On the world’s largest online flea market, any private user can offer his or her products. Buyers are private customers. In the case of manufacturing platforms (B2B), the intermediary integrates global production

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capacities via standard online interfaces. The platform operator coordinates manufacturing requests from its business customers with the help of artificial intelligence. For each request, algorithms determine the best composition of quality, production costs, logistics costs, delivery reliability, and other criteria. The advantage for the customer: He has to interact only with the platform operator; he receives timely feedback on the feasibility of the order, the process, prices, and other details. The principle of Consumer2Manufacturer platforms can be outlined using the example of Pinduduo. The C2M business model of the Chinese company founded in 2015 is based on the pooling of bargain hunters. Private users enforce lower final prices with the supplier via collective orders. If a sufficiently large number of interested buyers can be found, they all receive a discount. The customer communicates directly with the manufacturer (consumer-to-manufacturer). B2C platforms serve customer requirements without investing in equipment. Online brokerage services can consequently also be described as “no asset” business models. Examples of this: Airbnb mediates as a platform between landlords (as providers) and consumers of an accommodation. Uber brings together commercial drivers and passengers as a mobility broker. Without the platform, ridehailing providers and passengers would not have found each other—or only with significantly greater effort. The mediation is digitized by means of a smartphone app. The particular advantages of the platforms for private users are the reduction in search effort and transaction costs. Another example of success of an innovative digital business model is offered by a seemingly traditional industry: the long-distance coach business. Flixbus has built up a dominant market position within just a few years. Today’s market leader in Germany did not define long-distance bus travel as an infrastructure business when it entered the market in 2011. Flixbus’ vision was summed up in one sentence: In the age of digital transformation, bus tourism is a network business! Customer data—and not vehicles—represent the resource critical to success. The resulting core competence is the implementation of the customer information gained in the route offering, route planning, pricing, etc. Flixbus became the dominant Internet platform for travelers with this business model (Alvares de Souza Soares, 2021). Flixbus is not a bus company. It does not own vehicles. The company operates an online platform through which customers can book tickets digitally. The number and quality of contact options are the key values to customer. Therefore, at the beginning of market penetration, it was critical for success to establish as many connections as possible as quickly as possible. Flixbus was able to force the competition out of the market with penetration prices by constantly increasing market shares with subsequent economies of scale. On the domestic longdistance bus market, the “no asset” platform was already without significant competition in 2018 following strong market consolidations—Flixbus is a monopolist today (Kluge, 2018; Schlesiger, 2018). The platform company also used the dramatic collapse of the travel market in the wake of the pandemic (as of January 2020) to expand to the USA (Alvares de Souza Soares, 2021). Following the acquisition of Greyhound, it now dominates the US market. The lessons from the Flixbus success story are as follows:

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1. Market share as a key performance indicator plays a central role in digitized industries. The supplier with the largest market share very often achieves the most favorable cost position. Due to these dynamic relationships, market share has a discrete value as a determinant of future profit potential. 2. High market shares have a positive impact on all three profit drivers. Fixed cost degression and improved price enforcement potential lead to a dual positive profit effect. The increase in market power also leads to greater potential on the sales side and thus to a volume effect. Thus, based on its monopoly position in the long-distance bus market, Flixbus created excellent opportunities to expand its business model in the direction of the mobility group Flixmobility (Schlesiger, 2018; Alvares de Souza Soares, 2021). 3. Market share-oriented rules of thumb are highly relevant, especially in dynamic markets and for innovative business models. In essence, Amazon’s strategy in online retailing is comparable to Flixbus’ approach, even if the two business models, the relevant competitors, etc. are very different. 4. In other digitized industries (such as the bicycle industry or food delivery services), too, the innovation drivers—as in the case of Flixbus—do not come from the core sector, but from the digital economy. At their core, they are data experts. One example is bikesharing, in which the Asian technology group Tencent has invested heavily (Hecking, 2018b). Critical to success is the ability to apply user data and their movement profiles profitably. Access to the customer and control of end user data are of crucial importance (Burfeind, 2018). Re-commerce platforms represent another variant of the brokerage model. This business model supports the trade in second-hand goods via the Internet. Consumer electronics such as cell phones and tablet PCs as well as secondhand books, compact discs, and computer games are sold online. Pioneers of re-commerce in Germany include Momox for books and Rebuy for PC games. The “sustainability” megatrend is being served by other business models. Selected examples are as follows: • Zalando offers second-hand fashion and the “Care & Repair” repair service. • Vaude, an outdoor equipment manufacturer from Germany, offers a business model for the rental of mountain sports equipment and outdoor clothing with its “irent.it” platform. • FairGrapes stands as a company for sustainable viticulture. In contrast to many traditional wine retailers, the focus is on quality wines. The concept of sustainable consumption is rounded off by nature or species conservation projects in which consumers can participate. • Virtual kitchens (ghost kitchens) replace fully equipped physical restaurants. This business model offers restaurateurs the opportunity to flexibly rent a professional kitchen infrastructure. The trend of food delivery is served with much less initial investment than traditional restaurants can afford. Exclusive restaurant concepts are scalable cost-effectively via cloud kitchens.

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• In B2B sectors, information technologies enable the sharing of key industrial goods. Collaborative consumption models remove barriers to the usage of solutions. Asset utilization is improved—waste for customers is reduced. The revenue models of platform operators are diverse and differ according to the categories outlined above. Consumer-to-consumer platforms are essentially based on a direct compensation by the customer. The user pays for the presentation of the offer or the actual transaction. This results in different price models: fixed participation fees, variable brokerage fees, or a combination of both approaches. eBay combines both price models and thus is able to monetize in two ways: 1. A fixed fee for a listing of the offerings. 2. Revenue per transaction (paid either as an absolute amount or as a percentage of revenue). The services of B2C platforms such as Facebook are often free of charge for users and are financed by advertising. To their corporate customers, the large digital platforms offer the opportunity to personalize and contextualize advertising. In the case of Facebook, the user is in this respect the “product” (or provider); the advertising company represents the customer. A distinctive feature of digital platform models is that they scale very often. However, this is not always true. There are exceptions to this rule! The statement that digital networks always mean scalability must be viewed critically. The winner takes-all principle does not always apply to digital platforms. The example of Spotify proves this essential insight. Case Study Music Streaming: Spotify The business model of the global market leader for music streaming must be critically evaluated in terms of profitability (Anonymous, 2022). As an audio streaming platform, Spotify connects artists with consumers of music. Spotify’s levies on music companies (labels) are so high that the streaming service has suffered losses over several years. In 2017, despite revenues of EUR 4.1 billion, it made a loss of over EUR 300 million (Hajek, 2018; Rest & de Souza Soares, 2018). There is no way out of the losses via scaling effects. This sets Spotify apart from some other digitized enterprises. Spotify’s costs grow with the number of users, as royalties to labels are to be paid per music stream. In contrast, the video streaming service Netflix pays fixed prices (flat fees) for third-party content. Netflix also produces a lot of content itself. The consequence of this: Netflix profits from economies of scale! The revenue-cost gap is widening as users grow (Kerkmann, 2018; Hecking, 2018a). In the case of Spotify, the effect will never materialize—unless the core elements of the business model are addressed. Since it will hardly be possible to significantly influence the cost structure, Spotify must necessarily address a second pillar of the profit model—the revenue sources (see Chap. 4).

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The example of the world market leader Spotify proves that there are indeed business models in the platform economy that do not achieve economies of scale. Other critical digital industries from a profit perspective are: 1. Ridehailing: The market leader Uber accumulated over USD 21 billion in losses from 2015 to 2020 (Rest, 2021). The negative trend in profitability is worsening; the net operating profit margin after tax was minus 30% in the last fiscal period in 2021. 2. Bikesharing: Chinese market leaders Ofo and Meituanbike have chronic cash flow problems; customer deposits are used as financial reserves (Anonymous, 2019). 3. Food delivery services: The dichotomy of high variable service costs and relatively low enforceable prices is problematic here; Uber Eats has not yet been successful in terms of “value extraction” in this market either. 4. Car sharing: The business model is problematic with regard to all three profit drivers: low usage, low willingness to pay, high capital intensity due to the need for a broad offering; ShareNow—the joint venture between Daimler and BMW— had to cope with monthly losses in the double-digit millions of EUR at the beginning of 2020 (Anonymous, 2018c). 5. E-scooters: The intensity of competition on the German market is enormous. Voi, Lime, Bird, and the price-aggressive newcomer Bolt are fighting against the market leader Tier Mobility. Price wars are not the only negative profit driver. Sales restrictions and additional costs arise from increasing public resistance. Numerous European cities are limiting the number of devices and the parking areas allowed; in parallel, special usage fees are being significantly increased (Anonymous, 2021). As a consequence, most B2C platforms from the outlined sectors are making losses (and this was already the case before the Corona pandemic began). In contrast to this is ByteDance from China. The technology conglomerate bundles business activities in the fields of gaming, music streaming, education, etc. The video sharing platform TikTok, founded in 2017, is also part of it. Short video formats are becoming increasingly popular, especially among younger segments of the population. The rapid increase in the number of users of the video app resulted in a profit of USD 3 billion for ByteDance in 2019 (based on revenues of USD 18 billion). Practical Examples: Marketplaces and Platforms Electronic marketplaces are digital sales channels that enable trade unrestricted by time and region. Marketplaces are highly relevant for both digital and tangible goods. A wide variety of products are traded via the Internet. An important success criterion for marketplaces is a critical mass of participants. Network effects explain why large companies dominate the marketplace (continued)

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business model. In detail, different variants of this business model can be distinguished: Business-to-consumer marketplaces such as Amazon have the greatest relative importance. Amazon’s marketplace generates the highest share of online sales in Germany, ahead of Otto and Zalando. Fifty-three percent of all online sales in Germany were via Amazon Marketplace in 2021. External retailers and manufacturers use the digital sales channel partly on their own account and at their own risk (Amazon Seller Program). Amazon thus multiplies the choice of products for its customers, which increases the appeal of the marketplace from the buyer’s perspective. Sales from third-party sellers account for 58% of total revenue. The global market leader in online retailing also uses its marketplace to sell third-party products from niche providers (Hielscher, 2021; Hielscher, 2022). Dominant business-to-consumer platforms exploit their market power strategically—they dictate the rules of business and enforce high trade margins. Amazon Marketplace collects a commission from its partners in the amount of a fixed percentage of the sales price. From the partner companies' perspective, there is a shift from previously necessary marketing and advertising budgets to transaction fees (commissions). In the course of determining the commissions, the world's largest online retailer takes a differentiated approach based on the business model (industry, product specifics, etc.) of the partner seller. The amount of the transaction fees is determined, among other things, by the type and value of the product. The price model of the online retailer reflects the margin and, in part, the sales potential of the partner products (Salden et al., 2017). Taobao, which belongs to the Chinese digital enterprise Alibaba, is the largest online retail platform globally (Reccius, 2019). Unlike Amazon, it does not sell its own brands. Third-party sellers distribute goods to end consumers. Taobao does not own any warehouses and is not acting as a retailer. The B2C company imposes sales commissions as well as fees for marketing and advertising. Numerous digital marketplace providers have expanded their product range to include a large number of niche products. The business model is referred to as long-tail business (Brutscher, 2015). On the one hand, the portfolio consists of focus products that are available almost everywhere in online channels. These fast-moving offerings are subject to tough price and margin pressure. On the other hand, there are rarely purchased niche products. These are hardly noticed by the majority of customers or are perceived as interesting by only a few consumers. From a pricing perspective, the long-tail business model offers opportunities for differentiation (see Chapter 10). However, the long-tail business model for digital goods has one major difference compared to traditional products. In traditional product sectors such as mechanical engineering, the number of variants is a key complexity driver. In contrast to digital offerings, an increased number of variants for physical products does not necessarily

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lead to positive effects for profit and customer loyalty. On the contrary, the number of variants and company earnings are negatively correlated. Apple, Tesla, Volkswagen, and Mercedes are worth mentioning here. At the beginning of 2022, Tesla was focusing on four models in its core vehicle production business. Instead of introducing new models, the electric car pioneer focuses on scaling the existing versions from a profit perspective (Freitag & Rest, 2022). Digitization is changing the value creation processes of companies. The management of linear value chains is being overridden by the management of crosscompany networks. In many cases, digital ecosystems are evolving—out of the platform model. Ecosystems are based on a radically different concept of value creation. The value creation model goes far beyond individual offerings that can only fulfill a partial need (e.g., car rental or car sharing). The focus is on the overarching customer benefit, such as “unlimited mobility independent of time and place”. Services that contribute to an overarching customer need are bundled under a holistic concept. The classic supplier-customer model is being replaced by a networked ecosystem with various value creation partners (suppliers, technology partners, sales intermediaries). The core competencies of the partner companies are combined in such a way that the needs of the customers can be served optimally. Cross-industry ecosystems can be found in particular in the areas of mobility (“smart mobility”) and living (“smart home”). Tesla offers a constantly growing ecosystem of products and services. The electric pioneer does not only sell cars. Additional revenue streams result from vehicle software, charging networks, car insurance, solar roofs, and household batteries. Software for autonomous driving represents Tesla’s largest source of potential profits. The electricity storage division (including the electricity trading platform Autobidder) will grow even faster than the vehicle division in the future (Freitag & Rest, 2022). The development of business models in the direction of comprehensive ecosystems has consequences for key factors influencing price management. The implications are as follows: • Increasing mergence of industries; less traditional sectors with clear boundaries (e.g., pharma and automotive). • The sharp distinction between B2C and B2B is increasingly dissolving. • The strategic openness required to build ecosystems calls for comprehensive control mechanisms; for example, access rights to data must be regulated. (which data is available to which partner?) • Competitors are more difficult to identify. The dividing line between competitors and cooperation partners is becoming blurred. The large digital enterprises are increasingly competing with each other, which has massive implications for their pricing. – Direct competition is especially strong in public cloud computing. The marketleading competitors in the cloud business with IT services and storage space on

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the Internet are (in descending order): Amazon (Web Services), Microsoft (Azure), Alibaba, and Alphabet (Google). Four of the major digital companies account for much of the digital voice assistant business: Apple (Siri), Google (Assistant), Amazon (Alexa), and Microsoft (Cortana). In the payments sector, competitors from outside the industry are competing with the established player’s Visa, Mastercard, and PayPal. Digital payment services include Apple Pay, Google Pay, Alipay from Alibaba, and WeChat Pay from Tencent. In the growth sector of self-driving cars, the competitive intensity of the digital groups is growing massively. Alphabet (with Waymo), Apple, Tesla, Baidu, and Amazon (Zoox) are active in this growing market. The video streaming and music streaming sectors have interesting similarities: a dominant market leader specializing in this industry (Netflix; Spotify); increasing competition from broadly based digital enterprises (Apple, Amazon, Alphabet), conglomerates (AT&T, Disney), and small niche providers. The digital advertising sector (online ads) is dominated by Google, Meta (Facebook), and Amazon. Another focus sector with above-average growth and enormous competitive intensity is game streaming. Meta (Facebook Gaming), Google (Stadia), Amazon (Luna), Tencent as well as the console market oligopolists Sony (psNow), Microsoft, and Nintendo are in direct competition with each other in cloud gaming.

This increasing competition is being countered in numerous sectors by cooperative approaches on the part of the digital enterprises. Business models can basically be defined in three different ways: 1. Product oriented 2. Competency based 3. Needs based. The relevant questions are: What do we offer? What are we able to do? What do our customers need? The customer’s need is the business basis! In contrast, a product-oriented business definition is too narrow. There are two main arguments in favor of this: 1. A customer never pays for a product, but ultimately always only for the satisfaction of a need. The problem solution perceived by the customer determines his decision-making process. 2. The customer’s perception of products is dynamic. Priorities and preferences are shifting. Established products are potentially at risk from changes in these needs structures. In the age of digitization, this is even more true than before. Offers can become technically obsolete very quickly. Or they can potentially be displaced by competing solutions that serve customer needs in a better way. Customers are

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always deciding anew on the continued existence of the company (Carlzon, 1992; Stauss, 1991). Example: Cell Phones Companies like Siemens, Motorola, and Nokia were simply overwhelmed by the enormous pace of development in the mobile communications industry. At the beginning of the millennium, Siemens got caught up in cutthroat competition primarily based on price due to an undifferentiated strategy. Nokia ignored the technological leap of the mobile Internet. In the period from June 2007 (introduction of the first iPhone by Apple) to the end of 2011, the Finnish cell phone company lost half of its global market share. The crash in the relative market position (from 51% to 27%) was followed in early 2012 by further drastic declines in the three central KPIs of pricing: revenue, unit sales, and average prices each fell by double-digit percentages. Nokia’s losses were not limited to the premium smartphone segment, however. With low-price phones in the emerging markets, the Finnish group lost even more. Nokia’s brand value, which allowed premium prices for a long time, eroded rapidly due to technological evolution and changing needs structures (Eisenlauer, 2018; Anonymous, 2018b). Nokia’s crash at its core is based on a perception problem: the cell phone company ignored the change in customer needs. The behavior of competitors was also misjudged. Apple, on the other hand, understood that access to the Internet became more important than single cell phone functions. Charging time—as one of Nokia’s prioritized features—was nowhere near as critical to customers as it had been a few years earlier (Eisenlauer, 2018). The multitouch display of Apple addressed a key need of smartphone users: an ingeniously simple user interface. An analysis of successful business models in a wide range of industries leads to the following findings: 1. Customer acceptance is the decisive factor in explaining the success or failure of business models. 2. Failed companies neglected the customer perspective when designing their business model. Value generation and value extraction were not in balance. 3. Companies run the risk of missing out on new trends. Skimming profits in established markets and on existing technology platforms is very often the focus. 4. In the context of structural changes due to digitization, there are early signals for new trends. Example a): Microsoft continued to pursue its proven business model for its Windows operating system even when Google was already consistently shifting the value creation of software in the direction of services. Microsoft only expanded the license-based revenue model after losing enormous shares to Google and other competitors in the area of open-source software. For a long

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time, cloud computing was not dominated by the software company Microsoft, but by the former bookseller Amazon. It took 5 years for Microsoft to react to the pioneer Amazon Web Services (market launch 2006). Example b): Newspaper publishers ignored the Internet for many years. It was assumed that the new technological possibilities would endanger the paper-based business model. Innovative digital business sectors were conquered by new competitors (Parship and Tinder, ImmobilienScout24, AutoScout24). Announcements and advertisements in the segments of partnership, real estate, job placement, and automotive were lost completely. The book industry was also transformed by online providers (such as Amazon). 5. If there is an insufficient willingness to pay in the market or if the business model is not viable, the best algorithm cannot compensate for this. Pricing alone cannot fix structural problems of a company. Tools, methods, and automated pricing mechanisms are important, but only a necessary condition. “Digital pricing” approaches such as “dynamic pricing” alone are consequently no guarantee for entrepreneurial success. The rideshare service provider Uber—one of the protagonists of dynamic pricing—has been making losses in the billions for years (Rest, 2021). The shocking aspect is that the losses are increasing dramatically (for example: USD 3 billion loss on USD 11 billion revenue in 2021). 6. Speed (in the sense of rapid implementation of customer requirements) is a key success factor. However, the pioneer in a digital industry is not always successful later on! Facebook (social networks), eBay (C2C platform), Google (search engines), and Spotify (music streaming)—although they are market leaders today—were not pioneers. About Google: When the domain “Google.com” was launched in 1998, there were already 14 other search engines. However, Google was able to quickly establish itself because its technology was superior to its competitors (i.e., generated better search results from the user’s point of view). Google took over the idea of combining search results with text ads from its competitor Overture. On Spotify: The pioneer in the music streaming services sector was the company, Pandora. If one uses these findings with foresight, it remains to be said: Even the seemingly unassailable business models of digital corporations like Meta (Facebook) and Alphabet (Google) are threatened by new technological developments. The increasingly significant voice control technology is profoundly changing the advertising business. The dominance of Google or Facebook in the stationary and mobile advertising business is undisputed. The two digital groups generate returns because they know more about users than others. This allows Google and Facebook to generate increasingly effective targeted advertising. However, competitors such as Amazon are catching up (Anonymous, 2018a, 2018b, 2018c), and their relative share is already 7%. With its digital voice assistant Alexa, Amazon controls a mission-critical interface that significantly influences both value generation and monetization. The interface controls:

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• The research of information and offerings on the net. • The online purchase of users via its own platform. • The generation of advertising revenues. Already today, many users prefer the voice assistant Alexa to the long-dominant search engine Google (Postinett, 2018). In the USA, more than 50% of users’ searches start on Amazon. Amazon is used twice as much as Google. More importantly, there is a strong correlation between search and purchase. This leads to Amazon’s outstanding competitive advantage: For selling products, Amazon’s knowledge about its customers is much more valuable than Google’s and Facebook’s expertise. “Spending patterns” are much more important than “browsing patterns”. Meta (Facebook), on the other hand, is investing heavily in the Metaverse, the next evolutionary stage of the Internet. Meta has already developed its own interfaces to the metaverse (Hecking, 2021). The most important gateways for augmented reality include: The virtual data glasses Oculus as well as the video conferencing computer Portal—which can be used via voice control (Hecking, 2022a, 2022b).

3.3

Value Creation Through Data and Data-Driven Business Models

In a digital economy, information is at the heart of value creation. The economic use of new technological leaps such as the Internet of Things and artificial intelligence requires historical information as well as current economic data. Technological development manifests itself in economic statistics: For several decades, the highest valuation in the S&P 500 stock index in the USA was accounted for by those companies that produced physical goods or offered them as sales intermediaries (e.g., pharmaceuticals and retail). Today, technology firms (e.g., software companies), platform operators (social media companies, online retailers, etc.), and digital corporations (Apple) lead the list in terms of market capitalization (Rottwilm, 2018). Eight of the ten most valuable companies in the world at the end of 2021 came from the USA—Apple, Microsoft, and Alphabet (Google) make up the top 3. The Corona pandemic has fuelled this digitization push. In the case of successful manufacturers of end devices (Apple, Nintendo, Tesla, Sony, Trumpf, Bosch, etc.), hardware is increasingly serving as the value creation basis for innovative software offerings and digital services. The result of all these developments: Physical assets and goods tend to lose importance in business models. Today, trade in data already contributes more to global economic growth than the exchange of goods. Data is the core resource of the world’s most valuable companies (Meckel, 2018). The success of technology corporations such as Apple, Amazon, Alphabet, Microsoft, Alibaba, and Tencent is based on a digital business model. Three key drivers of the data economy are as follows:

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1. Multi-optionality: The same data point can be used for a variety of value creation options. High-quality information can be used to improve existing services, develop new products, and build creative new services. Netflix, Spotify, and Amazon—to name just a few examples—use data with outstanding excellence. There is an opportunity for the multiple marketing of data. The price of data is derived from the value creation options that business customers and private users can realize from the information. This contrasts with physical goods, which can only be sold or leased once. The payment flows for traditional products are limited by physical boundaries! 2. Network effects: The quantity and quality of the available data are critical to success. The more extensive the data sets of companies, the greater their value creation options. Linking information increases its utility and the potential for networked solutions disproportionately. In this respect, large technology groups such as Alphabet, Meta, Apple, Alibaba, Tencent, and Amazon have a competitive advantage that is hard for new providers to catch up with. This is because the systems of smaller companies—starting from a significantly lower information base—are literally too slow to learn (Kharpal, 2016; Lietzmann, 2018; Pander, 2018). 3. Data as a means of payment: The provision of personal data is regarded as a potential means of payment for free online services. From a legal perspective, data is equivalent to a monetary payment. In Germany, this has applied since January 2022. The value of data, the speed of market development, and network effects are interdependent. The goal must be to tap as many value adding data sources as quickly as possible. The faster the market penetration, the: • • • • •

Larger the network of users Greater the volume of data Higher the potential for managing data flows Greater the value of data Greater the pricing potential. The pricing challenge in this case encompasses two dimensions:

1. The pricing of data as a value driver. 2. Optimizing prices for innovative services generated based on the data. Both sources of revenue are mutually dependent because data (dimension 1) is the basis for digital services (dimension 2), which generate new, high-quality data. The value of customer data can be described succinctly using the example of search engine operators. A search engine like Google or Baidu coordinates advertisers (companies) and information users (business and private customers). Customer information is the critical resource within the business model. The more individuals use the search engine, the more data Google or Baidu own. Of critical importance:

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customer data determines the perceived value of both of Google’s target audiences. Both advertisers and search engine users benefit from the quantity and quality of information. Customer data is a lever to optimize search results (and thus determines the value for the user). At the same time, they increase the value of the platform for advertisers. The larger the search engine’s market share, the greater the demand from advertisers to place their ads on the platform. An expansion of demand, in turn, strengthens the negotiating position of the search engine provider for the pricing of advertisements. Within the group of search engine users, different segments with different business values exist. The data of wealthy customers is more valuable than the profiles of low-income users. The income of users correlates with their shopping budget and their willingness to pay. As a result, high-income earners are more attractive than average users as a target group for advertising. Successful companies differentiate themselves through data, the ideas that arise from it, and the implementation via digital business models. Two influencing factors explain digital business model innovations: 1. A customer-centric business definition. This serves previously unresolved customer problems or responds to a change in user needs. 2. Technological changes. Both innovation drivers can be outlined using the example of the automotive industry and two other sectors. Practical Example 1: Data-Driven Business Models The enormous potential of digital transformation manifests itself, particularly in the area of mobility. Tesla’s business model is unique in terms of the three essential pillars of a business definition. On “value to customer”: Constant vehicle improvements via software updates lead to an added value per vehicle that is superior to most competitors. Users always have an up-to-date vehicle—regardless of the year of manufacture. Due to low wear and tear, expensive repair and maintenance work is not necessary. Tesla’s “operating model” is based on an integrated and centralized IT architecture—the operating system is unique in the industry. For the "profit model" this results in an outstanding operating margin (Freitag & Rest, 2022). New business ideas from the two German premium manufacturers BMW and Mercedes are based on a deeper understanding of the value to customer of car users. For a significant proportion of customers, mobility is more important than owning a vehicle. New transportation concepts such as car sharing are fueling the OEM business: "People want easy access to mobility beyond their own vehicle" (Heckel & Ermisch, 2018). This change in the definition of customer needs results in a new business model. Customers should no longer be restricted to just buy cars. They can switch to flexible rental or leasing (continued)

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models that serve their temporally and contextually different needs far better. A subscription is a simple price model. During the individual term of validity, it is possible to switch between different vehicles. With this new overarching business model, automakers are moving away from the pure sale of vehicles. At its core is the offer of comprehensive mobility services. Five key pillars of the digital transformation in the automotive industry are outlined below. The relevant dimensions include data, digital services, target groups and partners, strategic success factors, and revenue models. (a) Data: An enormous amount of usage-related data is generated via the Internet of Things. The information collected by vehicle sensors, scanners, and cameras include consumption, distance, tire pressure, steering movement, acceleration, and braking behavior. Detailed movement profiles are created via integrated navigation systems and the sensors. Map services can be updated with real-time data. (b) Digital services: The movement data results in numerous innovative services for end customers. Digital mobility information linked to locations and updated navigation maps assists in the search for parking spaces. In addition, individual parking garage reservations can be made at the push of a button. Car users can be offered personalized shopping suggestions (location, price promotions, etc.) during their journey. Depending on the time and weather, convenient appointments can be coordinated at the nearest car wash. These are coordinated with the capacity utilization of the facility and possible time-based price differentiations of the operator. Based on the customer-specific use or rejection of these offers, insights can be drawn for improved service offers in the future (Eckl-Dorna, 2018; Fasse, 2018; Meyer, 2018). (c) Value creation and revenue partners: The stakeholders of vehicle and motion data are diverse. The list includes original equipment manufacturers (OEMs), auto dealers, parts manufacturers, and tire suppliers, among others. Wrecking services, sensor manufacturers, traffic information providers, and software companies are added to the list. Service providers also derive significant benefit from a car’s mobility and location data. Financial service providers are at the forefront (Reiche, 2018). The group of stakeholders from the service sector continues to include parking garage operators, repair stores, retail companies, restaurants, and hotels. (d) Strategic success factors: Whoever is able to control user and movement data will dominate the promising market for mobility services in the long term. A crucial prerequisite for the success of mobility platforms is a critical size. This explains the former cooperation of the major German (continued)

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premium manufacturers (e.g., BMW and Daimler). With their selfdeveloped mobility services, the carmakers are in direct competition with car rental companies (such as Sixt). Sixt’s goal is to build a global platform that covers the entire value chain of digitalized mobility (Fritz et al., 2018). Under the umbrella brand “One”, Sixt offers a global ecosystem for mobility: car rental (“Sixt rent”), carsharing (“Sixt share”), and ride services (“Sixt ride”) are integrated by means of an app for the customer. The decisive component of this business model for networked mobility is the bundle of several services. Ridehailing is not profitable for Sixt—viewed in isolation. Nevertheless, Sixt offers cabs and ridehailing services from cooperation partners (such as Lyft). Mobility users are tied to the ecosystem—the app is the catalyst for more detailed user insights about their data. Station-independent car sharing (freefloating carsharing) has also not yet progressed beyond a niche status. The profitability of the business model is limited to high-density metropolitan areas. Carsharing can be distinguished from car rental as follows: Short-term use; usually billed by the minute; mainly in demand at weekends. Car rental and car sharing complement each other, especially in terms of the time of use. (e) Monetization and revenue models: How can the new data-based services be optimally monetized with the help of innovative revenue and price models? This is the key question for all companies involved. Access to users is critical to success in monetizing the added value. This favors all those companies that have direct customer contact (e.g., car manufacturers, rental car providers, insurance companies, or repair shop operators). Revenue sources include advertising and commissions for value-added services. To promote network effects, car users could also be incentivized for proactively submitting additional data. The use of driver assistance systems, lane keepers, parking aids, and numerous other digitized services can be encouraged by insurance companies with lower-price add-ons. Start-ups like Byton define a car as a “smart device on wheels” (Eckl-Dorna, 2018). Customers can download apps via a central interface. In analogy to the revenue model of Apple’s App Store, Byton earns a share of these transactions as the operator of the platform. The central prerequisite is a direct business relationship with customers (Eckl-Dorna, 2018; Anonymous, 2022).

3.3 Value Creation Through Data and Data-Driven Business Models

Practical Example 2: Data-Driven Business Models Herbert Kannegiesser GmbH is the world market leader for industrial laundry technology. The company produces equipment for industrial laundries. End customers are hotels or hospitals. The business foundation of the supplier of laundry technology has changed massively in recent years. Customer requirements in terms of performance and economic efficiency (life cycle costs) have become much more demanding (Wocher, 2017). The strategic solution of the laundry service provider is in key points: 1. Automation of value creation processes, stronger integration with customer processes. 2. Transformation from machine builder to solution provider. 3. Significant expansion of the solution portfolio to include software and services, offering a complete package consisting of machines, software packages, and consulting. The value creation architecture is based on software and data. Laundry items are connected to the network via radio chips. From the analysis of the status information, the use of resources can be optimized (Capital-Redaktion, 2018). Customer benefits are significantly increased in two ways: customers are supported in increasing their productivity and customer benefit transparency is significantly promoted. With this innovative business model, the company is able to cope with the increasing price and cost pressure.

Practical Example 3: Data-Driven Business Models Digital technology is also leading to completely new business, revenue, and price models for elevators. Innovative companies such as the elevator and escalator manufacturer Schindler are expanding their business definition. They no longer define themselves as manufacturers of equipment, but as comprehensive solution providers (Sauberschwarz & Weiß, 2018; Buttlar & Fahrion, 2018; Lietzmann, 2018). Automated error messages from defective elevators or escalators result in significantly faster repair and maintenance services. As artificial intelligence continues to develop, elevators are already reporting impending faults before they fail. In addition, services can be offered throughout the customer’s entire transportation process. One example of this is the transmission of information for end customers embedded in the elevator doors. Customers are offered a significant increase in time efficiency and an improvement in the service experience—across all digital services. The benefits of the value-added services can be captured via creative price models tailored to the usage behavior of different customer segments (see Chapter 8).

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3.4

Business Models

The Three-Level Model of Digital Pricing

In digital business models (platforms, marketplaces, ecosystems, etc.), price is no longer a reliable metric for competition. Two main reasons are: 1. Many companies (like Alphabet, Amazon, Alibaba, or Tencent) cross-subsidize parts of their business. Not all business units have to contribute to profit. Services are therefore often offered for free (Google; search engine) or below cost (Amazon; Kindle). This decision is an elementary part of the revenue model. The revenue model definition is upstream of the pricing process. 2. Traditionally, monetization models are based on the exchange of a good for money. In digital business models, customers can pay with an exchange value other than money. The equivalent can include non-monetary components or be entirely without monetary payment. Modern monetization models increasingly include non-monetary countervalue components. Example a) Attention in the context of freemium models (example: Spotify). Here, users accept advertising in order to be able to use the “free” digital service component free of charge. Example b) Users pay with their data (as in the case of Facebook and Google). Practical Example: Modern Monetization Models The IKEA furniture enterprise introduced a new monetization model in its stores in Dubai in 2020. Compensation by the customer is neither in monetary units, nor with data, nor by means of attention. Under the slogan “Buy with your time”, IKEA introduced a price in form of a “time currency” for each item. The more time customers spend traveling to the furniture stores, the more they can buy. The logic: the longer the trip, the more time credits, the lower the bill (Buttlar & Fahrion, 2018; Lietzmann, 2018). The “operating model” behind the innovative monetization measure is as follows: 1. a “timeline” on the app Google Maps documents how much time IKEA customers have invested on their way to the stores. 2. an algorithm calculates the monetary value of the trip (input factors: time spent, distance traveled, average hourly wage, etc.). For a limited period of time, each item was expressed in two monetization units: a monetary amount (in local currency) and a time amount. All the key influencing factors of digitization lead to the following core statement: price optimization for business offerings (e.g., products such as game consoles, services such as air travel, and digital services such as video streaming) is only one facet of digital price management! Important business decisions precede price setting: (1) the definition of revenue sources (the revenue model). (2) defining value to customer as a central pillar of the business model (Fig. 3.2). Professional price management must go beyond the pure optimization of the pricing process and also reflect the higher-level decisions on the business model and the revenue model! These interactions have reached a new dimension with the

3.4 The Three-Level Model of Digital Pricing Fig. 3.2 The three-level approach “digital pricing”

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Digital Pricing

Business model

Revenue model

Pricing process

increasing digitization of business life. Numerous success stories from companies demonstrate how customer benefits can be generated and successfully captured with the help of digital technologies. All practical examples relate to the linkage of business and revenue modeling and the pricing process. Practical example 1: Hitachi (B2B) Hitachi changed the value creation architecture (“operating model”) in one of its B2B business units a few years ago. The latest sensor technologies were integrated into Hitachi’s train systems. These new measurement methodologies allowed a significant improvement in the punctuality of trains (value to customer). The business model was transformed from “selling products” to “offering software-based services”. B2B customers (such as UK Rail Networks) were offered “punctuality” in the sense of a “train as a service” concept. The consequence of the business model innovation: the revenue model changed from one-time payments for products to continuous payment streams for a software-based service. The price model is outcome based. It is derived from the overarching revenue model as follows: the better the on-time performance rate, the higher the price.

Practical example 2: AVE (B2C) The following B2C example is based on the same revenue model as in the case of Hitachi. However, the Spanish train operator AVE went one step further in its price model for end users: AVE offers passengers a punctuality guarantee for the intra-spanish train connection Barcelona—Madrid. If the performance promise is not met, the traveler receives a full refund.

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Level 1: Business model Content is the core of value to customer

Level 2: Revenue model Hardware: favorable prices

Content: profitable prices

Price model hardware: e-book reader

Price model content: electronic books

Level 3: Pricing process Analysis

Strategy

Structure

Implementation

Monitoring

Fig. 3.3 The three-level approach “digital pricing”: example Amazon

Practical example 3: Amazon (B2C) Amazon’s revenue model for electronic books is based on the digital business model of providing content. The business definition (“provision of content”) was described by Jeff Bezos a few years ago as follows: “We don’t want to make money with the devices. We make profit after the device is sold. When customers buy books, MP3s, or movies!” (Anonymous, 2013). Amazon defines its value delivery to customers primarily in terms of content. Hardware is a second priority. Electronic devices such as the ebook reader are the leverage for the profitable core business with digital content (Fig. 3.3). Content (such as ebooks) is the most important revenue driver and tends to be sold at profitable prices. Hardware (e.g., readers for electronic books) is sometimes even offered below variable costs. The ebook reader Kindle Fire has repeatedly been offered in the USA at bargain prices below USD 100. At this price level, the product business is not profitable.

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Practical example 4: Google (B2C) Google’s revenue model for its hardware offering follows the same principles as Amazon’s consumer device business. Google defines hardware (such as the Pixel smartphone) primarily as a lever for profitable follow-up business with software, services, and applications. End devices are intended to motivate customers to use Google services. Profits result primarily from the use of online services. Profit generation is determined by the frequency, intensity, and duration of use. Setting prices for products is not sufficient for profit optimization. Critical to the success of digital pricing is the consideration of all four components of the business model. Of crucial importance here is the “value to customer”. According to Hermann Simon, the most important aspect of price management is “value to customer”. If the customer’s subjective perception of value is the starting point for pricing, professional price management must necessarily start with the higher-level business model (level 1). However, the linkage occurs in both directions. Creative pricing measures are one of numerous examples of this principle. Innovative price models not only lead to better monetization of the benefits (“value capture”), but are also an independent value driver for the customer (“value generation”). Creative price models increase the value to customer (and thus enhance the business model)! So price management is by no means just monetization. Digital pricing can also contribute to value generation. The revenue model is defined on the basis of a clear understanding of a company’s added values (“value to customer”) and the underlying value creation processes (“operating model”). The revenue model (level 2) defines the sources of revenue (i.e., the services to be priced); this includes, among other things, defining the services that—measured in monetary units—are offered free of charge. The core decisions within the pricing process (level 3) are derived from this: Price strategy, price structures, and models up to concrete price levels (Fig. 3.4). The decisive factor for the clear separation of levels 2 and 3 is: Most companies work with multi-part revenue models! In the case of digital enterprise groups, in particular, these are based on the conscious decision not to generate revenues with certain offerings. This explains the integration and logical linking of levels 2 and 3: For all offers that are not to be monetized, the pricing process (level 3) is not relevant! Revenue models and business definitions are often ignored in works on price management. However, this disregards a significant challenge in profit optimization. After all, the much-discussed digital transformation is essentially about four pricingrelevant challenges: 1. 2. 3. 4.

The definition of new value propositions. New product architectures and digital services. Innovative revenue and price models. Technology-driven changes to the price management process.

All three levels—business definition, revenue model, and pricing process—must be seen and optimized from the customer perspective. The elements of the three-

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Level 1: Business model Target customers + Value to customer

Value creation architecture

Profit model

Level 2: Revenue model

Product

Service

Software

Digital content

Data

Structure

Implementation

Advertising

Digital services

Level 3: Pricing process Analysis

Strategy

Monitoring

Fig. 3.4 The three-level approach “digital pricing” in detail

level digitization system include the customer benefit, the value creation system, the profit model, the revenue sources, the individual elements of a price model, etc. The decisive criterion in connection with the three-level model of digitization is consistency. The better the individual modules are aligned with the customer requirements and the more coherent the relationships between the individual elements within the three-level system, the greater the market success. Practical Example: Amazon (B2C) An important element of Amazon’s business model is the digital assistant Alexa (Postinett, 2018; Armbruster, 2018a, 2018b; Jacobsen, 2018). The voice assistant enables a wealth of services for the user. With the help of the digitized speaker “Echo”, it answers the user’s questions, orders goods via the online portal, controls the playback of music, and steers household appliances such as lighting systems or heating. With the software solution, Amazon is consequently opening up a portfolio of revenue sources in the area of digital services. When customers want to use the digital assistant to control the music system as well as the coffee machine, refrigerator, or heating system, data is constantly being sent as part of the Internet of Things. All controllable and networked devices must be constantly on standby (“always on”). This has a direct impact on price models and the level of prices. Depending on the customer segment and usage details (time, place, application, context, etc.), the perceived benefits of these digital services can vary greatly. The resulting willingness to pay must be optimally exploited.

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The prerequisite for this is a professional analysis and optimization process consisting of the following steps: 1. 2. 3. 4.

Quantification of the customer benefit of individual applications. Segment-specific design of digital services. Determination of the resulting revenue sources. Decision regarding the price model and the price level of services.

The necessary basis for the three-stage digital transformation is new technologies (sensor technology, Internet of Things, cloud computing, artificial intelligence, blockchain, etc.). But only viable customer requirements and their servicing through innovative approaches turn technological potentials into a market opportunity that can be monetized. This applies to the large technology corporations (such as Amazon, Alphabet, Apple, Microsoft, Tencent, and Alibaba) as well as to startups (e.g., Google Waymo) and SMEs (small and medium-sized enterprises). Industrial companies that are facing future challenges with innovative digital business models (such as Trumpf, Bosch, Siemens, and Schott) also offer a portfolio of successful practical examples. For detailed questions on the business model definition, I have developed the Business Model Map (Fig. 3.5). This comprehensive method is a further development of the COMSTRAT approach (cf. Simon and Gathen, 2002). Even though the business definition has to start with the customer value: Competencies and resources are mandatory to include in the course of the business model definition. The capabilities of the company are often the limiting factor in expanding or redefining business models (Meyer, 2018). The business model of selling contacts (e.g., Parship and Tinder) requires completely different competencies than the value creation of selling online products (Amazon) or information (Google). Based on this logic, the business model map methodically integrates, among other things, the resources and competencies of a company.

3.5

Method Tip: Business Model Map

The business model map is a tool for deriving a business definition. In an overall system it integrates: – – – –

An assessment of the attractiveness of business segments. A competitive analysis on relative performance. A company analysis on internal competencies. The priorities of market development, resulting from the company’s goals.

The application of the business model map is based on the following insights and premises: – A business model aims at uniqueness. It is about creating unique values for the customer.

(index)

Value capture

Segment X

"too good"

Performance advantage

(index)

Value generation

Relative performance

consistent

Performance disadvantage

"too good"

Strength

Competence index

Relative competence

consistent

Weakness

Competence matrix

Competitive advantage matrix

3

Fig. 3.5 Business model map, overview

Value generation

Segment Z

Segment Y

Business Model Portfolio

Value capture

Operating Model

Importance

Value to Customer

Relevenace

Profit Model

74 Business Models

3.5 Method Tip: Business Model Map

– – – –

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A superior strategic position results in above-average returns. Differentiation from competitors requires unique competencies. Competitive advantages and competencies are based on the control of resources. Resources include technologies, know-how, patents, product and corporate brands, access to raw materials, and production facilities. Against the backdrop of increasing digitization, data as a strategic resource is a minimum requirement for market success. The control and application of data explain the success of Amazon, Google, Facebook as well as Flixmobility, Schindler, and numerous other companies. The outstanding advantage of the business model map is the uniform evaluation of business activities. A step-by-step aggregation of strategically significant information forms the basis for priority setting. Case Study: Resources as a Starting Point for the Business Model Definition One question in the context of the business model methodology is: What resources and competencies do we have in the company that we have not yet used systematically to create value? A creative and structured analysis of this question can result in opportunities for differentiation and profitable new sources of revenue. Amazon repeatedly derives new business units and strategies from this core question. One example of this is the B2B business model of Amazon Web Services. It is based on the value creation structure developed for the core B2C business (especially the server capacities for online retail). Building on these unique resources, Amazon developed the leasing of IT services. In 2017, Web Services generated USD 12 billion in revenue with USD 4 billion in operating profit. The business model which was derived from unique resources (AWS) is now the tech company’s cash cow. In 2020, global revenue was approximately USD 45 billion. More than half of the digital group’s profit came from AWS 5 years ago (Anonymous, 2018a). In 2022 it was more than 70%.

The specific characteristic of the business model map method is the logical linking of the relevant questions of a business model in a stringent system. A typical sequence of questions is: 1. Which business segments (products, regions, sales channels, etc.) are most attractive in terms of our targets (including contribution margin and market share)? 2. What elements of our business performance do customers in these business areas particularly appreciate? 3. Based on what competencies and skills do we create these performance elements? 4. What resources are the basis for these competencies and skills?

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Project Outline for the Business Model Map Objective – Definition of a business model for companies, business units, and/or product lines. – Integration of the essential business model dimensions into an overall system. – Segmentation of the business into self-contained areas: Business units, product lines/products, regions, customer groups, or distribution channels of the company. – Segmentation is usually based on four dimensions. Example: Different product lines (dimension 1) are positioned within a business unit. The perception of several customer segments (dimension 2) in individual regions (dimension 3) is to be recorded in a differentiated manner. Sales channels can be added as a fourth dimension (see Chapter 1, Dimensions of Price). The visualization of the product lines in the positioning diagram is based on the main targets of the company. The target achievement of the individual product lines in terms of quantity, sales, or profit is plotted graphically. The size of the dots visualizes the relative contribution of the individual product lines to the target achievement. Information Base and Key Figures 1. Use of quantitative data (example: primary data on customer perception of company offers; secondary data on target achievement). 2. Addition of qualitative expert judgments by the management (example: assessment of the organization’s capabilities). 3. Consolidation of internal and external information using a standardized system. 4. Evaluation of different business activities according to uniform metrics (e.g., market attractiveness can be assessed uniformly on the basis of market share and contribution margin). 5. Condensation of information into key figures (indices for value capture (business segment attractiveness) and value generation (competitive strength)). 6. Visualization of information through business diagrams (competitive advantage matrix, competence matrix, business model portfolio). 7. Identification of opportunities and risks based on quantitative data. (continued)

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Value Capture Index Business segments are assessed quantitatively to determine the value capture index. The index determines the attractiveness of a business segment from a company perspective. The steps are in detail: 1. Segmentation of business activities (e.g., product–customer–region combinations) into homogeneous areas. 2. Selection of criteria for value capture (e.g., profitability). 3. Weighting of the value capture (attractiveness) factors. 4. Assessment of business activities (e.g., product–customer–region combinations) against the criteria. 5. Calculation of the “value capture” index as a weighted average (sum of importance [step 3] and assessment [step 4] per product–customer–region combination). Competitive Advantage Matrix The competitive advantage matrix integrates two dimensions: 1. Requirements of the customers to the suppliers. 2. Assessment of one’s own company in comparison to its most important competitors. The competitive advantage matrix (value generation matrix) clearly shows which strengths differentiate the company from the competition and where action is needed in terms of performance positioning (Fig. 3.6). The competitive strength index aggregates importance and relative performance across all purchase decision parameters (value drivers). The steps are: 1. Determination of the relevant competitive environment (most important competitors). 2. Identification of the performance parameters to be evaluated from the customer’s point of view. 3. Assessment of the importance of individual performance features (value drivers) by customers. 4. Perception of the performance parameters from the customer’s point of view (assessment for all relevant companies). 5. Visualization of the parameters in the competitive advantage matrix. 6. Determination of the competitive strength index (“competitive index” = weighted average of relative performance as an indicator of the competitive position resp. value generation). Competence Matrix The competence matrix is created methodically analogous to the competitive advantage matrix. In contrast to the competitive advantage matrix, which condenses

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Inferior position

Superior position

Importance for the customer

very important

Business Models

unimportant

Consistent position

Misallocation of resources equal

worse

better

Relative performance

Fig. 3.6 Competitive advantage matrix

performance parameters from the customer’s point of view, the competence matrix visualizes company characteristics. The aim is to determine the relative internal performance of the company in comparison with its competitors. The basis for this are the future prerequisites for success and the current capabilities of the organization. These include R&D competence, sales competence, market orientation, digital know-how, data management, agility, negotiation competence of the sales staff, and numerous other capabilities. The measurement of relative performance in a competitive comparison is based on a competence index. This aggregates internal strengths and weaknesses into one metric. The measurement of competence strength is the starting point for identifying the skills that need to be specifically fostered in order to be able to realize strategic objectives. The competence matrix must also include all those skills that are relevant for the management of digital business models. Spotify’s business model, for example, is based on several hundred data specialists who collect customer data and evaluate it according to patterns. This know-how of the global market leader is a unique resource on the basis of which innovative digital services are created for its users (Hajek, 2018; Albert & Schultz, 2018; Postinett, 2018). Business Model Portfolio The portfolio visualizes the two core questions of the business model (where to compete?; how to compete?) on two dimensions (Fig. 3.5):

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• Value capture: Attractiveness of business segments (economic success, market share). • Value generation: Competitive strength of the company. The value capture (segment attractiveness) dimension answers the question of which business areas can create the greatest value. The company’s own performance is an indicator of its competitive position (value generation) as a basis for capturing the value created. The assessments of market attractiveness (vertical axis; value capture) and competitive position (horizontal axis; value generation) are visualized using the portfolio. The two central business model dimensions can be represented for existing and new business activities. The overall strength of the company on the horizontal dimension can also be a combination of two assessments: competitive position and competence strength are then aggregated on the horizontal axis. The positioning of the business activities in terms of attractiveness and strength is the starting point for simulating possible competitive strategies and for deriving strategic decisions on the business model. Due to the quantitative data basis and the integration of all relevant dimensions, the business model map has a significantly higher practical relevance than qualitative methods.

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Anonymous. (2022). Umstellung auf Agenturmodell: Mercedes will Verkaufsflächen verringern. Retrieved April 22, 2022, from https://www.autohaus.de/nachrichten/autohersteller/umstellungauf-agenturmodell-mercedes-will-verkaufsflaechen-verringern-3153835 Armbruster, A. (2018a). Giganten unter Druck. Frankfurter Allgemeine Woche, 6, 39. Armbruster, A. (2018b). Giganten unter Druck. Frankfurter Allgemeine Zeitung. Retrieved April 22, 2022, from http://www.faz.net/aktuell/finanzen/die-naechste-billion-dollar-wette-laeuftdiesmal-amazon-15564030.html Bieger, T., & Reinhold, S. (2011). Innovative Geschäftsmodelle: Konzeptionelle Grundlagen, Gestaltungsfelder und unternehmerische Praxis. In Bieger et al. (Eds.), Innovative Geschäftsmodelle (pp. S. 13–S. 70). Springer. Brutscher, C. (2015). Long Tail – Business Model unter der Lupe. E-Business. Retrieved April 22, 2022, from https://ebusiness2020.wordpress.com/2015/06/18/long-tail-businessnessmodel-unter-der-lupe/ Burfeind, S. (2018). Datenschutz. Verkaufe dich selbst! Retrieved April 22, 2022, from https:// www.wiwo.de/futureboard/datenschutz-verkaufe-dich-selbst/20652936.html Buttlar, H., & Fahrion, G. (2018). Interview: “Auf den Baustellen wird das Material knapp”. Retrieved April 22, 2022, from https://www.capital.de/wirtschaft-politik/auf-den-baustellenwird-das-material-knapp Capital-Redaktion. (2018). Glossar - Künstliche Intelligenz. Retrieved April 22, 2022, from https:// www.capital.de/wirtschaft-politik/glossar-kuenstliche-intelligenz Carlzon, J. (1992). Alles für den Kunden: Jan Carlzon revolutioniert ein Unternehmen. Campus. Eckl-Dorna, W. (2018). Ein Smart Device auf Rädern - das ist das Neue. Retrieved April 22, 2022, from http://www.manager-magazin.de/unternehmen/autoindustrie/elektroauto-china-startupbyton-soll-alsluxusmarken-alternative-starten-a-1204351-4.html Eisenlauer, M. (2018). Von 89 bis 749 Euro – Kult und Klasse - das sind Nokias Neue. Retrieved April 22, 2022, from https://www.bild.de/digital/smartphone-und-tablet/mobile-worldbarcelona/nokia-neuheiten-54924600.bild.html Fasse, M. (2018). Mobilitätsdienste: Daimler und VW fordern Uber heraus. Handelsblatt. Retrieved April 22, 2022, from http://www.handelsblatt.com/unternehmen/industrie/ mobilitaetsdienste-daimler-und-vw-fordern-uber-heraus/20760266.html Freitag, M. & Rest, J. (2022). Wie Elon Musk Apple übertrumpfen will. Retrieved April 22, 2022, from https://www.manager-magazin.de/unternehmen/autoindustrie/tesla-wie-elon-musk-sogarapple-ueberholen-will-a-7872dbee-0002-0001-0000-000189635030. Fritz, M., Hohensee, M., Berke, J., Maier A., Schlesiger, C., & Deuber, L. (2018). Softbank: Weltherrscher der Mobilität. Retrieved April 22, 2022, from https://www.wiwo.de/ unternehmen/handel/zukunftsbranche-softbank-weltherrscher-der-mobilitaet/21046126.html Hajek, S. (2018). Streamingdienst Börsengang. So funktioniert die Erfolgsformel von Spotify. Retrieved April 22, 2022, from https://www.wiwo.de/technologie/digitale-welt/ streamingdienst-boersengang-groesserer-datenschatz-als-apple-und-netflix/21121318-2.html Heckel, M., & Ermisch, S. (2018). Turo: Der nächste Carsharing-Anbieter greift an. Retrieved April 22, 2022, from http://gruender.wiwo.de/turo-der-naechste-carsharing-anbieter-greift-an/ Hecking, M. (2018a). Warum Spotify trotz Horror-Verlusten an die Börse geht. Tech-Wette für die Massen. Retrieved April 22, 2022, from http://www.manager-magazin.de/finanzen/boerse/ spotify-boersengang-des-streaming-dienstes-trotz-horror-verlusten-a-1201045-2.html Hecking, M. (2018b). Uber kommt jetzt mit dem Fahrrad Kampfzone Innenstadt - warum Techfirmen die Städte mit E-Bikes fluten. Retrieved April 22, 2022, from http://www. manager-magazin.de/unternehmen/it/was-uber-tencent-und-alibaba-mit-bike-sharing-wollen-a1202146.html Hecking, M. (2021). Kampf um das Metaverse. Wie Meta-Chef Zuckerberg das moderne Internet beherrschen will. Retrieved April 22, 2022, from https://www.manager-magazin.de/ unternehmen/meta-vs-epic-warum-mark-zuckerberg-den-kampf-ums-metaversum-nochlaengst-nicht-gewonnen-hat-a-3d734ca5-96c3-42c1-877e-51850cef01ab

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Revenue Models

4.1

Delineation: Revenue Models

The business model definition results in potential revenue sources (products, services, software, digital content, advertising, digital services, etc.). The revenue model of a company or business unit answers the following questions, among others: (a) With which company offers do we want to generate sales? (b) Which revenues come from which sources? (c) Can individual revenue sources be combined? Or do we want to offer and invoice products, services, software, etc. separately? (d) At which levels of the value chain do we want to generate sales? (e) Who are our revenue partners? From whom do we obtain sales? (f) Is it possible to tap completely new sources of revenue? How would our business model have to be changed to enable us to exploit new revenue potentials? Traditional corporate revenue sources rely on the following: 1. Sale of products (e.g., car manufacturer). 2. Rental of goods as part of a service (e.g., rental car companies). 3. Leasing (e.g., mechanical engineering). Digital business models are leading to a major shift in the revenue models of companies. In most sectors, revenue shares are shifting from products (hardware) to online services, software, and digital content (see Fig. 4.1). Online advertising (e.g., Google, Facebook, and Amazon) and data have also gained in importance as sources of revenue. In many B2B sectors, digital transformation consists of the transition from traditional product sales to comprehensive solution offerings (including software, services, and digital solutions). # Springer Nature Switzerland AG 2023 F. Frohmann, Digital Pricing, Management for Professionals, https://doi.org/10.1007/978-3-031-24591-6_4

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Product

Service

Software

Digital content

Data

Online advertising

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Digital services

Fig. 4.1 Seven selected revenue sources at a glance

Practical Example 1: Google Waymo The Google subsidiary defines four revenue streams under its “autonomous driving” business model: sales of software and know-how; transportation services (robot cabs; autonomous delivery services); online advertising and in-vehicle content offerings. Self-driving cars, on the other hand, are not sold. Put another way: Products are not part of the revenue model.

Practical Example 2: Automobile Manufacturer OEMs are supplementing their core business (selling cars) with digital services such as carsharing, ridehailing, or more flexible rental models.

Practical Example 3: Amazon The digital group defines value delivery to customers primarily in terms of content and digital services. Hardware has second priority. Electronic devices such as the ebook reader are the lever for the profitable core business with digital content. Hardware (e.g., readers for electronic books) is sometimes even offered below variable costs.

Practical Example 4: Google Google’s revenue model for its hardware offering follows the same principles as Amazon’s business for end devices. Profits essentially result from the use of online services. Ninety-five percent of Google’s revenue is generated from advertising sales (Rabe, 2022). Online advertising is also becoming increasingly important in the case of Amazon (example 3) and in the mobility sector (example 1). An important distinction between revenue sources relates to their origin (Zerdick et al., 1999, p. 25):

4.1 Delineation: Revenue Models

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1. Direct revenues: Revenues are generated from the users of the service. 2. Indirect revenues: Revenues are generated with market partners who have an economic interest in the consumer’s use of the service. The revenue model of traditional media companies is based on both sources: • Direct revenues from the sale of products (e.g., magazines) to end customers. • Indirect revenue from the sale of advertising space to companies. It is not uncommon for the use of information goods (e.g., electronic books) to include four interrelated revenue streams: • • • •

Content Digital services Hardware Software.

Apple sells its devices (including iPhone, iPad, and Apple Watch), the content that goes with them, and the accompanying services as part of a comprehensive ecosystem (Meyer, 2018; Anonymous, 2022a). Software offers (such as the sale of additional storage capacity) are also increasingly earning money (Anonymous, 2018c; Jerzy, 2021; Freitag & Rest, 2022). Across all four central revenue sources, the technology group is benefiting from increasing digitization in several ways. Case Study Music Streaming: Spotify The business model of the global market leader for music streaming was outlined in Chapter 3. The cost structure cannot be influenced due to the necessary copyright fees. It is therefore imperative that Spotify addresses a second pillar of the profit model—the revenue sources. The question is whether profits can be realized in the future by tapping other revenue sources. The starting point and basic prerequisite for redefining the revenue model is the analysis of the value creation processes. The process of value generation can be mapped using value chains. Each value chain begins with the definition of the customer benefit and the creation of a service based on this. The benefit for end customers is already very high in the case of Spotify. This is evidenced by the enormous growth figures and the relatively high proportion of paying subscription customers in the premium version of the freemium revenue model. Facts on this: Among the 418 million monthly active users at the end of Q1 2022, 183 million were paying subscription customers (Anonymous, 2022b). A decisive question results from the end-customer assessment: Who are the other participants in the value chain? Musicians, record companies (labels), and concert promoters. All stakeholders can gain additional value (continued)

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from Spotify’s vast database (Dunkel & Steinmann, 2018; Postinett, 2018b). With its unique knowledge of user preferences, Spotify could provide both labels and artists with direct access to fans. As one of numerous examples of new value creation opportunities, consider the following: Artists’ tour schedules can be tailored more precisely based on big data analytics. Spotify has full transparency on which artists and tracks are listened to particularly often in which countries. This additional benefit for the value creation partners can be monetized via corresponding revenue and price models. The revenue base would thus be significantly expanded. Spotify would be able to generate revenues from end users as well as artists, event organizers, and music labels.

4.2

Services and Revenue Sources on the Internet

The Internet business of companies can be divided into four main categories (Simon & Fassnacht, 2009). These services and the resulting revenue sources are the subjects of the following section. 1. Content: The content area is based on the sale of digital products, services, and rights. Digital content is the most important category of offerings in the Internet business. Central to price management is the distinction between: – Paid content: Content for which users have to pay a price. – Free Content: Content financed by online advertising. The revenue partner in the first case is the user. In the second case, revenues are generated with advertising customers. Both revenue sources are often used in combination, e.g., via a freemium model. Paid digital products (paid content) relate to a broad portfolio of information goods: – The electronic book of an online bookseller. – Electronic newspaper or magazine articles from publishers. – Music downloads. – Films and teaching materials made available online. – Software services. The providers of free information (free content) include platforms such as YouTube or Wikipedia. In both cases, the offerings are based on content generation by the customer. YouTube’s business model differs significantly from music streaming providers such as Spotify. Spotify is a content provider—it pays fees to the license holders (labels) whose music is offered on the platform. YouTube is a host provider. Fees to license holders are not required, because YouTube only provides the digital infrastructure in which users upload their own creations. In the further course of the work, this first pillar of the Internet business is differentiated: content, digital services, and software are presented in a differentiated manner in the further considerations.

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2. Commerce: The commerce area consists of electronic transactions involving products or services. A distinction is made between three variants and associated business forms: – Pure Internet retailers. – Manufacturers and mail-order companies that sell via multiple channels. They use the potential of online retailing to supplement stationary sales. Internet customers pay to purchase a product, e.g., to buy a CD from an online music store. Distribution takes place via the medium of the Internet. In contrast to the content business, physical transport of the product is required. – Online service providers. These offer their customers a portal for the online purchase of services such as rail and airline tickets, concert tickets, or travel. Brokerage commissions are charged for the booking of admission tickets. 3. Context: The service consists of the aggregation and distribution of information. Navigation aids and search engines such as Google are part of this business model. Revenues are generated from two sources: advertising and the provision of contacts to third-party providers. Online offers from value creation partners (pages, links, information, etc.) that fit the context are presented at an appropriate place on a website (e.g., on a search engine such as Google). The goal from the partner company’s point of view is for the Internet user to click on the page. For each such brokerage service, the provider of the search engine or navigation aid receives remuneration. 4. Connection: The service sold is contacts. The business model based on this is the offer of a platform for the exchange of information on the Internet. Examples of this are social networks (Facebook), career platforms (LinkedIn; Xing), or dating portals (Parship, Tinder). At the end of the value chain is the advertising customer or the contact recipient as the user. Three possible sources of revenue result from this business definition: – Direct revenues for the contact brokerage service, membership fees of paying members (revenue partner: users). – Indirect revenues from the placement of advertisements (revenue partners: advertising customers). – Incomes from commissions based on the referral of customers to other companies (revenue partner: company). Some community platforms such as social networks are based on free memberships. Free access enables rapid growth via network effects. Advertising revenues are the main source of income. For career portals and dating platforms, on the other hand, paying members are the most important source of revenue. As a rule, various revenue sources are used in parallel (Skiera & Lambrecht, 2007; Skiera et al., 2005). For example, many companies offer both content and commerce services. One example of this is publishing houses, whose revenue generation is based on the sale of printed and electronic books. One of the key challenges in optimizing revenue models is to consider the interdependencies between the individual revenue sources. Revenue components sometimes conflict with each other. Example: the more digital

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content is offered on a portal for payment, the fewer users the website will attract. As reach decreases, revenue from the sale of contacts decreases. Conversely, the more users a portal has, the greater the attractiveness of the Internet platform for advertisers due to the network effect. Facebook is a particularly striking example of this. But there is also a trade-off in the lucrative advertising business. This is because advertising is generally perceived as annoying by consumers. In this respect, numerous ads generate higher revenues through the sale of contacts (e.g., banner ads). However, this is offset by lower revenues from the sale of products (content and commerce). The goal is to optimize the total revenue across all potential revenue sources. One example of this is the media industry. Newspaper publishers such as the Wall Street Journal generate revenues from the distribution of current news, but also from the sale of advertising space. Fluctuations in the advertising market can be absorbed by the content business. On the other hand, advertising revenues serve as a buffer against a possible decline in the number of customers in the product business. For a long time, the online auction house ricardo.de relied on three sources of revenue at the same time—the sale of products, contacts, and information: • Products are sold through auctions. • Within the framework of the online auctions, advertising is placed. • Auctions are used to gather information about consumers’ willingness to pay. The choice of a company’s revenue model is largely dependent on which stages of the value chain it takes on. The decision depends on: • • • • • •

The requirements of different levels. The necessary resources and competencies. The level of revenue achievable at the individual stages of the value chain. The positions of power of the various market participants. The objective of balancing risk in the sense of a portfolio approach. Access to end customers.

For example, if an online bookseller wants to sell the collected customer data, he is on the value chain for the sale of information. Due to the direct customer contact, the company obtains information about the preferences of the individual persons. This enables the online bookseller to create and market detailed user profiles. An overall view and integrated optimization are important. In order to develop profitable sources of revenue in the long term, it may be necessary to take over stages of the value chain that do not appear attractive in the short term with a view to financial objectives. Amazon’s dynamic business model provides an example of such interactions. The low-margin music streaming of the technology group (Amazon Music) has a positive impact on the profitable business model with the voice assistant Alexa. Music usage plays a central role in voice control systems. Since the acquisition of the music streaming value creation stage supports growth in the profitable value creation model of voice services, the overall constellation is optimal for Amazon. Another interaction exists between music streaming and

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Amazon Prime, the world’s most successful customer loyalty program. The cost of a Prime membership (amounting to EUR 89,90 per year in Germany) includes, among other things, a limited music streaming service. However, Amazon Prime is not only booked for streaming, but also by customers who only use the e-commerce area or Amazon Prime video. The main argument for Prime users is: they can eliminate all kinds of shipping costs. From a revenue model perspective, this means: Amazon uses shipping as a loss leader to strengthen its brand and revenue in other business segments. Prime, the customer loyalty program introduced in the USA in 2005, is a cross-subsidy instrument from a revenue perspective. The revenue source interactions outlined (Level 2) have a direct impact on pricing (Level 3). Amazon can charge lower prices because it is less dependent on revenue from streaming than other providers. Netflix (video) and Spotify (music), on the other hand, have to earn money directly by streaming content. Consequently, both cannot avoid keeping their prices at a relatively high level. The position of power of a company within the value chain is of outstanding importance. The position within the value chain is ultimately decisive for who is able to capture revenue potentials and to what extent. In digitized industries, direct contact with the end customer is critical to success. Value chain stages that are close to the end customer tend to have a high potential for generating revenue by selling contacts or information about the user. Online booksellers like Amazon are in a very good position to generate revenue. The basis for this position of power is the very broad range of products and direct contact with readers. Digitization is not only having a massive impact on B2C revenue models. B2B revenue models will also expand significantly—they will be determined more by the end customer in the future. B2B companies can use digital technologies to strengthen the end-customer contact and increase value creation. Revenue models are subject to strong dynamics. The relative importance of individual revenue sources can shift over time. This can be caused by changes in customer requirements, a redefinition of the business model, technological trends, the market entry of new competitors, or cross-industry influences. In the wake of the 2020 pandemic, revenue shares shifted massively across all industries. The share of digital services (video conferencing, video streaming, gaming, etc.) among the major digital groups increased exponentially. Case Study: Revenue Sources for Smartphones Against the backdrop of the steady technical upgrading of smartphones, the share of software and digital services in the overall performance has become ever greater. Business in mobile communications has shifted massively in recent years. The value creation shares are shifting from device manufacturing to the sale of software and related services (Eisenlauer, 2017; Fröhlich, 2018a, 2018b; Hohensee, 2018; Jacobsen, 2018; Kharpal, 2016).

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Case Study: Revenue Sources for Games Profits are generated in particular from software and special services. Hardware sales are sometimes even associated with losses due to intense competition and price pressure. For example, Microsoft suffered losses with one version of its X-Box within five years from market launch (2002) to market exit (2006) in the USA (Simon, 2015). The total deficit was USD 4 billion. Sony’s revenue model and resulting pricing strategy is as follows: Devices are sold at a loss, and the focus is on exclusive games for users with large budgets. Microsoft’s future revenue model in gaming is based on its xCloud service. The digital service will eliminate the need to own a console. Games will run in data centers (Anonymous, 2018a). The results can be streamed on smartphones, Internet-enabled TVs, etc. In the online segment, revenues are increasingly generated by selling virtual products with innovative price models. Epic Games’ revenue model is as follows: Many games—such as Fortnite—are free; billions in revenue are generated with in-game purchases (in-app purchases). Players buy clothing, weapons, or other items for their avatars. Another source of income for game providers is advertising.

Case Study: Revenue Sources for Films The media industry offers an example of the change in user behavior, a high level of dynamics in business models, and a resulting change in revenue models (Ahlig, 2018; Harengel, 2017). A broad spectrum of free-to-air TV channels is contrasted by paid offerings such as Sky (pay TV) or Netflix (streaming). In addition, there is an enormously diverse range of video content (YouTube, etc.). The preferences of users in the TV and media market have changed significantly—also in the course of the expansion of offerings. Individualization and exclusivity are the two overriding customer wishes. Free choice and flexible use of the desired content are the core requirements. Streaming providers such as Netflix, Disney, Amazon, and Apple are investing more and more in the production of their own content. They are upgrading their video services with high-quality in-house productions. This is the only way to offer customers the desired exclusivity. This is the only way to achieve sustainable differentiation from the competition (Mansholt, 2018). With regard to the business model—and in particular, the operating model—the following applies: New sources of revenue can be tapped under certain conditions. These include the following constellations: 1. The company develops a standard that is important for the customer. Software provider Adobe was able to build a broad customer base with its free product

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Acrobat Reader. With the basic software for reading PDF documents, Adobe defined the market standard. The high distribution of the free basic product ensured higher demand for complementary goods through indirect network effects. Revenues were generated through the sale of the creation software (Buxmann et al., 2008, p. 123; Anonymous, 2018b; Simon & Fassnacht, 2009). Sony achieved great success with the introduction of the Blue-Ray technology. The reason for this was the following realization. The introduction of a new standard depends on two criteria: acceptance by customers; dissemination of the technology standard (e.g., via distribution channels and media). There is a causal relationship between the two criteria: in order to achieve customer acceptance, dissemination must be ensured. The consequence of this for the revenue model and pricing: Blu-Ray players (hardware) and movies (content) had to be available at very attractive prices. The lower the price level, the faster the player (and thus the technology standard) would be accepted. To bring the devices to market at a low price, Sony chose a two-part revenue model. The hardware was offered together with the Playstation 3 game console. Every buyer of a Playstation 3 received a Blu-Ray player in addition. 2. The company controls (or dominates) an interface to the customers. Interfaces are of enormous importance in the context of digitization. At interfaces, revenue can be generated from several revenue sources simultaneously. One example of this is the long-distance bus business with its enormous relevance of network effects. The crucial interface in this dynamic market is access to end customers, which Flixmobility (formerly Flixbus) took over from its subcontractors. As it expanded its business model into the rail business, Flixmobility leveraged this interface (Kluge, 2018). Alphabet’s (Google) prominent position is based on the original realization that the Internet cannot function without search engines—the wealth of information is unmanageable. Google strategically occupied the central interface to the Internet starting in 1998. Its search engine became an indispensable infrastructure for the Internet. Google expanded its sphere of influence by offering the mobile operating system Android free of charge (Beuth, 2016). Last but not least: Google pays a double-digit billion amount annually to Apple to be used as the default search engine on its hardware (especially the iPhone). 3. A resource of the company occupies a position in the value creation system that is indispensable for the customer. In the case of Apple, the sales portal occupies a central position in the value creation system. To illustrate this relationship, Apple’s revenue model is described below. Case Study: Apple Revenue Sources Physical products (hardware) are at the core of Apple’s business model. Nearly 80% of Apple’s global revenue in 2020 came from its devices (Anonymous, 2022a). Major product lines include iPod, iPhone and iPad, Apple Watch, AirPod, Apple TV, Mac computers, and HomePod speakers (continued)

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(Kerkmann, 2018). A launch of glasses was planned for 2022. The revenue significance of content (online products) and services for the technology group has increased significantly in the recent past. Customers who buy a new hardware model automatically get access to digital content and services (e.g., apps). Services include the Apple Music service, the TV+ video streaming service, and the Apple Pay payment service. Other services include: Arcade (computer games), News+ (online service for newspapers and magazines), Care (insurance). Apple gained access to the growth drivers’ content and online services with the help of an openness strategy (Meyer, 2018). The open business model is based on value creation cooperations with external service providers (open value innovation). Close partnerships across the entire value chain of the stationary and mobile Internet were successively developed into an inter-company value creation system. The focus was on third-party development services. This unique ecosystem led to the following effects on Apple’s revenue models and pricing: 1. The online store App Store offers owners of Apple hardware the opportunity to choose from a wide range of applications for the various end devices (such as iPhone or iPad) (Meyer, 2018). A clear double-digit billion amount (in USD) in sales is generated via app downloads. 2. For the most part, the applications do not come from Apple itself, but from independent programmers. The products of the value-added partners complement the core products of the technology group. 3. Opening up to its value-added partners created new revenue potentials for Apple. This is because each additional complementary product strengthens the value of the core offering and thus increases customers’ willingness to pay. 4. Based on its position of power, Apple obliges its value-added partners to sell the applications exclusively via the online store. 5. Revenue partners pay an annual fee for listing the applications. For each application sold, Apple receives a significant double-digit percentage of the revenue generated by the value-added partners as a commission. 6. The services represent an increasingly significant business for Apple (Kharpal, 2016; Kerkmann, 2018). In the fiscal year 2021 (October 2020 to September 2021), revenue in this segment already amounted to more than USD 68.4 billion. In 2017, the division still generated sales of USD 37 billion. This means that the service unit already represents the second most important business unit in terms of sales. Google also tapped into the growth market of the mobile Internet with the help of an openness strategy. The Google Maps business model is based on particularly intensive value creation by the public (open value innovation). (continued)

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The offerings of the value creation partners complement Google’s core products. Complementary third-party services include, for example, location information for the Google Maps service (Dämon, 2016). The consequence of the significant expansion of revenue models is that there are many more challenges for pricing than 10 years ago. In more and more cases, the first question is which potential revenue sources should be priced at all. This challenge must be optimized before the details of the pricing process (including strategy, price level, price model, rebates, discounts, and incentives) can be addressed.

4.3

Overview of Selected Revenue Models

Most companies work with multipart revenue models (cf. Fig. 4.2). In the case of digital groups, in particular, these are based on a deliberate decision not to generate revenue from certain offerings. Examples of two-part revenue models are: Revenue Model 1: Free Software; Revenue Generation Via Advertising Users do not pay for the company’s software offering. Revenue is generated through the sale of advertising. On the mobile Internet, Google generates revenue through

Hardware

Online service

Software

Software + digital advertising

Advertising

+

Online service + digital advertising

+

Software + online service

+

+

Bait-and-hook

+ Freemium

= free of charge

+ = profitable price

Fig. 4.2 Five selected revenue models

= favorable price

= paid advertising

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context-specific advertising. To promote the mobile advertising business, Google offered its Android operating system to cell phone manufacturers free of charge (Hackhausen, 2013; Hauck, 2014). Android achieved a global market share of over 80% in M-Commerce via the free model. The advantage of the ad-financed revenue model is the enormous network potential; a large number of users can be acquired very quickly via free access to the digital offering. Increases in market share and the resulting attractiveness of the portal form the basis for a rapid increase in advertising revenues. The main risk of the free model is the fixation on one source of revenue, advertising. This leads to a great dependence on the advertising market and its price development. Revenue Model 2: Free Digital Service; Revenue Generation Via Advertising or the Sale of Information Advertising- or information-funded revenue models are based on users not paying for the company’s digital services. Revenue is generated through the sale of advertising or information: • The search engine provider Alphabet (Google) offers its service—information research on the Internet—free of charge. Google creates interest profiles from users’ search queries. The search engine operator generates revenue from the sale of these customer profiles to advertisers. The company can only offer its users free search queries on the basis of the highly profitable advertising revenues. In Google’s business and revenue model, the user is the actual product. Advertising companies are the paying customers (Hackhausen, 2013). Another source of revenue for Google is payments from companies and private individuals for preferential mention in search queries (premium search results). • Comparison platforms (such as Verivox) offer price information free of charge. They are financed by selling advertising space and advertising links (Simon & Fassnacht, 2009). • Some Internet service providers, such as Microsoft or GMX, also based their revenue model on the provision of contacts. In contrast to competitors such as T-Online, revenue was not generated by providing e-mail accounts. Advertising is the central source of revenue. • At Meta, users get the social medium for free because advertisers pay for access to users. Advertising revenues are the main source of income. The sale of advertising space accounts for 98% of the company’s revenues. In 2017, 88% of the advertising revenue of Facebook, which was founded in 2004, was generated by ads on mobile devices (Postinett, 2018c). Revenue Model 3: Free Software; Revenue Generation Through the Sale of Complementary Software Products or Services This revenue model is based on offering a basic software product free of charge. This creates network and lock-in effects among users. Revenues are generated with the sale of complementary offerings (Zerdick et al., 1999). An example of this is offered by providers of open-source software (e.g., Red Hat Linux in the B2B segment). In

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this software segment, the core component is free of charge. Revenues result from complementary services such as consulting, implementation, documentation, and maintenance (Buxmann & Lehmann, 2009). These product-related services are associated with high-profit margins. Revenue Model 4: “Bait and Hook” “Bait and hook” is a revenue model that aims to generate revenue in all components. It is based on the linkage of two products that are used together by the customer (“tied products”). Cross-product relationships exist in a wide variety of industries, especially in B2B markets (Jensen & Henrich, 2011). The purchase of a main product (e.g., a machine) attracts purchases of other products (e.g., spare parts) over time. If more of one product is sold, sales volumes of the other product also increase. In such complementary product relationships, cross-price elasticity is negative. Price reductions of the basic product lead to sales increases of the related product. Fixed complementarity is referred to in the case of static input ratios (e.g., car—car tire). The quantity of the coupled product can also be variable. A linkage that is very important for pricing consists of a durable product and a consumable good that is used at regular intervals. Examples of this are the following product links: • • • •

Copier and paper Razor and razor blade Printer and cartridge Water softener and water filter.

The reusable basic component (e.g., a water softener) is offered at a very low price. This motivates customers to use the high-priced component (e.g., water filter). The consumed—and regularly repurchased—product is sold at a relatively high price (Anonymous, 2018e). A Gillette razor is offered at a low entry price. The profit for the retailer or the online store comes mainly from the downstream sale of razor blades. Gillette’s success is largely based on a consistent implementation of this revenue model. The average price of disposable blades was driven up significantly through the successive expansion of the product line. The transition from the Sensor Excel version to the Mach 3 system and finally to Fusion (with five blades) involved a price increase by a factor of three. At the same time, Gillette has a dominant competitive position with a global market share of about 70% (Gassmann, 2016). The goal is to optimize profit for the product group. In Gillette’s case, it is also about the optimal mix of margin and volume (market share). “Tying” is also used in industrial goods industries. In the case of capital goods, the purchase price of the main product tends to be of little importance compared to the subsequent transactions. The value of the subsequent products and services is often many times higher than the initial price. Profitable prices can be achieved for subsequent products and the continued supply of additional parts. The aim is to achieve an optimum overall result. In this respect, the conclusion of an initial business at prices

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that do not cover costs can be justified. Another case study of the “bait and hook” model is consulting services. Basic seminars are offered at very low prices. The aim is to cover fixed costs and to arouse customer interest by demonstrating competencies. In price psychology, this is described with the term “entry effect” (Kopetzky, 2016). Personal consultations and in-depth seminars can be charged at premium prices. Revenue Model 5: Freemium With the increasing penetration of digitization, freemium has become a very successful revenue model in numerous industries. The basic idea behind the freemium concept is the combination of free and paid-for parts of the offering. The term freemium combines two aspects: Free (free basic services) and premium (for an additional charge). The basic version is cross-financed with the revenues of the premium services sold. Freemium is a two-tier revenue model. The distinction between the user perspective and the provider perspective serves as an explanation: From the user perspective: Part of the offering (e.g., a standard service) is provided free of charge. The free entry-level version is associated with limited functionality for the user. Extensions to the basic version (e.g., via premium features) are subject to a charge. From a provider’s perspective: The world’s largest music streaming provider, Spotify, operates with a “freemium model” that relies on two main sources of revenue: Advertising revenue and paid content. The basic version is crossfinanced with revenues from paid premium services. Various price models are available for the premium component: for music streaming, for example, subscriptions (Spotify) or pay-per-use (Apple i-Tunes). The freemium model emerged with the evolution of the Internet. In the content area, service providers such as T-Online were already working with product differentiation at the turn of the millennium. Customers had a choice between free standard information and enhanced offerings in return for payment. Paid content programs were based on the users’ willingness to pay for enhanced services. The freemium model emerged from the combination of the two approaches. With the advancement of information technology and increasing penetration of digitization, freemium has become a highly successful revenue model in numerous industries. Two-tier revenue models are particularly relevant for the following digital services: software (e.g., Skype and Dropbox), content, video games (e.g., Farmville), apps (e.g., Angry Birds), contact platforms, and social networks (e.g., LinkedIn). Paid premium content is based on the core arguments of added value of time advantage (topicality of information), good summary, and more content (depth of detail). Case Study Music Streaming: Spotify The world’s largest music streaming provider Spotify operates with a two-tier revenue model. Users of the free version have to accept the insertion of (continued)

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advertising breaks between the individual songs. The paid premium version, priced at almost EUR 10 per month in Germany, entitles users to download more than 36 million songs from the databases of the music labels (Albert & Schultz, 2018; Hajek, 2018). The market leader for streamed music launched its app in October 2008, and by the end of 2017 Spotify had around 159 million users worldwide. Among them, about 71 million paying subscription customers. At the end of the first quarter of 2022, the music streaming service from Sweden already recorded 418 million active users. With 183 million subscription users, the “free to paid” conversion rate is 44% (Anonymous, 2022b). Unlike Spotify, its biggest competitor Apple Music—like Amazon— does not offer a free service (Postinett, 2018a; Mohammed, 2019). Consequently, in the case of Apple Music and Amazon, it is a single-tier revenue model. The example of Spotify can be used to prove that freemium is not a price model but a revenue concept. This is because a wide variety of price models are available for the paid revenue component, such as “pay-perstream” or subscription (flat rate or tiered subscription). The clear separation between revenue and price model is a prerequisite for professional price management. Career networks such as Xing and LinkedIn offer a free basic membership and a paid premium service. The premium version offers attractive additional services. Depending on the contract duration, the customer pays a basic monthly fee of varying amounts for the extended functionalities. In the case of contact platforms, the focus is on improved interaction options. Some partner agencies pursue a variant of the freemium concept. As part of a promotion, they offer a free membership for a limited period. The differentiation made by the provider between the premium offer and the basic offer is not initially perceived by the customer at the start of use. After the end of the promotion phase, the user is no longer allowed to interact with the network members. This event, which is unexpected for the customer, significantly increases the willingness to purchase the premium version. Variants of the freemium model include concepts such as “free-to-play” and “pay-to-win”.

Pricing Challenges with the Freemium Model 1. Attractive basic offering: A broad customer base for the free offering must be achieved. Sufficient benefits in the ad-financed basic model attract a large number of customers. As the network effect increases and the critical user mass is reached, the inhibition threshold to pay for the extended offer then also falls. Defining the right scope of services is a classic optimization problem: If the basic version contains too few features, follow-up purchases may fail to materialize. However, if the basic version is designed to be too comprehensive, the full version will be underutilized.

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2. Perceived additional values in the premium version: The second core challenge in the freemium model is building willingness to pay for the premium offerings. Attractive product features have to be integrated into the paid service offer. It is a matter of creating the right payment triggers. Payment triggers are incentives for customers to use a provider’s paid premium services. One successful value driver for career networks is the feature “who was on my profile?”, among others. In the case of Spotify, it is the instant availability of new albums or songs, among other things. If the new releases are reserved for premium customers for a longer period of time, users of the free version are more likely to be persuaded to take out a subscription. 3. Optimal monetization of frequent users: The price model must provide incentives for an increasing intensity of usage. The maximum payable price should not be capped, because frequent users are often much more willing to pay than average customers. The price premiums for the real value drivers (additional services) must be set correctly in order to exploit this profit potential. This applies in particular to sectors with products or services that have strong emotional values. The basic prerequisite for the successful introduction of a freemium model is the cost structure. Low marginal costs are a necessary condition for ensuring that the free basic option does not become an unreasonable burden for the provider. The market success and profitability of the freemium model can be measured by key performance indicators (KPIs). These include, among others: • Percentage of customers who use the paid version of the service (“payer conversion rate”). • Average price per customer (“average revenue per paying user”). • Cancellation rate of trial subscriptions. A special case of the freemium model can be observed in Apple’s services division. One of the services with revenue potential is the storage space for the hardware products (such as the iPhone). The free Basic Cloud service offers only five gigabytes of storage. Apps and message storage quickly reach their capacity limits with this free version. Additional storage volume in the iCloud to an extent of 50 gigabytes is sold as a premium version for 99 cents per month (Anonymous, 2018c, 2018d). The subscription for the limited storage space is a permanent source of revenue in Apple’s portfolio. The revenue model of hardware (iPhone or iPad) and service (storage space) is a hybrid form of “tying” and a freemium component in the storage volume.

References Ahlig, E. (2018). Welcher ist der beste Streaming-Dienst für mich? Retrieved April 22, 2022, from https://www.bild.de/unterhaltung/tv/netflix/und-co-bild-checkt-die-streaming-dienste-53921 980.bild.html

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Gassmann, M. (2016). Das große Geschäft mit dem Männerbart. Welt. Retrieved April 22, 2022, from https://www.welt.de/wirtschaft/article157279974/Das-grosse-Geschaeft-mit-demMaennerbart.html Hackhausen, J. (2013). Apple vs. Google: Das epische Duell der Internetriesen. Wirtschaftswoche. Retrieved April 22, 2022, from https://www.wiwo.de/finanzen/boerse/apple-vs-google-dasepische-duell-der-internetriesen/8535404.html Hajek, S. (2018). Streamingdienst Börsengang. So funktioniert die Erfolgsformel von Spotify. Retrieved April 22, 2022, from https://www.wiwo.de/technologie/digitale-welt/ streamingdienst-boersengang-groesserer-datenschatz-als-apple-und-netflix/21121318-2.html Harengel, P. (2017). Streaming und Fernsehen. Radikaler Umbruch: Wie wir TV-Sender, Netflix und Amazon zum Umdenken zwingen. Focus. Retrieved April 22, 2022, from https://www. focus.de/digital/experten/mediennutzung-streaming-wie-nutzer-tv-sender-netflix-und-amazonzum-umdenken-zwingen_id_7751533.html Hauck, M. (2014). Geschäftsmodelle von Google und Facebook. Daten für Milliarden. Süddeutsche Zeitung. Retrieved April 22, 2022, from http://www.sueddeutsche.de/digital/ geschaeftsmodelle-von-google-und-facebook-daten-fuer-milliarden1.2270247 Hohensee, M. (2018). iPhone X: Apple enttäuscht und begeistert zugleich. Retrieved April 22, 2022, from https://www.wiwo.de/unternehmen/it/iphone-x-apple-enttaeuscht-undbegeistert-zugleich/20919524.html Jacobsen, N. (2018). Amazon-Quartalsbilanz: Jeff Bezos nimmt den Börsenthron ins Visier. Absatzwirtschaft. Retrieved April 22, 2022, from http://www.absatzwirtschaft.de/amazonquartalsbilanz-jeff-bezos-nimmt-den-boersenthron-ins-visier-131351/ Jensen, O., & Henrich, M. (2011). Grundlegende preisstrategische Optionen auf B2B-Märkten. In C. Homburg & C. Totzek (Hrsg.), Preismanagement auf B2B-Märkten. S. 75–104. Jerzy, N. (2021). Das sind die neuen Apple-Produkte 2021. Retrieved April 22, 2022, from https:// www.capital.de/leben/das-sind-die-neuen-apple-produkte-2021 Kerkmann, C. (2018). Spekulation um Megadeal. Kauft Apple wirklich Netflix? Wirtschaftswoche. Retrieved April 22, 2022, from https://www.wiwo.de/unternehmen/it/spekulation-um-megadeal-kauft-apple-wirklich-netflix/20808824.html Kharpal, A. (2016). Apple captures record 91 percent of global smartphone profits: Research. Retrieved April 22, 2022, from https://www.cnbc.com/2016/11/23/apple-captures-record-91percent-of-global-smartphone-profits-research.html Kluge, S. (2018). Verbraucher fahren auf Flixbus ab. Wirtschaftswoche. Retrieved April 22, 2022, from https://www.wiwo.de/unternehmen/dienstleister/brandindex-verbraucher-fahren-aufflixbus-ab/21031196.html Kopetzky, M. (2016). Preispsychologie. In vier Schritten zur optimierten Preisgestaltung. Springer Gabler. Mansholt, M. (2018). Warum beim Streaming-Krieg der Kunde verliert. Stern. Retrieved April 22, 2022, from https://www.stern.de/digital/homeentertainment/netflix%2D%2Dwarum-beimstreaming-krieg-der-kunde-verliert7703198.html Meyer, J. U. (2018). Zu radikal, zu innovativ, zu schnell. Woran Tesla scheitern könnte. Manager Magazin. Retrieved April 22, 2022, from http://www.manager-magazin.de/unternehmen/ autoindustrie/tesla-woran-der-elektro-autobauer-scheitern-koennte-a-1203806.html Mohammed, R. (2019). Why is every streaming service using the same pricing model? Harvard Business Review. Retrieved April 22, 2022, from https://hbr.org/2019/11/why-is-everystreaming-service-using-the-same-pricing-model Postinett, A. (2018a). Das nächste Netflix? Was Sie zum Börsengang von Spotify wissen müssen. Handelsblatt. Retrieved April 22, 2022, from https://www.wiwo.de/finanzen/boerse/dasnaechste-netflix-was-sie-zum-boersengang-von-spotify-wissen-muessen/21019612.html Postinett, A. (2018b). Videostreamingdienst. Starke Ergebnisse von Netflix lassen Tech-Branche hoffen. Handelsblatt. Retrieved April 22, 2022, from https://www.wiwo.de/unternehmen/it/ videostreamingdienst-starke-ergebnisse-von-netflix-lassen-tech-branche-hoffen/21181586.html

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5

Pricing Process Part 1: Analysis

5.1

Introduction to the Pricing Process

One of the key messages of this book, as an interim conclusion, is as follows: There are important business decisions upstream of price setting. (1) The definition of the revenue sources (the revenue model). (2) The definition of customer value (value to customer) as a central pillar of the business model. Resulting from this: Professional price management must go beyond the mere optimization of the pricing process to reflect the higher-level decisions on the business model and the revenue model. What does professionalism in price management mean against the background of the fundamentals described? The outstanding value contribution of price management is to optimize the achievement of corporate objectives. The overriding goal must be to install a value creation and extraction process that understands digital pricing as an integrated management approach. This end-to-end approach defines all process steps from target prioritization to price enforcement and monitoring of target achievement. The pricing process is one of the most significant value creation processes of companies. Pricing processes consist of numerous challenges that have a different importance depending on the sector (B2C, B2B, C2M, C2C), industry, and company (Schmidt-Gallas & Lauszus, 2005; Roll et al., 2012; Homburg & Totzek, 2011; Frohmann, 2009a). Monetization (“value extraction”) and value creation (“value generation”) can be achieved in equal measure with this management process. I base this book on the following process steps: analysis—strategy—structure— implementation—monitoring (Fig. 5.1). This approach corresponds to the process sequence of tasks in management. The process begins with a comprehensive analysis of all pricing-relevant data. It translates the strategy—which follows the analysis—into structural pricing decisions (price points, differentiation approaches, innovative price models, etc.). These form the starting point for the design of price negotiations and the enforcement of prices on the market. Internal and external price communication is also part of the crossfunctional discipline of pricing. The process comprises a very large number of # Springer Nature Switzerland AG 2023 F. Frohmann, Digital Pricing, Management for Professionals, https://doi.org/10.1007/978-3-031-24591-6_5

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5 1 Analysis

2

3

Strategy

Structure

Pricing Process Part 1: Analysis 4

5

Implementation

Monitoring

Fig. 5.1 Pricing process

detailed tasks and process steps. As a central process phase, the optimization of price levels is of particular importance for value monetization. However, price management goes far beyond setting prices. Among other things, it is also about creating value for the customer, e.g., by introducing creative structures and price models. Successful companies develop and live a pricing process whose elements are consistent across all levels. Consistency from the customer’s point of view is the decisive indicator for assessing the pricing process. In the digital age, another criterion is added: agility—in the sense of speed and efficiency along the pricing value chain.

5.2

Determinants of Pricing

Price optimization must include at least 11 essential pieces of information. These can be symbolized by the “11 C” of pricing (Fig. 5.2). At the core of the analysis phase are the following questions:

Currency

tier 2

Context

tier 1

Company targets Profit Turnover

Price

?

Country

Volume growth Market share

Competitor

Price potential

Price image

Costs

Compliance

Fig. 5.2 The 11 determinants of pricing

Capacity

Cycle Stage

Channel

Customer

5.2 Determinants of Pricing

105

1. “Customer”: What are consumers willing to pay? What are the price elasticities of customers for our company offers (products, services, software, etc.)? 2. “Competition”: At what prices does the competition sell? How will competitors react to our price measures? 3. “Costs”: What is the composition of our costs? What is the ratio of variable to fixed costs? 4. “Capacity”: What is the capacity situation in the industry? How high is the utilization of production and service capacities? 5. “Cycle Stage”: In which phase of the product life cycle is our offering? 6. “Company Targets”: What is our strategy? What goals are we pursuing? 7. “Compliance”: What is the legal framework for pricing in our industry? 8. “Channel”: Which sales channels do we use? What is their strategic relevance? 9. “Country”: In which regions are we active (value creation, sales, etc.)? What are the pricing-relevant interdependencies between the individual countries? 10. “Currency”: How should variations of exchange rates be reflected in pricing? 11. “Context”: How does our price presentation affect customer perception? How can we change the context in which a price is presented? How can insights into “nudging”, “framing”, etc. be taken into account in price optimization? About the determinants in detail: • Customer: The acceptance of prices from the customer’s point of view is a key factor influencing sales and profits. • Competition: Customer preference for a product or service depends on the prices of competitors. The tendency here is that the lower the prices of competitors’ offerings, the lower the company’s own pricing potential. However, there are numerous examples of companies in various sectors which largely avoid price competition on the basis of a differentiation strategy. • Costs: The level and structure of costs determine the company’s pricing scope. • Capacity: The capacity utilization of production facilities or service units has a direct impact on pricing. The intensity of price competition results from the relationship between supply capacity and demand. In the case of excess capacity, price is increasingly used to control utilization. The strong correlation between capacity utilization and price levels is particularly true in commodity industries. In the case of homogeneous mass products, the market price is primarily determined by the relationship between supply and demand. Raw materials (crude oil, cement, steel, iron ore), electricity, certain basic chemicals, and many other product categories are among these “commodities”. At the beginning of 2022, two supply developments came together that had a massive impact on price developments in almost all sectors: the Corona pandemic and the Ukraine war. • Cycle Stage: The variation of prices over the product life cycle is one of the key levers for business success. Pricing strategies and levels differ fundamentally for the four phases of introduction, growth, maturity, and degeneration. Market penetration can be controlled by the company. For example, a low initial price can accelerate the diffusion process. The changing pricing potential over the life

106

• •

•

•

•

5

Pricing Process Part 1: Analysis

cycle can be described by the concept of “pricing power”. Pricing power is the potential of a company to enforce pricing targets on customers (Simon & Fassnacht, 2016, p. 26, 2019; Simon, 2015a, p. 30; Simon, 2015b, p. 24; Tacke, 2014). Conversely, the power potential of a buyer vis-à-vis its suppliers can also be measured. A company’s pricing power is one of the key leading indicators of long-term success (Tacke, 2018). The ability to enforce prices varies over the life cycle of offers. The pricing power of company offers tends to increase from launch through the growth phase. Pricing power peaks in the maturity lifecycle stage. Thereafter, pricing power generally declines again. Company Targets: The basic equation Profit = Quantity × Price - Cost illustrates the direct relationship between price and profit. All consequences resulting from a price action are condensed into profit as a target variable. Compliance: The opportunities and risks of market penetration are determined by legal details, especially in digitized industries (Schlieker, 2021). Some examples of this are: Technology companies such as Apple migrating to the financial sector must observe the regulatory requirements of the banking sector. Legal restrictions are also relevant in the highly profitable cloud computing business. Amazon, for example, is not authorized to directly evaluate the content of companies’ stored data. In other sectors, legal requirements apply to digitized price publication (e.g., gas stations) as well as restrictions on the potential of bundling (e.g., software). Last but not least: In Germany, there is a fixed pricing for books. All books (including eBooks) must be sold at the same price everywhere. Channel: Digitization has led to a significant expansion of sales channels, including web stores, online marketplaces, and platforms. In addition to online sales channels, there are indirect sales (via a distributor or dealer) and direct sales—this makes price management across all channels much more challenging. For certain customer groups and products, online stores have proven their worth (e.g., spare parts, simple products, customers with low service requirements, and add-on products). Even with a rough division of channels into “online” and “offline”, there are various options in pricing: no price differences, “best buy” model, price differentiation “offline” vs. “online”. Depending on the industry and strategy, online channels can also be positioned higher in terms of price than stationary stores. The latter strategy was pursued by Walmart in 2018. This was due to three of the influencing factors already outlined above: costs (higher logistics costs), customer (convenience), and company targets (traffic shift to the stationary channel). Country: Price variations depending on countries, regions, or sales territories can be explained by a variety of parameters (including differences in competition, costs or willingness to pay, tax influences). A particular challenge are commodity flows between countries that are not intended by the companies (reimports, gray imports). Parallel imports lead to profit losses through cannibalization, which can be actively countered by a price framework (corridor). A price corridor is a compromise solution between unit prices and independent country prices. Currency: Exchange rate variations play a prominent role in the price management of global companies. Looking at the price potential between the upper and

5.3 Costs

107

lower limits (see Fig. 5.2), it can be stated: The larger the variable unit costs in relation to the maximum price, the greater the influence of exchange rate variations on the optimal price (see Simon, 1992). • Context: The value and price perception of end users, customers, and sales partners depend on the context (situation, location, price presentation, etc.). The price presentation is more important for the perception than the objective price level. Consequently, pricing must necessarily incorporate the latest findings in behavioral psychology. The insights from brain research must be integrated into all major challenges of the pricing process (see Chap. 13). Figure 5.2 illustrates that the various criteria act on two levels. The first level contains those criteria which directly influence the level of a price. The second layer comprises factors that have a moderating influence on the price level. Professional price management requires the integration of all the influencing factors outlined above. Business mistakes are inevitable if individual factors are ignored. The most common mistakes include: • • • •

Consider costs, customers, and/or competition in isolation. Disregarding the legal framework. Not setting clear target priorities. Inconsistent pricing across sales regions and channels.

The multilayered complexity shown in the diagram is further increased in the context of dynamic pricing. Dynamic pricing is a time-based approach to price optimization that incorporates a variety of additional criteria beyond those outlined: temporal factors such as season, day of the week, or time of day; contextual criteria such as location and weather; customer-related factors such as end device or search agent through to product criteria such as perishability. The basic prerequisite for a profit-oriented pricing process is that all relevant information is prepared in the analysis phase in a way that optimizes decisionmaking. In many companies, simple pricing methods are used: competition-based pricing and the cost-plus approach. These are based on very simple analyses. Both determinants—costs and competition—are the subject of the following section.

5.3

Costs

In cost-oriented pricing, a target margin is added to the variable unit costs. The aim is to ensure the targeted profitability (Diller, 2008). The multipliers (standard markups in the cost-plus approach) depend on the industry. Markup pricing is the simplest pricing method (Bertsch, 1991; Buxmann & Lehmann, 2009, p. 521; Roll et al., 2012). It remains the most widely used, especially in time-dependent forms (such as for services). The problem with cost-plus pricing is as follows (see Simon, 1992; Buchwald, 2018; Homburg & Totzek, 2011):

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1. The cause-and-effect relationships are reversed. Price is not a function of cost. Rather, price changes lead to consequences for sales volumes. Changes in sales volumes subsequently influence costs. 2. The unit cost of a product or service is not static, but a moving target. Unit costs change with volume. 3. The one-sided focus on the company’s own costs disregards the market. The demand side is ignored. 4. The costs are irrelevant for the customer benefit (and thus for the maximum price)! Willingness to pay results from customer needs. Internal structural data are completely irrelevant for the market. 5. In a full cost calculation, a decline in sales volume leads to an increased allocation of fixed expenses and thus to a higher price. A price increase in response to a decline in volume exacerbates the quantity problem. 6. Another problem with cost-plus pricing is that it neglects competitors. These arguments result in three risks of the cost-plus approach with regard to profit as the most important target variable of pricing: (a) Efficiency gains are automatically passed on to the customer. Changes in benefits on the demand side are not taken into account. The fixed markup factors mean that savings in manufacturing costs or purchasing conditions are passed on in an undifferentiated manner. Potential profits are given away in this way. (b) Companies forgo margin when their price is too low. This is always the case when the willingness to pay for products is higher than the manufacturing costplus margin markup. Customers’ willingness to pay can be significantly higher than cost-plus target margin. Digital products are a concise example of this. Here, willingness to pay is often well above the very low cost of value creation. An even broader perspective on the most important sectors and companies shows that products are becoming increasingly similar in quality in an international comparison. There is great potential for differentiation through productrelated services. This strategy, which has long been valid, has become even more important against the background of digitization and the opportunities for digital services. If these additional values are not professionally reflected in the prices charged, the profit potential is wasted. With cost-based price management, it is not possible to capture added value. (c) Companies give away sales volumes if their price is too high. Excessively high costs or margin expectations result in a price that exceeds customers’ willingness to pay. Volumes and market share are then lost to competitors. The fact is: there is no causal relationship between costs (or margin expectations) and customers’ willingness to pay. The core problem with the cost-plus approach is its purely internal focus. The effect of a one-sided cost focus can be foreseen: Either the products are too expensive because the customers’ budget expectations are not taken into account. Or the customer’s perception of quality suffers because the price positioning is too low. Only in very large assortments and in spare parts pricing does

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109

markup pricing have a justification in an adapted form (Buchwald, 2018). But even in these cases, markup factors should not be distributed across the board. Product- or product-group-specific differentiation of markups is mandatory for-profit exploitation (Roll et al., 2012). A particularly important argument against the cost-plus approach is the significant expansion of revenue sources in the course of digital transformation. Data, software, digital services, and content: For all these revenue components, the traditional cost-plus approach is not applicable, as their variable costs approach zero. Despite the disadvantages described: Cost information is important for pricing (Homburg & Totzek, 2011). As a price floor, internal expenses are mandatory if a company wants to make profits. In investment-intensive industries, the cost implications of innovations or product expansions must be included in pricing considerations. For example, the formerly new OLED display as a value driver in the iPhone X cost EUR 93 per device in 2018 (Mansholt, 2018). However, looking at average costs in pricing is inadmissible. Fixed and variable components must be separated. Only costs that are dependent on the pricing decision may be included in the calculation. The price floor corresponds to the lowest price at which a product can be sold without incurring losses (Simon, 1992; Corsten, 1988; Bertsch, 1991; Diller, 2008). In the short run, a profit contribution is generated if the achievable price is higher than the variable unit costs. This is because in the short run, fixed costs cannot be changed and are therefore not relevant for decision-making. The profit-optimal price is not influenced by fixed costs. Consequently, only those costs that are dependent on the price decision (e.g., distribution costs) may be included in the price calculation. Sunk costs that cannot be influenced include, for example, expenses for research and development and the costs of market launch. In the long term, however, the price must cover both variable and fixed costs of production or service provision. Then the “costs to serve” are relevant: All costs incurred to comprehensively serve the customer—e.g., expenses for sales, customer service, marketing, and technical service. Let us look at the video streaming market. To expand their position, the market-leading providers (Netflix, Disney, and Amazon) are investing heavily in their own content (Postinett, 2018). Netflix invested USD 17 billion in its own original content in 2020 (Gürtler & Rauffmann, 2022). In the content business, investments in the billions of dollars are required to serve customer needs while achieving mission-critical mass. Long-term investments in the double-digit billions for licenses and productions must be financed by corresponding price increases. Consequently, the price floor corresponds to full costs. Case Study MediaMarktSaturn The retail chain MediaMarktSaturn have been investing more heavily in digitization for years. During 2018, electronic price tags were introduced in all German stores (Mitsis, 2018; Firlus, 2018). The costs of EUR 100 million (continued)

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are to be reflected in pricing in the long term according to the decision rule outlined. Many supply sectors—including services and digital goods—are characterized by very low marginal costs and high fixed costs (Bertsch, 1991; Buxmann & Lehmann, 2009, p. 521; Roll et al., 2012). Once capacities are built up in industries such as media, telecommunications, financial services as well as information technology, the marginal costs of creating another unit of output are marginal. This is particularly true for digital offerings. The profit impact of a change in sales volume is greater the higher the ratio of fixed to variable costs. Due to a high proportion of fixed costs, even a small increase in volume or capacity utilization leads to a significant change in profit (Simon, 1992).

5.4

Competition

In competitive pricing, prices are set with direct reference to competitors (Roll et al., 2012; Simon & Fassnacht, 2009, 2016, 2019; Corsten, 1988; Simon & Dolan, 1997). Defensive approaches (such as an orientation to the price leader) or active pricing strategies (e.g., consistently undercutting benchmarks as part of a niche strategy) are observable. Competitive pricing plays an important role for digital products: 1. Competitive pricing is closely aligned with market share objectives. It is often about achieving market share gains by undercutting competitors. Winning a high market share is crucial for digital offerings. Network effects and the resulting lock-in effects on customers are the decisive arguments for this (Buxmann & Lehmann, 2009). 2. Monitoring the competition is becoming easier as a result of digitization. For technological reasons, competition-oriented pricing is easier to implement than in the past. Data crawlers are algorithm-based tools that automatically search the Internet for information on competitors, products, prices, etc. Upstream competitive analyses can also be increasingly automated, e.g., through web scraping. 3. In the case of digital goods, an intensification of price competition has been observed in the recent past. Many modern industries—especially those most affected by digitization—are characterized by oligopolistic structures. Pricing is determined by a few strong competitors—the oligopolists divide up the majority of the market among themselves. Cloud computing is dominated by Amazon, Microsoft, and Google. Two-thirds of the market for online advertising is covered by Meta (Facebook) and Google alone. The two major players consistently accounted for more than 60% of global advertising revenue between 2017 and 2022. Amazon, Google, and Apple dominate the growing market for personal voice assistants.

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4. Market share targets, price competition, and concentration tendencies have a massive impact on industry profits. In many digitized industries, only the companies with the highest market share are profitable. In the smartphone industry, for example, there is a clear imbalance in the distribution of profits. Apple generates about 80% of industry profits with its iPhone. Samsung is also very profitable. Profitability drops significantly for the other manufacturers (Anonymous, 2018). On the importance of market share positions in digital industries, another example: the highly successful digital group Alphabet is in the red in one of its most important business areas—cloud computing. Compared to the market-leading and highly profitable companies Amazon (Web Services) and Microsoft (Azure), even the position 3 or 4 in the competitive ranking is no longer sufficient to generate profits. Similar constellations can also be found in more traditional industries such as air traffic. Concentration tendencies and the pursuit of size and market power will continue to increase in digitized industries in the future. In this cutthroat competition, price will continue to play an exposed role as a competitive parameter in the future. 5. The price action of one company often has a noticeable impact on the market share of other competitors in oligopolistic industries. Depending on the assessment of the background and the sales effects that have occurred, competitors react with price countermeasures. This competitive reaction has repercussions on the volume and profit situation of the price initiator. The “Black Friday” promotion day in Germany in November 2017 offers numerous examples of this. In many product categories, there were enormous competitive and price dynamics in the run-up to the discount promotion. This could be observed, for example, in the price development of the Playstation 4 game console from Sony. Immediately before the actual promotion day, three online retailers slashed their prices. The reason for this was a campaign by the retail discounter Aldi, which offered the Playstation 4 at a very low price of EUR 299 in the run-up to “Black Friday”. The aforementioned competitors obviously felt compelled to respond to the discount retailer’s price war. But competitors also react to price increases. One example of this is the market for video streaming. Investments worth billions of USD for licenses in the content business have to be financed by corresponding price increases. The market leader Netflix took the initiative at the beginning of 2018. Amazon followed immediately with price increases for its Prime members. The monthly subscription for Prime customers was raised from USD 10.99 to USD 12.99, an increase of 18% (Postinett, 2018). It is not only about downward trends (price wars). Price increases by competitors sometimes also result in erratic developments. Container shipping provides an example in the recent past. This was caused by a sharp increase in demand coupled with exceptionally tight capacities. Price developments in this oligopolistic market were extreme: On the very important China–Europe route, spot rates for containers (from around EUR 2000 in the past) have virtually exploded

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to well over EUR 10,000 in a short period of time at the end of the year 2020. The Shanghai Index (which summarizes the prices of the most important China routes) has tripled in 2020 alone. In the following, we take a closer look at the effect of price reductions. It becomes clear that competitive pricing approaches are very risky in fixed-cost-intensive industries. This is because cost structures often lead competitors with unused capacities to resort to price cuts. Manufacturers of mass products are particularly at risk. A price reduction then often leads to a dangerous price spiral in highly competitive markets. The consequences are (cf. Simon & Fassnacht, 2009): 1. The providers are continuously undercutting each other. 2. Only very rarely do price reductions translate into significantly higher sales volumes. 3. In the end, the industry as a whole finds itself at a lower price level with virtually unchanged market shares. 4. This automatism often drives all competitors into the red. This is because the reduced margin is often not compensated for by sufficient volume. Price wars are often forced on the assumption that the profits after a market shakeout will exceed the losses of the price war. The examples of a wide variety of industries such as telecommunications, food retailing, air transport, and many others show that this assumption is often wrong. Price wars often drag on much longer than originally expected. The result is permanently lower profits for all competitors. In many cases, price wars have permanently weakened entire industries. In the B2B sector, the price war that broke out in the German cement industry in 2002 is a cautionary example. In this industry, it took many years for companies to return to their previous profit levels after a price war (Simon, 2015a). Other examples of intense price wars in the past were provided by memory chips (Intel and AMD), game consoles (Microsoft and Sony), and book retailing (Walmart and Amazon). Food discounters (Aldi and Lidl) and drugstores (Rossmann) undercut each other fiercely in early 2018 (Schuldt, 2018; Anonymous, 2018; Hielscher & Firlus, 2018). Of decisive importance for the success of competitive price management is which competitor to orient oneself toward. It is a matter of defining the relevant competition. The relevant competition from the supplier perspective is defined by the selection of customers. Only the customer perspective is relevant in the context of pricing. In the course of his purchase decision, the customer carries out two selection steps. In the first process step, the awareness of the supplier is what counts. The second selection step is concerned with acceptance and preference aspects from the user’s perspective. Taking into account minimum requirements for quality, image, etc., as well as upper price limits, only a relatively small number of providers are then in direct competition with each other. These providers and their product alternatives are referred to as the relevant set or evoked set (Buchwald, 2018). Without a clear delineation of the relevant competition, wrong decisions may result. Against the backdrop of increasing digitization, there is a particular risk in defining the market too narrowly and ignoring potential competitors. This is because, in the

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recent past, numerous companies have been confronted with attacks from competitors from outside the industry. Microsoft’s core business with software licenses was directly hit by Google’s free concept for Google Docs. Microsoft reacted to the attack by open source software in the short term with price reductions. In the medium term, the revenue model had to be realigned. In addition to licenses (for installed software), the market leader expanded its portfolio to include online services based on Internet-based software (Buxmann & Lehmann, 2009). As an Interim Conclusion It Can Be Stated Competitive pricing and cost-plus pricing are highly relevant in practice. Costs and competitive prices are important as influencing factors. However, the methods should never be used in isolation, because both fall short. Competitors may not be pursuing the same business objectives as one’s own company. Pricing is a very important lever in achieving both revenue and profit goals. So why would you align with your competitor’s pricing strategy and put such an important lever at risk, especially since your competitors may have very different goals? How you should respond to a competitor’s price change depends on your relative position in the industry. This position results from pricing power and competitive advantage. In addition to cost and competitive considerations, the customer perspective must be taken into account. In highly competitive markets, it is only possible to capture what value is generated for the customer. In this respect, it is essential to know the customer’s perception of value. Profit-oriented pricing must take into account all relevant influencing factors: Competitive offers and the company’s own costs, but also customer requirements and willingness to pay.

5.5

Customers

Value-based price management aligns price structures and levels with customer benefits. This is because customers’ willingness to pay is always a reflection of their perceived value (Simon, 1992, 2015b). The key question from a management perspective is: What benefits does the customer associate with our company offering and how high is his resulting willingness to pay? The aim is to take the added value of the company’s own offerings into account in pricing. The decisive task in the run-up to any price and product decision is to measure the perception of value. Value-based pricing requires a differentiated analysis of customers and the competition. The effort involved is higher than with cost-plus pricing or an adjustment to the competition (Hinterhuber, 2004; Frohmann, 2009b). Especially for companies that have traditionally set their prices by adding a markup to the cost of goods sold, the application of value-based pricing can lead to significant profit increases. In certain supply categories (e.g., software), value-based pricing is mandatory. Chap. 9 shows in methodological detail how the value-based approach can be used for systematic profit optimization.

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References Anonymous (2018). Einnahmen mit Cloud-Anwendungen. Wie Apple mit Ihrem iPhoneSpeicherplatz Geld macht. Manager Magazin Online. Retrieved April 22, 2022, from https:// www.focus.de/digital/computer/apple/apple-das-geschaeft-mit-zu-geringem-speicher_id_84 56497.html Bertsch, L. H. (1991). Expertensystemgestützte Dienstleistungskostenrechnung. Poeschel. Buchwald, G. (2018). Pricing-Lexikon. Prof. Roll & Pastuch Management Consultants. Retrieved April 22, 2022, from https://www.roll-pastuch.de/de/unternehmen/pricing-lexikon Buxmann, P., & Lehmann, S. (2009). Preisstrategien von Softwareanbietern. Wirtschaftsinformatik, 6, 519–529. Corsten, H. (1988). Betriebswirtschaftslehre der Dienstleistungsunternehmungen. Oldenbourg. Diller, H. (2008). Preispolitik (4. Aufl.). : Kohlhammer. Firlus, T. (2018). Künstliche Intelligenz im Handel. Der Code weiß, was Sie morgen kaufen möchten. Retrieved April 22, 2022, from https://www.wiwo.de/unternehmen/handel/ kuenstliche-intelligenz-im-handel-luxusindustrie-probiert-ki-aus/20961980-2.html Frohmann, F. (2009a). Erfolgreiche Preisstrategien und Produktpositionierung (Lektion 1). In Strategisches Preismanagement. Schriftlicher Lehrgang in 13 Lektionen (2. Aufl.). Frohmann, F. (2009b). Der Neuproduktmanager. Management Circle Seminar. Gürtler, T. & Rauffmann, T. (2022). Hat Netflix doch noch ein entscheidendes Ass im Ärmel? Retrieved April 22, 2022, from https://www.wiwo.de/unternehmen/it/streaming-wars-hatnetflix-doch-noch-ein-entscheidendes-ass-im-aermel/27998172.html Hielscher, H., & Firlus, T. (2018). Rabattaktion bei Aldi. Beginnt jetzt ein neuer Preiskampf mit Lidl? Retrieved April 22, 2022, from https://www.wiwo.de/unternehmen/handel/ rabattaktion-bei-aldi-beginnt-jetzt-ein-neuer-preiskampf-mit-lidl/20872504.html Hinterhuber, A. (2004). Towards value-based pricing – An integrative framework for decision making. Industrial Marketing Management, 33(8), 765–778. Homburg, C., & Totzek, C. (2011). Preismanagement auf Business-to-Business-Märkten: Zentrale Entscheidungsfelder und Erfolgsfaktoren. In C. Homburg & C. Totzek (Eds.), Preismanagement auf B2BMärkten (pp. S. 15–S. 69). Gabler. Mansholt, M. (2018). Smartphone-Konkurrenz. iPhone X verkauft sich schlechter als gedacht – Das stellt Samsung vor Probleme. Stern online. Retrieved April 22, 2022, from https://www. stern.de/digital/smartphones/iphone-x-verkauft-sich-schlechter-als-gedacht—und-stelltsamsung-vor-probleme-7869276.html Mitsis, K. (2018). Kein Preis-Chaos mehr bei Media Markt: Elektro-Riese plant langersehnten Schritt. Retrieved April 22, 2022, from http://www.chip.de/news/Kein-Preis-Chaos-mehrMedia-Markt-und-Saturn-planen-langersehnten-Schritt_134574723.html Postinett, A. (2018). Online-Videodienst: Die Welt schaut Netflix. Handelsblatt Online. Retrieved April 22, 2022, from https://www.wiwo.de/unternehmen/it/online-videodienst-die-welt-schautnetflix/20875584.html Roll, O., Pastuch, K., & Buchwald, G. (Eds.). (2012). Praxishandbuch Preismanagement. Strategien – Management – Lösungen. Wiley. Schlieker, K. (2021). Zoff um Handy-Daten. Wiesbadener Kurier, page 7, December 3, 2021. Schmidt-Gallas, D., & Lauszus, D. (2005). Mehr Markt. Bonn. Schuldt, S. (2018). Einzelhandel: Aldi und dm starten nächsten Preiskampf – diesmal mit MarkenParfüm. Retrieved April 22, 2022, from https://www.stern.de/wirtschaft/news/aldi-und-dmstarten-naechsten-preiskampf%2D%2D-diesmal-koennte-douglasverlierer-sein-7857280.html Simon, H. (1992). Preismanagement: Analyse – Strategie – Umsetzung (2. Aufl.). : Gabler. Simon, H. (2015a). Preisheiten. Campus. Simon, H. (2015b). Confessions of the pricing man. Copernicus. Simon, H., & Dolan, R. J. (1997). Profit durch power pricing: Strategien aktiver Preispolitik. Campus.

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Simon, H., & Fassnacht, M. (2009). Strategie – Analyse – Entscheidung – Umsetzung (3. Aufl.). : Gabler. Simon, H., & Fassnacht, M. (2016). Strategie – Analyse – Entscheidung – Umsetzung (4. Aufl.). : Gabler. Simon, H., & Fassnacht, M. (2019). Strategy, analysis, decision, implementation. Springer Nature. Tacke, G. (2014). From good to great – Achieving pricing excellence in competitive markets. Vortrag bei Evonik Industries AG. Tacke, G. (2018). Digitization: “Think big, start smart”. Presentation European Sales Conference SKP 2018. April 19, 2018.

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6.1

From Corporate and Competitive Strategy to Pricing Strategy

The corporate strategy and the competitive strategies of individual divisions derived from it are fundamental to price management. Profit-oriented corporate management requires that the pricing strategy be integrated into the overall strategy of a company. Pricing must be oriented to higher-level requirements. The reasons for this are summarized as follows: 1. Every pricing decision a company makes involves six dimensions. Price is an amount a buyer pays for a certain volume of a product—region, distribution channel, and timing are added dimensions. 2. Price is a strategic instrument that companies use to position themselves in the competitive environment. The fundamental question of positioning in the priceperformance perception of customers concerns both the level of the company as a whole, the dimension of individual divisions, as well as product lines and products. 3. At all strategy levels, the aim is to define the strategic thrust for various focus segments (products, regions, customer groups, or sales channels of the company). In essence, the strategy definition reflects the various dimensions of pricing. Price is thus by definition a component of a company’s strategy process. 4. Pricing makes an important contribution to unlocking a company’s potential for success. Prices are a reflection of perceived performance. Prices reflect internal costs, customer requirements, the competitive situation and, last but not least, corporate objectives. 5. As a long-term monetization plan, the pricing strategy should be aligned with the business strategy. 6. Pricing strategies are regularly reviewed, elaborated, and optimized. 7. New pricing strategies are to be defined in particular in the following constellations: (a) change in corporate strategy; (b) structural change in the # Springer Nature Switzerland AG 2023 F. Frohmann, Digital Pricing, Management for Professionals, https://doi.org/10.1007/978-3-031-24591-6_6

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revenue model; (c) entry into a new market; (d) introduction of a new product; and (e) development of a new distribution channel. 8. A central component of strategy development is the definition of targets. As part of budget planning, management determines how sales volumes (market shares) and margins are to develop in the following periods at the individual planning levels. Target prioritization is carried out for business units, product lines, and focus products. As price dimensions customer segments, regions, and sales channels are part of the target setting at product level. The core elements of the pricing strategy must be clarified at the various levels of strategic planning at different levels of detail and time intervals. It is about the strategic positioning of the company, from business units and product lines to individual products. Corporations such as Apple, Amazon, Siemens, or Microsoft first define an overarching corporate strategy. The corporate strategy comprises long-term fundamental decisions for achieving the company’s market-related goals. These serve to channel the strategies of individual business units. Central questions of the corporate strategy are: • In which business areas do we want to compete? • How should central resources be distributed to the individual business units? • Which products or services should be offered to which customer groups? The answers to these questions result directly from the specifications of the overarching business model and the results of the business model map (cf. Chap. 3). Competitive strategies are derived for the individual strategic business units on the basis of the corporate strategy. In the case of Apple, these business areas are, among others: iPhone, iPad tablet, Mac computer, the services business, and the computer watch Apple Watch. The business unit strategies (example: Apple Services) serve as direct specifications for the various product lines and products within the organizational units (including the streaming services Apple Music and TV+, the online storage iCloud, and the payment service Apple Pay). The core challenge of strategic planning for all areas is a competitive alignment in terms of performance and price. This chapter outlines the different levels of strategic planning and their influence on price management.

6.2

Dimensions of the Pricing Strategy

The pricing strategy defines the central cornerstones for optimizing prices. Important guidelines for operational price management are derived on the basis of the pricing strategy. Four strategy dimensions can be summarized in the following exemplary questions:

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1. What are our goals? – How do we weigh the competing goals of market share and margin? – What is the priority of revenue optimization versus profit generation? 2. How do we want to position our offerings in terms of price? – What price-performance level compared to the competition are we targeting? – In which price ranges do we position our products and services? – What price image are we aiming for? 3. How do we want to behave in the price-performance competition? – Should price be actively used as a competitive parameter? – Do we want to be the price leader, do we align ourselves with competitive prices or do we pursue a niche strategy? – How do we respond to competition? 4. What price structure do we choose? – What is the logic behind our pricing? – How do we tier prices across the service portfolio? – What criteria should be used for price differentiation? – How much do we want to differentiate our prices? The approach to strategy definition is similar to navigation with its three elements of determining the location, describing the goal, and determining the route. The corresponding questions of the pricing strategy are: where are we? where do we want to go? how do we get there? In the chapter on pricing analysis, the main factors influencing the pricing strategy (costs, competition, customers, etc.) were briefly described. The analysis of the determinants (11 C) serves to determine the starting situation (where do we stand?). The following section outlines the target description (where do we want to go?). Then various route options (wow do we get there?) are discussed. Numerous aspects have to be taken into account in the context of strategy development. The necessary trade-offs can be reduced to four overarching dimensions and questions: • Attractiveness of target markets: Where do we want to compete? • Own performance in competitive comparison: How do we want to compete? • Company competencies compared to the competition: What skills are required to grow profitably? • Objectives: What targets result from the budget objectives for our pricing? The first three sets of questions are answered methodically as part of the business model definition (see Chap. 3: “business model map”). The bridge between business model definition, revenue models, and the pricing strategy are the company’s objectives.

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Pricing Targets

Pricing strategies have an outstanding leverage effect on the company’s target achievement. The selection of options in pricing must therefore be based on the company’s objectives or the strategies of individual business units. The challenge is anything but trivial: after all, companies pursue several objectives (such as profit, market share, and growth) that are very often in conflict with each other. Objectives differ in detail for product lines, regions, customer groups, or sales channels. Pricing priorities are also often fundamentally differentiated for individual sectors and product categories. This can be outlined using four selected sectors and product categories as examples: 1. For digital products, generating network and lock-in effects is a top priority. Market share targets are of paramount importance (Heiny & Rest, 2022). In many cases, active cutthroat competition is conducted via price (e.g., ridehailing and e-scooters in Germany in 2022). 2. This contrasts with traditional product sectors such as automotive, mechanical engineering, and most consumer goods sectors. Here, significantly more emphasis is placed on balancing volume and margin. The core objective is profitable growth (Ziesemer, 2021). 3. In the commodity business—with interchangeable offerings—prices must be set flexibly. This is the only way to ensure sufficient utilization of production and service capacities. In commodity markets, the pricing strategy is to follow the market price. 4. In the specialty sector, margins are more important than volumes—the value of the products must be captured through optimal price markups. The following pitfalls are found in corporate practice: • Goals are not explicitly formulated. • There is no consideration of the overall context. The interdependencies between the goals in different areas of the company (online offerings, commodities, services, etc.) are ignored. • Inconsistencies across planning levels dilute the implementation of strategies. • Different functional areas (sales, controlling, and marketing) have different ideas about where the journey in pricing should lead. • Conflicting goals are not transparent. The necessary prioritization is simply not possible as a result. Target prioritization is imperative because pricing moves a large number of levers at the same time: Prices have an impact on financial ratios, on corporate image, on brand values as well as on demand behavior, and much more (Simon, 1992). Examples of possible objectives of price management are:

6.3 Pricing Targets

• • • • • • • • • • • • • • • • • • • •

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Increase sales volume per customer. Increase market share. Ensure customer loyalty. Force competitors out of the market. Win back lost customers. Increase purchase rate. Increase profit. Attract new customers. Reduce price sensitivity. Promote cross-selling. Bridge short-term capacity utilization valleys. Reduce transparency. Avoid price wars. Facilitate upgrading (purchase of higher value products). Increase price per customer. Increase average selling prices (ASP). Support brand strategy. Prevent competitors from entering the market. Increase customer attention through creative price elements. Reduce volatility.

This list was deliberately chosen without a categorization or logical order to illustrate the diversity of possible pricing approaches. Some of the objectives listed above are mutually exclusive. Against the background of the enormous diversity of objectives, the risk of overlooking conflicting relationships between the individual approaches is correspondingly high. Method Tip: Target Prioritization A very simple but effective method can be used to reveal the target weightings of individual functional areas. The starting point is a workshop with all functional areas involved in the pricing process (product management, marketing, sales, controlling, customer service). Within this framework, each organizational unit first defines all criteria to be included as targets. In the second step, each workshop participant evaluates the relative importance of each target using a constant sum scale. The question is: Which price targets do I have to orient myself to in my function? One-hundred percent is to be allocated to all objectives. The constant-sum scale is particularly well suited for identifying conflicting goals. Weighting using the cardinal scale (from 0 to 100%) is more valid than rating on an interval scale (e.g., 1–5) because it forces a trade-off. The results can be evaluated, visualized, and presented for discussion in the workshop using a simple tool. It is advisable to link this target discussion with the expert estimate for price optimization, which is presented (continued)

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in Chapter 9. The target prioritization tool is an excellent quantitative support for anchoring the strategy definition across the individual functions and hierarchy levels (Tacke, 2014). The target weighting in pricing can be consistently derived from the specifications of the higher hierarchy and planning levels. Conflicting target approaches can then be ruled out. In strategic approaches to price management, there is a particular conflict between the goals of margin and sales volume (or market share). Price decision makers are faced with a fundamental dilemma here. High prices are necessary to achieve high margins. However, these slow down the growth of a company. If volume increases are to be pushed, lower prices are recommended, which in turn are at the expense of unit contribution margins. The simultaneous increase of sales (or market share) and unit contribution margin (margin) is only possible under special circumstances (Simon, 1992; Simon & Fassnacht, 2009, 2019). In a dynamic view, the relations of the two core objectives change: 1. Depending on the development stage of a company or its products, focusing on short-term market share targets can fuel long-term gains. 2. In digital industries, a company’s relative market share determines its relative cost position. 3. In dynamic growth markets, market shares are an important indicator of a company’s innovative strength and the resulting customer benefits. 4. Dominant market positions are one of the main reasons for “pricing power”. Pricing power describes the ability to enforce higher prices and margins (Simon, 2015a). A company’s pricing power is one of the key leading indicators of longterm success (Tacke, 2014, 2018). High “pricing power”—and the resulting profit potential from price changes—tends to arise in the following situations: – Innovative offers – High market share (dominant market position) – High customer benefit – Complex offers with low-price transparency – Scarce capacities – Superior brand image Market share is of prominent importance within the criteria. In the context of the lifecycle phases, investment in market share is critical to success, especially for digital offerings. In many digital sectors, quickly achieving a critical mass is a prerequisite for harvesting the value created in later lifecycle phases via higher prices. 5. The target options of maximum market skimming and rapid market penetration must be evaluated against each other and prioritized for each company.

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Case Studies: Amazon ebooks and Apple iPhone For many years, achieving short-term profits was a subordinate priority for Amazon. In retrospect, the Amazon approach proves that start-up losses can make strategic sense in the long term. The global market leader among online retailers pursued a clearly defined pricing strategy: It was all about increasing market share and conquering dominant market positions in the long term (Anonymous, 2013, 2018a; Schütte, 2017). The shift in market power constellations in the electronic distribution of books in the US market serves as an indicator of this. Amazon’s position of power is enormous due to its high market share and direct end-customer access. Smaller publishers, in particular, have felt the purchasing power of the online retailer in discount negotiations in the recent past. Apple’s development shows that priorities can shift over the course of a product’s life cycle. The iPhone—the cash cow in Apple’s corporate portfolio—struggled with growth problems over a relatively short period. Sales actually declined slightly in 2016. The established markets (e.g., Europe and the USA) are saturated. Against the backdrop of stagnating market shares, Apple focused on drastically increasing margins in the course of its product line expansion. The iPhone X was launched at prices significantly above EUR 1000 in fall 2017. In the run-up to the market launch in Germany, there was already a very intense public discussion about whether the extraordinarily high price would be accepted. The price-performance ratio was sharply criticized in social networks (Kuhn & Berke, 2018). The XS and XR versions were (with a price of EUR 1,649 at the peak) the extreme point in Apple’s price development. At the end of 2018 as well as at the beginning of 2019, the first significant slump in sales figures occurred. Revenues and profits developed negatively in the short term. This consistently negative development of the most important KPIs (sales, market share, revenue, profit, perceived priceperformance ratio) led to a rapid response by the company: lowering the prices of various iPhone versions such as XR and 8, among others; more balanced pricing structure for the following new product versions.

6.4

Competitive Strategies

Value to customer as the starting point for pricing should be seen from the customer’s point of view. A company can only be successful if it has competitive advantages perceived by the customer. This always involves a comparison with competitors. Good performance on important parameters is not sufficient from the customer’s point of view if competitors are perceived to be even better (Simon, 1991a). The position in relation to the competition is determined by two factors, by:

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1. The performance (the benefit offered; value to customer) 2. The price demanded (the economic sacrifice from the customer’s point of view) Value to customer and price determine success in competition. To put it another way: There are only two options for successful positioning (Simon, 1991a; Porter, 1980). The possible competitive advantage lies: • On the performance-benefit side (performance differentiation) • On the price-cost side (cost leadership). Performance Differentiation The primary objective of the differentiation strategy is to offer the customer a special incentive to buy by means of a unique performance. The possible range of values here includes the core offering, additional services, communicative services and sales, including distribution channels (Porter, 1980). The brand can also make a decisive contribution to differentiation from competitors. Many customers are willing to pay more for a superior product or brand image than for competing offerings. Accordingly, a higher price can be demanded. The prerequisite for an increase in profit is that the added value for the customer (and thus the willingness to pay to be captured) is higher than the additional costs of the higher performance. The importance of the individual value drivers and the resulting willingness to pay differ depending on the industry, product category, customer segment, etc. Examples of a differentiation strategy can be found in all sectors and product categories. In the classic product business, examples include Porsche, Starbucks, Red Bull, Evian, Lindt, Montblanc, Miele, Gillette, and Boss (Simon, 2015a, 2015b). As a result of their differentiation from competitors, the aforementioned companies impose higher prices in the market. In all examples, the superior brand image compared to the competition is one of the core drivers for higher average prices, margins, and profits. Case Studies • Apple is a prime example of the consistent implementation of a differentiation strategy in the smartphone sector. All competitors combined achieved a profit of USD 54 billion worldwide with smartphones in 2016. With an amount of USD 45 billion, the pioneering product iPhone, launched in 2007, accounted for a large part of the total industry profit (Anonymous, 2018d; Jacobsen, 2018; Dämon, 2016). Despite significantly intensified competition in subsequent years, Apple maintained its dominant revenue and profit position. In Q2 2021, Apple accounted for 40% of revenue and 75% of profits in the overall smartphone market (Anonymous, 2022a). The market share of the various iPhone versions by sales volumes was much lower (up to only 13% in recent years). However, against the background of the excellent products and the brand image, significantly higher prices can (continued)

6.4 Competitive Strategies

•

•

•

•

•

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be enforced compared to the competition. In the course of successively expanding its product line (versioning), Apple is increasingly expanding its premium price strategy. The situation is similar for tablets. Apple devices are significantly more expensive than tablet computers from the competition. For example, the cheapest iPad cost USD 329 in the USA at the beginning of 2018. Competitors’ devices were available for less than USD 200 (Postinett, 2018a). The significance of the brand can be described using an example of the automotive industry. In the segment of MPVs, Volkswagen (with its Sharan model) has been significantly more successful in the past compared with the identical version from Ford (Galaxy model). The monitoring of success relates to the two core objectives in pricing—margins and sales volumes. Volkswagen achieved a significant price premium and was also able to attract significantly more customers with the Sharan than the competitor model (Frohmann, 2009). Volkswagen’s brand image is primarily responsible for this dual premium (sales volume and price). In the case of Starbucks, too, a superior brand positioning is one of the value drivers from the customer’s perspective (Simon, 2015a). With its differentiation strategy and premium prices, Starbucks is successful in an industry whose core product, coffee, was long considered a classic “commodity”. Evian (mineral water) and Montblanc (ballpoint pens) succeed in differentiating themselves in product categories that, as mass-produced goods, are subject to high-price pressure (Simon, 2015a). The example shows: Customer perception can be influenced in terms of differentiation from the competition even in the case of seemingly interchangeable products. This process must be creatively controlled. The active influencing of customer perception must not be neglected. Otherwise, there is a danger that an industry will slip into pure price competition. In digitized industries, performance differentiation from the customer’s point of view manifests itself particularly in the attributes “faster” and “easier/convenient”. Both value drivers together—especially fast delivery and outstanding convenience throughout the entire search and ordering process—explain Amazon’s unbroken dominance in global online retail.

Cost Leadership This strategy option is based on the competitive advantage of a lower price (Porter, 1980). Based on significant cost advantages, a company can offer its customers significantly lower prices than comparable competitors. Due to the low-cost base, one still generates attractive margins. The important point here is: Low prices alone are not the key to success. Profits with low prices are only generated by companies

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whose entire business model is consistently focused on efficiency (Simon, 2015a). Examples of this can be found in numerous industries. The basis of Dell’s low prices in the PC market were efficiencies under the built-to-order model (Simon, 2015b, p. 54). In air travel, Southwest in the USA was the first airline to implement this strategic approach in the late 1960s (Dirlewanger, 1969; Doganis, 1991; Meyer, 1992; Pompl, 1991; Frohmann, 1994). Ryanair transferred the cost leadership strategy to the European market and is both profitability and market leader among low-cost airlines. The low-cost segment has grown disproportionately in recent years, fueled by the permanent low fares of Ryanair, Easyjet, and other low-budget airlines. Within Europe, the low-cost segment accounted for 48% of flights in 2017 (Machatschke, 2018, p. 52). The relative share of discount airlines has continued to increase in Europe in the years of the Corona crisis (2020 and 2021). The advantages over globally positioned carriers such as Lufthansa or Air France are both on the market side and in cost structures (Kiani-Kreß, 2022). On the market side, low-cost airlines serve “short haul leisure”—nonstop flights for vacationers, migrant workers, and visitors. Compared to business travel, connecting flights and long-haul routes (differentiation strategy), this product–customer segment slumped much less during the pandemic. On the cost side, the low-cost carriers are clearly superior to the premium carriers in the decisive efficiency measure (transport costs per passenger kilometer). The average value is 3 cents (vs. 6–9 cents for network airlines). In recent years, the cost leader model and the resulting price strategy have been able to establish themselves in almost all industries. Numerous consumer goods companies adopted the strategic approach of Aldi, the pioneer of this business variant in food retailing. Low-cost approaches are also increasingly found in industrial goods sectors (including mechanical engineering), with which particularly price-focused segments are addressed (Simon, 2015b). In the European ridehailing sector, Bolt’s low-cost strategy is based on cost leadership (Heiny & Rest, 2022). The cost advantage in the “operating model” could be used for two main levers of market penetration: low commission demands on drivers; low end-user prices. The effect: an expansion of the company’s own supply combined with an increase in demand. Porter’s strategy approach outlined (performance differentiation vs. cost leadership) corresponds at its core to an economic model created much earlier. David Ricardo’s concept of comparative cost advantage relates to the trade between nations. According to Ricardo, countries increase their prosperity when they specialize in the goods they can produce very cheaply or particularly well. Consequently, the strategy involves the classic trade-off between low costs (via efficiency in value creation) and high benefits (via innovation and performance differentiation). In a comparison of the two strategy approaches, Hermann Simon showed that the strategy option of cost leadership is associated with an increased competitive risk. A key success factor is economies of scale—i.e., cost advantages in value creation via high volumes or more efficient processes (example low-cost airlines: 11 flight hours per day vs. 9 hours in the case of premium carriers). The fundamental relationships of the cost leadership strategy are very similar to the specifics of digital goods: sales volumes, market shares, and critical mass in competition are the key levers for long-

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term profitability. In all markets in which low-cost providers are active, usually only two to a maximum of three providers succeed in competing profitably in the long term (Simon, 2015a, 2015b). This applies to the retail sector, telecommunications (including mobile communications), tourism (airlines, hotels), and financial services. For the remaining companies, there is simply no possibility of achieving the critical mass necessary for profitable survival. Simon shows that cost leaders have failed significantly more often in the market than companies that position themselves through competitive advantages on the performance side. In sum, there is a larger number of permanently successful premium providers across all industries compared to low-cost companies (Simon, 2015a, 2015b). As an interim conclusion, it is possible to state the following with regard to competitive strategic positioning: 1. The choice of the business model and the competitive strategy decisively limits the scope for price management. The long-term positioning decision sets the framework for all downstream price measures. 2. A company’s cost structures play a decisive role in determining its price competitiveness and thus its chances of survival (Simon, 1992). 3. Low-price strategies are only suitable for cost leaders. This is because a price advantage can only be maintained in the long term if the company operates more efficiently. Otherwise, competitors can easily follow suit with price cuts (Simon, 1992). 4. Higher prices can only be achieved by offering real benefits and added values for which the market is willing to pay. Attractive innovations and superior quality require investments in research and development, sales, service, etc. 5. The effects of global digitization are also reflected in research and development budgets. The ranking of companies with the highest share of investment has shifted significantly in the course of the digital transformation. The most important industry in economic terms (the automotive sector) dominated research and development budgets for a long time. Volkswagen ranked first in global research and development (R&D) budgets for five consecutive years (2012–2016). In 2017, the ranking changed significantly. Amazon occupied the position of the global budget leader for the first time (Anonymous, 2017). Other pioneers of digitization (Alphabet, Meta, Apple, etc.) followed in the places (Schütte, 2017). Amazon (with an innovation budget of USD 36 billion) also led the R&D rankings in 2019. 6. Based on value to customer considerations, only genuine value drivers should be offered. Technological masterpieces that no customer wants to pay for are punished by the market just as much as low-quality, low-cost solutions. 7. Price is a fundamental component of communication to the customer. The brand promise is reflected by the price-performance ratio. Prices must be designed consistently with the brand image. 8. Since competitive advantages are based on unique capabilities, it is important to protect these core competencies and resources. It must be prevented that other

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market participants (competitors, partners, sales agents, customers, etc.) can replace the own resources and imitate competencies. 9. Platforms shift the value creation within a market, but also between countries. Before digitization, value creation and value monetization were bundled within a country much more often. When buying a product or using a service from a German company, the revenues could also be collected by the respective domestic providers. Due to the enormous dominance of large platforms (e.g., Alibaba, Tencent, and Amazon), the revenue streams are shifting. This is because for every transaction on an international platform, revenue flows into the country of the platform operator. The result: a significant shift of value creation from Europe to North America and Asia (Schmidt, 2016; Rest, 2018). 10. In digitized business models, the two options of performance differentiation and cost leadership tend to converge more closely. Scaling effects (via the cost structure) and high customer value (via new digital services) are not mutually exclusive in principle. Overall profitability depends on the specific constellation of the three profit levers (price, volume and costs). This insight brings us full circle to the first chapter. For example, Spotify, the global market leader in music streaming, succeeds in combining high customer value and relatively low prices. However, due to its value creation model and high royalty payments, the scaling opportunities necessary for sustained profitability do not exist (Anonymous, 2022b). Apple Case Study Controlling its value creation system is a top strategic priority for Apple. The technology group imposes strict rules on its market partners (suppliers, customers, value creation partners). Programmers of applications for Apple end devices are prohibited from imitating core functions of the value creation model. This includes, among other things, the playing of music files. The core of Apple’s strategy is an optimal combination of “openness” and “isolation”. In the production of services, maximum openness is achieved for its value creation partners. At the same time, Apple consistently seals off its resources and competencies. By strictly protecting its resources, the technology group controls, among other things, its dominant market position with digital services. The service unit is the second most important sector in Apple’s portfolio from a profit perspective (Kharpal, 2016; Kerkmann, 2018; Anonymous, 2022a). Quite a few companies engage in “overengineering”. Their products contain many more features than customers need or are willing to pay for. A pronounced internal orientation is added to this. It is ignored that it is never about the objective performance. Only the customer perspective counts! Competitive advantages are always perceived subjectively (Simon, 1991a, 1991b). They are often supported by communication measures and resulting image positions.

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Despite technically identical products, significant price premiums can often be realized through superior brand values. These statements apply to all product categories, including apparent “commodities”. Even raw material suppliers, energy providers, and suppliers of supposedly interchangeable products can set themselves apart from the competition by means of a unique brand image. The outstanding characteristic of the brand as a competitive parameter is the durability of the competitive edge. Price changes can be parried by competitors in a matter of seconds (Simon, 1992). In contrast, image positions can only be positively changed in the long term. In summary, two more important practical findings from numerous projects in a wide range of industries: 1. The search for differentiation opportunities is a creative task. The smallest differences in the performance of the offering compared to the competition can become highly relevant in the market. In the case of eyeglass frames, for example, weight is a decisive argument for many customers. 2. Without a strategic competitive advantage, long-term profitable survival cannot be ensured. It is not uncommon for positioning dilemmas to result not from a technical-objective performance disadvantage (and correspondingly insufficient willingness to pay), but from perception deficits.

6.5

Strategic Segmentation and Positioning

Regardless of their specific design, competitive strategies always include the product and customer dimension. The starting point is the definition of price (cf. Chap. 1). Accordingly, there are two central starting points for capturing the value created: the product and the customer. Put simply, there are at least as many strategic price points as there are products (P) and customer segments (K) in a company. If all other price dimensions (sales channel, region, etc.) are excluded for the time being, this results in P times K price points. These must be set optimally and coordinated across the individual planning levels (e.g., division and product line). The question of price segmentation and positioning concerns: • The selection of target segments (customer dimension). • The performance- and price-side alignment in the competitive environment (product dimension). Segmentation and positioning comprise the decision as to which priceperformance combinations are to be used to serve which segments. The core requirements in pricing are therefore as follows: 1. Segment markets 2. Differentiate offers and prices.

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This question builds on the basic competitive strategies (performance differentiation or cost leadership), but goes beyond them. Segmentation and differentiation are at the heart of price management. An impetus for expanding market penetration can result from the well-founded measurement of customer requirements. Market analyses usually reveal a differentiated picture—different customer segments exist in almost all industries. These are characterized by significant differences in their performance requirements and willingness to pay. Offering only one product in such a situation leads to two negative effects: 1. For some customers, the price is too low. They would be willing to pay more for higher quality; the company loses margins. 2. For another part of the customers, the price exceeds their willingness to pay. They will not buy; the company loses sales volumes (Simon & Fassnacht, 2009, 2019). The various demands cannot be satisfied in this way. This dilemma can be resolved through targeted product and price differentiation. Segmentation and positioning build on customer requirements; they are the strategic responses to differentiating customer needs. The perspective of segmentation and positioning can be subdivided as follows for the individual hierarchy levels: 1. The view of a company that aligns its business units in relation to its competitors as part of its corporate strategy. 2. The perspective of a business unit that positions its various product lines in terms of performance and price as part of its competitive strategy. 3. A product line that serves different subsegments in a differentiated manner with individual products in different price ranges. The methodical approach of segmentation and positioning is based on the alignment of products in a price-performance portfolio. The starting point is the customers’ requirements and willingness to pay. Market penetration can be limited to a specific segment or targeted at several groups. High and low-price strategies can be used in parallel. Most companies work with several offers in diverse submarkets. They differentiate their product offerings across the price-quality spectrum. Many companies serve the specific customer requirements of three different segments in a differentiated manner (Homburg & Totzek, 2011). Consequently, price positioning can be divided into three basic positioning options: low-price, mid-price, and highprice positioning. 1. With a premium product, the focus is on the quality of the offering. Special offers and price promotions are deliberately avoided so as not to dilute the premium brand positioning. In many cases, the superiority of premium products is based on innovation. Mercedes-Benz, Miele, Nespresso, Starbucks and Apple cover the premium product segment—in the service sector, Singapore Airlines and Cathay Pacific, among others.

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2. Low-price offers are particularly favorable in terms of performance compared with competitors. Relatively low performance is associated with a permanently low price. Price plays the decisive role in the marketing mix. Profits are based on high sales volumes or unit sales. Low-price positioning is closely linked to the cost leadership strategy (examples: Ryanair, Aldi, and Lidl). 3. Many companies position themselves in a medium-price range that serves the typical mass market (Simon & Fassnacht, 2016). Medium-price offerings cover the middle price-performance radius of a market. The mid-price segment is located between discount prices and premium positioning. Branded products in the consumer goods market (Beiersdorf, Henkel, Danone, Nestlé, etc.) characterize medium-price positioning. In almost all industries, the three price tiers have expanded in the course of economic and social development—both upward and downward. Simon distinguishes five price tiers (Simon & Dolan, 1997; Simon & Fassnacht, 2019; Simon, 2015b). Consequently, the basic positioning can be extended by two extreme price tiers: ultra-low price and luxury price positioning. 4. Luxury price positioning is based on the perception of an extremely high performance at a very high price. The price level as such accounts for a part of the attractiveness from the customer’s point of view. Two major consequences result from this: (a) Price is a quality characteristic and (b) Price increases are not necessarily associated with volume decreases. In many cases, price elasticity is positive. In the case of luxury products, exclusivity also arises, among other things, from the fact that the product is only available to a very limited extent. Scarcity effects in turn explain the sometimes exorbitant prices of luxury brands. One example of this is the luxury car brand Bugatti. The Divo super sports car was offered at a price of USD 6 million in the recent past, but sold out immediately after it was announced. Brands such as Louis Vuitton and Hermès are examples of a luxury price positioning in the fashion industry. Luxury customers are increasingly found among young people—the share of Generation Y and Z customers in total sales of luxury brands will continue to rise worldwide. The resale market also plays an important role in the luxury industry. Sustainability and circular fashion consumption are becoming increasingly important in the luxury segment. 5. Ultra-low price offerings offer the greatest growth potential—they are used in emerging markets in particular (cf. Simon & Fassnacht, 2016, p. 80). The basis is the combination of “extremely low price” and “minimum performance”. At the core of ultra-low price positioning are performance features that are indispensable for the customer and are offered at a basic quality level. Dacia Logan stands for this ultra-low price positioning in the automotive industry (Fig. 6.1). Depending on the size of the company, the outlined alignment of the basic positioning options takes place with:

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high Luxury price

Value

Premium

Standard

Low budget

Ultra low price low low

Price

high

Fig. 6.1 Segmentation and positioning (based on Simon & Fassnacht, 2019, p. 42)

• Different divisions within a group • Several brands within a business unit • Several products or brands within a product line. Case Studies 1. The Volkswagen Group is represented by several business units in the automotive market. Four brands cover a large part of the automotive competitive radius in terms of performance and price: the premium brand Audi; Volkswagen, the leading core brand in the mid-range segment, and the Seat and Škoda divisions, which are positioned more favorably in terms of price. Within the four divisions, there is a very granular product and price differentiation. Different customer segments are addressed, for example, within a model family through performance differentiation and within a car model through different equipment variants. 2. Lufthansa’s strategy in the passenger business is based on a positioning in the premium segment. This is achieved with the core brand. In parallel, (continued)

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Lufthansa participated in the low-cost segment, which appeals to priceconscious travelers, with the secondary brand Eurowings. 3. Mercedes-Benz Cars (MBC) is the business unit of the Mercedes-Benz Group for the various passenger car brands. With a large portfolio of models (including C-, E-, G-, and S-Class as well as Maybach and AMG), MBC covers various subgroups of the automotive market. Against the backdrop of massive macroeconomic changes (parts shortages, rising input costs) in the recent past, a change in the strategy was emerging in early 2022. This applies to Mercedes-Benz, but also to its German competitors BMW and Audi. The reason for this is the objective of increasing earnings per passenger car. The resulting strategy is as follows: 1. the model range will be limited at the bottom and expanded at the top; 2. luxury models with higher profit contributions will be prioritized; 3. smaller and lower-priced models will no longer be produced (Ziesemer, 2021)—the VW subsidiary Audi will no longer offer smaller models (like A1 or Q2). Price segmentation and positioning are complex problems for which individual solutions must be found in each case. The use of highly developed methods is essential in order to systematically exploit the potential of a differentiated market development. This applies to all necessary sub-steps such as data collection, analysis and decision support. For price segmentation and positioning, multivariate methods can provide valuable support (Backhaus et al., 1990; Green & Tull, 1982). These include the following methods and objectives (Fig. 6.2): 1. Conjoint measurement: Elicitation of customer requirements. 2. Analytic hierarchy process (AHP): Determination of the importance of purchase criteria. 3. Factor analysis: Identification of the customers’ overriding decision parameters. 4. Cluster analysis: Identification of customer segments. 5. Discriminant analysis: Revealing the characteristics that can be used to explain segment differences. 6. Multidimensional scaling (MDS): Positioning of company offers from the customer’s perspective. Decision support is provided on a case-by-case basis using the appropriate statistical methods. Depending on the problem, several methods are also used in combination.

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Customer Conjoint

Rating of most important providers

Segment 1

Segment 2

Segment 3

Cluster analysis Operationalization of target segments Discriminant analysis

Positioning: Suppliers

Positioning: Target groups

Factor analysis

Cluster analysis

MDS

Discriminant analysis

Competition matrix

Conjoint measurement

Fig. 6.2 Overview of multivariate analysis methods

Implementation Tip 1. Homogeneous market segments are compiled with the aid of cluster analysis. Cluster analysis is a method of group formation. It identifies homogeneous subsets from a heterogeneous totality of consumers. 2. Discriminant analysis is a suitable method for distinguishing between the segments. Discriminant analysis is a method for revealing those group characteristics that explain the differences between individual target groups. A combination of sociodemographic and behavioral characteristics is recommended. 3. Benefit segmentation is of particular importance for price management. This is done on the basis of the subjective benefit perception of various offers. Methods such as conjoint measurement are available for measuring the benefits. Conjoint measurement can be used to determine the utility values of different services. (continued)

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4. A first indicator of the differences between customers results from measuring the relative importance of individual product features. Very often, the importance of features differs between segments; one would be giving away profit potential if the company were to target an average customer. Against the background of different preferences of different customer groups, services must be differentiated. Whether and how additional services should be priced separately depends on the strategic objective. Is the primary objective to expand volumes and market share? Or is it to secure margins by putting a price tag on cost-intensive services? 5. An excellent method for price segmentation and positioning is multidimensional scaling (MDS). Multidimensional scaling is used to: (a) Identify requirements and preferences of current and potential customers. (b) Capture customer perceptions of all relevant competitors. (c) Visualize competitor positions from the customer’s point of view. The MDS is excellent for structuring markets. The result of MDS is the positioning of products in the map of customers’ perception. A central task in the development of segment strategies is the identification and description of customer groups. Competition within a price segment is generally stronger than between the different target groups. It is of crucial importance to be able to clearly describe the specific characteristics of the price segments on the basis of quantitative data. The following price-relevant criteria are usually used in market segmentation (Fig. 6.3): Segments

Products Value to Customer

Customer requirements A

Premium

Standard

B

Low budget

C

Willingness to pay

Fig. 6.3 Segmentation and positioning based on willingness to pay

Price level

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– Willingness to pay (the maximum price a person will pay for a product) – Price sensitivity (as a measure of how a customer reacts to price changes) – Price attitude (this can be elicited, for example, by asking whether a particular price is perceived as high, reasonable or low).

6.6

Competitive Advantage Matrix

The competitive advantage matrix is one of the three pillars of the business model map already outlined. As a very helpful tool for strategic pricing, it can also be used as a stand-alone method. The relative performance from the customer’s point of view is clearly shown with the help of this tool. The perceived performance of the company in comparison with its competitors is visualized with regard to various important product parameters. Beyond pure product features, service aspects, the brand and intangible values must also be included. The two dimensions of the competitive advantage matrix (importance and relative performance) are measured as follows: 1. Relative performance is the company’s own performance divided by the performance of the strongest competitor. Alternatively, a group of competitors can also serve as a benchmark for measuring the company’s performance. The performance perceived by the customer for each individual product characteristic serves as an indicator. This requires an appropriate data collection in the market. An internal assessment of customer perception by industry experts is recommended in parallel. 2. Importance is the relative contribution of the features (value drivers) to the purchase decision. Suitable methods of measurement are conjoint measurement, analytic hierarchy process (AHP) or a direct query on an interval scale. In the competitive advantage matrix (Fig. 6.4), each individual feature is shown separately from the customer’s point of view. The perceived performance of a supplier is shown on the horizontal axis. The vertical dimension reflects the importance of the purchasing criteria. The main objective of the matrix concept is to identify strategic strengths and weaknesses. The importance of a parameter from the customer’s point of view and the relative performance of the company in this feature are to be matched. Various recommendations for action result from the analysis (Simon, 1991a): 1. Expand competitive advantages in the particularly important purchasing criteria! 2. Accept a less good performance on unimportant features under consistency aspects! 3. Avoid technological feats on less important features! 4. Urgently correct a qualitatively inadequate performance for important features!

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very important Secure advantage

Maintain consistent position

Reduce performance

I m p o rt a n c e f o r the customer

Improve performance

unimportant

worse

better

Relative performance

Fig. 6.4 Competitive advantage matrix (Simon, 1992)

A dynamic approach is critical for success: importance shifts over time. This must be recorded in a structured manner during the survey phase in discussions with customers. If purchasing criteria (delivery time, design, etc.) become more important in the future, a performance advantage perceived by the customer will have a greater impact on the overall decision. Strengths in these features should be highlighted in communication. Competitive Advantage Matrix and Price The importance of the price is shown graphically by the vertical positioning in comparison to all other decision parameters of the customer. Customer feedback on the relevance of price reflects its potential as a competitive parameter. An identification of the competitive position necessarily requires the inclusion of the second dimension (relative performance); valid conclusions can only be drawn by comparing the performance profiles of competing companies. Importance scores are often not interpreted correctly. The relevance of the purchase criteria reflects the general requirements of users in the context of a product category. The relative importance ratings are therefore not related to a specific competitor. Product quality, service aspects, etc. very often rank ahead of price. However, if competing products do not differ perceptibly in terms of features with outstanding importance, the overall picture changes. Customers then consider the next most important feature in their deliberations (Simon, 1992). In these cases, price

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is the decisive factor. As a rule, price does not play a prominent role from the outset. Rather, competitors have not succeeded in noticeably differentiating themselves from the competition in terms of even more important performance parameters. This constellation is regularly found with interchangeable products (commodities). In the worst case, the competitors do not differ perceptibly on any of the performance characteristics—the path to pure price competition is thus marked out. This can also be observed increasingly in digital sectors (e.g., ridehailing, carsharing, online food delivery, and e-scooters). The correct assessment of the relative importance of price compared to the benefit parameters (quality, brand, service, etc.) is one of the most important prerequisites for corporate success. The downfall of some companies can be traced back to the misjudgment of price effects. Case Study Praktiker The DIY chain Praktiker’s very one-sided focus on price was its undoing. For many years—starting in 2007—Praktiker tried to set itself apart with a simple price advertisement in Germany: “20 percent off everything”. With its one-size-fits-all discounts, Praktiker massively raised its profile, but over the years it made ever greater losses. No other DIY store was perceived as offering such low prices. The problem with this is that price only plays a secondary role for many customers. Professional craftsmen and DIY enthusiasts very often prefer premium products when it comes to tools, as Bosch’s success with its Power Tools unit proves. The misjudgment of price and a wrong discount strategy explain the failure of Praktiker (Simon, 2013; Schüür-Langkau, 2013). In 2013, the DIY chain had to file for bankruptcy. The situation in the German food industry is quite different. Preferences and willingness to pay are significantly more differentiated here. There is a very large customer segment that prefers low-priced food (Schuldt, 2018; Reiche, 2018; Anonymous, 2018e). Positioning via low prices is a profitable strategy option; it explains the lasting success of Aldi and Lidl.

Case Study Loewe In the case of the German TV manufacturer Loewe, positioning problems were foreseeable over a long period of time. LCD televisions were already gaining global acceptance by the end of the 1990s. In an extremely competitive market, televisions tended to become cheaper and cheaper. Loewe’s premium positioning proved to be increasingly difficult to implement. For many consumers, design was less relevant than sheer form (“as flat as possible”). However, Loewe cultivated design as a brand core. The focus on “high-end” retailers also proved to be a mistake. Large retail chains (such as Saturn) were neglected in contrast. In 2013, Loewe posted losses for the third year in a row.

6.7 Strategic Behavior in Competition

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Strategic Behavior in Competition

In digital sectors, companies must always expect to face competitors who cover previously unserved customer requirements with new business models. Among other things, this can also result in the development of a new segment or the occupation of a new price range. Competitors’ new business definitions can lead to changes in revenue models. One possible consequence of this is that competitors demand prices that are incompatible with the own corporate design (i.e., cost structures, positioning, and targets). A current example is the music market. Spotify is by far the global market leader among streaming services. The pioneer operates with a freemium revenue model. Spotify’s business model is primarily based on music streaming. There is no risk balancing as part of a business unit portfolio. At streaming newcomer Amazon, the business definition is different against the backdrop of the broad corporate portfolio. Amazon Music Unlimited undercut its strongest competitors Spotify and Apple Music with its monthly prices in early 2018 (Ballein, 2017). This contrasts with a customer segment of predominantly younger users who would be willing to pay premium prices for upgraded streaming services (earlier releases, etc.). To date, Spotify has failed to capture this premium segment. Netflix faced a similar challenge in the video streaming sector. The pioneer started in 2007 with a basic price point in the USA: USD 4.99. By expanding the flat-rate subscription into a more differentiated structure (Basic, Standard, Premium) and continuously raising prices, Netflix managed to steadily increase sales volume, revenue, and profit. However, with Apple (TV+), Amazon (Prime Video), Disney+, and HBO Max, competition intensified exponentially in a few years (as of 2019). In November 2019, Apple drastically undercut the market leader with its video streaming service TV+ (and a low price of EUR 4.99/month in Germany). Consequently, the business model of competitors should be validly assessed in advance of possible reactions. These considerations are not necessarily about profit targets. The overriding strategy is always relevant. In addition to financial objectives (profit, sales, margin, etc.), this usually also includes market-oriented criteria (such as market share, image, and customer satisfaction). This competitive strategic thinking requires a significant additional effort in terms of analysis and decision support. The potential for risk assessment has increased significantly as a result of digitization. Scenario analyses and simulations of competitive behavior can be used much more systematically than before with the help of IT tools. In addition, price comparison platforms provide an efficient overview of competitors’ prices. “Price crawlers” are used, among other things, to assess in advance of a planned promotion which competitors would follow our own price steps. In the context of discount campaigns—such as Black Friday—price search engines are used by some manufacturers to simulate competitors’ reactions to their own price variations (Rieken, 2017). In terms of game theory, thinking several steps ahead is critical to success in competitive pricing. Game theory serves as a strategic approach to predicting the reactions of competitors. To do this, one puts oneself in their position mentally. It is

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crucial to answer the question of which reactions to one’s own measures would be optimal for the competitors. A reliable answer to this question requires a large amount of information. This includes possible targets of the competitors, their cost and capacity situation as well as the financial situation. The goal in terms of data mining is to use all competitive information to run through future options based on realistic scenarios. Quantitative scenarios are data-based pictures of the future that describe conceivable developments. The scenarios are multidimensional, since the competitor’s assumptions about our own response are relevant to his thought processes. In the course of scenario development, the potential of artificial intelligence should be exploited (Joho, 2018). All conceivable options can be run through in a short time. This results in a significantly greater variety of solutions than with primarily intuitive planning by management. One of numerous instruments for professionalizing and dynamizing the scenario process are “tipping points”. These are data (on events, economic parameters, or trends) that cover uncertain events with an impact on the competitive scenarios. “Tipping points” allow a clear definition at which points a scenario changes decisively. This is the case when key determinants of the strategy evolve beyond a previously defined threshold. This form of scenario planning makes it possible to react flexibly and rationally to significant changes. Tipping points relevant for the pricing strategy include, for example, forecasts of economic growth, changes in the legal situation, or a change in the strategic objectives of competitors (Sprenger, 2018; Schmidt, 2015; Student, 2017). Price wars can be easily explained with a game-theoretic model, the prisoner’s dilemma In the original concept, two prisoners receive different offers separately. In essence, it is a trade-off between confrontation and cooperation. The best outcome for both (the lowest sentence) occurs when the competitors cooperate. It is crucial for the joint success of the adversaries that they pursue the same goals. This explains the high relevance of the model for competitive pricing (see Jensen & Henrich, 2011). In the language of price management, the lowest penalty means the highest profit for both competitors. I assume the following initial situation for two companies: Both competitors position themselves in the same price range. The initial price is EUR 20. Company A plans a price increase to EUR 22.50. If competitor B does not follow suit, A will find itself in a very poor profit position with the higher level. Some of the customers now buy from the significantly cheaper company B. A’s volume loss exceeds its margin profit. However, if B follows the initiator to EUR 22.50, the profits of both oligopolists increase compared to the initial situation. Customers have no incentive to switch if prices are equal again. In this respect, sales volumes remain the same with significantly increased margins. The decisive question from the point of view of company A is whether it trusts competitor B to have this strategic foresight. If A has to assume that competitor B is pursuing a different strategy, the company will leave the price at EUR 20. The problem of the prisoners in the game-theoretic model can be traced to two causes:

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– Neither inmate knows the other’s intentions. – There is no way to coordinate. In this respect, everyone initially tends toward confrontation. In the case of a price war in the real economy, companies are literally the prisoners: They have to take note of the customers’ choice decision, but often cannot accurately estimate its outcome. Two pieces of information are of utmost relevance: – Transparency about the goals of competitors. – Information on the prices of competitors. On transparency: In price competition, no company knows the exact intentions of the other side. The second point—the level of information on prices—varies considerably depending on the pricing systems and price models in the sector. In sectors with price fixing or reporting systems (publishing products, pharmaceuticals, gas stations, etc.), there is almost complete price transparency. In the case of negotiated prices (e.g., industrial suppliers or project business), however, it is difficult to detect the prices and conditions of competitors. In addition, in almost all industries there is no possibility of official price coordination for legal reasons. Antitrust laws prevent this. The result of this enormous uncertainty is that as soon as a company becomes active in terms of pricing, competitors often lower their prices almost as a reflex in order to maintain their market position. Price wars are not always exclusively due to a lack of discipline and strategic thinking. There are also industry-related reasons for price erosion (cf. Simon & Fassnacht, 2016, p. VI). The most important causes include: – – – – – –

Excess capacity. Lack of market growth. High fixed costs. Increasing use of online sales channels. Increased price transparency on the demand and supply side. Little differentiation of products.

The rapid quality alignment of new products and services is one of the most important drivers of fierce price competition. Many products (including the accompanying services) are interchangeable for most customers—price is then often the decisive selection criterion. Price wars often arise in saturated markets where growth is only possible by squeezing out the competition. But even in growth markets, ambitious expansion targets can fuel intense price competition. Amazon, for example, is the market leader in cloud computing by a wide margin. However, as the market matures, competition from competitors such as Microsoft (number 2), Alibaba and Google is increasing massively (Anonymous, 2018b). Despite strong market growth, Amazon has been losing share in the recent past. There are ways to break out of the prisoner’s dilemma and escape the risk of price erosion (Wübker, 2006). The most important measure will be briefly outlined here.

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The degree of substitutability—and thus of dependence on competitor prices—can be influenced by the company primarily on the supply side. The better this differentiation succeeds, the greater the upward price potential. Particularly in digitized markets, value differentiation via a niche strategy is promising. One of the pioneers of a digital niche strategy is the music store Thomann. The online mail order company is the market leader in Europe (Salden et al., 2017, p. 15). Thomann’s success can be explained, among other things, by its consistent servicing of the very demanding customer requirements in the online trade of musical instruments. With clear competitive advantages in terms of availability of goods, expertise in music and an international hotline, the medium-sized company successfully occupies a niche in the online business. This shows: The best strategy is to avoid direct price competition and focus on increasing customer value. A niche strategy can be used specifically as a means of avoiding undesirable competitive reactions. Niche brands can partially escape price competition. The potential for differentiation is constantly increasing in the course of digitization. It is not uncommon for the entire business model to be realigned. The method outlined in Chap. 3 can be used to support decision-making. The business model map methodically identifies precisely those business areas in which the company can operate profitably in the long term on the basis of competitive advantages and sustainable competencies (see Fig. 3.5). In addition to the supply-side measures described above, the portfolio of pricing levers also offers scope for avoiding price wars. Creative discount and condition models are an effective instrument for increasing customer loyalty and weakening the focus on list prices. Alignment with a price leader is another solution for stabilizing competition (Homburg & Totzek, 2011; Jensen & Henrich, 2011). In this context, it is important to clearly define price leadership. Three cases must be clearly separated. – Case 1: A form of price leadership relevant from a profit perspective is based on a large market share or market leadership. The price leader steers its main competitors toward stable prices or higher profitability. Such a form of price leadership was observed in the video streaming market in early 2018. The global market leader Netflix initiated a significant price increase, which was promptly responded to by the competitor Amazon with a comparable increase (Postinett, 2018b). – Case 2: Another variant of price leadership in the sense of game theory results from performance advantages and related price premia. An example of this is the smartphone market, where Apple, as the price leader, generates a large share of industry profits. By continuously expanding its product line, Apple is successively tapping into higher price tiers. For competitors such as Samsung, this offers the opportunity to serve new price segments. In the first two cases, the role of price leadership is to provide orientation, guidance, and predictability to the industry as a whole. The price leader significantly influences the market price level and can thus contribute to the profitability of the industry (Simon, 1992).

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– Case 3: A diametrically opposed form of price leadership is a positioning as the lowest-priced supplier in the industry. Market power and economies of scale are relevant for influencing competitors. Compared to the first two variants, the intention is different. Competitors are to be specifically prevented from catching up in terms of price. Price leadership in this variant consists of the ambition to be the lowest-priced supplier in the industry. An example of this is the food retail sector. Aldi is the price leader in the discount segment. For certain leading products, Aldi claims the positioning as the lowest-priced supplier and enforces this without compromise. One of the core elements of Aldi’s strategy is to consistently defend its price leadership in frequently purchased consumer goods that are under particular price scrutiny by consumers. The discounter responds to attacks on its price leadership with drastic countermeasures. Both Lidl (in the core range of low-price providers) and Rossmann (for drugstore items) had to buy this with significant margin losses (Hielscher, 2018; Anonymous, 2018c). Modern media and software tools (price databases, price comparison systems, bots, etc.) as well as price reporting systems are an excellent technical basis for successful price leadership and competitor adjustments based on this. On the basis of these systems, prices can be viewed freely by all relevant competitors to the greatest possible extent. Strategies can be indirectly traced and are thus easily and quickly copied. This almost complete transparency simplifies joint actions in the direction of more profitable prices. Admittedly, this often fails in practice due to the lack of common sense of individual industry participants. However, the example of video streaming (case 1) shows that adjustments to a strategically far-sighted price leader are possible even in dynamic markets. The latter variant of price leadership (case 3) involves the enormous risk that a price-aggressive competitor will challenge the role of the company with the lowest cost. If a competitor deliberately breaks with the standards inherent in the industry, the stability of competition also can be undermined. Not frequently, this leads to a bitter price war. Case Study Retail Germany In January 2018, Aldi irritated its competitors with special offers for branded products in the drugstore sector. Up to that point, there had been no temporary discount campaigns in the standard range of the strong market retailer. The core element of Aldi’s strategy was permanently low prices. The previous permanent low prices were replaced by short-term special offers on branded products. With this course correction, Aldi directly targeted drugstore chains such as dm and Rossmann. The discounter’s action was followed by very quick reactions from competitors: Lidl, Kaufland and Rossmann undercut Aldi with temporary special offers. The risk of a margin-destroying price spiral is evidenced by the following quote from Rossmann management (Hielscher, 2018): “We don’t need to hide from Aldi, we can match any price promotion”.

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Price wars are also increasingly evident in online industries. In February 2018, the comparison portal Check24 announced a EUR 1000 loan with an effective interest rate of minus 1.5% in Germany. The borrower receives an online loan of EUR 1000, but repays only EUR 992. If one adds possible credit defaults, operating expenses and the advertising budget to the loan costs, the campaign is associated with costs in the millions. The motivation for this loss-making business is the fierce competition that Check24 has been waging for years with its strongest competitor Smava. Since Smava first introduced zero-percent credit in 2015, the two rivals have been regularly undercutting each other. Check24’s objective is the same as in the case of Aldi: “The message we want to send out is very clear: We are the ones who basically offer the customer the cheapest deal on the market” (Dohms, 2018). “Our agency fee is always cheaper than that of our competitors”. This statement by Bolt at the beginning of 2022 describes the direct attack on the two main rivals in Germany: FreeNow and Uber. The competitors’ reaction: At the time of Bolt’s market launch in Frankfurt am Main, they also lowered their fees (Heiny & Rest, 2022). Conclusion on the Pricing Strategy • Pricing strategies must be designed dynamically in the age of digitization. Strategic determinations are based on assumptions about the future. It is impossible to reliably predict the success of strategic decisions because the influencing factors change very quickly. The risk can be reduced by companies not sticking rigidly to a strategy. Whenever significant changes occur, the strategy must be questioned. In the age of digitization, strategic flexibility represents a core competence that is particularly relevant for price management. • Short-term mistakes and long-term strategic positioning errors must be avoided at all costs. Avoiding serious pricing errors (as in the case of Praktiker) is critical to success. • Data-based segmentation enables optimal positioning and thus better profit exploitation. The greater the degree of differentiation, the greater the increase in profits. This is described in more detail in the following chapter on price differentiation.

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Pricing Process Part 3: Structure (3a: Price Differentiation)

7.1

Basics of Price Differentiation

Price differentiation is one of the most important profit levers within the pricing process. This is particularly true for digital services. Simply put, price differentiation means different prices for separate customer segments based on the same - or modified - products (Wübker, 2006; Roll et al., 2012; Simon & Fassnacht, 2016, 2019; Skiera & Spann, 2002; Corsten, 1998; Simon & Dolan, 1997; Diller, 2008). Differentiation allows market opportunities to be exploited and profit potentials to be leveraged. This is because unit prices are not profit-optimal. Even with an optimized unit price—compared to simple heuristics—a large part of the profit potential is not leveraged. Consumers in all industries differ in their preferences and willingness to pay. The more successfully these differences can be reflected in prices and products, the greater the market exploitation. Customers are divided into subgroups that are as homogeneous as possible according to specific criteria. This allows the target groups to be addressed with differentiated prices. The overarching objective is to optimize profits by exploiting differences in preferences and price elasticities of the segments (Wübker, 2006; Roll et al., 2012; Simon & Fassnacht, 2009, 2016, 2019; Skiera & Spann, 2002; Corsten, 1998; Simon & Dolan, 1997). For both digital consumer goods and durables, there is enormous potential for price differentiation due to their outlined characteristics. In principle, the more differentiated the pricing, the higher the profit exploitation. This can lead to individual prices for each individual consumer. Examples of individual pricing can be found in negotiation situations (e.g., real estate, used cars, and especially industrial goods). For a long time, however, individual customer pricing was neither practicable nor economically sensible in numerous industries. The following reasons spoke against individual pricing (Simon, 1992; Roll et al., 2012):

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1. Buyer-individual price elasticities were difficult to measure for consumer goods and mass-produced goods. 2. It was not possible to set prices for individual customers for organizational and technical reasons. 3. In the case of a broad product portfolio, the workload was too high because integrated tool-side support was missing for a long time. In the course of the digitization of the pricing process, the technological and organizational prerequisites for exploiting pricing potentials have improved significantly. This section outlines the basic elements of price differentiation. A particular focus is the impact of digitization on price individualization. Three forms of price differentiation can be distinguished (Wübker, 2006; Roll et al., 2012; Simon & Fassnacht, 2016, 2019; Skiera & Spann, 2002; Corsten, 1998; Simon & Dolan, 1997): 1. In first-degree differentiation, the provider demands the individual maximum price from each customer. The total consumer surplus is monetized. The consumer surplus corresponds to the amount that the consumer saves if the unit price to be paid is below his willingness to pay. 2. Second-degree price differentiation is associated with a choice for the consumer. The supplier divides customers into segments with different maximum prices. The price structure is aligned with the target groups. Different product-price combinations are on offer. The customer decides for himself which variant he will choose. They are free to make their own purchasing decisions, i.e., it is possible to switch between segments. Price differentiation with self-selection plays an important role in digital goods (Skiera & Spann, 2002; Buxmann & Lehmann, 2009). 3. Third-degree price differentiation links access to different prices directly to criteria. The customer is not free to choose (Skiera & Spann, 2002; Diller, 2008). It is generally not possible to switch between segments. One example in the B2B segment is price structures that are based on the size of the company. According to Simon, price differentiation is based on differences in value to customer and maximum prices with respect to three essential dimensions (Simon, 1992): 1. Market segments 2. Quantities 3. Products. The most important variants for digital pricing are outlined below along these three basic forms of price differentiation.

7.2 Variants of Price Differentiation

7.2

Variants of Price Differentiation

7.2.1

Price Differentiation According to Market Segments

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This is based on differences in value perceptions and willingness to pay for the same offering across various dimensions. Four dimensions are particularly relevant for digital services: Region, time, user, and distribution channel (Simon, 1992). Accordingly, a distinction is made between regional, time-related, and user-related price differentiation and the differentiation of prices according to distribution channels. Regional Price Differentiation In the course of regional price differentiation, different prices are set for the same product in different areas. In many industries, there are significant price differences between countries. Price variations depending on countries, regions, or sales territories can be explained by a variety of parameters. These include region-specific differences in the following influencing factors: price elasticities, demand, competitive conditions, logistics costs, exchange rates, and inflation rates (Miller & Krohmer, 2011). There is also an organizational reason: many companies have a decentralized sales organization—national subsidiaries sometimes have considerable autonomy of decision. Processes and methods of international price management are a fundamental part of securing profits for globally positioned companies (Wübker, 2006; Roll et al., 2012; Simon & Fassnacht, 2016, 2019; Skiera & Spann, 2002; Simon & Dolan, 1997; Frohmann, 2007). This is especially true for manufacturing companies in the traditional product sectors (consumer goods, automotive, mechanical engineering, etc.) based in the euro area. Here, significant price differences between the individual euro markets lead to the risk of price erosion in high-price countries. Professional pricing proactively counteracts this development. The aim is to avoid excessive price differences between a company’s various regional markets. Price corridors are an important instrument for controlling regional differences. A price corridor is a centrally defined price limit for local pricing. It defines a price range that no country is allowed to leave. The autonomy of local distributors is limited in order to gradually reduce existing differences in regional prices. The centrally defined price framework is a workable compromise between unit prices and independent country prices. Arbitrage transactions by reimporters and gray market traders are minimized as a result. The challenges in setting the price corridor are mainly methodological. Three essential steps are as follows: 1. Determine the optimal price for each country (with reference to the price range). 2. Gradually reduce excessive variations in country price levels. 3. Achieve the global optimum.

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Project Outline Pedelecs Preferences and willingness to pay for electronic bicycles (e-bikes) were the subjects of an international consulting project conducted by the author in 2010. Potential users of the product to be developed in various segments (mountain bikes, trekking bikes, etc.) were surveyed on their value perceptions using conjoint measurement. The resulting optimal prices varied greatly for neighboring countries. Depending on the product segment, the sales-optimal prices differed by up to 30% between two countries in the eurozone. Willingness to pay was significantly higher in the company's sales region with the highest market share. Due to the high transparency in the market, an international coordination of the price ranges according to the methodology described above became necessary. The definition of the corridor was supported by a digital decision support tool. In the case of digital offers, regional price differentiation can be efficiently supported technically via routing. This is the practice of tourism providers such as airlines and international hotel chains. The technological basis for price differentiation is the assignment of Internet Protocol (IP) addresses to geographical regions. Users of a website—differentiated by region—can be presented with varying offers at different prices (Simon & Fassnacht, 2009). “Contextual pricing” is a special form of regional price differentiation. Value perceptions for the same product sometimes differ greatly depending on the locality and environmental conditions of consumption. From a psychological perspective, willingness to pay changes depending on the customer’s local environment as well as the context of use (Trevisan, 2015). Consumer goods manufacturers, in particular, are capturing these differences in values through sometimes drastic price differentiation. Stationary retailers use “geofencing” to provide customers with contextrelated individual offers. Geofencing is based on the technical possibility of localizing customers’ devices (e.g., smartphones) via location sensors. Customers only receive push messages about current offers if they are in the vicinity of a stationary store. The interface to the smartphones of their customers is a very important resource for retailers as a basis for customer-specific price offers (Forster, 2018). The real estate exchange ImmoScout24 also differentiates prices on its marketplace by region. The core criterion is the variance in values across areas. For example, attractive properties are in short supply in metropolitan areas—there is a steady increase in demand. Anyone who wants to insert an advertisement for a house or apartment there pays a higher price than a portal user who posts a comparable property in structurally weak regions in Germany. Time-Based Price Differentiation Time-related price differentiation is primarily aimed at customers’ different levels of willingness to pay at different times (Skiera & Spann, 2002). Price differences for the same services can, for example, be based on the time of day, the day of the week, or

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the season. In service sectors, time-based price differentiation plays a prominent role. In air travel, each individual seat can be sold at a different price based on time. Stronger demand at later departure times, during vacations or at certain booking times justifies a higher price. The price to be paid by the customer—based on the dimension of time alone—depends on various questions: How long before departure do I book? On what day and at what time is the booking confirmed? On which day of the week do I fly? At what time does the flight take place? What is the season at the points of origin and destination? (Dirlewanger, 1969; Doganis, 1991; Meyer, 1992; Pompl, 1991). Time-related price differentiation is also of particular importance for digital goods. Identical services are valued and used differently at different times. For this reason, prices for digital products are also differentiated according to their timeliness, among other factors. The sequence of publication is price determining. It is a question of the time delay with which the information goods are made available to different customer segments (Skiera & Spann, 2002). Time-varying price differentiation is easy to implement on the Internet. Demand effects of time-varying prices can be efficiently captured and used for optimization. Time-based price differentiation has the following effects for the supplier (Roll et al., 2012; Simon & Fassnacht, 2016, 2019; Skiera & Spann, 2002; Corsten, 1998; Simon & Dolan, 1997): 1. Fluctuations in demand can be equalized. 2. Reduced prices at times of day, days of the week, or seasons when demand is low have the effect of stimulating demand. 3. Higher prices during peak periods serve to exploit a relatively high willingness to pay when demand is increased. 4. Demand smoothing results in a more even capacity utilization. Resource planning is facilitated at the same time. Time-based price differentiation should be implemented with great care. There are two significant risks: • Isolated actions can lead to a cannibalization of the overall business. This is because temporary price actions do have an impact on demand and competitive conditions in another time interval. This relationship can be described using the example of the “Singles Day” in China in 2017. On the day of the discount promotion, November 11, 2017, online retailers achieved sales records. Alibaba, China’s largest Internet retailer, sold over EUR 20 billion worth of goods; 92% of purchases were made via smartphones. The decisive factor in assessing this online sales promotion is: the buying behavior of many customers in China had changed long before—in anticipation of the discount campaign. Many users postponed planned purchases until the promotion period. The net effect of the price action is significantly lower when these temporal arbitrage effects are taken into account (Salden et al., 2017, p. 16; Ankenbrand, 2018). For consumer goods, hoarding purchases have a similarly negative effect. They merely lead to a shift in demand, not to a sustainable increase.

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Unit price

Differentiated prices Sales volume

Sales volume Lost profit (1): prices too low

P1 Budget segment

P2 Medium segment

Profit

Lost profit (2): prices too high

Profit

Price

P3 Premium segment

Price

Fig. 7.1 Price differentiation (based on Simon & Fassnacht, 2019, page 224)

• Customers are changing their planning and buying behavior as they become more accustomed to low prices and discounts. In online retailing in particular, there is hardly a product left that does not come with a temporary discount. In many product categories and also in services, customers are now so conditioned that they expect price reductions and are increasingly active in demanding them. Put another way: List prices are becoming less and less relevant. In the medium term, retailers run the risk of sliding into a price spiral. The chapter on “Dynamic Pricing” takes a closer look at the negative business dynamics that can develop with the increasing automation of pricing. User-Related Price Differentiation Price differentiation is tied to the customer’s personal criteria. Dating apps, for example, charge different prices for male and female users. Price differentiation according to customer characteristics takes into account the fact that the willingness to pay of different users differs (Roll et al., 2012; Simon & Fassnacht, 2016, 2019; Skiera & Spann, 2002; Corsten, 1998; Simon & Dolan, 1997). This is shown in a simplified form in Fig. 7.1 on the right. The profit achieved at a unit price is visualized by the rectangle in the left part of the figure. Profit potentials in the order of the two smaller rectangles in the right section of the figure cannot be realized in this case. The profit that can in principle be captured is given away in two respects with unit prices. Losses occur on both sides of the pricing lever, in sales volumes and margins (Fig. 7.1): 1. Margin loss: Certain customer volumes that could also be realized at higher prices are only contracted at the unit price. Corresponding willingness to pay (P1 in the graphic) cannot be exploited. 2. Volume loss: Potential customers whose willingness to pay is below the unit price (P3 in the chart) migrate to the competition. The price points (P1–P3) are used to

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capture the different maximum prices of three customer segments (Simon, 1992). The segments can be easily defined on the basis of their characteristics. In organizational terms, customer data, which has been refined in the course of digitization, is increasingly helping here. Information on customers is updated with increasing frequency. Case Study: Price Differentiation for Advertising Customers on the Internet The digitization of processes associated with the Internet means that advertising messages can be tailored to user groups in a much more targeted manner. For advertisers, large platforms such as Alphabet (Google) and Meta (Facebook) are associated with lower wastage and greater efficiency of communication campaigns. The main challenge for the platform operator is to optimally exploit the added value and the associated willingness to pay of the advertising customers. In order to optimize revenue, various forms of price differentiation are available to the platform provider. One very successful variant is price differentiation by target person. The pricing logic is: the more targeted the appeal to users, the higher the advertising price. On Facebook, for example, advertisers who want to place an ad can narrow down their target segment very specifically by income, education, and interests. The more precisely the target group is defined (i.e., the more focused the ad), the higher the price demanded by the network provider. Depending on the price model, this can be “cost-per-mille” (CPM) or “cost-per-click” (CPC). Customer-driven pricing (CDP) is a special form of user-based price differentiation. CDP is based on the technological possibilities of digitization. An online platform is used to record the maximum price an individual customer is willing to pay (Simon, 2015, p. 258). It is an automated variant of first-degree price differentiation that was first widely used in the travel industry. Tourism customers deposit their willingness to pay for flights, package tours, rental cars, or hotels directly on supplier portals (such as priceline.com). Similar to an auction, the customer with the highest maximum price wins the bid. Whereas auctions offer price transparency, this information advantage from the customer’s point of view does not exist with the CDP method. The reduced transparency also tends to reduce price elasticity—which promotes the willingness to buy. CDP provides an excellent technological basis for profit optimization, as the consumer surplus is fully captured. Price Differentiation According to Sales Channels Sales channel differentiation is becoming increasingly important with increasing digitization. It is currently the focus of price management in many companies. The same product is offered in different channels at different prices (Roll et al., 2012; Simon & Fassnacht, 2016, 2019; Skiera & Spann, 2002; Corsten, 1998; Simon & Dolan, 1997). Price differentiation between direct and indirect distribution channels

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exists in numerous industries. Online distribution channels, which are associated with lower costs for the supplier, are made more attractive to the customer via discounts. In many sectors, the lowest prices across all sales channels are found on the Internet. The clear differentiation between offline and online prices is one of the main reasons for the massive growth of the Internet as a sales channel. In 2017, interactive Internet sales in Germany amounted to EUR 78 billion. In addition to traditional product sales, interactive commerce also includes online sales of services such as rail and airline tickets, concert tickets or travel. The strongest growth was recorded by online mail order companies whose core business is stationary retail (Anonymous, 2018a). These use the Internet as an additional distribution channel. Double-digit growth followed in the years thereafter, before an even stronger increase occurred in 2020—in the wake of the Corona crisis. The greatest growth momentum was recorded by everyday goods (including food and drugstore goods), but also medicines. In the recent past, companies such as Amazon (Pharmacy) and Douglas (Disapo) have also entered the drug distribution market. The Internet has led to massive changes in pricing. The term “online” stands for a variety of channels: own web stores, sales via online marketplaces or platforms, and sales via social shopping channels. Social shopping evolved as social media and e-commerce merged (Schneider, 2021). The boundary between social networks and shopping websites that used to exist in Western markets does not exist in China. There, consumers buy products directly from social media channels. The WeChat app from the digital group Tencent, which has over 1.2 billion users, is of central importance. WeChat has interfaces to the leading e-commerce companies JD.com and Pinduoduo. In addition to online sales channels, channel management must also integrate traditional sales channels into pricing (e.g., indirect sales via a distributor and direct sales). Key questions in the context of multichannel management are (Friesen & Heintze, 2015): • • • • • • •

How do we want to position distribution channels in terms of price? What are the costs associated with each channel? What benefits do we offer the customer? What requirements do customers have for different sales channels? What are the effects of the requirements on the willingness to pay? Should price differentiation be applied? How high should the price differences be?

A core argument for lower online prices is the cost advantage of digital sales channels. Higher competitive intensity would also argue for differentiating “online” from “offline” in terms of price. Benefit arguments are neglected here. For a large number of products, the Internet is associated with greater convenience when ordering. In addition, valuable information from users serves as a possible incentive for ordering a book or downloading a piece of music online. In many cases, these benefits and the resulting profit potential remain untapped. Walmart’s channel

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strategy in 2018 was based on a price premium for online channels. Of the eleven influencing factors (11 C) outlined in Chap. 5, three criteria were decisive: • Customer (more convenience for the customer) • Costs (additional costs for the necessary shipping in e-commerce) • Company targets (target: more customer traffic in the retail store). Problems in channel pricing can arise if the price differences between the sales channels are too great. The reason for the sometimes very high-price differences between online and offline channels is the significantly higher margin requirements of stationary retail. The active management of pricing for the various distribution channels is of decisive importance (Friesen & Heintze, 2015). In the course of this, all price-relevant specifics of the individual channels must be taken into account when allocating conditions (e.g., the functions of the channel, current margins and profitability targets, cost structures, and competition). The purchasing behavior of customers also has an influence on price differentiation. For example, the following applies to online stores: • Spontaneous purchases, which generate higher margins in brick-and-mortar retail, are rare in online sales. • Online customers often buy low-margin items that are hard to make money on. Case Study Media-Saturn Retail Group The perception of customers is of particular importance for price differentiation by sales channels. Consistency from the user’s point of view is a central requirement for the pricing of multichannel retailers. In February 2018, the Media-Saturn retail group took this finding as an opportunity to eliminate the previously significant price differences. For electronics products, prices were no longer differentiated between the store and the online shop from this point on. The price differentiation previously applied led to irritation among customers: Certain products were offered at lower prices in the online shop than in the store; for other electronic items, however, it was the other way around (Mitsis, 2018). Any disparity in prices between different channels undermines customer trust and thus loyalty. Regardless of the standardization of prices across channels, there will continue to be selective price adjustments in response to regional promotions. However, these competitor-related responses will only take the form of temporary and locally limited promotions at selected stores. Price uniformity will be supported by electronic shelf labels. In the more than 1,000 stores of Europe’s leading consumer electronics retailer Media-Saturn, prices are to be adjusted in real time. Online price levels are coordinated accordingly.

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Case Study Boss Boss’ strategy as a premium supplier requires active control of the price level across all distribution channels. In the premium segment of the textile industry, the quality indication of price plays an important role. In customer perception, there is a clear correlation between the price of a suit and its quality. This explains why Boss is critical of active participation in discount campaigns by distribution partners (e.g., fashion stores). The customers’ perception of quality should not be cannibalized by special offers. Despite its very restrictive approach to discount promotions, Boss has not succeeded in the past in achieving a full price control of the market. A significant portion of original merchandise was sold in gray channels at deep discounts several years ago (Weishaupt, 2018; Hofer & Bastgen, 2017). Such price erosion is dangerous for premium goods. In such cases, stronger price control can only be enforced through a higher share of direct sales channels. In the case of direct sales, however, margin control is dearly bought with significantly higher fixed costs. A special form of channel-based price differentiation is found in media offerings. Print, audio or video content is priced according to the distribution channel. For example, movies are released in different channels (e.g., movie theaters, media libraries, pay TV, free TV, and streaming platforms) at different prices over time (Wirtz, 2009). Due to the multidimensionality of prices, the various forms of price differentiation are often applied in a complementary manner. In the media example, channel-based differentiation is combined with time-based price differentiation. In the course of digitization, the boundaries between traditional distribution and online retail are becoming increasingly blurred. Alibaba—the market leader in e-commerce in China—offers an example of this with its “new retail” concept. Alibaba customers use their smartphones while shopping to scan the barcodes of the selected products themselves. Payment is made using the company’s own payment app Alipay (Ankenbrand, 2018; Hirn, 2018).

7.2.2

Quantity-Based Price Differentiation

With quantity-based price differentiation, the average price changes from the customer’s point of view (Simon, 2015, p. 175 ff.). The price per unit decreases as the user’s purchase quantity increases—it is non-linear. Therefore, price differentiation by quantity is also called non-linear pricing (Roll et al., 2012; Simon & Fassnacht, 2016, 2019; Skiera & Spann, 2002; Corsten, 1998; Simon & Dolan, 1997; Tacke, 1989). Non-linear pricing applies primarily to the “variablequantity” case: a customer purchases multiple units of a consumer good, service, or digital offering in a given time period. Quantity-based price differentiation is used to tap profit potentials. Differentiated prices reflect differences in willingness to pay. The advantage of the non-linear system is, among other things, efficiency in

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Sales volume 5 Quantity unit

Maximum price (*)

1

10 EUR

2

7 EUR

3

6 EUR

4

4 EUR

5

3 EUR

3

Unit price 6

10

Willingness to pay (*)

Fig. 7.2 Non-linear pricing (based on Simon, 1992)

implementation. All customers receive the same price offer. Each customer pays a differentiated price—according to their actual usage. The table in Fig. 7.2 shows the purchase quantities and maximum prices of the buyer of a digital product. The premises for the sample calculation are: • The goal of the company is to maximize profits. • The marginal cost is 2 EUR. Case Study Quantity-Based Price Differentiation • Option 1: The company would like to set a unit price that is not dependent on quantity in order to make pricing as simple as possible. An amount of EUR 6 is optimal for profit in this constellation. Three units are sold. The profit (price * sales volume - marginal costs) is 12 EUR. Alternative pricing leads to lower profits. At a price of 7 EUR, only two units are sold. With a price of EUR 4, four units could be sold. In both cases, the profit (EUR 10 and EUR 8, respectively) is lower. With the unit price of EUR 6, consumer rents arise. The consumer rents are EUR 4 (EUR 10 – EUR 6) for the first unit and EUR 2 (EUR 8 - EUR 6) for the second unit. Consequently, a margin potential of EUR 6 is not utilized. In addition, the consumer does not use a fourth and fifth unit. The reason for this: At EUR 6, the unit price is higher than the maximum prices (EUR 4 and EUR 3) of the additional quantities. Here, too, the profit potential inherent in the market is only incompletely exploited by the unit pricing. In contrast to the first case (price too low), in the second case (price too high), potential additional quantity is foregone. • Option 2: In non-linear pricing, one sets the price for each unit according to the user’s respective maximum price. Five units are sold. The profit is EUR 20.

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The example shows: With uniform pricing, profit potentials are only partially captured. Graphically speaking, only a rectangle results with respect to the profit function. Non-linear pricing, on the other hand, makes it possible in principle to fully exploit the profit triangle (Tacke, 1989). In the example outlined, this enables a significant increase in sales and profits. Two effects contribute to this: • Willingness to pay is fully monetized. • A new demand with willingness to pay below the uniform price is activated. The best solution, graphically speaking, is to charge each consumer a fixed amount corresponding to the individual area of the profit triangle (Simon, 2015, p. 175 ff.). In practice, this was not possible for a long time due to organizational reasons. In the course of digitization, however, the potential has increased significantly. Non-linear pricing can be found in numerous forms: • • • •

Volume discounts Two-part prices Flat fees (flat rates) Discount forms such as “15% cheaper with an annual subscription”, “buy two and get one free”, and “20% discount for 10 or more”. • Multi-person prices (group prices).

Multi-person pricing is a variant of non-linear pricing used in B2C markets as well as in B2B sectors. Spotify’s “Premium Duo” tariff is an example of multiperson pricing in the music streaming sector. In 2020, two customers in Poland paid the equivalent of USD 6.40—a significant discount compared to the single customer subscription rate (USD 5.10). In Germany, up to six people can use the family subscription at a price of EUR 14.99 per month—a massive discount compared to the single customer rate of EUR 9.99. Wübker describes an interesting variant of non-linear pricing: tiered pricing for credit cards from a financial services provider. The fee decreases with the annual turnover made with the card (Wübker, 2006). Volume discounts are the best-known form of non-linear pricing (Miller & Krohmer, 2011). They are used in different variants (see Chap. 11). Bonus programs are based on a special form of a volume discount that is particularly important in the service and retail sectors (Frohmann, 1994). They are part of higher-level customer loyalty concepts such as Payback or Miles & More. Price differentiation according to the number of users is familiar from software licenses. Scaling models are based on discounts for additional licenses. This form of volume discounting is widely used as a purchase incentive, especially for large customers. Similar non-linear models are based on price discounts depending on the duration of the contract and the amount of available data. In the latter variant, a price incentive is provided for migration to higher data volumes (Buxmann & Lehmann, 2009). Streaming providers in the music (e.g., Spotify) and video (e.g., Disney+) sectors use an incentive to use long-running contracts. Spotify’s annual subscription (EUR 99.99) offers a significant discount compared to the monthly rate (EUR 9.99). The subscription model of

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the music streaming market leader corresponds to the discount logic “pay 10, take 12”. Amazon offers a similar contract duration-based incentive in music streaming. In video streaming, Disney+ applies the same logic as Spotify: “12 for the price of 10” (as of the end of 2021). The monthly subscription in the US costs USD 6.99, and the annual plan costs USD 69.99. Aldi implemented a special variant of the volume discount at the beginning of 2018. The volume discount was backed by very strict conditions (fencing). The condition for the discount was that customers redeem a coupon, buy at least three different products and spend more than EUR 9 on them (Anonymous, 2018b).

7.2.3

Price Differentiation According to Products

This is based on differences in the customer’s appreciation of at least slightly different products (Diller, 2008). Two cases can be distinguished: 1. Price bundling 2. Performance-based price differentiation.

7.2.3.1 Price Bundling Price bundling is another lever for profit optimization. This involves combining different products into a bundle and selling them at an attractive package price. The package consists of at least two different products or services (e.g., a package of repair services and spare parts). As a rule, the bundle price is lower than the price of the individual components (Homburg & Totzek, 2011). One of the pioneering cases of bundling is the pricing of vacation trips. Package prices for air travel including hotel and rental car represent the origin of bundle pricing. Case Study: Microsoft Office Package The potential of product bundling can be vividly illustrated by Microsoft’s Office packages. By cleverly combining individual services into program packages, the software company was able to extend its dominance from word processing (Word) and spreadsheets (Excel) to graphics programs (PowerPoint) and database applications (Access). Sales of a high-margin— but slow in demand—by-product such as Access were boosted by bundling. By bundling prices of products in great demand with less attractive offerings, Microsoft succeeded in increasing the contribution margin per customer. With market shares of over 80% at times, it achieved an almost monopolistic position in the office packages. The program packages were the cash cow in the software company’s portfolio for a long time. In 2020, Office (with a revenue share of 25%) was only just behind Azure Services (26%), Microsoft’s highest-revenue business area.

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Depending on the type of product, a distinction is made between two forms of price bundling (Miller & Krohmer, 2011): pure bundling and mixed bundling. In “pure bundling”, products are offered exclusively as a package; it is not possible to purchase the individual components. This form of package pricing requires a very popular focus product that the customer wants to buy in any case. Vendors who have appropriate pricing power in their core product can stimulate sales of additional products through pure bundling. An example of pure bundling is patent-protected spare parts that are only sold as a package with components that face a highly competitive environment. With “mixed bundling”, the customer has a choice. He can buy the bundled offer or he can purchase the respective products individually. Mixed bundling is found in the majority of restaurants. There, complete menus are sold at a lower price. In addition, customers are offered à la carte meals. The components are offered separately and billed individually. The decisive factor is the distribution of preferences among the individual components on offer. Customers who highly value specific menu components and choose to forego other components prefer the à la carte mode. If the individual components are more or less equally valued, the bundled offer is the preferred alternative. “Customized bundling” is a variant of bundle pricing with special significance for information goods. Within the provider’s technical specifications, users can choose for themselves which products they want to include in the bundle. The provider only sets the framework, via prices and the scope of the offering (Buxmann & Lehmann, 2009). Customized bundling is becoming increasingly important in online retailing. Case Study “Customized Bundling” At special machine manufacturer Trumpf, customers can configure tools on the homepage. Millions of variants can be combined digitally. In the course of the customer-specific configuration of the packages, the price is calculated online. Once the customer has made an order decision, an automated order transmission takes place. The tool component or parts bundle is produced immediately. Delivery can take place after a few hours. The total time between order and delivery was reduced by a factor of 10 a few years ago. Processes that previously took four days were reduced to four hours with the help of the digital value creation model (Meckel & Seiwert, 2016; Voss, 2018). Companies pursue various goals with price bundling: 1. Increase in sales. Additional sales are based on two demand effects: – Cross-selling (customers who previously purchased only one product buy the package). – Acquisition of new customers. 2. Creating barriers to entry.

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3. Cost savings (economies of scope: production cost advantages; sales cost reductions, including costs for invoicing and delivery). 4. Increase in capacity utilization. 5. Reduction of price transparency in the market. Bundling makes it more difficult to compare offers. The more complex the bundled offer, the lower the priceperformance transparency tends to be. The role of price in the customer’s selection process can be reduced via bundling. 6. Reduction of price pressure from the customer side. Attractive bundled company offerings take the place of a discounting of individual products. The quality perception of individual offerings is not diluted by open price concessions. 7. Influencing customer perception. From the perspective of price psychology, the “complete price effect” influences the price sensitivity of customers. Complete prices seem more favorable! Customers perceive several small prices more strongly as a loss than a total price corresponding to the sum of the individual amounts. The whole (the bundle price) is perceived by the buyer as lower than the sum of its parts (Kopetzky, 2016, p. 11). This behavioral economics effect is independent of the objective price advantage of the bundle over the individual prices. 8. Profit increase. The central objective of price bundling is to exploit profit potentials by simultaneously reducing costs and increasing sales (Wübker, 2004). Microsoft has been able to realize higher profits with its Office packages. The additional sales (compared to individual pricing) more than compensate for the loss of margin due to the bundle discount. Internal complexity reductions and the resulting cost savings have a parallel positive effect on profits. Two particularly important design factors in price bundling are: • The components of the bundle • The level of the package price. Case Studies Product Type The partial components of the bundle can be of very different types. In the software industry, the product type is essentially the software as the core offering, its maintenance, and other support services. Traditionally the total revenue of a software manufacturer could be divided into three equal parts: Licenses, maintenance, and other services (Buxmann & Lehmann, 2009). The services from these three areas were offered in various forms as bundles. In the telecommunications industry, products for fixed line network, mobile communications, and Internet access were offered separately for a long time. In 2006, the German market leader launched T-Home Entertain, its first triple play product, which bundled all communications into one broadband connection at flat monthly rates. In the entertainment industry, music titles by various artists were sold as compilation packages. For the price advantage, customers accept purchasing certain songs in the package that they hardly value.

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Amazon’s customer loyalty program Prime is a special form of bundling. Since 2017, Amazon has linked its Prime offering with a video streaming portal. This consists of an extensive range of content (movies and series). The content is available from Amazon Prime for a membership fee of EUR 89,90 per year or EUR 8.99 per month. Customers who use a Prime subscription receive the video offer for free. Amazon Prime Video is automatically available to all Prime members (Ahlig, 2018). Bundling models of this kind are particularly relevant for information goods. Established products can be enhanced with new applications (or formats) to increase customer loyalty. Amazon launched a special bundled offer in Germany in 2013. The Prime customer segment was offered MP3 versions of music titles in addition to the offline product. Those who purchased a product (e.g., a CD) got the digital versions of the content for free. This example shows the importance of a consistent pricing process. The overarching strategy (digitization; expansion of content business) is transferred into concrete pricing measures (in this case, bundling digital content with products). Individual elements of the business and revenue model as well as the pricing logic are constantly being expanded and refined at the digital enterprise group Amazon. Bundling is also successfully used as a competitive tool in B2B sectors such as mechanical and plant engineering. Packages of hardware, software and services lead to a win-win situation for both parties. Suppliers achieve profitable growth because clever bundling enables them to exploit customers’ full willingness to pay. At the same time, they exploit cross-selling potential through increased value creation for the customer. In B2B business, companies thus have the opportunity to distinguish themselves as system providers. By offering comprehensive customer solutions, they escape the risk of an intensified price competition. The advantages for the purchaser are both the lower bundle price and the simplification of procurement processes. In the sense of one-stop shopping, the business customer can efficiently obtain all services from a single source. This increases customer loyalty. The price level for the bundle can be set in different ways: • In the case of an additive package, the bundle price is equal to the sum of the individual prices. • Sub-additive bundling is the normal case. It is based on discounts compared with the individual prices (Diller, 2008, p. 240; Buxmann & Lehmann, 2009). For example, the price of the Microsoft Office bundle has traditionally been significantly lower than the sum of the individual prices of its components. • If significant added value is generated by bundling, a price premium is optimal for profits. One example of superadditive bundling is spare parts packages in mechanical engineering or the automotive industry. By assembling parts for a standard repair, customers save search costs to a considerable extent. Beyond cost savings, bundling also leads to added benefits. The increased willingness to pay results from the certainty of having all the necessary parts available in case of emergency.

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Two particular challenges for the provider are: 1. The intelligent composition 2. Optimized pricing of the bundled components. Challenge 1 The example of Microsoft can be used to describe a key success factor of bundling. It requires an attractive focus product that the customer wants to purchase (for example, Word and Excel). In addition, the package contains high-margin add-on products whose sales would be significantly lower without bundling (PowerPoint and Access). Two constellations must come together in traditional bundling (sub-additive price!): • Individual components strongly preferred by the customer are combined with relatively unattractive components. Only then is bundling advantageous for both sides! • The total sales volume can be increased significantly. Only then does the waiver of margins in bundling pay off for the supplier! As part of a bundle, components with an attractive cost-benefit ratio are of particular interest from the manufacturer’s point of view. They hardly cause any additional costs in value creation, but are associated with a clear additional benefit for the customer. Bundling strategies are all the more advantageous the lower the variable costs of the components. For this reason, price bundling is of particular importance for information goods. Challenge 2 The basis for profit maximization through price bundling is a different willingness to pay for different products of a manufacturer. The following simple calculation example illustrates this. The initial situation is as follows: Product A is clearly more preferred by the customer than product B. The willingness to pay for A is EUR 11, the maximum price for B is only EUR 3 from the customer’s point of view. In the initial situation, the supplier charges unit prices of EUR 8 (for A) and EUR 5 (for B). The strong preference for product A is reflected in the consumer surplus of EUR 3. The customer would be willing to pay significantly more. In contrast, he refrains from buying product B because the unit price is too high. The decision of the supplier is: The bundle price is set profit-optimally against the background of these value differences. The unexploited willingness to pay for product A is transferred to the other bundle component. This makes the bundle interesting for the customer. In this example, the profit optimization takes place through a bundle price of EUR 12. The customer buys the bundle (i.e., product B in addition to individual product A). Compared to single pricing, sales volume and profit increase.

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Method Tip Profit improvement over individual pricing requires particularly detailed information on demand behavior. A well-founded assessment of the individual maximum prices for both the products and the bundle is indispensable. This is because the optimum number of products in a bundle also depends, among other things, on the customer’s budget constraints. The application of a special conjoint measurement design is recommended, with the help of which the necessary information for optimization can be determined (Simon, 1992). The progress of information technology also offers far better possibilities for measurement and optimization in this area (cf. Chap. 9). Machine learning should also be used in the optimization of bundle prices. Artificial intelligence supports companies in automatically determining promising combinations from the variety of possibilities. The bundling of products can be restricted by competition laws. An example of this is the action taken by the European Commission against Microsoft in 1998. The reason for this was the bundling of the Windows operating system with Internet Explorer. Microsoft was accused of exploiting its alleged monopoly in the PC operating system. The software company had to remove the coupling of the Internet browser Explorer with Windows (Buxmann et al., 2008). The decoupling served to promote free competition from rival products (such as Mozilla Firefox or Netscape). As early as 1969, IBM was legally forced to unbundle hardware and software. The passing on of software by a hardware manufacturer without additional payments from customers was punished as a distortion of competition. Irrespective of the legal framework, there are also substantive reasons for unbundling in certain constellations. Debundling means splitting a previous unit price (e.g., bundled price for hardware plus services) into several components (Buxmann & Lehmann, 2009; Kopetzky, 2016). For example, services are decoupled from the main product and charged separately. The pioneers of unbundling were the telecommunications sector and the IT industry. Services that were previously only available as part of a package were offered separately. One example of this is the unbundling of the DSL line from the fixed line network. Partitioning is also supported by price-psychological findings. By dividing a package price into individual components, product features can be emphasized that differentiate the company from the competition. Competitive advantages that would have gone unnoticed by the customer in the case of a package price are used by debundling to capture the full value. New markets can be opened up by selling components as stand-alone products. In many industries, the share of value added is shifting over time from hardware to software and services. Consulting, training and support services as well as software offerings are gaining in importance relative to the core product almost everywhere. Traditionally, services have not been invoiced separately, but were compensated for with a system price.

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The strategy of unbundling is one of the main reasons for the success of Amazon iTunes and the streaming platform Spotify. This is shown by a comparison with the traditional business model of the music industry. In the labels’ traditional distribution model, customers were virtually forced to buy complete albums (or CDs), even if they only valued certain songs. In the digital business model of Apple and Spotify, the purchase or download of individual tracks is possible. In terms of the price model, both companies have taken different paths: Apple iTunes: Pay-per-use; Spotify: subscription for the premium component of the freemium revenue model. With Apple Music, the digital group launched a rival offering to Spotify in 2015. In order to control the perception of price via unbundling (“price partitioning”), a precise analysis is required. Comprehensive transparency is crucial: Which customer types value and use which components? Banking services in the retail segment serve as a concise example. A checking account traditionally included a double-digit number of price and service components. The basic monthly fee has long been the focus of private clients (Wübker, 2004, 2006). Based on the price level of the core service, the customer evaluated the price positioning of the entire checking account. Other price parameters were largely unknown to the user and consequently offered scope for price increases. Partitioning is about two dimensions: 1. Vertical: Across mental price categories (e.g., air travel, accommodation, and excursions in the destination for a vacation trip). For each component, analyze the following criteria from the customer’s point of view: – Disposition to buy. – Curve of the loss benefit. The concept of loss benefit from price psychology describes the customer’s perceived sacrifice by paying the price. The loss benefit correlates with price elasticity. 2. Horizontal: Within a category (e.g., the accommodation on a vacation trip). At this level, the following questions are crucial: – Which price components does the customer use to assess the priceperformance ratio? – What is the revenue contribution of the components? Methodical optimization is recommended. All price and product components are to be analyzed with regard to the following criteria: – Number of transactions of a component. – Profit contribution of the components. – Sensitivity of the customer to price increases.

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Ultimately, an optimal solution is always dependent on the three key determinants of pricing: – The cost structures. – The competitive offers. – The constellation of consumers’ willingness to pay.

7.2.3.2 Performance-Related Price Differentiation Most companies offer their products, services, and information goods in different quality categories and price ranges. In product line pricing, differentiated prices are set for product categories (Diller, 2008; Simon & Wübker, 2000). The different quality levels and product variants serve different price segments. Total market exploitation is increased by product and price differentiation, as different groups of buyers are addressed in a differentiated manner. Customers decide individually on the product-price combination that is best for them. In line with their preferences, they allocate themselves to different product packages and prices. As a result, both margins and sales volumes can be increased across the entire portfolio. A classic example is provided by first, business and economy classes in the airline industry. The change in the basic service is relatively small. Nevertheless, the segment-specific variation of additional services (seat pitch, check-in, catering, on-board service, etc.) enables an enormous price differentiation and the resulting increase in profits. Performance-based price differentiation is widespread in the case of digital goods. In connection with product differentiation, the term versioning is often used (Buxmann & Lehmann, 2009). The technical characteristics as well as the cost specifics of information goods trigger the offer of different versions. Digital goods are characterized by two cost effects that reinforce each other: economies of scale and economies of scope. • Economies of scale are cost degressions due to larger quantities. The average costs of information offerings fall as sales volumes increase. • Economies of scope means cost reduction with simultaneous production of multiple versions. Digital products such as music files, electronic books, or software can be reproduced almost free of charge and changed with very little effort. It is very easy to offer two or more quality versions at different prices. Highquality versions can be used to appeal to frequent users. The premium product versions serve to capture the higher valuations and willingness to pay. The additional features of the premium offerings justify a higher price. At the same time, a lowerpriced version—reduced to the bare essentials—is offered for less demanding segments. Against the background of network effects, low-priced versions lead to greater market penetration. Versioning makes it possible to capture a segmentspecific willingness to pay. The trade-off between margin and sales volume is thus

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overcome. As a result, a significant increase in portfolio profitability can be achieved across the individual versions. Case Study Netflix The market launch in the video streaming segment in 2007 initially took place with a uniform price in the USA. In subsequent years, the original flat-rate subscription was converted into a more differentiated structure in line with versioning. The diverse product and price variants are differentiated using the terms “basic”, “standard”, and “premium”. The premium subscription version offers the most comprehensive service. Streaming customers can watch series and movies on four devices simultaneously. The three-tier versioning was later expanded downward in terms of performance and price: firstly, with an entrylevel subscription of USD 2.49; secondly, with a particularly low-priced subscription in India and other Asian countries (Rottwilm & Lange, 2022). In the content business, product-price variations are common practice. Full versions with all details are sold more expensively than abridged versions. Differentiation on the performance side takes place at costs that are significantly lower than the achievable price markup. In the case of digital music, the compression quality of the versions is an important differentiation criterion. Different willingness to pay for different levels of compression can be effectively captured via the versioning of digital music tracks. The value of digital consumer goods such as electronic books to customers is primarily derived from their scope (richness of detail) and topicality. Amazon offers three fundamentally different price-performance versions in its music streaming business segment: Prime Music, Amazon Music Unlimited, and Amazon Music HD. The price gradations can be explained by differences in the number of songs that can be used, the playback quality, and the option of a membership in the Prime customer loyalty program. At market launch, Amazon Music HD was almost twice as expensive as the basic Prime Music offering (USD 7.99) at USD 14.99 in the USA (for non-Prime members). The software industry provides another example of versioning. For software manufacturers, the starting point for performance-based price differentiation is a high-quality and comprehensive product. Subsequently, certain functionalities are taken out. Specific versions can thus be offered to different customer segments (Shapiro & Varian, 1999, p. 63; Buxmann et al., 2008). Microsoft practiced this technique very early on with its Windows operating system (Buxmann & Lehmann, 2009). Windows Vista serves as one of several examples. The “Home Basic”, “Home Premium”, “Business”, and “Enterprise” versions differed significantly in terms of functional scope and price. The “Starter” and “Ultimate” versions were positioned at the lower and upper end of the portfolio.

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Two Brand Strategies as a form of Performance-based Price Differentiation In many sectors, premium brands are losing market share to competitors from the low-cost segment. Low-cost products include low-priced offerings from manufacturing companies and service providers as well as low-priced private labels from retailers. In order to better reflect market requirements, many premium suppliers respond by offering low-cost brands. As part of two-system strategies, they work with different brands, each with a different price positioning and differentiated services. The parallel offering of premium products and low-cost offers is the strategic response to a differentiation of customer demands (Homburg & Totzek, 2011). Premium manufacturers in German B2B sectors, in particular, have also had to open up to market segments with lower price levels in order to secure their survival in the long term. This results, in particular, from the different growth rates of the segments. Low-price segments are growing disproportionately strongly in many sectors. Against this background, a restriction to premium segments is out of the question. After all, premium niches are too small in many industrial goods markets. Competitiveness in the high-growth low-price segments is an important defense strategy for the premium and mid-price segments. Lucrative low-price segments do not exist only in the Asian growth markets. Low-price positions are also experiencing strong growth in the Western industrialized countries. In B2B sectors, in particular, premium suppliers are differentiating their product portfolios to better serve the requirements of different segments. Secondary brand versions or “less expensive alternatives” (LEA) are of increasing relevance not only in the mechanical engineering sector but also in other industrial goods sectors. Product differentiation into a technologically advanced product and a consistently simplified product concept requires a segmentation on the basis of customer requirements. In mechanical engineering, a leading global supplier of materials handling technology differentiated its equipment into two variants: a basic version for priceconscious customers and a premium machine for the performance-oriented segment. The lower-cost basic version offers lower speed and a lower lifting capacity than the premium variant. In return, the low-cost version is almost a third cheaper. The aim is to meet differentiated customer requirements. A previously unserved market segment with lower budgets and reduced performance requirements is targeted with the basic variant. The low-cost Xiameter brand from Dow Corning provides an example of a two brand approach in the chemical industry. Apple introduced the fifth generation of its iPhone in two variants (C and S version) in 2013. The price difference between the two versions was EUR 100. There was an intense discussion in social media and in the specialist literature as to whether the price advantage of the fighter brand (iPhone 5, C version) of EUR 100 was too low (Maessen, 2013). This example leads us to evaluate the price positioning of a low-cost brand (LEA). Here, several criteria have to be included. These can be summarized with the following questions using Apple as an example:

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• Will the premium variant (S version) be cannibalized with the low-budget product (C version)? • Is the fighter brand competitive with low-priced competitors (especially Asian smartphone manufacturers)? • Is the lower-priced version profitable? Apple’s price differentiation was based on the following premises and objectives (Maessen, 2013): 1. It makes strategic sense to offer an LEA to the segment of price-sensitive customers. 2. The price gap is used to achieve the optimal profit over the product line. 3. Apple’s competitive spectrum in terms of price and performance is reasonably extended downward. 4. A displacement of the premium version 5S by the 5C variant is not to be expected. The qualitative difference is too high from the premium customers’ perspective in the developed markets to fear cannibalization. 5. The relatively small price gap of EUR 100 deliberately keeps a price leeway open. This creates a strategic window to lower the 5C price in the future. 6. Several scenarios justify a future price reduction of the 5C variant. A further reduction would be possible if growth in price-focused customer segments (especially in Asian markets) is to be accelerated. A no-frills brand must meet two requirements: • Address price-conscious customers. • Fall short of the expectations of premium customers. The second criterion in particular was fully met with the C variant. The greatest dangers associated with offering an LEA are: • Cannibalization of the premium brand: To prevent profit dilution of the core brand, the two versions must be clearly separated from the user’s point of view. With respect to the behavior of premium customers, the following two guidelines can be derived for the company: – Make the fighter brand less valuable or less readily available to the customer! – Successively upgrade the premium brand through performance improvements! • Lack of market power of the fighter brand: Companies from various industries have protected their premium product too much in the past. As a result, the fighter brand lost attractiveness and ultimately had to be withdrawn from the market. Examples include the low-cost brands Funtime (from Kodak) and Zocor MSD (from Merck). In 2003, patent protection expired on Merck’s blockbuster medicament Zocor. Generics (i.e., cheaper but equally effective imitation pharmaceuticals) threatened the cholesterol-lowering medicament. Merck launched the Zocor MSD fighter brand 4 months before the patent protection

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expired. The problem was not the timing of the launch, but the excessive price positioning. In order to exploit the monopoly position to the maximum, the price of the LEA was set only slightly below the premium brand. Since it was not possible to compete with the imitation products, high losses were incurred in a short time. However, the company was not agile enough to correct this mispositioning. Only one day before the patent expired did Merck react and significantly reduce the price of the fighter brand. The reaction came too late. Merck was forced to eventually take Zocor MSD off the market. Conclusion A wide variety of objectives can be pursued by offering price alternatives. Three exemplary functions and effects of versioning are: 1. The entry of new customers is made possible via particularly favorable offers (LEA). 2. Price pressure on high-value products can be reduced by offering lower-cost alternatives. This prevents price-conscious customers from switching to the competition. In addition, profit-destroying discounts on the premium product can be avoided. 3. The customer’s migration to the next higher quality level (upgrading) is promoted by attractively graduated price levels. The greatest challenge lies in optimally serving the performance requirements and willingness to pay of the various customer segments. These differences can be captured in the most profitable way possible by: • Generating very high values in the top price category. • Offering a deliberately very low benefit in the lowest price range. It is a matter of optimally harmonizing the performance reduction of the fighter brand with its price advantage. The price and performance range of the LEA must be adjusted until the right compromise is found within the framework of the conflicting objectives outlined. It is not uncommon for the performance and price spectrum to be expanded both upward and downward. The objective is to optimize the overall profit within the product line.

7.3

Prerequisites for Price Differentiation Concepts

Six core conditions must be met for profits to increase significantly through price differentiation (Simon, 1992): 1. Customers differ significantly in their preferences. 2. The price elasticities of individual customers or segments are known.

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3. The segments can be identified and addressed with regard to clearly defined criteria. 4. The separation of the segments is enforceable. Unwanted switching by the customer can be prevented by fencing. Clear demarcation lines are drawn between the individual customer groups. Consumers with high and low maximum prices are to be separated. The main fencing instruments used to separate segments are conditions and application rules for prices as well as service differentiation. Segments tend to be easier to separate in the services business than in the product business. On the one hand, customers are very easy to identify because service creation and use often coincide. On the other hand, customer loyalty often exists over a long period of time. In the case of products, separate treatment of customers is easily possible in the case of sociodemographic segmentation. In the case of regional or temporal differentiation, however, there is a risk of arbitrage effects. Demand can be selectively shifted, affecting profit absorption. It is then no longer possible to fully capture differences in willingness to pay. In the case of information goods, price differentiation is supported by systems for digital rights protection. The objective of digital rights management (DRM) is to control copyrights or exploitation rights to digital services. This makes it possible to restrict unwanted use and distribution. DRM systems facilitate the protection of intellectual property on the Internet. 5. Segmentation is possible at reasonable cost. The additional revenue from price individualization must be significantly greater than the costs incurred as a result of differentiation. In many cases, segmentation is achieved through self-selection by customers. Examples of this are mobile communications contracts, special equipment in the car sector, or software services. In online industries, the conditions are even more favorable. Here, the identification, segmentation and addressing of customers can be fully automated. 6. Price individualization is based on criteria that are considered fair by customers. The price differences must not be greater than the benefit differences. Amazon failed a few years ago with its attempt to differentiate prices according to the Internet browsers used. User resistance and negative public attention led to a quick correction of the price differentiation measure. In the context of digitization, public media are discussing how far price differentiation can go in online markets. This raises the fundamental question of user neutrality. The same prices are demanded for the same products, irrespective of the device used or access location. Notwithstanding these demands, the strict rules on net neutrality in the USA were abolished at the end of 2017. Internet providers in the USA now have the opportunity to give priority to certain data streams on the Internet as a result of the change in the law. Access to certain content can be made

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more expensive or possibly even prevented for standard customers. The change in the business model will manifest itself in new price models. Data tariff models in particular will be further differentiated. Customers with higher willingness to pay can be offered increased rates of transmission. Alternatively, content (e.g., music or movies) can be activated in return for a subscription to a special streaming channel. For providers with market power, the abolition of network neutrality leads to a further strengthening of their position. Smaller companies will find it more difficult to operate in the market. Data-driven business models enable multidimensional price differentiation (Skiera & Spann, 2002, p. 279). One example of this is simultaneous price differentiation according to region, user, and the quantities purchased. Different prices per person and country are supplemented by a non-linear price model. The goal of multidimensional price differentiation is a finer segmentation based on the outlined price dimensions. This allows the existing willingness to pay to be captured even better. It should be noted, however, that the complexity of the price structure can still be grasped by the buyer. The more similar customers, products, timing, and/or sales channels are and the more different the prices offered at the same time, the higher the risk potential for the companies. Overall Conclusion Price Differentiation The fine art of pricing is clever differentiation. The goal of price differentiation is to enhance profits by exploiting differences in preferences and price elasticities of segments. Higher profits result from: 1. The absorption of relatively high willingness to pay in certain constellations with increased demand (margin effect). 2. A stimulation of demand through selective price cuts (quantity effect). 3. The more even utilization of capacities through targeted demand management (cost effect; Fig. 7.3). Price differentiation is highly relevant, especially for digital goods. The following reasons, in particular, speak in favor of this: 1. Due to the low variable costs of digital services, sales to customers with low willingness to pay are also profitable. 2. Digital goods are particularly easy and inexpensive to change. This favors price differentiation approaches. The different value perceptions of different segments can be reflected in the price. 3. The implementation costs of price differentiation are significantly lower on the Internet than in the offline economy. Leading pioneers of digital business models (such as Alphabet, Amazon, and Apple) differentiate their products and services in terms of price, primarily according to the benefits they provide. Prices are differentiated according to all six relevant dimensions (products, people/customer segments, times, volumes, sales channels,

References Fig. 7.3 Three effects of price differentiation

175 Skimming willingness to pay

Demand stimulation

Margin effect

Quantity effect

Cost effect

Capacity control

and regions). It is very important to understand the principles of differentiation. Only in this way can the appropriate approaches for one’s own company be selected from the wealth of possible techniques. The forms of differentiation that should be chosen are those that: (a) Best fit the business model. (b) Have the greatest profit potential. (c) Can be implemented efficiently without friction. The most important criterion is the acceptance from the customer’s point of view.

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Buxmann, P., & Lehmann, S. (2009). Preisstrategien von Softwareanbietern. Wirtschaftsinformatik, 51(6), 519–529. Corsten, H. (1998). Betriebswirtschaftslehre der Dienstleistungsunternehmungen. Oldenbourg. Diller, H. (2008). Preispolitik (4. Aufl.). : Kohlhammer. Dirlewanger, G. (1969). Die Preisdifferenzierung im internationalen Luftverkehr: Eine empirische Studie. Lang. Doganis, R. (1991). Flying off course: The economics of international airlines. Routledge. Forster, L. (2018). Ortung im Supermarkt: Wie Händler Smartphones für Werbung nutzen. Retrieved April 22, 2022, from https://www.wiwo.de/technologie/digitale-welt/ortung-imsupermarkt-wie-haendler-smartphones-fuer-werbung-nutzen/20981382.html%20,zuletzt%20 zugegriffen%20am%2001.08.2017 Friesen, M., & Heintze, F. (2015). Digital Pricing: Wie Hersteller der Preiserosion im Onlinehandel entgegenwirken können. DMC Commerce Consultants GmbH. Frohmann, F. (1994). Preispolitik im Luftreiseverkehr. Diplomarbeit, Johannes-GutenbergUniversität Mainz. Frohmann, F. (2007). Mit erfolgreichen Pricingstrategien Produkte optimal positionieren (Lektion 2). In Strategisches Preismanagement. Schriftlicher Lehrgang in 13 Lektionen. Management Circle. Hirn, W. (2018). Digital-Supermächte streiten um Weltherrschaft: Jack Ma gegen Jeff Bezos Duell der Giganten. Retrieved April 22, 2022, from http://www.manager-magazin.de/magazin/ artikel/e-commerce-kampf-um-die-weltherrschaft-zwischen-amazon-und-alibaba-a-1202347. html Hofer, M.B., & Bastgen, J. (2017). Wenn ein hoher Preis den Absatz steigert. Sciam Online. Retrieved April 22, 2022, from http://www.sciam-online.at/wenn-ein-hoher-preis-den-absatzsteigert/ Homburg, C., & Totzek, C. (2011). Preismanagement auf Business-to-Business-Märkten: Zentrale Entscheidungsfelder und Erfolgsfaktoren. In C. Homburg & C. Totzek (Eds.), Preismanagement auf B2B-Märkten (pp. 15–69). Gabler. Kopetzky, M. (2016). Preispsychologie. In vier Schritten zur optimierten Preisgestaltung. Springer Gabler. Maessen, A. (2013). Was Sie von Apples Preispolitik lernen können. Harvard Business Manager Retrieved April 22, 2022, from http://www.harvardbusinessmanager.de/blogs/iphone-5-dieintelligenz-der-apple-strategie-a-929071.html Meckel, M., & Seiwert, M. (2016). Interview: Maschinenbauer Trumpf. Veränderung ist wichtiger als Wachstum. Retrieved April 22, 2022, from https://www.wiwo.de/unternehmen/mittelstand/ hannovermesse/maschinenbauer-trumpf-veraenderung-ist-wichtiger-als-wachstum/13357928. html Meyer, A. (1992). Dienstleistungs-Marketing: Erkenntnisse und praktische Beispiele, Augsburg. FGM. Miller, K., & Krohmer, H. (2011). Ausgewählte Entscheidungsfelder des Preismanagements auf B2B-Märkten. In C. Homburg & C. Totzek (Eds.), Preismanagement auf B2B-Märkten (pp. 105–126). Gabler. Mitsis, K. (2018). Kein Preis-Chaos mehr bei Media Markt: Elektro-Riese plant langersehnten Schritt. Retrieved April 22, 2022, from http://www.chip.de/news/Kein-Preis-Chaos-mehrMedia-Markt-und-Saturn-planen-langersehnten-Schritt_134574723.html Pompl, W. (1991). Luftverkehr: Eine ökonomische Einführung. Springer. Roll, O., Pastuch, K., & Buchwald, G. (Eds.). (2012). Praxishandbuch Preismanagement. Strategien - Management – Lösungen. Wiley. Rottwilm, C. & Lange, K. (2022). Diese Corona-Gewinner werden jetzt abgestraft. Retrieved April 22, 2022, from https://www.manager-magazin.de/finanzen/boerse/biontech-netflix-pelotonsartorius-investoren-lassen-corona-gewinner-fallen-a-427f752f-c3b7-47f4-8b9f-306c9c397c82 Salden, S., Schaefer, A., & Zand, B. (2017). Der Kunde als Gott. Der Spiegel, 2017(50), 12–19.

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8.1

Delimitation and Definition: Price Models

One of the many motifs of this book is the presentation of the interplay between business model, revenue model, and pricing process. The starting point for price management is the business model (see Chap. 3). It is about a clear understanding of one’s own added values and the underlying value creation processes. This results in potential revenue sources and revenue partners (see Chap. 4). Suitable price models are derived on the basis of the revenue model. Price models (“how to charge?”) define the qualitative basis on which quantitative price levels (“how much to charge?”) are based. Price models are systems with multiple interacting parts. The object of price modeling is to answer the questions for what, when, by whom, and on the basis of which parameters the price is defined. Figure 8.1 shows an example of four revenue sources (software, hardware, training, and hotline) for the business unit of a car supplier. The four revenue components in this B2B business model result in four different price models for the components of a diagnostic system. Another example of the delineation of the two integrated challenges (revenue model definition and price model definition) is visualized in Fig. 8.2. The figure shows an example of four potential revenue sources (software, digital services, advertising, and digital content) for Google’s automotive division (Waymo). Waymo’s exemplary four revenue components result in four potential price models for the “autonomous driving” business model of Google: license fee, two-part tariff (basic fee and price per minute), pay-per-click, and subscription. With reference to the first question (“what is a price defined for?”), it can be stated: Price models traditionally refer to one unit of an offer. One-dimensional price models are set by the supplier on the basis of a very simple logic: It is about the price for a household appliance, a car, or a smartphone. The customer makes a one-time payment. These simple price models still apply to numerous products based on the classic sales concept. From the customer’s point of view, this is based on paying for a product in advance and then using it. In service industries, in particular, price # Springer Nature Switzerland AG 2023 F. Frohmann, Digital Pricing, Management for Professionals, https://doi.org/10.1007/978-3-031-24591-6_8

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Revenue sources and price models Example: OEM supplier (B2B)

Software

Training

License fee

Fixed price per day

Revenue- & price model

Hardware

Hotline

Fixed price per product

Two-part tariff

Fig. 8.1 Revenue sources and price models; example: passenger car supplier. (Source: Own representation)

Revenue sources and price models Example: Smart Mobility

Software

License fee

Advertising

Auction

Digital services

Revenue- & price model

Fixed price+ Cost per minute

Digital content

Subscription

Fig. 8.2 Revenue sources and price models; for example: smart mobility (Source: Own representation)

models comprise several components. A two-dimensional price model, for example, consists of a basic price and a variable fee depending on usage. Payments accrue at different points in time. In telecommunications, the questions outlined—for what and when is the price defined—are answered in a more differentiated manner: Here,

8.1 Delimitation and Definition: Price Models 1 Analysis

2

3

Strategy

Structure

Price level

Numerator

How much to charge?

Chapter 9

181 4 Implementation

5 Monitoring

Price model

How to charge?

Denominator

Chapter 8

Fig. 8.3 Pricing process and price model optimization (Source: Own representation)

a data transmission can be billed per volume, a telephone call per minute, and Internet access can be billed at a flat rate per month (Roll et al., 2009; Roll & Wricke, 2005). There are hundreds of different mobile tariffs to choose from. In addition, customers have numerous options for billing data volumes, making international calls, or using special streaming services. A price model can also be based on the number of users, such as T-Mobile’s community rate introduced a few years ago. Each price model influences customer acceptance and user retention to varying degrees. Billing per unit of time is easy for customers to understand. The risk for the provider, however, is a reduced usage by the customer. Flat rates, on the other hand, increase the user’s financial planning security. This not only gives the customer an incentive to increase usage—a flat rate also increases the level of willingness to pay. This proves that the price model and the price level are not independent of each other. The aim of this chapter is to structure the very dynamic field of action which is “price models”. First, a definitional delimitation is made. This is based on numerous projects of the author in a wide variety of industries. A comprehensive empirical analysis of price models in all economic sectors was carried out in parallel over two decades. The price model delimitation lays the foundation for a method to develop new monetization approaches. Chapter 8 (price models: “how to charge?”) lays the qualitative foundation to which Chap. 9 (price optimization: “how much to charge?”) refers. “How to charge” offers particular differentiation potential in price management. The explanation for this is as follows: Increased price transparency in the course of digitization increases the likelihood that competitors will undercut each other. Many companies are subject to the temptation to give in to price pressure via automated processes. However, pure price reductions are rarely successful. Prices are data: quickly copied, interchangeable! Price models are systems: difficult to imitate, potentially unique (Fig. 8.3).

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The Six Pillars of a Price Model

A price model is based on the revenue model definition. It is created by logically linking six pillars. The six dimensions of a price model can be defined using the following questions (Fig. 8.4): 1. Are company offerings (e.g. products/services) combined into a package or is an individual product/service billed? → Scope 2. What does the customer pay for? → Reference base 3. How many components are included in the price? What is the unit of measurement? → Price metric 4. How does the customer pay? → Form of payment 5. Who sets the price? → Degree of interaction 6. At what time is the price determined? → Time of the price setting. All six dimensions of a price model are logically connected. Behind all six pillars are different options. The content-based linking of the answers to the six outlined questions defines a model in each case. The possible combinations of the various options will become apparent in the further course of this chapter—for this purpose, we will delve deeper into the individual dimensions. Pillar 1: Scope Digital business models have led to a massive expansion of companies’ revenue models. In most sectors, revenue shares are shifting from products to services, software, and digital content. Online advertising and especially data have gained importance as revenue sources—in many cases resulting in multiple revenue models.

1. Scope

2. Reference base

Single source of revenue

Transaction

Bundling of revenue sources

Access

Usage

4. Payment form

5. Degree of interaction

3a) Number of components

One time payment

Interaction

Ex-ante price

3b) Unit of measure

Regular payment

No interaction

Ex-post price

3. Price metric

Irregular payment

Outcome

Financial success

Fig. 8.4 The six pillars of a price model (Source: Own representation)

6. Time

Real-time price

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The logical link between the revenue model (digital pricing, level 2) and the price model (level 3) is made via the “scope” dimension. As a direct consequence of the overarching revenue model definition, the number of revenue sources is defined. In some cases, offers are billed individually. In many cases, however, several revenue sources are bundled. The entertainment company Disney sells musical tickets and cruises (services) as well as merchandise (products) in connection with its streaming service Disney+ (digital service). Another example is the price model “Apple One”. This involves the bundling of six content revenue streams (music streaming, video streaming, newspapers and magazines, games, and others). Apple One bundles Apple Music, TV+, Arcade, and News+ and two additional offerings in one price model. Pillar 2: Reference Base The reference base is of enormous importance in the definition of the price model: It is a first milestone with regard to the basic price adjustment (Buxmann & Lehmann, 2009). The reference base of a price model is based on the question: For what does the customer pay? Potential reference bases are: (a) Transaction: Customers pay to purchase a product, service, or another company offering (revenue stream). (b) Access: The customer pays for access to a company offering or revenue stream (products, services, software, digital content, digital services). (c) Usage: The customer pays for the use of a company offering or a revenue source. (d) Result: Customers pay based on the outcomes achieved by the provider. Fulfilled performance promises are monetized. (e) Success: Customers pay for a measurable economic result that has been achieved by the company. Success and the resulting price to be paid is tied to economic KPIs, including cost reduction, profit increase, and profitability improvement. Traditionally, customers pay for a product or service; it is an amount based on the products or service units sold. Transaction-based price models are based on an input relation: price per product, price per unit, etc. In access-based models (subscriptions and memberships), the reference base is the access to a resource. In these pricing systems, the amount to be paid relates to the right to use a company offering for a period of time. How intensively the customer uses the company offering is not relevant. Example: an elevator company that previously sold equipment to building owners; it now offers elevator rights—to be paid in monthly rates. As an interim conclusion, it should be noted: usage-independent price models are based on the reference base “transaction” or “access”. They promote price transparency in the market. Across different offers, a common denominator is established to which the price amount (in the numerator) refers. This promotes the comparability of prices from different competitors. Thus, usage-independent price models trigger the intensity of price competition. The effects of increased customer price focus on competitive dynamics and industry profitability have already been discussed

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in detail. Ultimately, however, the customer is concerned with a different question. Specifically, what contribution does a product make to satisfying my needs? The customer’s needs (and thus his willingness to pay) are generally not directed at owning a company offering. Customers are concerned with the use of services; or in other words: with the fulfillment of needs (Carlzon, 1992; Simon, 1991). The needs-based perspective generates a much broader basis for price modeling—it is the catalyst for creative price models that are more outputoriented. Innovative price models focus more on the value to customer rather than on the ownership of a product or pure access (cf. Stoppel, 2016). Pillar 3: Price Metrics The price metric refers to two dimensions: • The number of price components. • The unit of measure from which the amount of the price results. The questions here are: what is the price-determining criterion (e.g., purchase volume, number of users, and intensity of use)? What influence does the measurement unit have on the total amount to be paid by the customer? The unit of measure, in particular, can take on different forms, depending on the revenue source and the reference base. Examples are: Price per transaction, price per storage requirement in gigabytes, price per usage in hours. The respective unit of measure results in different options for billing. The price metric is closely linked to the objective of customer retention via intelligent price differentiation. Targeted incentives for customers are to be set. This brings the average price into the user’s focus. Interesting leverage effects on customer loyalty result from a usage-dependent reduction of average prices. In the course of digitization, innovative approaches to differentiation are increasingly emerging. The technological basis for this is the improved ability to analyze customer preferences. In this way, price-based incentives can be used to promote specific behavior. In mobile communications, creative rates were developed several years ago—they promoted customer loyalty through price incentives within communities. T-Mobile launched a discount for five selected friends or family members under the MyFaves logo. This was based on a systematic analysis of usage behavior: in mobile communications, an average of 80% of calls were made to a maximum of five people (Frohmann, 2014a). Against the backdrop of technological developments, community rates will become significantly more important in the future. The C2M business model of the platform company Pinduoduo also reflects the community idea. The price model is simplified: the more users, the higher the chance of a discount. Another example of digitally supported group pricing is based on the growing mobility services in metropolitan areas. With the help of digital technology, the idea of a shared cab for ridesharing was developed (Fasse, 2018). An app is used by the platform provider to coordinate passengers and facilitate billing processes.

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Algorithms calculate routes and optimize passenger boardings. With each additional passenger, the average fare per user decreases. Pillar 4: Form of Payment When designing a price model, there are basically three payment variants. For digital goods (e.g., software), this means in detail: 1. The customer makes a one-time payment. He hereby acquires the right to use the service for an unlimited period of time. The one-time payment corresponds to the long-established model of licensing a software. 2. The customer pays for an information good at regular intervals, e.g., via a subscription price. 3. Irregular payments result in particular from price models that are based on “use”, “result”, or “success”. Recurring payments can be optimized with respect to two dimensions: • Payment frequency. • Duration of payments. As a price model for the use of software, for example, a monthly or annual subscription price could be set for a period of 2 years. In the case of bundling, hybrid forms of one-off and regular payments are feasible (Skiera & Spann, 2002). The purchase of a software license linked to the conclusion of a maintenance contract is widespread. The service component comprises annual payments for the maintenance service in the amount of a certain percentage of the one-time license payment. Alternatively, billing can be on an hourly basis. Case Studies New revenue and price models in the area of standard software are based on the technical possibilities of providing customers with software offerings as a service. Software as a service solution replaces traditional licensing concepts with rental models. Customers use the provider’s services via Internet connections; they no longer have to buy and install the software (Buxmann et al., 2008). The revenue model results in new price models. Subscriptions are not necessarily the best alternative as a price model from the user’s point of view. The decisive advantage of cloud systems for the customer is flexible utilization. The customer only pays for the scope of services that is used. Rented software and maintenance services can therefore also be used economically for short intervals of use. For providers, the implementation of these service-oriented rental models results in access to new customer segments with a tendency toward lower willingness to pay. One example of this is the cloud (continued)

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offering Office 365 from Microsoft (Anonymous, 2018a). Online versions of various programs are free of charge; these include Word, PowerPoint, Excel, Outlook, and OneNote. In addition to the free Office Online, service offerings can be purchased for a fee. Web spaces for an Internet site or professional e-mail solutions are available as monthly or annual subscriptions. The Office 365 subscription includes different rates depending on the detailed service. The evolution of Adobe Systems was similar to the business model variation of Microsoft outlined earlier. Against the background of changing customer requirements and technological developments, Adobe changed its business definition. The sale of software packages was increasingly supplemented by cloud computing. The cloud approach meets the changing usage requirements in the digital age with off-site storage of data and deviceindependent, location-neutral access. The overarching business model change was reflected at the level of price modeling. Creative Suite—a software package for a one-time license payment—was replaced by a software-as-aservice offering several years ago. The Creative Cloud offering is a contribution to the success of the US software manufacturer. By switching from one-time license sales to regular web subscriptions, Adobe generates continuous and predictable revenues (Anonymous, 2018c; Buxmann & Lehmann, 2009). In the area of payment services, technical development is proceeding rapidly (Anonymous, 2018d; Reimann & Sokolow, 2018). Mobile payment methods are gaining massive importance. The Asian technology group Alibaba designed a smile-to-pay solution in 2017. The technical basis for this innovative billing system in restaurants is the facial recognition of users (Ankenbrand, 2018). The identification of customers, their operation and service, and subsequent payment via app are fully digitized across the interaction process (and all “customer touchpoints”). In addition to payment time and frequency, the means of payment (as a third action parameter) has an influence on customer behavior. Pillar 5: Degree of Interaction The unilateral setting of the price is referred to as non-interactive pricing. In classic product pricing, the customer has no influence on the price level, except for a possible discount. The supplier sets the price in advance (“posted price”). In contrast, pricing approaches such as “pay-what-you-want” place unilateral price setting in the hands of the customer (Simon, 2015b). In interactive pricing, the price results from the exchange between customer and supplier. Examples of participatory pricing approaches are: • Price negotiations • Tenders

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• Customer-driven pricing • Name your own price • Auctions. Auctions exist in many different variants (Simon, 2015a, p. 271 f.). Two fundamentally different approaches can be distinguished from each other as follows (Homburg et al., 2006, p. 83; Homburg & Totzek, 2011; Rese, 2011): • Classic Auction (“Forwards Auction”): – Price influence by the buyer. – Interested parties outbid each other regarding a seller’s offer. – The actual price is determined by the competition of different price offers of the consumers. – The interested party with the greatest willingness to pay is awarded the contract. • Purchase Auction (“Reverse Auction”): – Price influence by the seller. – Suppliers undercut each other regarding a buyer’s request. – A significant influence on the final price is exerted through predefined price levels. – The seller with the lowest price offer is awarded the buyer’s bid. Among the numerous special forms of an auction, the Vickrey auction is particularly noteworthy from a pricing perspective: The highest bidder is awarded the contract, but pays the amount of the second-highest bid. The Vickrey mechanism motivates bidders to disclose their true willingness to pay. Advertising-financed Internet platforms use the auction mechanism to determine prices. The advertising company, as a customer of the platform operator, receives an offer in the form of a price specification. Two price models are used: • Cost-per-mille (CPM) • Cost-per-click (CPC). In the CPC model, the advertiser pays per click of a user on its advertising banner. Algorithms determine which ads are presented to the platform user. The level of the cost-per-mille (thousand-contact prices) is based on the specifics of the target group (income, region, age, etc.). The higher the price, the more specific the address. Internet auctions exist in all major sectors. They have gained acceptance for B2B, B2C as well as C2C business models (Google Ads, eBay). The Internet offers enormous potential by massively reducing transaction costs. Auctioneer and bidder come together in a very efficient way on virtual marketplaces or online platforms.

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Case Study Aladoo Aladoo is one of the largest online auction houses in Germany. Under the motto “You set the price”, Aladoo holds more than 1000 live auctions every day. The online auction portal has a significant competitive advantage: Users bid for products directly at Aladoo. They do not communicate (as is the case with eBay, for example) with the sellers. Interaction takes place directly with the portal operator. Damages and faulty deliveries can be complained directly to Aladoo. The online portal then takes care of the settlement. Another unique selling point compared to traditional online auctions is the bonus round. In some cases, it takes place after the end of the auction. The bidders have just under a minute to secure the prize at the final bid plus an extra amount. So, the auction is not automatically lost if the user is not the highest bidder. In B2B markets, e-bidding as a procurement process has steadily gained in importance in recent years (Homburg et al., 2006, p. 83). The electronic auction is technically carried out in the form of the reverse auction process. Pillar 6: Time of Price Setting Three basic variants can be distinguished: • Ex ante fixed prices • Case-specific prices determined ex post • Real-time prices. Ex ante prices are communicated prior to the transaction. This classic market pricing is particularly relevant for anonymous consumer goods. Fixed prices are also suitable for standardized services. The basis for ex post pricing is the actual time input or usage. In contrast to fixed pricing, case-specific pricing places the full price risk with the customer. The more individualized the customer’s requirements, the more suitable case-specific pricing is. The dichotomy outlined (cf. Simon, 1992; Simon & Fassnacht, 2016, p. 512) must be extended to include real-time pricing in the course of digitization. The main reasons for this are: • The scope of offerings (the number of revenue sources) has grown significantly. • Real-time pricing has gained importance in numerous industries. • In the course of technological progress, the options for the reference base have expanded significantly. One example: “buy now, pay later” models. These allow customers to pay in interest-free installments after the purchase. The two supporting pillars of a price model—reference base and pricing metric— are described in detail below.

8.3 Reference Bases in Detail

8.3

189

Reference Bases in Detail

Basically, I distinguish between usage-independent, usage-dependent, and valueoriented reference bases.

8.3.1

Usage-Independent Reference Bases

Price models based on transaction (ownership) or access determine usageindependent price models. The simplest price model is transaction-based. The reference base is the purchase of a product, service, or software. Fees are charged per transaction—or the price is linked to the size of the transaction. An example of transactional models is fees that are charged when a property is changed. An agent’s commission as a percentage of the fare—as in the case of the German ridesharing platform Mitfahrgelegenheit.de—also counts as a transaction model. Depending on which revenue model is used, different dynamic pricing systems (peak load pricing and yield management) can be applied. Access-related price models are based on the access to a company offering, respectively, revenue sources (product, service, software, content, etc.). Access models can be divided into subscriptions and memberships: Payments are tied to time periods. One example is cloud computing. Companies pay for the right to access resources—for the ability to perform complex calculations without having their own server capacity. An example of membership is the loyalty program Amazon Prime. Similar to the German BahnCard, in the case of Amazon, payment of the fee affects consumption over time (e.g., Amazon Prime Day). The retail company Real launched a customer loyalty program in 2019. The annual membership in “RealPro” costs EUR 69 per year. For this “investment” or precommitment, real customers receive an incentive in the form of a 20% discount on stationery purchases (Schader, 2020). Subscription models monetize the purchase of products or services by means of a recurring payment (e.g., monthly or annually). They have been established in numerous industries for a very long time: Publishing/newspaper industry, amusement parks, package tours, gyms, and buses and trains. A successful example in the service industry is Deutsche Bahn AG’s BahnCard 100. With regard to the revenue source “product”, it can be stated: Subscription price models are widespread for bicycles, razor blades, cars, clothing, pet articles, and food (wine, coffee). The decisive difference to transaction models is that the ownership rights to the resources used remain with the provider. Breitling offers a smooth transition from two reference bases—access and transaction—with one of its price models. The brand of high-end watches offers customers two different subscription models as part of its “Breitling Select” program. The “squad points” that can be acquired via the access model are to be credited at a special price when a watch is purchased (transaction model). The widespread use of subscription models for products and services can be explained by technological progress. The facilitation of access (price model) is based on dramatic improvements in the “operating model” in terms of prediction, monitoring, and payment. Against

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the backdrop of digital technologies, subscription price models can be applied in any context where purchases were previously impeded or impossible. The “Care by Volvo” mobility subscription primarily addresses the issue of finances: a monthly fee integrates car payment, insurance coverage, comprehensive maintenance, and additional digital services. Subscription models for printer ink, such as “HP Instant Ink”, serve the need for “simplicity”. The company Dollar Shave Club makes the same value case for its razor subscription. The common denominator of access models is: revenue is a function of time. In this respect, access to high-value consumer goods (including cars) no longer fails due to limited financial resources. From the provider’s point of view, the trend toward “more sustainable consumption” (sustainability) can be excellently served by means of subscription models. One example of sustainability is the Swiss sports brand On. On offers fully recyclable shoe models on a subscription basis (Steinkirchner, 2021). One variant of a usage-independent access model used in particular in digital industries is the flat rate. A flat rate is understood to mean unit prices independent of the actual use of the service. A flat rate is a variant of non-linear pricing, since the price per unit decreases as usage increases (Skiera & Spann, 2002). “All you can eat” systems are well-established flat-rate price models used by restaurants. The time limit on unlimited consumption confines the risk for the provider. For a deeper understanding of flat rates, it is interesting to take a historical look at the example of telecommunications. Case Study Telecommunications Until the liberalization of the German market in 1998, tariff structures in telecommunications were comparatively complex (Anonymous, 2018f). The market leader Deutsche Telekom differentiated its mobile tariffs on the basis of two dimensions: region and time. Six tariff periods and three price regions determined the price per minute. From 2005 onward, a clear trend toward simpler price models evolved. Uniform prices were introduced by competitors who were able to achieve cost advantages on the basis of leaner business processes. E-Plus founded Base, the first low-cost brand, which differentiated itself from the competition by offering time-independent flat rates. In 2005, E-Plus introduced the first data flat rate for EUR 40 per month, putting all competitors under pressure. Simple flat rates proved to be a suitable trigger for communicating a favorable price positioning. Tchibo’s prepaid rate in 2005 was also aimed directly at increasing transparency in the market. At 35 cents per minute and at any time of the day, the mobile phone retailer’s offer was significantly cheaper than competitors’ prices. As a consequence of better comparability and more intense competition, average prices fell steadily. In 2007, the rate level for some low-cost providers was already below 10 cents per minute. The exponential growth of mobile Internet services was fueled by data flat rates. Internet services are now predominantly billed as flat rates.

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The mobile communications example shows that an intensification of competition is very often reflected in simpler price models. Price levels successively follow price structures (Roll et al., 2009; Frohmann, 2014a). In other service industries (including airlines, hotels, and car rental providers), the trend was similar. For both airlines and hotels, low-cost providers (e.g., Ryanair) and budget brands (e.g., Ibis) were the main drivers of simple price models. The widespread application of subscription models for information goods (music, software, media content, games, etc.) can be explained in the context of digitization. Readers of electronic books have been able to choose from numerous flat-rate variants for years. Streaming platforms offer unlimited access to digital content in the area of music and film. The two global market leaders Spotify (in the case of music) and Netflix (for movies and series) are well-known examples. 1. Music: Spotify is a prime example of the digital transformation of an industry that had entered a downward trend at the end of the last century. Spotify’s revenue model is based on the fact that music users can obtain individual songs flexibly via a streaming service. They no longer have to buy an entire CD. Spotify serves the megatrend of the flexible use of services. The value to customer can be described with two advantages of the business model from the user’s point of view: “flexible selection” and “small-scale consumption”. The two-part freemium revenue model results in a simple price model for premium customers: a monthly or annual subscription (Postinett, 2018b). For a flat price of EUR 9.99/ month (alternatively: EUR 99/year) in the premium version, users have complete access to a central music database. Spotify is the world’s largest streaming service for music with 195 million paying customers (September 2022). Apple iTunes initially started with a unit price per download of 99 cents, which had to be paid in the pay-per-use model. Apple Music relies on a subscription model with a flat rate of EUR 9.99/month. Despite the late market entry (2015; 7 years after Spotify), Apple Music is number 2 worldwide in terms of market share. 2. Movies: The former Internet video store Netflix has risen to become the world’s largest video streaming service. Netflix recorded 120 million subscribers globally at the beginning of 2018 (Ahlig, 2018; Harengel, 2017; Postinett, 2018a). At the beginning of 2022, the market leader already had 222 million customers (Breustedt & Skolow, 2022). Spotify and Netflix operate with tiered subscription models. Different tiers are associated with different price levels. Spotify serves separate customer segments (students, couples, families) in a differentiated manner with different price levels. The examples outlined provide evidence: The optimization of price models and price differentiation techniques (cf. Chap. 7) are closely linked. There is a linkage of the core components of the price model (scope, reference base, price metric) with the previously defined options of price differentiation. In the case of Spotify, the tiered subscription model is linked to various second- and third-degree price differentiation options. With a tiered subscription model, additional differentiation options can be used compared to a flat rate. Profitability potentials are exploited.

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Subscriptions are increasingly being used not only for information goods. Flat rates are also being used to penetrate the market for digital business models in traditional product sectors—above all in the mobility industry. Providers include online portals specializing in subscription models (Cluno or Faaren), car manufacturers (Volvo, Volkswagen, etc.), and car rental companies (Sixt). One of the pioneers of subscription models in Europe was Volvo Cars with “Care by Volvo”. The monthly subscription “Access by BMW” allows customers to switch between different car models at short notice. Bookings are made via an app. BMW offers several rental packages which are differentiated in terms of price. Porsche (with “Porsche Passport”) and Mercedes (with “Me Flexperience”) introduced similar subscription programs several years ago (Vetter, 2017). Tesla, in turn, offers a comprehensive range of software solutions on a subscription basis. Subscription models are conceptually located between “leasing” and “car sharing” in the automotive industry. Subscription models in the new car business are comparable to leasing in terms of the assignment to the customer. However, shorter and more flexible validity periods distinguish “subscription” from leasing (Essegaier et al., 2002, p. 140; Stoppel, 2016). Leasing, on the other hand, does not involve transaction or ownership. Recently, some companies (Lynk, ViveLaCar) have introduced hybrid models. These allow subscribers to share the cars they use with third parties as well. In 2021, 13% of all new car customers in Germany opted for a subscription (Preu, 2022). In terms of price acceptance, a significant price threshold was to be observed at a level of EUR 499 (cf. Chap. 13). The real estate industry also uses simple flat rates. Instead of billing their tenants for heating costs based on usage, some housing associations offer flat rates for heating. A particular advantage for residents is the planning security resulting from the flat rate: the levels of the flat rates are fixed over a longer period. Access models are also proving their worth in B2B sectors. Two recent examples prove that: • Amazon Business: Amazon launched a flat rate for business customers in Germany at the beginning of 2018. Products from the business-to-business division are delivered via Business Prime within 48 h for an annual flat rate. • Software companies are increasingly using subscription price models in B2B business. The customer switches from purchasing a perpetual enterprise license (transaction model) to an access model. Regular payments spread over time replace a very high one-time payment; capital expenditures (CapEx) are replaced by operational expenditures (OpEx). Flat-rate price models (subscriptions) are neither new nor innovative. Here are a few facts (cf. Monti, 2020): • Subscription models for content were widely introduced (e.g., for books, maps, and music manuscripts) in the early seventeenth century (Monti, 2020). • The first successful subscription dates back to 1617; it was designed for a book.

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• Music (as further information good alongside media content such as books) could also be subscribed to several centuries ago. The pioneer was a manuscript workshop in Vienna. The subscription model was introduced in 1783. • Mozart’s business model consisted of two parallel offerings: concerts as well as manuscripts. For both revenue streams (service and product), the artist opted for a subscription price model (Monti, 2020). The advantages and disadvantages of a usage-independent price model (subscription) can be summarized as follows: • Advantages from the customer’s perspective: – Full cost transparency – Simplicity – Budget control (due to the decoupling of consumption and payment) – Convenience – Low capital requirement (regular fee replaces one-time initial cost) – The average price per unit decreases as the customer’s total usage increases. For frequent users, this results in economic advantages over a usagebased rate. • Advantages from the supplier perspective: The advantages of subscriptions for companies arise primarily from customer perceptions and psychological findings: – Many customers find it difficult to correctly assess their actual usage. Consumers very often overestimate their consumption behavior. The benefits of a flat rate are overestimated by infrequent users. This “flat rate bias “ benefits providers. – The option of an unlimited use tends to be associated with a higher willingness to pay on the part of customers (Buxmann & Lehmann, 2009). – The reduction in the provider’s price complexity is also rewarded by a higher willingness to pay on the part of the customer. The simplicity of the price model is a value driver. Simplifying consumer decision-making increases the maximum price paid by users as well as customer loyalty. – Customers avoid both losses and risks. Subscription rates appeal to users’ security awareness. With a flat rate, customers are protected against negative effects of their demand fluctuation. This explains why some users choose subscription models even when an alternative pay-per-use rate is objectively cheaper. From a psychological perspective, a subscription acts like an insurance against the risk of unplanned additional costs (Kopetzky, 2016, p. 26). – Internal processes such as sales training, customer communication, and billing become much more efficient thanks to a simple price model. Lower process costs compared to usage-based rates are the result. – Further advantage arguments are: Securing a steady cash flow Promoting a continuous relationship with the customer Securing a base load of demand.

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The advantages of subscriptions apply, in particular, to frequently consumed consumer goods, especially information goods. In the case of digital offerings, the increasing data consumption of users favors the acceptance of flat rates. More and more customers prefer unrestricted carefreeness in the form of unlimited data volume at a guaranteed price. The differences in usage behavior between different customer segments form the economic basis for flat rate price models. The flat rate level is determined in the course of a mixed calculation. The profit of customers who make little use of the service is above average for the company. They contribute a share to the financing of frequent users, whose profit contribution is negative due to their high use of company resources. • Disadvantages from the customer’s perspective: According to findings of pricing psychology, people find it difficult to correctly classify future behavior. In industries such as mobile communications, customers often find that they are paying too much in view of their usage behavior. People often misjudge their own usage habits before signing a contract. It is not uncommon for contracts with excessively high flat rates to be oversized (Anonymous, 2018f). • Disadvantages from the supplier perspective: Consumers differ in their preferences and willingness to pay. The more successfully these differences can be reflected in prices and offerings, the greater the market exploitation. The profit that can in principle be skimmed off is given away in two respects in the case of uniform prices (flat rates): – Margin loss: Certain sales volumes that could also be realized at higher prices are only contracted at the unit price. Willingness to pay above the flat rate level cannot be captured. – Loss of sales volume: Potential customers whose willingness to pay is below the flat-rate price level cannot be won. The introduction of a flat-rate price model is not without risks. Four factors play a prominent role in the profit potential or risk of a subscription model: 1. The cost structure (ratio of fixed costs to marginal costs): The lower the marginal costs, the lower the risk. 2. The distribution of usage across all customer segments: The lower the proportion of frequent users, the lower the risk. The conflict situation can be summarized as follows: Low marginal costs are a prerequisite for the commercial viability of flat rates. Rapid sales growth (or sufficient utilization of capacities) is essential for fixed cost degression. However, growth can very quickly lead to two undesirable effects: a jump in fixed costs; a disproportionate increase in frequent users. Detailed forecasts and simulations are required to assess the risks. 3. Cash flow implications: Revenues are deferred (to a point in the future). In the short term, low revenues are offset by high investment costs (transition costs). Short-term losses are possible. This requires a correspondingly high prioritization of long-term growth. This closes the circle to target prioritization (cf. Chap. 3).

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4. Customer loyalty: Compared to transaction models (purchase of a machine or purchase of a software license), the switching costs for the customer are lower. The user can exit the business relationship more easily. Conclusion: Flat rates tend to be more suitable for providers with a strong market position and a large customer portfolio. A broad customer base allows a stable calculation of a profitable flat rate. For smaller providers, even a slight increase in the proportion of frequent users can lead to a significant shift in the mixed calculation. Subscription models are interesting for many companies but are only one among numerous price model options.

8.3.2

Usage-Dependent Reference Bases

In consumption-based pricing, there is no sale of products or services (or another company offering). Customers can use a product or make use of a service—the fee is calculated on the basis of the customer’s intensity of use (Stoppel, 2016). Usage is captured by applying different price metrics. Carsharing services, some of which are offered as pay-per-use models in Germany, serve as an example. Share Now charges by the minute (9 cents per minute). WeShare (Volkswagen) and Sixt Share also relate usage to “time” as a metric. Miles differentiates itself in the course of its “free floating” business model with an innovative price model (“pay for the ride, not the traffic”). The usage (reference base) is measured on the basis of the distance traveled (price metric). So instead of minutes, kilometers driven are billed. Other dimensions of the price model: The price is set by the provider (degree of interaction)—the total bill is determined ex post (point in time). These are one-time payments. Usage-based Price models are gaining importance in the course of increasing digitization, especially for information goods. They are replacing the traditional price model in more and more industries. The customer no longer pays for what is offered, but only for its actual use. Pay-per-use approaches exist in various forms (Stoppel, 2016). In the case of software, various price metrics are derived from the decision in favor of a usage-based reference base, such as, among others: • Price per transaction. • Price depending on the customer’s storage needs. • Price depending on the time of use. The service packages of the market leader for cloud computing—AWS—are also partly paid for according to use. This reduces comparability and interchangeability. Xerox is one of the pioneers of a usage-based price model with continuously recurring payments. The manufacturer of copiers relied on usage-based billing per copied page in the B2B sector. A decisive argument in favor of this innovative price model was the continuity of payment flows for both parties. Both the costs on the user side and Xerox’s revenues could thus be distributed over time.

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German car manufacturers in the premium segment (Audi, Mercedes, and BMW) have implemented a change in the business model for optional extras as part of their digitization strategy. Digitized features such as heated seats, and air conditioning can be made available on demand. The extras are either physically available in every car (via built-in hardware) or they can be recalled digitally as software-based services. Payment is only made when the features are used (pay-as-you-go model). Given the enormous costs of installing hardware, the potential lies mainly with software-based services. The additional services can be activated before or during the journey. The selected service, the billing period desired by the user, and the usage-dependent price are shown to the driver via an interface on the central display (for example, “the comfort control is activated at a price of X EUR for 48 h”). The range of services that can be activated by software has been successively expanded by manufacturers in recent years. Examples include an increase in engine power, suspension adjustment, and activation of the parking assistant. These can be offered, used, and billed on a case-specific basis and depending on the context (route, driving environment, time, weather, road conditions, etc.) (Anonymous, 2015; Eckl-Dorna, 2018; Fasse, 2018; Meyer, 2018). Numerous variations of the pay-per-use model developed in the course of digitization. Pay-per-click models ensure the billing of online advertising. The basis of payment is not the placement of advertising (as in classic print media). Only the use of digital advertising (e.g., the clicking of ads by interested parties) results in costs for the advertising customer. New opportunities for value creation and the monetization of additional benefits are also opening up for insurance companies. Sensors and end devices in the vehicle are used to record the driving behavior of insurance customers. The data collected can be used for innovative pay-as-you-drive models. Telematics tariffs assess the insurance premium according to the customer’s driving behavior, among other things. Pay-per-risk models are based on the driver’s risk factors. Driving behavior, details of driving times, and route information are aggregated into a risk value per customer. Price variations are applied depending on the measured risk level. Discounts on the insurance rate offer users incentives to reduce their risk scores. Conversely, the riskiest drivers often switch to competing companies as a consequence of the introduction of pay-per-risk models. The technical requirements for implementing such models are constantly being improved. These include, for example, the measurement of actual usage and the efficient transfer of usage data (EcklDorna, 2018; Fasse, 2018; Meyer, 2018). Further examples of usage-dependent reference bases can be found in B2B sectors: • Winterhalter: The starting point for the usage-based price model is a modified business concept: dosing models for commercial dishwashers, cleaning agents, and water treatment products. The customer pays for the completed wash cycles (“pay-per-wash”). • Signify: Corporate customers (including airports, steel companies, etc.) pay for the light they use. Signify retains ownership of all lights and installations used.

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The advantages and disadvantages of a usage-based price model can be described as follows: • Advantages from the customer’s perspective: – No one-time investment. – Linking payment to the service actually used. If customers do not use the product, they do not pay for it. – Companies set price incentives for a certain usage behavior of the customer. – Lowering the barrier to purchase (the trade-off between “buying a product” versus “buying a right to use” is easier for the customer than with a subscription). – The duration of use is more flexible than with subscription models. Usage can be increased or decreased—as needed. • Advantages from the provider perspective: – Securing continuous cash flows. – Reduction of price transparency. – Facilitation of the value argumentation toward the customer. – Opportunity to promote a particular usage pattern that leads to economic benefits (including internal cost savings and improvement of planning processes). – Potential to manage the customer portfolio (focus on profitable segments). – Potential for rounding out the price model portfolio (especially for: entry-level segments or price-sensitive customers; segments that fear financial risks due to flat fees). – The transition to usage-based pricing is supported by larger trends: (1) availability of data; (2) better prediction based on technical developments (AI; deep learning). Predictions about usage are increasingly efficient to make—this makes usage-based pricing more attractive. • Disadvantages from the customer’s perspective: – Cost transparency requires active monitoring. – No budget control (due to coupling of consumption and payment). • Disadvantages from the provider’s perspective: From the provider’s perspective, the numerous advantages of usage-based price models have to be bought with a significant disadvantage. The application of usage-dependent reference bases leads to fixed and variable costs (including sales training, usage monitoring, and billing costs).

8.3.3

Value-Dependent Reference Bases

Further development of usage-dependent price models leads to value-based assessment bases. The reference parameter is “output” (outcome-based) or “economic result” (success-based). For B2C customers, the following applies: Ultimately, a customer only ever pays for the satisfaction of a need. In B2B business, it is the solution to the problem that

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counts. This needs-based or problem-oriented perspective generates a much broader basis for price model design. Innovative price models focus neither on a transaction (product, service, software) nor on access or usage. In creative approaches, the reference basis is aligned with the value drivers of a product. A distinction must be made between outcome- and success-based price models. The basic idea of an outcome- or performance-based price model can be described using a case study from the B2B sector. In mechanical engineering, pricing is traditionally done on a unit basis: The business customer pays for a machine or the purchase of certain components. The business customer is only indirectly interested in the product. The actual benefit results from the service provided by the machine and the resulting end product. Therefore, an outputoriented reference base for the price model is appropriate. The backbone of the price model is then no longer the machine, but its performance. The performance is operationalized via the products manufactured or the number of operating hours. Outcome-based models exist in various forms: – – – –

Price per mileage (Michelin) Price depending on the rock blasted (Orica) Price depending on the transported weight (Schindler) Price per laugh (pay-per-smile; Teatreneu; comedy theater in Barcelona). Case Studies: Outcome-Based Price Models (Cf. Figure 8.5) 1. Elevators – Company: Schindler (Switzerland). – Traditional price model: Price per elevator, billing of maintenance services according to time spent. – New price model: Billing according to the weight that the elevator transports over a certain height. – Trigger for new price model: Change in Schindler’s business model. Value proposition: Selling transportation services instead of elevators. Value creation model: Transformation of a physical product into a service; remote control and maintenance of elevators. 2. Industrial explosives – Company: Orica (Australia). – Traditional price model: Price per kilogram of explosives. – New price model: Price depending on the amount of rock blasted (“broken rock”). – Trigger for new price model: Change in Orica’s business model. Value proposition: Repositioning from manufacturer of explosives to solution provider (focus: quality of blasting). Differentiation of industrial explosives (as interchangeable mass-produced goods) via a new price model. (continued)

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Value creation model: Digitized processes (comprehensive data analysis; accurate forecasting of output of blasting process; “rock-on-ground” contracts; end-to-end monitoring for customers). 3. Truck tires – Company: Michelin (France) – Traditional price model: Price per tire – New price model: Price depending on mileage – Trigger for the new price model: Product innovation: A modified version of Michelin’s truck tires offers significantly higher mileage than competing products. Change in Michelin’s business model. Value proposition: From tire supplier to mobility service provider. Value-added model: Digital data acquisition, measurement of a tire’s mileage directly on the vehicle. Revenue model: The higher mileage of the tire leads to an automated revenue generation. Revenue increases continuously as the tire lasts longer. 4. Comedy theater. – Company: Teatreneu (Spain). – Traditional price model: Fixed price per visitor and event. – New price model: “pay-per-smile” (“pay-per-laugh”); each laugh of a visitor is charged with 30 cents; maximum fee: 24 EUR per performance (cap; price ceiling). – Trigger for the new price model: Change in Teatreneu’s business model. Value proposition: Visitors to the theater should pay according to their enjoyment; admission is free. Value-added model: Use of sensor technology; facial recognition software is attached to the back of the seats; the facial recognition system registers when visitors laugh during a performance. All four examples demonstrate the enormous potential of digital technologies. The optimization of products, the development of new services, and the design of creative price models go hand in hand. In the case of innovations or significant product improvements, a variation in the price model is often the decisive lever for profit optimization (Anonymous, 2014). Of the detailed examples above, Michelin’s “pay-per-mile” approach in the B2B segment (truck tires) should be examined in more detail for this purpose. With the traditional pricing approach, Michelin would not have been able to implement a price increase in the clear double-digit percentage range despite the enormous improvement in performance. The existing market prices as an anchor would not have allowed a significant price increase. The perception of users is also of decisive importance here. Monetization of the technical advantage could only be achieved in the course of an objective proof of performance. This

200 Fig. 8.5 Outcome-based price models: selected examples (Source: Own representation)

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Outcome-based price models

Price per weight lifted

Price per mile

Price per blasted rock („broken rock")

Price per laugh

required a completely new price model (from “price per tire” to “price per kilometer”). Hitachi changed the value creation architecture (“operating model”) in one of its B2B businesses a few years ago. The latest sensor technologies were integrated into Hitachi’s train systems. These new measurement methodologies allowed a significant improvement in the on time rate of trains (value to customer). The business model was transformed from “selling products” to “offering software-based services”. “Punctuality” was offered to B2B customers (such as UK Rail Networks) as part of a “train as a service” concept. The consequence of the business model innovation: The revenue model changed from one-time payments (for products) to steady, time-distributed payment streams for a software-based service. The price model is outcome-based, i.e., it is based on the service provided (Fig. 8.6). It is derived from the higher-level revenue model as follows: the better the on time rate, the higher the price. The price model for the AVE high-speed train in Spain reflects a special form of user orientation (Feth, 2008; Köhn, 2018). The AVE connects the 635 km distance from Madrid to Barcelona in just over two and a half hours. The Spanish railroad company Renfe Operadora guaranteed its customers to complete the route within 2 h and 37 min. In the event of a train delay of more than six minutes, the entire fare was refunded. The outstanding competitive advantage of the Spanish railroad operator at the time of introduction was “punctuality”. This performance promise (value to

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Revenue model

Product

Service

Software

Digital content

Data

Advertising

Digital service

Price model € / train

€ / punctuality

Fig. 8.6 Outcome-based price model: Hitachi (Source: Own representation)

customer) could be reliably delivered due to the outstanding quality of the value creation processes (operating model). The technical basis was a comprehensive monitoring using sensors based on IoT technology. The price model derived from the value proposition could only be realized profitably on the basis of digitized services. Customers were strongly bound to the rail company by the quality guarantee. In practice, they could only profit. Either the high-speed train is on time or customers receive a full fare refund. The railroad company’s cooperation with its supplier Siemens is based on an operator model, not a classic sales model (Feth, 2008). Operator models or performance contracting (Stoppel, 2016) are the starting point for outcome-based price models in B2B sectors. In the automotive sector, companies such as Eisenmann and BASF (in the “coatings” business unit) use pay-per-unit models or pay-on-production systems. The system operators install a final assembly line or a paint line at their automotive customers’ sites. Material supply is also guaranteed (Stoppel, 2016). Automakers pay a price for the result. The pricing metric is based on the parts produced or the vehicles successfully painted. General Electric—drill by the day—is another example from the B2B sector. The starting point for the transformation of the price model was the change in General Electric’s value creation model as a result of the Internet of Things. Necessary maintenance work on machines or components can be identified and scheduled at an early stage. Customers are offered an all-round carefree service package to be paid for on a daily basis. The price includes all costs incurred during machine use as well as expenses for maintenance and repair. Rolls-Royce’s price model also relates to the use of its equipment—it is charged per hour used (Cohen, 2007). The supplier of aircraft

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turbines installs the engines for its airline customers and provides all the necessary services (maintenance, wear and tear, etc.). The wind turbine manufacturer Enercon offers a successful example of a quantified distribution of added value between suppliers and customers. Enercon only realizes turnover when its turbines generate electricity for the customer (Simon, 2015b, p. 67 f.). The higher the running performance of the wind turbines and the electricity generated as a result, the higher the customer’s payment to Enercon. The basis for this is digitized value creation processes—including automated recording of the necessary process data and sophisticated measurement technology. The decisive metric is the running performance of each individual wind turbine recorded via sensors. Through its creative price model, the manufacturer takes some of the risk off its business customers. If the running performance is low, Enercon earns correspondingly less. In technological terms, outcome-based price models represent a further development of usage-based reference bases. The decisive prerequisite is a further development of the forecasting systems in the direction of causal modeling. An outcome must be clearly defined in order to be able to be a reference base of a price model. To be able to function as a KPI within the framework of a price model, an outcome must fulfill three criteria: • Importance for the customer • Measurability • Neutrality. The output must be quantifiable by the company and verifiable by the customer. Manipulation by both parties must be ruled out. The “number of units produced” or the “number of usable units” is a suitable metric for outcome-based price models. Outcome-based price models are much more complex than the options described so far (transaction, access, usage). This brings us back to causal analysis—most outcomes depend on multiple causes. The easier it is to quantify and monitor a company’s performance, the easier it is to implement outcome-based arrangements. The complexity of output-based price models increases when customers or intermediaries have a stake in the outcome. Managing the value creation processes and sharing the risk between both parties are key challenges. A performance-based price model is associated with numerous requirements for companies: – Consistent quality results are to be achieved. – Comprehensive capabilities with respect to data collection and analysis must be in place (impact data). – Risk management is particularly necessary when customers are involved in value creation. In success-based price models, the revenue is based on the economic benefit that the customer derives from the company offering. The reference value is the

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economic success from the interaction between provider and customer. Billing is not based on a discrete unit (e.g., time or data volume). Customers pay a price only in the event of economic success. A wide variety of metrics can be used to measure economic success: cost reductions, higher contribution margins, or increased profitability. The basic idea of a success-based price model can be described using a case study from the B2B sector (energy contracting). Energy suppliers or larger heating contractors optimize the energy efficiency of a complex of buildings. On the customer side, the higher energy efficiency results in cost savings. The saved costs are used as a reference value for calculating the price level (Stoppel, 2016). The supplier and the customer divide the saved costs between themselves according to a key metric that is contractually agreed.

8.4

Price Metrics in Detail

Price metric and reference base are closely linked. The decision for a reference basis results in the selection from various options for the price metric. Let us assume a usage-dependent reference base for the company offering (revenue source) software. Selected examples of resulting price metrics are (Buxmann & Lehmann, 2009): • Price per transaction: The price is determined depending on the number of transactions that can be performed with the software (e.g., per call or download). • Price depending on the customer’s storage requirements: The amount to be paid is measured in units of the customer’s storage requirements (e.g., per gigabyte). • Price depending on time usage: The price level depends on the actual duration of software usage (e.g., billing by the minute). A key decision in the context of a price metric relates to the number of price components (Buxmann & Lehmann, 2009). 1. One-Dimensional Metric The price model contains only one component. One example of many is: The price per output quantity is completely variable. The tariff is linear. Revenue for a given quantity of the product is proportional to the quantity purchased. Customers pay only according to actual usage. This price model is used, for example, by an international technology company that sells production machines for electrical components (Frohmann, 2014b). Against the backdrop of the increasing digitization of its value creation processes, the company switched to a calculation of usage quotas. 2. Two-Dimensional Metric Two-part tariffs are composed of two components (Voeth & Herbst, 2011): – A usage-independent basic fee that is paid once per period. – A variable, usage-dependent price component. The importance of the reference base becomes clear once again with these examples: The access right to a resource is the reference value of the basic charge.

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The use of the same resource is the reference base for the variable component. Two-part tariffs can, of course, also be based on one and the same reference base. For example, the Breitling watch brand. Under the Select program, two different subscription models are offered. A flat rate in the amount of EUR 1815 (annual payment). And a two-part subscription model (basic fee: EUR 425; monthly fees: EUR 125). Two-part rates are used in the service sector in particular. Block tariffs are typical for energy suppliers. Here, the customer can choose from various two-part tariffs. Split pricing has also been used by Deutsche Bahn AG since the introduction of the BahnCard in 1992. Customers paid a one-time basic fee in the original model. In return, they received a 50% discount on every rail journey. The customer initially invests the basic amount. He benefits from this through significantly lower kilometer prices over the course of a year. The more often the BahnCard is used, the lower the average price. The parallel offer of standard fare and BahnCard provides a simple segmentation. Deutsche Bahn customers assign themselves to one of the two price segments according to their travel behavior and willingness to pay. Travelers who rarely take the train pay the comparatively high standard rate. Frequent travelers have an incentive to buy the BahnCard. As a result, they pay only half the price (Firner & Tacke, 1993). The concept was successively expanded to include different variants and forms. Since 2003, there have been three variants: Bahncard 25, Bahncard 50, and Bahncard 100, each with versions for second and first class. Like all price model details, two-part metrics are constantly evolving over time. Mobility business models (ridehailing, e-scooters, bikesharing) are often implemented using two-part price models. A base price and a distance-based surcharge are combined: The price per distance unit (mile or kilometer) is consequently non-linear (Fasse, 2018; Anonymous, 2018e). One of the newer mobility providers—Moyem—composes its two-part tariff of the following two components: a start-up fee of EUR 299 and a usage-independent flat rate of at least EUR 259 per month (Wildberg, 2018). 3. Three-Dimensional Metric Also in this non-linear model, the average price decreases with the amount purchased. The more complex—three-part—rate is even better for price differentiation than a two-part metric. For some car rental service providers, the monthly flat rate is capped in terms of maximum mileage. Above a set distance, each kilometer must be paid for with an additional charge. With the usage-based component, the two-part model (starting fee and flat rate) becomes a threedimensional rate (Wildberg, 2018). Other Price Metrics 1. Flat rate cap with usage-dependent surcharges Network operators offered special rates for mobile Internet for many years. A flat rate with limited performance was supplemented by a consumption-based rate. One example was Telekom’s Magenta Mobil L rate. Two-part rates of this kind are more in line with users’ habits. In the case of flexible data rates with a

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consumption-based component, customers can add higher data speeds at short notice if necessary. The surcharges for this are non-linear. 2. One-dimensional metric with flat component Vodafone and Telekom offered examples of this with their StreamOn or Vodafone Pass services. With these rates, certain music or video services were not counted toward the data volume. In the case of T-Mobile, this involved services from content partners such as Apple Music or Netflix. It was a flat rate with limited data capacity. 3. “Bucket pricing” is another alternative to the two-part tariff. The variable (usagedependent) price component is limited to fixed quotas. In the case of pricequantity packages, the basic charge, which is not dependent on usage, applies for a period of time (Schlereth & Skiera, 2012). 4. Price models based on the freemium revenue model. The freemium revenue model traditionally results in a one-dimensional price model. The paid premium component, for example, is based on subscriptions that are billed monthly. This constellation is associated with two major disadvantages: – Many customers shy away from concluding a contract. The necessary commitment to a provider represents a barrier to purchase (loss of volume). – In many digital offerings (video games, movies, etc.), there is a segment of frequent users. The fans’ willingness to pay is often significantly higher than the undifferentiated monthly subscription fee. Rigid subscription prices cannot capture this willingness to pay (loss of margin). Both restrictions are circumvented by an à la carte concept (based on upfront payments). It combines the subscription structure with one-time transactions. Two-part à la carte models are standard in online gaming. Based on subscription payments, customers can use a game for as long as they wish. In addition, it is possible to purchase additional services for surcharges. The variable price component allows the monetization of intensive users’ willingness to pay. The average price (ARPU) can be significantly increased by the variable price component. The two-stage approach of the à la carte price model has proved to be particularly successful in product categories with high customer involvement. Service companies and providers of digital services can achieve significant increases in profits through a two-tier tariff compared to a single price (Buxmann & Lehmann, 2009). The following effects are associated with two-part tariffs from the perspective of users and providers: • Customer perspective: – Volume increase: Unit prices are lower than the unit rates of an alternative one-dimensional price model. This encourages consumption or usage. – Strengthening customer loyalty: In order to achieve a reduction in the average price, the customer uses the services of one provider throughout. Switching to the competition is associated with opportunity costs (switching costs). • Provider perspective:

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– Yield enhancement: The combination of the fixed and variable components results in higher profits compared with a unit price. The variable component is set lower than a comparable unit price. This activates new demand (sales volume effect). At the same time, the basic price leads to an increase in the average margin (margin effect). – Improving liquidity: The usage-independent basic charge leads to the absorption of fixed revenues. – Steady cash flows: The variable component enables the provider to realize continuous cash flows. – Increasing planning stability: The early sale of quotas (e.g., to subscription customers) ensures a basic capacity utilization. This improves internal planning processes. – Reduction of market transparency: Two-dimensional price models make it difficult to compare prices. Traditional price models are defined by a common denominator. This automatically directs the focus of customers and competitors more strongly on the price (in the numerator of the equation). The sum of the effects outlined results in an increase in sales and profits. However, this requires an appropriate control of customer perception. Only if customers and potential users understand the advantages of creative models will the expected sales effect materialize. For example, the basic fee can act as a barrier to acceptance if its effect on the average price paid is not understood.

8.5

Competitive Strategies and Price Models

Competitive strategies determine the complexity of price management; they directly affect the possible options for price model design. At the two extremes, pricing is either differentiated and complex or price models are simple and transparent. For established companies pursuing a premium strategy, complex models are the obvious choice. These make direct comparisons more difficult. As a result, they simplify the monetization of willingness to pay. In contrast, it can be advantageous for aggressive challengers to address customers with a very simple pricing. Increased transparency for the customer inevitably pays off for low-price providers. This explains why in numerous industries, newcomers, in particular, have used flat rates as a price model (Roll & Wricke, 2005). Simple models have proved to be an excellent instrument for breaking up established oligopolistic market structures. Transparency can be implemented much more easily today with the help of information technology. The reference bases of price models are becoming increasingly differentiated as price management becomes more professional. The following trends contribute to this: 1. The digitization of business models and corporate processes is fueling the creation of entirely new approaches. One example: Household appliances can

8.5 Competitive Strategies and Price Models

2.

3.

4.

5.

6.

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be controlled automatically on the basis of the Internet of Things. They can also be operated by cell phone. The devices react to each other, communicate with each other and are linked to central control devices. Washing machines and refrigerators communicate with smart electricity meters. They receive automated information about the time of day when electricity is cheapest to purchase. Smart metering has direct implications for pricing and results in new billing models. The washing machine is activated when electricity is particularly cheap. In the same way, electricity from one’s own solar system can be sold to the grid at attractive prices (Anonymous, 2018b). In many industries, the use of products is becoming more important than the traditional transaction model of “purchase and ownership”. Customers increasingly want to use services on a flexible basis. Exchange and sharing portals serve this trend and rely on creative price models (Wildberg, 2018). With increasing digitization, the quantity and quality of data available for pricing is rising. Significantly more detailed information about the temporal use of offers, the context, and the location of users offer completely new possibilities for price management. Price models will evolve toward multidimensional models in the increasingly digitized business world. Context aspects can be integrated. The potential of information technology enables the inclusion of local and temporal aspects as well as a better integration of behavioral criteria. With the increasing importance of data as a value driver, two factors will help determine the price level in the future: the volume of data and the speed of Internet transmission. In the future, customers will increasingly pay for the right to use resources flexibly. B2B companies have not been paying for the ownership of server capacities in the context of cloud computing for years. Prices result from the right to use software capacities flexibly. Customers obtain what they need and pay accordingly. Increasing technological integration results in a greater bundling of services. The basic ideas of ecosystems such as “smart home” and “smart mobility” reflect this trend. The development toward cross-sector ecosystems is emerging in numerous other industries. The range extends from the automotive industry to mechanical engineering and mobile communications. The trend is toward networked value chains whose sub-offers and prices are composed of the overall network. “Scope” (as the first of the six pillars of a price model) is gaining in importance against the background of digital ecosystems. Customers are being integrated into companies’ business models to a much greater extent than before. This is because customer resources—and in particular, user data—are becoming increasingly important for internal value creation processes. Selected examples of this: – Users take over parts of the value creation. – Customers generate data and make it available to the company in return for compensation. – The cleaning technology manufacturer Kärcher developed a digitalized solution for its fleet management. As part of the value-added service “Kärcher Fleet”, data about the cleaning equipment is recorded centrally. The decisive

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value added for the customer is the significantly increased transparency. Users can manage company inventories across locations and make maintenance processes more efficient. On the basis of its data-based business model, Kärcher succeeds in achieving a significantly stronger integration with its customers’ value-added processes. The added value created for customers offers Kärcher potential for developing new sources of revenue and price models. User data in particular is the basis for new revenue models. 7. Numerous success stories prove that innovative price models have a very strong impact on perceived customer benefits. With value-based reference bases, the customer’s attention is shifted to the performance or the economic impact of the company offering. Innovative price models not only lead to a better monetization of benefits, but are a value driver in their own right for the customer: they increase the value to customer (and thus enhance the business model)! The consequence of this is that price management is not just “value capturing” (monetization). Pricing can also contribute to value generation.

8.6

Methodical Innovation: Concept for Optimizing Price Models

Price model design is an analytical and creative task. Creativity can be managed and enhanced via appropriate processes. It is important to create a framework for action that fuels innovation. Only in this way can the enormous potential of price modeling be captured. The creative process begins with a clear definition of goals. Analogous to the pricing strategy, it is of fundamental importance to clearly define the strategic direction for price modeling. This also includes the determining factors, including the specifications of the business model, legal restrictions, financial aspects, and many more. At the core of the optimization process is a logic that has already been described in the context of the pricing strategy. It involves two aspects that are evaluated and linked quantitatively: • Prioritization of goals (dimension 1: importance). • Assessment of the contribution of each price model to the achievement of objectives (dimension 2: performance). Numerous success criteria must be considered when introducing new price models. Each model under discussion should be critically examined with regard to the following objectives: 1. Customer acceptance: Three central criteria determine the customer’s preference for a new price model: – The price model is simple and easy for the customer to understand. – The price model is perceived as fair by the customer. – The user is bound to the provider by the model.

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2. Revenue and profit security: – The price model ensures a steady and predictable cash flow for the company. – The customer pays according to the costs incurred by the provider. 3. Differentiation from the competition: – The price model helps to differentiate the company from its competitors. – The price model is an independent value driver from the customer’s perspective. In the logic of the competitive advantage matrix (cf. Chap. 6), this means visually: the price model is an independent “point” in the customer’s perception of value. 4. Influencing price transparency (from the perspective of customers and competitors): The supplier’s interests are asymmetrical. Price and benefit advantages over competitors should be perceived as strongly as possible by customers. Higher prices are to be valued relative to the benefits. The four factors outlined represent only the essential success criteria. Depending on the company, industry, and business model, these should be expanded accordingly. Before a new price model is introduced to the market, the associated opportunities and risks should be intensively analyzed and evaluated. The outlined process of deriving, evaluating, and prioritizing price models can be efficiently supported via a simple tool. The evaluation process is based in detail on the following steps: 1. 2. 3. 4. 5. 6. 7. 8. 9. 10.

Definition of all goals and objectives for the price model. Operationalization of the goals. Prioritization of goals (rating of importance on an interval or cardinal scale). Brainstorming: List of potentially possible price models. (Basis: higher-level business and revenue model). Strategic prioritization of price models (rough selection). Evaluation of each realistic price model with regard to the contribution to the achievement of objectives. Calculation of an overall score for each price model (linking of Steps 3 and 6; quantitative selection). Selection of the optimal price model. Preparation of a detailed economic efficiency calculation. Testing of a prototype with selected focus customers.

Objectification and traceability of the selection are of decisive importance. Operationalization and prioritization of the objectives (Steps 2 and 3) serve this purpose, among others. The outlined examples of successful companies (including Michelin, Orica, Enercon, and Schindler) prove that a methodical and structured approach leads to creativity. An initial strategic prioritization (filter 1: rough selection) and the quantitative evaluation (filter 2: scoring) result in a model suitable for the specific situation (business model, customer segment, region, and sales channel). Customer-related targets should account for a relatively high proportion of the total

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weighting of all goals (cf. Step 3). This results from the needs-oriented perspective and a clear focus on customer benefits.

8.7

Success Criteria of Price Models

The evaluation criteria presented result from a meta-analysis of successful price models. Criteria such as the acceptance of the price model from the customer’s point of view are core prerequisites for market success. Approaches that have failed in the market can be explained in the same way. By far the most important explanatory factor for the failure of a price model is the lack of customer acceptance. Example 1: Software (SAP) Price models for standard software have long been based on fees for maintenance services. At the end of 2009, the German market leader SAP experimented with a clause in contracts that automated price increases depending on a wage index. The decisive criteria for the success of a price model were initially underestimated: the perception and acceptance of regular users. Resistance from major customers became so strong that SAP withdrew the originally planned unilateral termination of contracts (Frohmann, 2014b).

Example 2: DVD Rental (Netflix) Today’s global market leader in streaming services originally started as a DVD rental service in the USA. Customers could rent as many movies as they wanted for a monthly fee. Netflix was effectively unrivaled and accordingly had a high pricing power. However, its own market power was overestimated. The result of the misjudgment of customers manifested itself in 2011 in a massive price increase of 60%. Netflix’s justification was based exclusively on internal factors; it referred to the sharp rise in licensing costs. The massive price increase ultimately failed due to a lack of customer acceptance. This closes the circle to the statement in Chap. 5: costs and internal restrictions are irrelevant for users! Willingness to pay results from the satisfaction of customer needs and a clear comprehensibility of price structures.

Example 3: Occupational Disability Insurance (WWK) At the beginning of 2018, insurance companies came under public scrutiny for their business practices. The reason for this was unusually high—and for some (continued)

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customers unforeseeable—price increases. The reason for the anger of insurance customers in key points: 1. Contracts covering an occupational disability risk contained clauses on pricing. 2. German providers such as WWK, Signal Iduna, and some others operated with two insurance contributions: They differentiated between gross and net amounts. Numerous contracts contained a price range that was risky for customers. Transparency for users was not always provided. 3. The lack of transparency from the customer’s perspective was further exacerbated by differences in the range between gross and net amounts— between providers and across individual contracts. 4. The price range was linked to the insurance company’s surplus. In the case of high profits, the insurance company passed on correspondingly high discounts; the premium to be paid by the customer approached the lower limit. In the case of a lower surplus, the discount was reduced. Customers automatically paid the maximum amount whenever the insurance company made no profits (Langenberg, 2018). Insurance surcharges averaged just under 50% in a representative study. 5. The problem from the customer’s point of view: The insurance contribution structure was not easy to understand. This was particularly true for the online business. There, some companies only communicated the low net premium. On comparison portals—such as Check24—only one amount was mentioned for all providers at the beginning of 2018. The insurance example proves that digitization does not automatically lead to greater value and price transparency. From the customer’s point of view, there is still a risk that price structures will be very intransparent. If the provider goes too far in this respect, a non-transparent price structure leads to great dissatisfaction and negative effects in the long term. Via social media as well as classic channels (including special broadcasts on TV channels), these cases very quickly become known to a broad mass. The scooter sharing company Coup changed its price model in April 2019. The old model consisted of a flat rate of EUR 3 for 30 min. The new model was as follows: Users pay 21 cents per minute; the minimum rental time is 10 min. The consequence for frequent users: 30 min cost EUR 6.30. Many users communicated their disappointment with the provider on social media (Dahlmann, 2019). Intransparency on the Internet does not only prevail at the level of individual providers. Modern technologies can also have an impact on an entire industry. One example is the influence of bots on airline pricing. Automated programs affect a significant proportion of the traffic on airline websites. The business model of bots is based on trading seats. Price comparisons are only the starting point. The risk for the traveler is a targeted manipulation of the price level (Meckel, 2018).

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Recommendations can be derived from a meta-analysis of successful as well as failed price models. In order to develop creative price models and successfully introduce them to the market, companies must take the following essential requirements seriously: 1. Consistent derivation from the business model: Price models must be derived stringently from the business model. A price model must fit the value proposition for the customer. It should reflect the internal value creation processes. And the model should ideally exploit the benefits created in such a way that customers are retained over the long term. 2. Consistent alignment with strategic goals: Lasting customer relationships and regular cash flows can be promoted through subscription structures, for example. The addition of a variable component (e.g., as part of a two-part tariff) increases the average revenue. 3. Needs-based perspective: All potential price models should be evaluated from the customer’s perspective. One of the crucial success factors is knowledge of the customers’ value creation processes. Often, greater integration into the customer’s process organization is the prerequisite for innovative revenue and price models. 4. Early involvement of customers: Strategic customers should be involved in the discussion and planning of new price models as early as possible. It is very helpful to learn: how satisfied are customers with traditional industry models? How open are they to variations? Which customer needs are being ignored with the existing price models? 5. Creativity: Based on the six essential dimensions of a price model, there are a large number of design and variation options. Innovative approaches to pricing and payment can trigger an enormous market effect. Better exploitation of the added value of improved products and services is very often only possible by varying the price model. Creative price models from other sectors and product categories can possibly be adapted and extended for their own business model. In this context, it is also imperative to observe legal framework conditions and compliance rules. 6. Simplicity: Price models should be as simple as possible. Comprehensibility is a key success factor. Business and private customers are put off by overly complex price models. 7. Differentiation: Differentiated price models for different customer segments are becoming increasingly important against the backdrop of digitization. Successful companies in numerous industries address different segments with price models specifically tailored to their needs. 8. Fairness: Perceived fairness depends on two criteria. Transparency and consistency. A balanced distribution of shared values (e.g., via performance-based price models) also fuels the user’s perception of fairness. 9. Careful planning and implementation: Changes in reference bases and price metrics are not easily enforceable. The introduction of new price models in the market should be carefully planned and prepared. The benefit argumentation for

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the customer is crucial. The chances of successfully introducing a price model are the higher the – better customer needs are addressed that were not previously considered, – more innovative the performances of the company is, – stronger the company’s market position is.

Conclusion Price Model Optimization Price models that stand out from the competition are a helpful tool for avoiding discount battles. They do not only serve to improve “value extraction”, but are also an important building block in “value generation”. Innovative price models have an enormous revenue and profit potential. This applies to all revenue sources. A price model is based on the fundamental decisions about the business model and the definitions of revenue sources and revenue partners. The coordination and consistent design of the individual elements are basic prerequisites for optimizing the achievement of financial targets. At the same time, technological developments increase the potential for creating new price models. The digitization of processes is important, as software support can be used to efficiently implement, measure, and bill even elaborate price models. User perception and customer benefit are the starting point and benchmark for successful implementation. This is especially true in industries with standardized and interchangeable products. The introduction of new price models to the market should be carefully planned and prepared. The decisive factor is the benefit argumentation for the customer. With the help of innovative price models, products can be enriched in such a way that they stand out from the competition in the customer’s perception. A new approach can become a unique selling point in the market. Creative price models in commodity industries are proof of this. Almost unlimited options are available for the design of price models, which can be successively expanded with increasing digitization.

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Anonymous. (2018b). Smart metering. Intelligente Stromzähler haben es noch schwer. Wirtschaftswoche. Online. Accessed Apr 22, 2022, from https://www.wiwo.de/technologie/ digitale-welt/smart-metering-intelligente-stromzaehler-haben-es-noch-schwer/21144186.html Anonymous. (2018c). US-Softwarekonzern: Adobe gelingt deutliches Plus bei Umsatz und Gewinn. Handelsblatt. Online. Accessed April 22, 2022, from http://www.handelsblatt.com/ unternehmen/it-medien/us-softwarekonzern-adobe-gelingt-deutliches-plus-bei-umsatz-undgewinn/21078512.html Anonymous. (2018d). Neue Zahlungs-Richtlinie PSD2: Der Trend zur kostenfreien OnlineZahlung ist gesetzt. Manager Magazin. Online. Accessed April 22, 2022, from http://www. manager-magazin.de/unternehmen/banken/psd2-zahlungsdienst-richtlinie-setzt-trend-zukostenfreier-online-zahlung-a-1188623.html Anonymous. (2018e). Nach Ausstieg bei DriveNow Sixt greift BMW und Daimler mit eigenem Carsharing an. Manager Magazin. Online. Accessed April 22, 2022, from http://www. manager-magazin.de/unternehmen/autoindustrie/sixt-nach-ausstieg-bei-drivenow-kommteigenes-carsharing-a-1198272.html Anonymous. (2018f). Früher gab es hohe Minutenpreise. Wie Telefonieren so billig wurde. Bild. Online. Accessed April 22, 2022, from https://www.bild.de/geld/wirtschaft/handy-vertrag/wietelefonieren-billig-wurde-54681162.bild.html Breustedt, H. und Skolow, A. (2022, April 21). Netflix im Abwärtstrend. Wiesbadener Kurier, 21. Buxmann, P., Diefenbach, H., & Hess, T. (2008). Die Software-Industrie: Ökonomische Prinzipien–Strategien–Perspektiven. Springer. Buxmann, P., & Lehmann, S. (2009). Preisstrategien von Softwareanbietern. Wirtschaftsinformatik, 2009(6), 519–529. Carlzon, J. (1992). Alles für den Kunden: Jan Carlzon revolutioniert ein Unternehmen. Campus. Cohen, M. (2007). Power by the hour: can paying only for performance redefine how products are sold and serviced? Accessed April 22, 2022, from https://knowledge.wharton.upenn.edu/ article/power-by-the-hour-can-paying-only-for-performance-redefine-how-products-are-soldand-serviced/ Dahlmann, D. (2019). Minutenpreise sind ein schlechter Coup. Accessed April 22, 2022, from https://www.businessinsider.de/gruenderszene/automotive-mobility/minutenpreise-coopdrehmoment/ Eckl-Dorna, W. (2018). Ein Smart Device auf Rädern–Das ist das Neue. Accessed April 22, 2022, from http://www.manager-magazin.de/unternehmen/autoindustrie/elektroauto-china-startupbyton-soll-alsluxusmarken-alternative-starten-a-1204351-4.html Essegaier, S., Gupta, S., & Zhang, Z. J. (2002). Pricing access services. Marketing Science, 21(2), 139–159. Fasse, M. (2018). Mobilitätsdienste: Daimler und VW fordern Uber heraus. Handelsblatt. Online. Accessed April 22, 2022, from http://www.handelsblatt.com/unternehmen/industrie/ mobilitaetsdienste-daimler-und-vw-fordern-uber-heraus/20760266.html Feth, G. G. (2008). Hochgeschwindigkeitszug AVE: Wie im Flug vergeht die Zeit. Accessed April 22, 2022, from http://www.faz.net/aktuell/technik-motor/technik/hochgeschwindigkeitszugave-wie-im-flug-vergeht-die-zeit-1668202-p2.html Firner, H., & Tacke, G. (1993). BahnCard: Kreative Preisstruktur. Absatzwirtschaft, 36(5), 66–70. Frohmann, F. (2014a, March 14). Gewinn maximieren: Big Data für kleinere Shops. i-Business. Accessed April 22, 2022, from http://www.ibusiness.de/aktuell/db/191465veg.html Frohmann, F. (2014b). How B2B enterprises implement pricing innovation to capture value. Leverage point webinar. Accessed April 22, 2022, from https://www.youtube.com/watch?v= VaSMkEC3Fn8 Harengel, P. (2017). Streaming und Fernsehen. Radikaler Umbruch: Wie wir TV-Sender, Netflix und Amazon zum Umdenken zwingen. Focus. Online. Accessed April 22, 2022, from https:// www.focus.de/digital/experten/mediennutzung-streaming-wie-nutzer-tv-sender-netflix-undamazon-zum-umdenken-zwingen_id_7751533.html Homburg, C., Schäfer, H., & Schneider, J. (2006). Sales Excellence. Gabler.

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9.1

Methods for Determining the Optimum Price

Professional pricing offers great potential for optimizing companies’ profits. Data on customer behavior is needed to optimize profits: (a) Historical information (b) Current statistics (c) Forecasts. The focus of the optimization is: • Actual or forecasted sales volumes at different prices. • Willingness to pay for products, services, and information goods. The following methods are suitable for data collection: 1. Observation • Price experiments • Econometric analysis of market data • Voice of consumer analytics (social listening) • A/B testing • Online auctions 2. Survey • Direct price query • Open line pricing • Gabor-Granger method • Price-sensitivity meter • Conjoint measurement 3. Workshops

# Springer Nature Switzerland AG 2023 F. Frohmann, Digital Pricing, Management for Professionals, https://doi.org/10.1007/978-3-031-24591-6_9

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Methods for price optimization

Observation

Survey

Workshop

Price experiments

Direct price query

Focus group interviews

Market data analysis

Open line pricing

Expert estimate

Social listening

Gabor Granger method

A/B testing

Price-sensitivity-meter

Online auctions

Conjoint measurement

Fig. 9.1 Methods to determine the price optimum (1) (Source: Own representation)

• Focus groups • Expert estimate. This chapter outlines the individual methods as well as their advantages and disadvantages. It shows in which decision-making situations which approaches can be used. These recommendations are based on well-founded knowledge of the methods as well as practical experience in over 400 projects for companies in a wide range of industries. Figure 9.1 provides an overview of the observation methods used to determine the optimal price.

9.1.1

Observation

9.1.1.1 Price Experiments This method of price optimization is becoming increasingly important in the wake of digitization. Today, the Internet offers a wide range of possibilities for researching buyer behavior. Without effort, it is possible to differentiate and publish different prices according to various price dimensions (customer, time, region, sales channel, etc.). Alternative prices can be tested on online portals regarding their acceptance. The effect on sales volume and market share is recorded on the basis of shopper behavior. Online retailing offers fully automated information on: • • • • •

Internet shopper identities Visits to online stores (dwell time, search process, etc.) User click behavior Purchased items Purchase times

9.1 Methods for Determining the Optimum Price

• • • •

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The use of company offerings (timing, frequency, etc.) Prices paid Abandonment of search processes or purchase rejections Purchase rates over time.

The key figures outlined can be easily measured and evaluated with the help of analysis tools (Jacobs, 2018; Wirminghaus et al., 2018; Anonymous, 2018d). Online retailers use the identified patterns in customer behavior to continuously optimize assortments and prices. However, price experiments should not only be used to test willingness to pay and price levels. Different structures and price models can also be efficiently tested for their effect in the market. The technological potential for measuring customer behavior is constantly improving in the course of digitization. The Internet of Things offers numerous new possibilities. One example from the retail sector: Beacons are used in stationery stores to locate customers and record their behavior. These Bluetooth transmitters, which are imperceptible to customers, are integrated into electronic price tags, among other things. This allows customers’ usage behavior to be measured in real time. Consideration processes (length, result, etc.) as well as walking routes of the customer and baskets of goods are analyzed in detail (Firlus, 2018). The measurement capabilities go down to the level of facial expressions and gestures. An intelligent mirror captures customer preferences and decision-making processes via facial recognition. Combined with sociodemographic data and user attitudes, a much more comprehensive picture emerges than traditional price research has been able to provide. The recorded preferences and behavioral patterns are used to make tailored offers and recommendations. Price acceptance, information on cross-price elasticities, preference for certain packaging concepts, and many other insights can be used for portfolio pricing (Macho, 2018). With the help of artificial intelligence, it is possible to interpret the mouse movements of customers in online retail. For example, it can be analyzed whether a customer threatens to abandon a purchase in the short term despite having already completed the compiling of a shopping cart. Measures are automatically derived from historical behavior patterns and the course of current order processes (Firlus, 2018). These technical possibilities significantly professionalize customer behavior steering. Case Study Offer Configurator In many sectors, customers can put together digitally supported product packages. As part of the individual selection of products and services, the willingness to pay can also be stored online. A case in point is the German online jeweler 123gold, which took advantage of the opportunities offered by digitization at a very early stage. With an online configurator, the jeweler created a significant competitive advantage. Traditionally, customers could only choose from a few available models in a collection. 123gold changed this (continued)

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in 2002 with the first digitized configurator. Based on the quotation tool, customers can design individual wedding rings online. Numerous parameters of the ring design can be varied. The ring variants displayed on the portal depend directly on the customer’s upper price limit in each individual case. Consequently, the configuration is increasingly reduced to those priceperformance variants that fit the requirements profile of the prospective customer. The budget specified online is included in the offer design (Salden et al., 2017; Schwab, 2017). Whenever the design is changed, corresponding price changes are automatically displayed to the user. From the customer’s selection behavior across variants, the system detects preference patterns. The ability to optimize an individualized product in terms of the price-performance ratio is a discrete value proposition for the user. A core principle of professional price management is automatically implemented with such offer configurators: It’s all about trade-offs! Higher performance requires higher prices. From the provider’s point of view, price reductions are only justifiable if they are also associated with service reductions for the customer. For the user, it is rarely just about the price—what ultimately counts for him is the optimal price-performance ratio. This is where digitization must come in—in the optimal servicing of customer needs! A configurator serves two central value drivers for the customer: convenience and speed.

9.1.1.2 Econometric Analysis of Market Data In many markets, standardized information can be used for price optimization. Price elasticity can be derived from historical data on sales volumes and prices of all competitors. These data are transformed into price-response functions using econometric regression methods. Online retailing provides an excellent information base that allows conclusions to be drawn about price effects. Price reporting systems are used in some sectors. In these cases, the standardized transfer of data on revenues and sales volumes is made to a common association or institute. In the course of digitization, the conditions for measuring price effects on sales volumes, revenue, and profit have also improved significantly for classic products (e.g., gasoline). It must be taken into account here that various disturbance variables (seasonal effects, competitive actions, etc.) can influence the price elasticity measurement. The measurement of cause–effect relationships is all about causality. Cause–effect relationships must be clearly separated from pure correlations. A special form of market data collection is price crawling. The technical basis of price crawling are smart bots—highly automated computer programs. Bots browse the Internet in search of price data and product specifics. Automation enables the collection of price data on a large scale and with very high frequency. Market data analysis conceptually includes the use of machine learning solutions to track price trends on the C2C platform eBay. Via market data collection for a newly launched

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product in 2003, Playmobil derived a leeway for an increase of its list prices. Playmobil had obviously underestimated the price acceptance of customers for the new toy “Noah’s Ark”. The retail price to the end customer was EUR 69.90. At the same time, significantly higher prices were realized on eBay.

9.1.1.3 Voice of Consumer Analytics (“Social Listening”) Social media platforms (such as Facebook, TikTok, and Instagram) are becoming increasingly important for conditioning consumers and influencing purchasing decisions. With the increasing relevance of social media for price management, it is becoming more and more important to know what people are communicating online about a company’s products, brands, and prices. Statements from customers about direct competitors can also be used efficiently for a company’s own target positioning. Social media monitoring will become increasingly important for pricerelated market research in the future. Companies will gain deeper insights into the market and receive timely information on the appreciation of their products. Statements on the relative price-performance ratio are made very clearly on social media by numerous users. Coupled with this, customers’ wishes and suggestions for improvement can be found again and again. This unstructured data from social media can be used for the further development of price management. The term “social listening” vividly describes what will be much more important in the future: the targeted use of all qualitative and quantitative information about the perceptions of customers. Streaming platforms like Spotify analyze customers even when they are not listening to music (Hajek, 2018; Albert & Schultz, 2018). Keywords about music—and thus preferences and interactions of listeners—are captured millions of times a day on social media (Facebook, Twitter, blogs). The free information on the web can be efficiently used to complement and validate market research methods as well as secondary statistics. They are often the best early indicator of the actual impact in the market. The early indication of social listening can be excellently demonstrated by the case study of the Apple iPhone. Predominantly negative user comments in the months as of November 2017 were followed by official reports starting in February 2018 that Apple’s price exceeded many customers’ willingness to pay (Anonymous, 2013, 2018a, 2018b; Eisenlauer, 2012; Schütte, 2017). A significant proportion of users commented on the significant exceeding of the EUR 1000 price threshold for smartphones as not justified. It was repeatedly emphasized that Apple’s performance is not commensurate with the price (Anonymous, 2013, 2018a; Eisenlauer, 2012; Schütte, 2017). When McDonald’s recently tried to raise the price of a cheeseburger by USD 0.39, it was met with fierce customer resistance. Within 48 hours, some 80,000 Facebook followers posted negative feedback in response to the price increase. The customers’ reactance forced McDonald’s to retract the price increase. The main reason for the latest case of angry reactions from customers to price increases in Germany was the short notice of an announcement: The gym chain McFit hit the negative headlines in March 2022 with a price increase of up to 25% (Huber, 2022). Standardized software is available for voice of consumer (VoC) analytics. Qualtrics, part of SAP, offers decision-support software that can capture

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unstructured data. Real-time data can include emojis in chats, sentiments in videos, comments under photos, and complaints in emails. Customer feedback can be systematically analyzed and used for decision-making.

9.1.1.4 A/B Testing A/B testing is a very efficient method for testing the customer acceptance of different variants. Numerous companies in various industries use this method systematically (Welbers, 2018). In retail, A/B testing is particularly well suited for measuring customers’ emotional reactions to displays. The products broadcast on shelves can be efficiently tested for their effectiveness. Systematic testing of versions is particularly important in online industries (e.g., software and web design). For information goods, the perceived value to customer depends on a variety of determinants. In addition to data timeliness and the level of detail of the information, the format is also a decisive value driver in the content business. One way to test the acceptance of different formats is to leave the price the same for each variant. Differences in preference can then be clearly attributed to the format. The British newspaper “The Independent” was the first renowned medium to switch to a smaller version. The decision was preceded by an intensive acceptance test (A/B testing). Two format versions were sold in parallel, with the same content at the same price. In this experimental setup, the smaller format actually sold better. When optimizing prices, it is essential to take into account the findings of price psychology. After all, whether an offer is perceived as expensive or inexpensive depends to a large extent on psychological factors (see Chap. 13). Controlled A/B tests are particularly well suited to measuring the effect of psychological pricing tactics. However, A/B tests have a crucial restriction: They cannot measure the absolute willingness to pay for offers. The test method is about relative comparisons. If a potential customer prefers option B to option A, this does not necessarily result in an optimum (cf. Krämer & Hercher, 2016). 9.1.1.5 Online Auctions Online auctions are a dynamic variant of market data collection. Online portals use auctions to systematically survey prospective buyers’ willingness to pay. Even the world’s oldest auction house, Sotheby’s, is expanding its auctions to forecast willingness-to-pay based on artificial intelligence (Anonymous, 2018d).

9.1.2

Survey

Figure 9.2 provides an overview of the survey methods used to determine the optimal price.

9.1.2.1 Direct Price Query The direct price survey is a simple methodology for determining the willingness to pay in the context of a customer interview. In the simplest version, consumers are asked directly how they react to certain prices or price changes (Simon & Fassnacht,

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Methods for price optimization

Observation

Survey

Workshop

Price experiments

Direct price query

Focus group interviews

Market data analysis

Open line pricing

Expert estimate

Social listening

Gabor Granger method

A/B testing

Price-sensitivity-meter

Online auctions

Conjoint measurement

Fig. 9.2 Methods to determine the price optimum (2) (Source: Own representation)

2016, p. 126; Drewes et al., 2010). A common questioning technique is: What is the maximum amount you are willing to spend for the product? The respondent indicates only his or her willingness to pay. These responses are used to derive demand functions (Pastuch, 2018). The direct price survey has some advantages. The method: 1. 2. 3. 4.

Does not overwhelm the respondent. Requires significantly less time compared to more complex processes. Is very easy to evaluate. Enables the testing of multiple products within one interview. However, the advantages are also countered by some serious disadvantages:

1. The questioning technique strongly draws the customer’s attention to the price. Price awareness is atypically increased as a result. 2. The price is considered in isolation. A weighing of the price-performance ratio does not take place. 3. Responses to pricing questions often do not match actual customer behavior. 4. The prestige effect of price statements must be taken into account when interpreting the results. 5. Respondents can manipulate the result very easily. The method is always problematic when customers have an interest in influencing the future price according to their ideas. 6. For new product categories where anchor prices are not yet available, the direct question technique is rather problematic. Due to its serious disadvantages, the direct survey approach is only of limited use for price management (Pastuch, 2018). There is a risk of wrong pricing decisions. In this respect, exclusive use of the direct method is not recommended. Advanced

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methods such as open line pricing or price-sensitivity meter are based on the basic principle of direct price queries. They modify it to a greater or lesser extent in order to significantly increase the validity of the data collected.

9.1.2.2 Open Line Pricing Open line pricing (OLP) is a simple market research methodology for determining the willingness to pay (Roll, 2018). The survey refers to price ranges that potential customers expect for a product. The advantage over the direct price survey is the differentiated specification of two price points. The reference to a price range results in a more intensive examination of the question of maximum willingness to pay. The OLP method is a supported survey. By specifying the scale, the evaluation is influenced. Aided open line pricing (AOLP) is an extension of OLP (Pastuch, 2018). The respondent is additionally shown the competitors’ prices for a comparable product. Despite the outlined extensions (price ranges; competitor prices), AOLP is also limited by the main disadvantages of direct price surveys. 9.1.2.3 Gabor-Granger Method The Gabor-Granger method is another approach to determining the willingness to pay using a direct price interrogation. Similar to the OLP method, it also involves price thresholds (Buchwald, 2018; Gabor & Granger, 1966). However, purchase probabilities are queried rather than expectations. Survey participants indicate the probability with which they would buy a certain product at a specified price. A typical question is: suppose you are faced with the decision to buy a new smartphone. What is the probability of purchasing the iPhone 13 pro from Apple at a price of EUR 1100? What is the probability of purchasing the iPhone 13 pro at a price of 1200 EUR? etc. The respondent indicates the probability on a scale from 1 (very unlikely) to 5 (very likely). For all price points within a realistic range of the product, the question is structurally repeated. Purchase probabilities and price sensitivities can be derived from the answers. The Gabor-Granger method does not establish a direct link to the competitive environment. The main disadvantages of all direct price surveys also apply to this approach. In order to measure willingness to pay validly, the direct approach should be supplemented by other survey methods. 9.1.2.4 Price-Sensitivity-Meter The van Westendorp method (price-sensitivity-meter, PSM) is a further development of the direct price query (Roll, 2018; Simon & Fassnacht, 2016, p. 129). Current or potential customers are asked four questions about a specific offer (Klarmann et al., 2011). A questioning sequence in the context of an interview can be as follows: You are faced with the decision to buy a new smartphone: 1. At what price would you consider the offer too cheap, so that you doubt the quality? 2. At what price do you consider the offer a good deal? 3. At what price would you consider the offer expensive, but still consider buying it?

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4. At what price would you consider the offer too expensive and not consider buying it? A special focus is placed on the graphical evaluation of the four price questions. The respondents’ answers are visualized in a two-dimensional diagram. Different insights can be gained from the intersections of the different curves. The main advantages of the PSM method are (Roll, 2018; Kopetzky, 2016): 1. 2. 3. 4. 5. 6.

Simple and efficient implementation. High transparency with the help of the graphical evaluation. Consideration of the quality indication of the price. Derivation of recommendations on the price range. Differentiated reflection on the willingness to pay. Covering findings of price psychology (consumers transfer objective prices into rough categories such as too cheap, acceptable, and too expensive). The main disadvantages of the PSM are (Roll, 2018):

1. 2. 3. 4.

No consideration of the competitive situation. No inclusion of the product properties. Strong focus on price (overestimation of willingness to pay). Lack of scientific basis for price recommendations.

The intersection points of the curves are not relevant for an optimization with regard to target variables such as quantity, sales, or profit. Price optimization is related to the best possible achievement of objectives such as sales volumes, revenue, contribution margin, or profit. However, in PSM, there is no connection between the intersections of the curves and an economic goal achievement (Krämer, 2015; Krämer & Hercher, 2016, p. 46; Krämer et al., 2017; Roll et al., 2010). In project practice, an adapted version of the PSM has proven to be useful. This variant uses three of the four original questions in the data collection phase (cheap, expensive, too expensive). This achieves greater validity of the data compared to the singular price question. In the data analysis, the adaptive approach concentrates on the answers to those price questions that target the maximum willingness to pay: Question 3 in the adapted version (price is expensive; purchase is still considered) and Question 3 (price is too expensive; no purchase because maximum willingness to pay is exceeded). Of highest relevance for price management is the maximum acceptable price threshold of each customer. In terms of the PSM method, one derives this key information from two of the three answers of the adaptive PSM approach per respondent. From the detailed information on Questions 2 and 3 in the adaptive version (3 and 4 in the original version), a price threshold can be identified per person that should not be exceeded. From this, an aggregated price response function (demand function) can be derived for a segment (Krämer, 2015; Krämer & Hercher, 2016, p. 46; Krämer et al., 2017; Roll et al., 2010). A profit-optimal price is to be determined from the sales curve, taking costs into account. The adapted version

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of the PSM technique should always be used in parallel with other methods as part of an integrated optimization approach. In the further course, the combination of different methods within an integrated approach will be shown. Additional pricepsychological interrelations and budget constraints are also mandatory to consider (see Chap. 13). The questions on price acceptance alone are not sufficient.

9.1.2.5 Conjoint Measurement The basis of professional price management is the knowledge of customer needs. Price should not be considered in isolation, but always in relation to the value drivers. In the real purchase situation, a customer never decides on the basis of financial aspects alone—he weighs up price and perceived benefits against each other. Conjoint measurement reproduces this trade-off (Simon & Fassnacht, 2009, p. 116; Homburg et al., 2006, p. 70; Klarmann et al., 2011). The decision-making process of buyers is mapped realistically (Mengen, 1993; Schweikl, 1985; Weisenfeld, 1989; Kucher & Simon, 1987). The specific characteristic of the method lies in the survey technique. It is possible to measure the willingness to pay without having to use direct price surveys. Survey participants are repeatedly presented with choice decisions. However, the survey does not refer directly to the price as well as to the appreciation of isolated service features. Rather, subjects are presented with alternative product-price profiles. These profiles correspond to combinations of different feature characteristics including different prices. Users’ preferences for the presented product-price alternatives are the focal point of the survey. The customer’s preferences for the different dimensions of the offer can be used to infer the impact of different prices on the company’s objectives. The preference data collected from users are transformed into price-dependent market share and sales volume data. The key steps in price impact measurement are: 1. 2. 3. 4. 5. 6. 7. 8.

Definition of features Determination of feature characteristics Design and programming of the interview Conducting the survey Analysis of the part worth utilities Calculation of the importance of the features Calculation of preference shares Forecast of the effects of pricing actions on sales volumes, revenue, and profit.

The results of conjoint measurement are influenced by the study design. The definition of the features and the feature characteristics is of central importance for the usefulness of a conjoint measurement study. In a conjoint survey, the attributes that are relevant to the customer must be included. If an important attribute is forgotten or if the feature characteristics are chosen incorrectly, there is a risk that the results will be distorted. The design should be determined in cooperation with the management in the form of a workshop. Management’s judgments should be validated through preparatory interviews with customers. Before conducting the

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interviews, a targeted selection of respondents is necessary. The survey must start with the persons in power of the purchase decision. Data collection is carried out by means of computer-assisted questioning. Different variants of conjoint measurement can be used for different applications. Software programs such as adaptive conjoint analysis (ACA) or choice-based conjoint (CBC) can be used for both data collection and analysis. In ACA, pairwise comparisons are constructed on the basis of introductory queries about the importance of features. CBC takes a different methodological approach and requires the respondent to make a clear commitment to an alternative. In the CBC variant, the respondent selects his preferred product-price profile from a portfolio of alternative concepts. Computer-aided variations of these procedures can also work with a high number of offer dimensions on the basis of a hybrid approach. Hybrid procedures combine the respective advantages of ACA and CBC. From the respondents’ answers, the importance of each feature characteristic for the overall preference is determined. These part worth utilities allow well-founded statements about the value a customer associates with product-price changes. This can be, for example, an improvement in features such as weight, range or battery charging time when purchasing an e-bike. The result is an estimate of the willingness to pay for a specific offer. This derivation is possible because the part worth utilities of all features and characteristics are directly comparable. They are measured uniformly on an interval scale during the interview—so they can be placed in relation to one another. On this basis, it is possible to determine, for example, Which loss of utility in the course of a price increase (decreasing part worth utility) is to be compensated by the corresponding added value of an improvement of the product on offer (increasing part worth utility)? In the course of this analysis, the importance of the feature characteristics within the design becomes obvious. Specifically, the question is: How strong must the product improvement be in order to compensate for a specific price increase? Performance improvement for five exemplary industries and product categories means, among other things: 1. 2. 3. 4. 5.

Reduction of the weight of the components of an e-bike. Increasing the storage space on a smartphone. Increasing the horsepower when buying a car. Extension of the seat pitch of an upgraded booking class in vacation air traffic. Reduction of the travel time on a route of Deutsche Bahn.

The particular advantage of the conjoint method is the quantification of these trade-off considerations of customers (Frohmann, 1994; Pastuch, 2018). Depending on the target segments, the utility functions usually vary. This means in terms of the example: all five features mentioned above are valued differently by different segments. As the differences in value estimates vary, so does the willingness to pay. The relative importance of the features in a conjoint study is directly derived from the part worth utilities of the individual feature characteristics (Theuerkauf, 1989). Part worth utilities and importance ratings serve as the data basis for the

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following core processes of price management (Simon & Fassnacht, 2016, p. 131; Homburg et al., 2006, p. 70): 1. Carrying out simulation calculations. 2. Estimation of price response functions (demand functions). 3. Derive strategic recommendations for product development and pricing. All three steps are described in detail later in the chapter. Excursus: Comparison of Methods (Conjoint Measurement Vs. Price Experiments) In the way it compiles the product-price alternatives, the conjoint method is very similar to the digital offer configuration on online portals. In both cases, the product price versions are derived automatically. In the conjoint interview, the variants presented online result directly from the customer’s answers. They are dynamically adjusted and correspond to the preferences of each individual user. The alternatives presented in the further course of the interview are increasingly reduced to price-performance variants that best match the priorities of the respondent. With each change in the design, the analyses are refined. The design of a conjoint study includes a selection of the current products or product-price variations that can be represented in the future. In contrast, digital offer configurators reflect the current product range and resulting price points. These can be list prices or customer-specific discounts. Configurators thus also refer to individual variants of a company’s product and price portfolio, which cannot be meaningfully depicted in a conjoint study. As a conclusion, it can be stated: conjoint measurement supports companies in all industries with regard to two fundamental aspects of price management. With regard to the “11 C of Pricing” (cf. Chap. 5), this means: (1) prices reflect the perceived value from the customer’s point of view (“customer”). (2) price points are optimally set taking into account the company’s objectives (“company Targets”). Conjoint analysis is very versatile. The method can be used for industrial and consumer goods as well as for services and digital products. It has proven equally effective for new products and established offerings (Simon & Fassnacht, 2016, 2019; Homburg et al., 2006; Mengen, 1993; Schweikl, 1985; Weisenfeld, 1989). Modern software programs such as EPIC Conjoint allow an implementation at short intervals—so it is possible to regularly check the extent to which customers’ willingness to pay and preferences for product features are developing. Weighing up the different product profiles is the focus of conjoint measurement. Survey participants pay much less attention to the fact that their willingness to pay is being queried. In addition to pricing, conjoint analysis also serves to optimize the products on offer. Comparative price evaluation is a major advantage of the conjoint method from a pricing

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psychology perspective. Customers find it difficult to evaluate prices according to absolute levels. In the real decision-making situation, prices are always compared with a specific benchmark—an anchor (Simon, 2015a, p. 90). This can be, for example, a price lately paid or a recommended retail price. Conjoint analysis allows these interrelations to be taken into account in a targeted manner. Regardless of these advantages, the conjoint interview should always be supplemented by direct questions. These refer to all the information that is required for product and price optimization beyond the benefit analysis: • • • • •

Customer budget Perception of the customer Further price psychological aspects (e.g., price threshold effect) Customer satisfaction Competitor perception.

9.1.3

Workshops

Figure 9.3 provides an overview of workshops for determining the optimal price.

9.1.3.1 Focus Group Interviews Focus groups are an excellent way to test newly developed concepts. The aim is to sound out the opportunities and barriers of innovative offerings in a structured manner with the involvement of key customers. Alternatively, potential users can also be surveyed. The opinions, attitudes and behaviors of the individual participants and the entire group are recorded in a standardized manner. The specific characteristics of focus groups are, in keywords:

Methods for price optimization

Observation

Survey

Workshop

Price experiments

Direct price query

Focus group interviews

Market data analysis

Open line pricing

Expert estimate

Social listening

Gabor Granger method

A/B testing

Price-sensitivity-meter

Online auctions

Conjoint measurement

Fig. 9.3 Methods to determine the price optimum (3) (Source: Own representation)

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– Open questioning situation: approximation of typical considerations and decision-making processes. – Moderation by an experienced coordinator. – Primarily qualitative discussion. – No claim to representativeness (small sample size, quota sample). – Supplement with quantitative survey components (e.g., adaptive PSM as part of a price discussion). – Very helpful input for preparing quantitative surveys (e.g., testing the survey design for a conjoint study). Structured group discussions are particularly helpful for gathering detailed information on budgets, maximum prices, and preferences for price architectures. Creative ideas for innovative price models and new discount structures can also be developed within this framework. Psychological aspects behind rational argumentation, emotional backgrounds to purchasing decisions, and hidden assumptions in the context of trade-offs—all these criteria relevant to pricing can be analyzed with the help of a group discussion. In the case of B2C studies, a maximum number of 20 participants is recommended. In the B2B sector, a smaller number of people is already sufficient. Focus group interviews reflect the philosophy of price management represented in the context of this book: it is about the profit-optimized satisfaction of customer needs, in the sense of a win-win constellation. Brainstorming sessions are excellently suited as a supplement to the quantitative methods described. Quantitative surveys can be validated with the help of parallel group discussions. Group discussions are particularly useful in exploring new price levels and the possible exceedance of certain price thresholds. Explorative focus group discussions can explicitly address aspects of price psychology.

9.1.3.2 Expert Estimation Expert estimation (price-volume assessment) is a method for deriving profit-optimal prices without direct user involvement (Simon & Fassnacht, 2009, p. 110, 2016, p. 123; Roll, 2018). The method is based on subjective estimates by in-house marketing and sales experts. It is concerned with sales volume potentials or risks at different prices. Typical questions for a new product to be launched are: 1. Where do we see a realistic upper or lower limit for the price of our product? 2. How high is the share of the total market that can be captured for our company? How does this vary depending on the price? 3. What sales volumes do we expect in the first year after product launch if the price is set at the upper limit, at the lower limit, and in the middle between the two? 4. How big is the increase in market share that we can achieve through price reductions? 5. What actions should we expect from competitors? An aggregate price-response function is derived from the experts’ individual estimates of sales volumes at various price points (Fig. 9.4).

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Company perspective

Sales volume

Sales volume

Turnover

Profit low

high

Price

Company experts estimate volume effects

low

high

Price

Tool visualizes price effects on KPI (sales, profit)

Fig. 9.4 Expert estimate to determine the price optimum (Source: Own representation)

In a moderated discussion, the implicit assumptions behind the individual sales volume estimates can be justified. The forecasts are discussed in the expert group. If the participants’ estimates change during the group discussion, the sales volume estimates must be corrected. The survey is tailored to the specific situation. Content details depend on the complexity of the business model, the life cycle phase of the product and the competitive position. In a multi-stage procedure, a market assessment is approached step by step. In the final step, the individual assessments are consolidated into a joint sales volume forecast. This serves as the basis for the target price positioning of the product. The experts that are familiar with the market should be composed from various organizational units. In addition to product and marketing managers, it is essential to include employees from field sales and customer service. The direct integration of experience from customer contacts is one of the method’s prerequisites for success. Up to ten experts should be interviewed. The more customer and market expertise is included in a structured way, the higher the quality of the results will be. The workshop is moderated by a neutral person. It makes sense to link the expert assessment with the method of target prioritization (see Chap. 3). Target setting (step 1) is followed by price optimization (step 2). Expert judgement is a very pragmatic methodology for determining a price optimum (Roll, 2018). Its application is recommended for both new and established products. In an unexpected situation—e.g., an imminent entry of a competitor— expert judgment is particularly suitable. This is because the method can be carried out quickly and without a great deal of preparation. The costs for implementing an expert estimate are very low (Simon & Fassnacht, 2016, p. 123; Roll, 2018). Particularly in the B2B environment, the price-volume assessment has proven its worth in terms of its forecasting ability. The expert workshop is generally recommended as a supplement to other methods, in particular to the direct survey of users based on the adapted PSM method.

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Excursus: Impact of Artificial Intelligence on Price Optimization Methods

The enormous technological potential of artificial intelligence (AI) is leading to a significant expansion of the possible applications of the methods outlined. Selected developments can be described as follows: 1. Focus group discussions and expert interviews can be significantly expanded. Participants no longer need to be physically present at workshops held as part of “artificial intelligence platforms”. AI platforms enable product and price concepts to be tested with several thousand potential customers, regardless of their location. 2. For simple surveys, “chatbots” are very well suited in the context of AI technology. 3. Social media monitoring will also be able to be used much more efficiently. Artificial intelligence (AI) has gained widespread acceptance in market research. The key advantages of using AI are the significantly higher speed and effectiveness of surveys. AI enables analyses that could not previously be performed by traditional market research (Maicher, 2017). Companies can thus react much more promptly to changes in the general market conditions (customer requirements, competitive actions, etc.). In this context, reference should be made to the internet platform Unanimous AI. The underlying software is based on the following objectives and premises (Bernau, 2018): 1. Individual forecasts by experts can be discussed in real time and successively further developed and refined by third parties. 2. Perceptions and evaluations of products, prices or market situations are systematically recorded and dynamically adjusted. 3. As many different experiences and approaches as possible are to be included in a structured way. Different perspectives are explicitly desired. The temporal evolution of the forecasts is transparent at all times. 4. At the end of the digitized group exchange, there is a joint solution. This is based on the best possible integration of individual expert opinions (Bernau, 2018). A particular advantage of the method is the efficiency and rationality of the group assessment: 1. The system serves as a kind of amplifier for the knowledge of individuals. 2. The aggregated community prognosis is more valid and reliable than singular forecasts. 3. Individual knowledge gaps are compensated for; personal preferences are eliminated.

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The software is an excellent complement to the classic expert estimation. It reflects a key premise of price management: professional pricing is based on a cross-functional management of the relevant stakeholders—finance, sales, marketing, and product management. As an overall conclusion, it can be stated: sophisticated insights into willingness to pay require a mix of methods. The choice of methods for price optimization must be determined on a case-by-case basis. The decision is based on the following parameters: 1. Status of the offering (a: product idea before development? b: already developed product? c: product already launched on the market?) 2. Budget for a price optimization 3. Time available 4. Impact on transparency in the market 5. Structure of customers (number, regional distribution, distribution of sales volumes and profits) 6. Revenue model (which company offering should be priced?). Possible revenue sources are: hardware/product, service, software, content, advertising, data, digital services). In more than 400 pricing projects of the author since 1996, survey-based methods (conjoint measurement as well as direct price queries via an adapted price-sensitivity-meter) have proven their worth. I recommend price elasticity estimates based on an internal workshop as a supplement to customer surveys. For the company’s most important focus products, a combination of the adapted price-sensitivity-meter method with conjoint measurement and expert estimation is recommended. Expert workshops can be significantly enhanced by the potentials of artificial intelligence.

9.2

Calculation of the Profit-Optimal Price

Professionalism in pricing manifests itself in the use of decision support systems for price optimization. The use of modern methods and tools requires an in-depth understanding of the structural relationships and profit effects of pricing. The fundamentals of price optimization are highlighted in the following section. The core argumentation has already been prepared with the chapter on the “11 determinants of pricing” (see Chap. 5): in fine-tuning the price level for a product, competition is an initial indication of the upper price limit. Internal marginal costs determine the lower limit with a view to profitability targets. The decisive factor is the perceived customer value of the company’s own offering compared with the competition. Value to customer is the decisive determinant in this complex decision problem (Frohmann, 2014). In the case of performance advantages, it is generally possible to impose a price premium over the competition. The exact determination of a price point within this corridor depends on the strategic product positioning and the

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Price elasticity = - 1 Sales volume

Sales volume

Turnover

Profit Profit maximum price

Price

Fig. 9.5 Calculation of the profit-optimal price (based on Simon & Fassnacht, 2019, page 189) (Source: Own representation)

associated financial targets. Against the background of maximum value extraction, the goal must be to push prices as far as possible to the upper limit. However, the market power and price transparency of customers are increasing in the course of digitization. An overestimation of willingness to pay or too much price discipline will be punished. A miscalculation threatens to result in a loss of market share. It is therefore a question of managing the classic conflict of objectives between securing margins and market share. The optimum price can be easily determined graphically in a coordinate system (Fig. 9.5). All realistic prices from the company’s point of view are plotted on the horizontal axis (abscissa). The effects of different price points on sales volumes, revenue, and profit are derived mathematically and visualized on the ordinate (vertical axis). With the aid of the function curves, the aim is to find the price at which the distance between the sales and cost curves is greatest. Any deviation from the optimum price leads to a decrease in profit. If the price is lowered, costs increase more than sales. If the price is varied upward, sales decrease more than costs (Simon, 1992; Simon & Fassnacht, 2009, p. 210, 2019; Roll et al., 2012). At the core of optimization is the opposite development of unit contribution margins and sales volumes. Compared to the profit optimum, a price increase leads to a higher unit contribution margin. However, the percentage increase in margin is lower than the decrease in sales volumes—on balance, profit consequently decreases. Even with a price reduction, the net effect on profit is negative. Depending on customer preferences and willingness to pay, completely different profit curves result. For each industry-specific situation, for each product and customer segment, these particularities have to be measured. However, regardless

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of product- and company-specific curves, general core statements can be made. The following insights are based on the teachings of Hermann Simon (1992, 2015a, 2015b) and Simon and Fassnacht (2016, 2019)). They have been confirmed in my own projects in numerous industries over almost three decades (Frohmann, 2007, 2008, 2009, 2014). 1. In any case, there exists an optimal price that maximizes profit. 2. The price optimum lies—from a graphical point of view—between the variable unit costs and the maximum price. The realizable contribution margin moves in principle between the customer benefit (upper price limit) and the variable costs (short-term lower price limit). 3. Only if a company regularly sets the optimum price point for key products can it secure its profits in the long term. 4. With a view to the classic trade-off between margin and quantity, it is a matter of finding the right compromise. Visually speaking, the “right middle” has to be found! 5. Minor deviations from the optimum price are less serious. The profit curve is often relatively flat in the area of the optimum. A slight deviation from the optimum price does not result in a serious loss of profit. However, profit decreases the further the chosen price point moves away from the optimum. 6. Only half of a change in variable unit costs or customer-specific maximum prices should be passed on in the price. It is not optimal to pass on cost changes fully in the price! The complete skimming of customer benefits should also be avoided. This is in line with the philosophy of value-based pricing (VBP). VBP aims at higher values for the buyer with simultaneous economic advantages for the supplier. Instead of skimming the customer’s entire willingness to pay for a product or service improvement, only a portion of the added value is monetized. A rule of thumb provides for capturing only 50% of the additional willingness to pay (Simon, 2015a, 2015b). This corresponds to a sensible combination of the two core objectives of “long-term customer loyalty” and “short-term maximization of profits”. 7. The optimal price can also be represented as an elasticity-dependent markup on marginal costs. As already explained in the introductory chapter, knowledge of the price elasticity of customers or segments is at the heart of price optimization. The higher the elasticity, the lower the markup on marginal costs should be. In other words, a company must earn high prices and corresponding margins through the value to customer. If there is no corresponding appreciation of the customer, the company will literally have to get by with lower markups. 8. The profit-optimal price is higher than the revenue-maximal price. The higher the marginal costs of the product, the greater this gap. The profit risk is all the more pronounced if a company concentrates its pricing exclusively on sales and their increase. This applies to business models of manufacturing companies in mechanical engineering, sub-segments of the automotive industry, project business, and numerous other sectors.

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9. Fixed costs do not influence the optimal price! If you set the price depending on the fixed costs (or the full costs), you make a logical mistake. 10. In the case of very low marginal costs, the usual trade-off between sales and profit is eliminated. When marginal costs are zero, sales- and profit-maximizing prices coincide. For supply categories with very low variable costs, revenue orientation can be justified. Consequently, revenue maximization is a sensible pricing target for digital company offers (software, digital services, online content). 11. The decision must always be made on a situation- and industry-specific basis with a view to the actual data. However, it is generally recommended not to push the profit-optimized point too far. Optimal prices should rather be slightly undercut. In growth markets, in particular, an optimal trade-off of the two goals of revenue and profit is recommended. In the case of digital offerings, market share is another important target figure. Case Study: Automatic Price Optimization Via a Repricing Tool A trend toward event-driven pricing is evolving in some industries. “Repricing” stands for the dynamic analysis and adjustment of digital prices. Repricing takes place in online stores, marketplaces, and price comparison offers. In online retailing, the following situation is emerging for numerous product categories: 1. A large number of competitors with largely identical offerings are competing for the customers’ attention. 2. Customers have almost complete transparency; they can efficiently compare the prices of competing companies. 3. On online platforms (such as Amazon or Idealo), buyers are guided specifically to lower-priced providers with the help of specific applications. Amazon occupies a prominent position among the portals. In the course of their product search on the Internet, many customers head for the world’s most important online retailer. 4. Against this backdrop, competition arises for the lowest price in a competitive comparison. The aim of the providers is to achieve a prominent position on the portal. In the case of Amazon, this means the competition for a position in the “BuyBox” (Anonymous, 2018c). More and more companies are relying on automatic price adjustments to optimize sales. The use of the digitized standard process leads to a significant efficiency advantage compared to manual price variations. This can be described using the following scenario. The premises for the case study are: (continued)

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(a) Two competitors compete in a price range of EUR 17.50 to EUR 24. (b) Company A uses a tool for automatic price adjustment as part of its digitization strategy. (c) Company B changes prices manually without a standard process. Some of B’s variations occur several times a day. The current price of supplier B is EUR 22. (d) Company A’s “repricing” is based on an optimization algorithm. This allows automatic price adjustments within a defined range. Let us assume a lower limit of EUR 18.95 and an upper limit of EUR 23.95. (e) Company B changes its price to EUR 21.50 as part of a daily routine check. (f) The repricing tool detects B’s change as part of an automated market monitoring and immediately adjusts company A’s price to EUR 21.45. This ensures that supplier A continues to be positioned in the “BuyBox”. A’s product also remains prominently positioned due to the automatism. In this scenario, companies with a higher price position have significantly reduced chances of selling their product. The disadvantage of the repricing process is a very strong focus on price. The relative positioning of competitors in the context of digital recommendation automation is reduced to a single datum. Benefit arguments recede into the background. Worse still, undercutting the competition is institutionalized and automated. As a result, many companies are undermining the brand value of their products. Profit margins are shrinking. Customers are becoming increasingly fixated on price. The race for the most favorable positioning described above explains why companies sometimes consciously decide against selling their products via platforms such as Amazon (Anonymous, 2018c). Some manufacturers of quality products refuse to participate in digital trading platforms. As part of a selective distribution system, they sell their products via specialist retailers, cooperation partners, and their own platform. This philosophy—moving away from a pure price comparison to a value argument—underlies Chap. 11 (implementation). The following subsection describes an approach of consistent orientation to customer benefits.

9.3

Simulation Analyses for Product and Price Optimization

The development of a new product or a portfolio of offerings is associated with enormous investments. In the content sector, companies sometimes invest doubledigit billions of USD for content and licenses (Harengel, 2017; Postinett, 2018). In

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the case of the video streaming provider Netflix in 2020, the following applied: with a turnover of USD 20 billion, they invested USD 17 billion in in-house productions (Rottwilm & Lange, 2022). At the same time, increasingly rapid innovation waves can be observed in the most significant industries. Technological changes and increasing digitization are leading to an influx of new and improved products. Market conditions are changing at ever shorter intervals. Here, too, video streaming offers a very succinct example. Just 5 years ago, the CEO of Netflix loosely translated stated as follows: “Our most important competitor is the sleeping quota of our customers” (Breustedt & Skolow, 2022). In the years that followed, Netflix faced an increasingly intense competition from large digital corporations (Apple, Amazon), entertainment conglomerates (Disney), and specialists (Joyn) at very short intervals. Another example of the increasing dynamics is the smartphone industry. The market leaders such as Apple and Samsung are introducing new product lines at ever shorter intervals (Fröhlich, 2018b). Not only for smartphones—strategic product and price planning is also becoming increasingly important in mechanical engineering or in the automotive industry, for software and memory chips. Professional processes and a high level of organizational discipline are critical to success in order to exploit profit potential. The technological possibilities are too diverse, the database on competitors, costs, and customer preferences too complex. It is too easy to get bogged down in various development projects. With advancing digitization, it is essential for the survival of a company to get a grip on this complexity risk (Giersberg, 2018). Complexity management is gaining importance as a core competence, especially in the fields of product development and pricing. The dilemma here: 1. Innovation is the decisive key to avoiding price pressure. The pricing power of companies correlates very strongly with the degree of differentiation of their offerings (cf. competitive strength index as indicator of “value generation” in the context of the business model map in Chap. 3). 2. The development and market launch of an innovative product represents a high entrepreneurial risk. Development expertise and customer focus alone are not enough. Especially in digitalized industries, financial resources can very quickly become a limiting factor. At the beginning of 2018, the automotive supplier Bosch announced its withdrawal from battery research. In order to compete with the leading Asian manufacturers and their scaling model, Bosch would have had to invest at least EUR 20 billion (Gomoll, 2018). 3. Three out of four new product launches fail. According to a meta-study by SKP, over 70% of all development projects fail to achieve the profitability targets set by the management (Tacke & Vidal, 2014; Ramanujam, 2018). Typical failures within the innovation process include: (a) Internal aspects dominate the much-needed focus on the end user. (b) Pricing managers are not involved at all—or not involved early on. (c) Customer benefits and willingness to pay are insufficiently integrated into product development and pricing.

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The risks are serious. Only three of numerous consequences are mentioned here: • “Overengineering”: The product contains features that are not desired by the customer. • “Overestimation”: Good performance is not enough, because competitors are perceived as even better by the customer. • “Overpricing”: The price demanded exceeds what the user is willing to pay. The latter risk is illustrated by the following quote from Apple’s development department: “We don’t start with the price when we develop a product. First of all, we always ask ourselves what technologies we can incorporate. That is reflected in the higher price” (Fröhlich, 2018a). Elon Musk puts it differently: The challenge for the CEO of Tesla is to “also make the vehicle affordable” (Heiny & Rest, 2022). The following lessons can be learned from these findings with regard to the enormous potential of digitization: 1. The top priority must be to identify changes in needs and develop new business opportunities (“exploration”). 2. At the same time, existing offerings must be optimized in order to exploit and control existing market potentials (“exploitation”). 3. Speed and agility are becoming increasingly important for innovation management in the digital age. In the worst case, a company develops a product over a long period of time in the course of “exploration” that no longer fits the needs of customers at the time of market launch. 4. Basic products that can be developed quickly serve to reduce complexity. These are initially reduced to the value drivers that are most important for the customer. The advantage of this is that the most urgent customer needs are covered. User feedback can be obtained at a very early stage. The development effort is minimized at the beginning and gradually expanded as the customer’s needs become clearer. The concept of the “minimum viable product” (MVP) is another element in the digital tool kit of product and pricing methods. It lends itself as a parallel method to more complex approaches such as conjoint measurement (Welbers, 2018). 5. In the course of “exploiting” existing products, it is important to identify changes at an early stage. Nokia was considered the dominant market leader among cell phone manufacturers until 2007. The shift in value creation in mobile communications was not seen for a long time. The increasing importance of software was recognized much too late. The potential of smartphones was also recognized too late. In other words: Nokia put too many resources into developing better devices for an increasingly obsolete technology. Investments in software solutions and smartphones were neglected for a long time. Siemens and Motorola have undergone similar developments in the cell phone sector. The core problem in Siemens’ case was an unbalanced product portfolio. By focusing on simple standard devices for the mass market, it risked to be trapped in a

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permanent price war. Siemens’ average selling prices fell in a very short time— from USD 230 in 2000 to USD 105 (2004). Especially in the case of fundamental innovations, it is essential to involve potential users in the development process as early as possible. On the way to the end product, agile adjustments to changes in the competitive environment or macroeconomic conditions are critical to success. However, the necessary orientation to the user is not sufficient as long as it is only passive or reactive. Customer needs can be influenced. They often change precisely when innovations are introduced to the market. Voice-controlled assistants are an example of an offering that users did not anticipate or explicitly demand. At the technology level, the same statement applies to the mobile Internet. A one-sided technology orientation must be strictly avoided. Internal orientation is the main cause of an oversupply of products and services for which there is no willingness to pay. In the age of digitization, the development process of many offerings goes beyond the market launch. In the course of market penetration, there is a continuous exchange of data between the manufacturer and the customers. Digitized products and services are continuously being refined. One of the pioneers is the e-mobility innovator Tesla. In the basic business, Tesla is one of the few car manufacturers worldwide that continuously improves its vehicles. Continuous software updates increase the value to customer over the course of the product life cycle.

Against the background of the developments outlined, the precise determination of the new product price is of great importance. Even minor misjudgments of customer and competitor reactions can result in major financial disadvantages. In the worst case, the innovation could fail, as in the case of Google Glass (data glasses) or Amazon (Firephones and Fire-Tablets). Suboptimal prices are often difficult to correct after market entry. This is especially true if you started too low. A systematic process is needed to determine the optimal price for innovations. The starting point is a comprehensive analysis of external and internal information. All steps—from analysis and target definition to market implementation—can be supported by modern data analysis and market simulation methods. Decision support methods bundle these sub-processes into a comprehensive model (Hofer & Ebel, 2002; Simon & Fassnacht, 2016, 2019; Roll et al., 2012). This also incorporates price–psychological relationships in a structured form (cf. Chap. 13). Digital decision support models provide answers to the most important detailed questions in the context of product price development: 1. 2. 3. 4.

How important are individual features from the customer’s point of view? Which changes can improve the service the most from a user’s point of view? What are the relevant value drivers for the customer? How high is the customers’ willingness to pay for performance improvements?

9.3 Simulation Analyses for Product and Price Optimization

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Fig. 9.6 Decision support model (based on Simon & Fassnacht, 2019, page 183) (Source: Own representation)

5. How will our market share change if we vary the price or the performance of a product feature? 6. Who is the most important competitor for us from the customer’s point of view? 7. What impact do competitors’ price variations or product changes have on our market share? 8. How strong are substitution effects in our company? Is there a risk of cannibalization of established brands by the new offering? 9. What is the value of our brand in price units? 10. Which product variant is to be launched on the market? 11. What is the profit-optimal price? Which price positioning corresponds to the strategic and financial goals? Decision support models simulate the purchasing behavior of potential customers under conditions that are as close to reality as possible. All relevant factors influencing pricing are taken into account. In order to be able to draw conclusions about potential market shares and profits from a customer preference perspective, the relevant competitive environment must be mapped comprehensively. Competing products also include brands of the own company portfolio that are positioned in a comparable price range. They can substitute the product to be optimized. With the help of a decision support model, as shown in Fig. 9.6, the optimal price positioning for a new product can be determined (Hofer & Ebel, 2002; Simon & Fassnacht, 2016, p. 204; Lauszus & Sebastian, 1997). Optimal list prices are derived

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on the basis of simulation calculations. The following steps determine the decision support process: 1. Data consolidation The choice of the optimal offer variant and the introductory price requires a great deal of market information as well as internal company data. In essence, this involves the determinants of pricing described in Chap. 5. Essential information for the product and price development are among others: – Product specification. – Company goals. – Variable costs for different product versions and service variations. – Positioning specifications for the product. – Prices and offerings (products, services, etc.) of all competitors. – Customers’ willingness to pay/purchase. – Customer preferences for all relevant value drivers. – Willingness to pay for performance improvements. – Capacity situation, etc. 2. Data analysis The decision support model condenses all external and internal information. A special role among the necessary internal variables take the framework conditions and the goals of the market launch. The target prioritization directly influences the calculation of the optimal price. The “optimal” price does not necessarily have to be the one that leads to maximum profit or contribution margin. In the following, however, it is assumed that the focus is on profit maximization. 3. Detailed simulation of different product-price variants With the help of the simulation model, it is possible to determine which changes in value to customer result from the product and service variations. The resulting willingness to pay can be derived simultaneously. Based on the willingness to pay and purchase intentions of potential customers, sales volumes can be forecasted as a function of price. To determine the price response function, preference shares are estimated first of all. From these predicted market shares, sales volumes can be inferred. All relevant competitors are included. The basis for the calculation is the attraction model. According to this model, the market shares of products can be predicted by setting their utility scores (part worth utilities) in relation to each other. The combination of prices and quantities results in the sales implication of various product constellations for the company (Frohmann, 1994). 4. Determination of the target price Despite the necessary market orientation, the cost aspect must not be disregarded. Customer benefits and costs must be weighed against each other in order to achieve the optimum. The profit effect of different scenarios (of company offerings and prices) results from the integration of cost data into the analyses. In a simulation of incremental performance changes, the following premise applies: product improvements only make economic sense if the incremental costs are more than compensated by a price increase. This is because, in some product categories, costs increase disproportionately as the technological level rises. In contrast, the additional willingness to pay of customers can take different

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courses of the function—very often, however, it is degressive (Lauszus & Sebastian, 1997). An optimal performance level results from the opposing development of cost and benefit increases. Product development focuses on features that provide real benefits from the customer’s point of view and can be implemented profitably. The simulation reflects the basic mission of a company: to serve customer needs profitably and at the same time to create competitive advantages over competitors. The optimal price lies at the apex of the profit function (cf. Fig. 9.5). The course of the profit curve around the optimum price is particularly important for decision-making. A relatively flat section of the curve means that a certain deviation upwards or downwards is not associated with a particularly high risk. Deviations from the financial maximum are not uncommon in practice. For reasons of the overriding portfolio strategy, product positioning, or competitive motives, it may be necessary to deviate from the purely mathematical optimum point. Price–psychological aspects must also be taken into account. 5. Market entry decision and selection of the product variant to be launched on the market The procedure outlined can be described by the term “target pricing”. Target pricing for a product to be developed is based on the benefits and the customer’s willingness to pay (Simon & Dahlhoff, 1998). In this outside-in approach, target price positioning is determined by incorporating customer requirements. The target price—as well as the profit margin specified by management—results in the costs allowed by the market. These target costs form the framework data for product development (Simon et al., 1993; Hermenau, 2009). It must be possible to manufacture a product at the target costs in order to be competitive and at the same time generate the target profits. The cost-plus calculation that still dominates in some companies is being replaced by a price-minus calculation. If a product cannot be manufactured at the target cost, the design of the offering must be changed. In extreme cases, production must be discontinued. 6. Determination of the price taking into account threshold effects and anchor aspects. Price thresholds are price points which, if exceeded, can lead to significant sales losses for reasons of customer psychology (see Chap. 13).

9.4

Method Innovation: Value-Price Optimization

9.4.1

Value-Price Optimization: Philosophy and Fundamentals

I have developed the integrated pricing method outlined below in the course of a wide variety of practical projects in many industries (Frohmann, 2008, 2009, 2014). It is based on concepts already described, such as the competitive advantage matrix and the methods of price optimization. The essential objective is:

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Superior priceperformance ratio

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Fig. 9.7 Value-price portfolio (Source: Own representation)

• Step 1: Systematic evaluation of the value-price positioning: from the customer’s point of view; in comparison with competitors. • Step 2: Derivation of the optimal product-price positioning—incorporating customer requirements, competitor offerings, and willingness to pay. To this end, one combines one’s own position in customer perception with the logic of price optimization. The value-price optimization (value driver analysis) described below combines the tasks of price strategy (positioning) and optimization already portrayed in a logical and stringent approach (see Fig. 9.7). A variety of methods of strategic pricing (competitive advantage matrix, target prioritization, etc.) and price optimization (price-sensitivity-meter, expert estimation, conjoint measurement, etc.) are combined depending on the initial situation of the company. The combination of methods for price optimization can be determined on a caseby-case basis. The decision is primarily based on the status of the company offering (product idea before development? developed product before market launch? product already launched on the market?). The portfolio of possible methods is the largest in the case of an already launched product. Value drivers are at the heart of value-price optimization. Value drivers are all purchasing factors that are relevant for the customer. They influence the decision in favor of a provider and the price paid. These purchase criteria go far beyond product performance. They also result from accompanying services (such as customer service, logistics, etc.), the brand, packaging, or intangible values (Macho, 2018). The starting point for discussing value drivers with customers is their need or the

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problem to be solved. The following specifics of value criteria are relevant for product and price optimization. Value drivers are: 1. Subjective (the customer’s perception is decisive). 2. Relative (relevance always results from a comparison with competitors). 3. Segment-specific (the weighting of value drivers differs considerably across different price dimensions, e.g., regions, customer segments, or sales channels). 4. Dynamic (the relevance can vary greatly over time). The combination of individual value drivers, their relative importance, and the feature characteristics across different competitors determine the company’s perceived performance in a competitive comparison. The target positioning of the company’s own products is derived from the customers’ perception of the value drivers. From a methodological perspective, the four specific characteristics of value drivers result in the following challenges for companies: Value drivers must 1. Be discussed together with customers. 2. Be examined for important current competitors and potential new competitors. 3. Be assessed in a differentiated manner in terms of their consequences for pricing segments (target groups, regions, sales channels, etc.). 4. Constantly be questioned. The phases of a value-price optimization are described in bullet points below as a project outline.

9.4.2

Methodological Steps of Value-Price Optimization at a Glance

The starting point of the value driver analysis is the perception of purchase criteria from the customer’s perspective. Three constellations can be distinguished: (a) Product ideas before the research and development phase. (b) Company offerings that have already been developed and are about to be launched. (c) Products introduced in the market. The individual phases of a value-price optimization (value-driver analysis) are as follows: Step 1: Survey design. Determination of: – Customers (Which customers are to be surveyed? Which functional units will be included in the case of business customers?) – Product (To which offering does the survey refer?) – Competition (Which competitors are included in the benchmarking?)

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Fig. 9.8 Competitive advantage matrix (example): product, supplier A (illustrative) (Source: Own representation)

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– Other price dimensions (Which regions and distribution channels do we want to include? What time horizon do we use as a basis?) Step 2: Data collection. Evaluation by customers in individual interviews. In B2B industries, multi-person interviews in the context of a buying center are particularly efficient. The customer assessment relates to two dimensions: – Importance of all value drivers: Interval scale; 1 (no relevance) to 9 (very important). The importance of price is also recorded on a scale from 1 to 9. The result of the price-related importance query serves as a first indication of the customer’s price sensitivity. – Perceived performance: Interval scale; 1 (very poor) to 9 (very good) for own company and all relevant competitors. Parallel to the customer interviews, experts from sales, application technology, customer service, etc. should also be asked to assess the value drivers from the customer’s perspective. An analysis of whether the management’s selfassessment (internal perspective) matches the customers’ perception (external perspective) is very helpful. When assessing performance, consistency must be ensured. Step 3: Creation of the competitive advantage matrix. Goal: visualization of the position of the company offering in a competitive comparison (cf. Figure 9.8). The first analysis step is only an intermediate stage in the integrated method of value-oriented pricing. The trade-off between price and performance is of decisive importance. This is made transparent methodically. For this purpose, the price must be conceptually separated from the value drivers. The separation is done both mathematically and graphically. The main goal is the derivation of a price dimension and a value dimension.

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Competitive advantage matrix

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Fig. 9.9 Derivation of a value-price portfolio from the competitive advantage matrix (Source: Own representation)

Step 4: Derivation of the value-price portfolio (value-price map): The perceived price-performance ratio compared to all major competitors can be derived with the help of the value-price portfolio (Frohmann, 2008, 2014). The goal is to condense the perceived relative value-price positioning. The total value of all competitors is shown on the vertical axis. Mathematically, the total value for each firm is the sum of the importance scores multiplied by the firm’s perceived performance across all value drivers. The perceived price positioning of all competitors is visualized on the horizontal axis of the value-price portfolio (Fig. 9.9). The particular advantage of the portfolio is the structured presentation of the two crucial dimensions of a business model in a perception map: – “Value generation” (what value to customer do we create?) – “Value extraction” (how do we monetize the value generated through price?) The diagonal range of the value-price portfolio corresponds to a consistent positioning. Consumers perceive the relationship between the value offered and the price demanded as balanced. Practical Tip The measurement of the price dimension (horizontal axis) differs fundamentally depending on the status of the company offering: 1. Product idea before R&D: In this constellation, willingness to pay data is visualized (methods: conjoint measurement, PSM, Gabor Granger, A/B testing). It is particularly useful to supplement this with expert estimates. (continued)

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2. Product before market launch: Queried willingness to pay is also visualized for products that have already been developed. 3. Products launched on the market: In this constellation, objective list prices can also be used for positioning. Expert judgment is particularly recommended as a method here, but also the econometric analysis of market data. Step 5: Detailed analysis of the value-price positions of all competitors. The core of strategic pricing considerations is the simultaneous consideration of price and the perceived performance of one’s own company offerings relative to the competition. It is about the positioning in terms of relative price and relative performance (Simon, 1992). The key assumptions are: – The price should be in a certain relation to the benefit. – In every product category, there is an area of intense competition in terms of price and value to customer. Here, significant price or product changes by one competitor lead to effects on all other competitors. I refer to this definable value-price framework as the competitive radius. – The positioning of the company within the competitive radius determines its exploitation of the market potential. In many of my projects, a high correlation between the two target variables has been proven, especially in B2B industries. Whenever possible, the relative price-performance perception and the relative market share should be considered in the overall context. The origins of the differences in value to customer between individual companies must be analyzed in detail. Relevant questions in this context include: – Why is our position better or worse? – How can we most effectively improve our positioning from the customer’s point of view? – Is there a potential—not least in terms of costs—to reduce services in a meaningful way? – What are the implications from all these findings for our target price positioning? It is imperative that this analysis is carried out on a segment-specific basis. Regional, customer group-specific, or channel-related details must be assessed. The willingness to pay off end customers and sales agents is influenced by different value drivers. Where requirements and willingness to pay differ significantly, market penetration must also be differentiated. The relative price position and the relative performance in relation to the competition must be optimized. A long-term, dynamic approach is crucial for market success. For company offerings not yet launched on the market—or for product ideas before the development phase—no price is available yet. In these cases, the starting point for portfolio positioning must be determined on the price axis. Competitors must be positioned in terms of price on the horizontal dimension. The core of the integrated method is the case-specific integration of the results of the price optimization methods into the value driver analysis now following. In the sixth

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Fig. 9.10 Optimization of the value-price positioning; integration of methodologies (Source: Own representation)

and seventh step, the portfolio’s starting point on the price axis (abscissa) is determined. Step 6: Integration of market research data on willingness to pay (adapted PSM method). First, the optimal price is calculated on the basis of the adapted PSM method. Step 7: Inclusion of the optimum price on the basis of an expert estimation: the aim is to cross-validate the results of the customer survey by means of an internal expert workshop. The starting price—as the starting point for further analysis steps— results from an overall consolidation within the framework of the adapted PSM method and the expert estimation. Step 8: Optimization of the value-price portfolio: In this phase, scenarios for portfolio optimization are derived (Fig. 9.10). It is about possible adjustments of the value-price position. Essential information for the optimization of the value-price portfolio are: • The additional customer benefit for performance improvements (e.g., a reduction in delivery time from 3 weeks to 2 weeks). • The internal costs for possible variations of the value drivers. • Estimates of quantity effects of price changes (elasticity). In the case of an unfavorable positioning, two main options exist: (a) Lower the price of the product with constant performance. (b) Improve the value positioning with no change in price. To determine the exact extent of the adjustment, the trade-off between performance and price from the customer’s point of view is relevant. This

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requires a focus on the value drivers that are particularly significant for the customer. The maxim is: differentiation from the competition should be based on the value drivers that are of paramount importance to the customer. When determining the target positioning, it is imperative to take into account the overriding input requirements of the strategy (product; product line; business unit) and the business model. The positioning does not necessarily have to focus on profit as a target. Revenue maximization and the achievement of a minimum sales volume or a target market share are further possible guidelines for determining the price optimum. Particularly in digitized industries, the price-performance perceptions of products launched on the market must be reviewed regularly. In certain constellations, companies choose a target positioning that combines a relatively low price with high relative performance. When entering digitized markets or in the course of expanding existing market shares, a particularly favorable price-performance ratio can make sense strategically. In the literature, it is sometimes assumed that price and quality should necessarily be in balance with each other. This is irrelevant for corporate practice! The target position results from the company targets, the competitive strategy, and the brand strategy.

Method Tip: Analysis of Value Drivers In practice, an interval scale (see Step 2) has proved to be a good way of revealing the significance of individual value drivers. Alternatives to the rating scale are the “trade-off method” and the “maxdiff method”. Maximum difference scaling is based on an indirect determination of the importance on the basis of trade-off considerations. In the course of the trade-off method, potential customers are shown two offers that differ in only one feature. Respondents must indicate whether there is a price difference between the two variants. If there is a perceived price difference, they are asked which product they think is more expensive. The product feature that the majority of respondents perceive as expensive is a value driver. A case study was already integrated into the first German edition of the book (2018) to illustrate the individual steps. The background was the planned launch of Apple’s iPhone X at the end of 2017. The focus of the case study was the description of methods; there was no claim to representativeness. The result of the case study was clearly supported by the opinions of experts interviewed in parallel and by extensive analyses of user statements in social media. The real development of the prices of the iPhone X completed the picture (Eisenlauer, 2017; Fröhlich, 2018a, 2018b; Hohensee, 2018; Jacobsen, 2018; Kharpal, 2016; Mansholt, 2018a, 2018b; Obermeier, 2018; Anonymous2018b, e, f, g, h; Schlieker, 2018). Professionalism in pricing manifests itself in the use of decision support systems. Method innovation in the application of approaches to price optimization is

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mandatory. The value-price optimization method, that I first introduced in the literature in 2018, is applicable to companies in all industries and for all company offerings (products, services, software, digital content, digital services, etc.).

9.4.3

Summary of the Value Driver Analysis

The overall objectives of the value driver analysis can be described as follows: • Integration and standardization. Methods for performance measurement (e.g., competitive advantage matrix) and price optimization (e.g., adapted PSM method and expert estimation) are linked in a logical sequence in terms of content. Value to customer data and price acceptance information can be stored in a decision support tool without media breaks. • Flexible application of methods Methods and analyses are tailored to the specifics and restrictions of individual product categories and industries. Key constraints include financial resources, time budget, product status, access to customers or responsiveness of potential users. • Cross validation. There is a combined application of methods to increase the validity of the results. The strong focus on price—as a disadvantage of isolated price queries—is eliminated. For example, the adapted PSM method is linked to a discussion of benefits. Willingness to pay can be measured for different product constellations. Value to customer and price are considered as a whole by combining the methods (Fig. 9.11). • Subsequent use of the results. The results are to be used efficiently in the context of upstream and downstream processes. The analysis of the value-price positioning of the relevant competitors can be used as an input for strategic scenario planning. The competitive strength index is one of the most important input data for the business model derivation (see Chap. 3). “Value selling” in the implementation phase of the pricing process (cf. Chap. 11) is based directly on the results of the value driver analysis. The perspective there switches in the direction of the individual customer: from the derivation of target prices for an offering, specifications for individual negotiations with customers are derived in the context of “value selling”. • Foundation of new product pricing. The method has a particular suitability for products that have already been developed and are about to be launched on the market. The positioning in the value-price portfolio as perceived by the customer is the starting point for defining the target position in relation to the competition. In essence, it is a matter of weighing up short-term profit (margin focus) against long-term growth (sales volume focus). In the first case, one will enter the market with a high price— relative to the value. If the priority is on sales volumes and market shares, this argues for a positioning in the upper left quadrant (Fig. 9.12).

B

Price

A

Sales volume

KPIs

Fig. 9.11 Process flow of the integrated methodology (Source: Own representation)

Relative performance

C

Value to customer

Value-Price Portfolio (status)

Perspective: Company A

Profit

Turnover

Price optimization

Price

C

B

A

Value to customer

Price

Value-Price Portfolio (target)

9

Price

Importance

Competitive advantage matrix

252 Pricing Process Part 3: Structure (3c: Price Optimization)

9.5 Pricing Strategies for New Products

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Price Target Matrix

Value-Price Portfolio Value

Market share focus Competitor B

Market share

+ New product? Competitor A

Competitor C

-

Margin

+

Margin focus

Price

Fig. 9.12 Derivation of the target positioning for a new product (Source: Own representation; price target matrix adapted from Simon & Dolan, 1997)

Two norm strategies resulting from the outlined considerations are described below: skimming pricing and penetration pricing.

9.5

Pricing Strategies for New Products

Five theses on the pricing strategy for new products are: 1. Initial pricing is a key factor in determining the success of products. 2. New product pricing is the most important factor affecting a company’s profitability. 3. Underestimating product benefits costs companies a great deal of profit. 4. If the entry price is set too low, it is difficult to compensate for the loss of profit over the life cycle. 5. The price point plays a decisive role in determining the brand image of a product. Skimming Vs. Penetration: Possible Applications, Advantages, and Disadvantages Based on the key targets “margin” or “sales volume/market share”, two ideal-typical strategies are distinguished in the pricing of new products: skimming and penetration pricing (Roll et al., 2012; Simon & Fassnacht, 2016, 2019; Skiera & Spann, 2002; Simon & Dolan, 1997; Buxmann & Lehmann, 2009). It is a matter of determining

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how much profit a product will generate in the long run. The crucial question is how this is to be realized: • In which phase of the life cycle? • In what way? • In what time sequence? Both strategies refer to the first phase of the product life cycle. They describe the price level in the launch period and development trends over time. The penetration strategy characterizes the introduction at a particularly low price. In the skimming strategy, a new product is introduced at a comparatively high price. The premium price is successively reduced over time. The choice of the strategy depends on the following five factors: – – – – –

Intended product positioning Financial situation Company goals Customers’ perception of benefits Revenue model. Possible company offerings (revenue sources) are: hardware/ product, service, software, content, advertising, data, and digital services.

Of outstanding importance is the target definition of the market launch as an internal specification. Weighing up the strategy options involves the following questions (Simon & Fassnacht, 2009, 2016; Pastuch, 2018; Frohmann, 2009): 1. To what extent is the company or business unit dependent on cash flow? Does the new product need to generate cash flow early? Or are start-up losses acceptable in the first periods? 2. What is the innovation level of the product? 3. What is the level of the customers’ willingness to pay? 4. Is the company a pioneer or does it follow the competitors? The entrepreneurial decision for one of the two strategy alternatives consists of a trade-off between two different target options. Do you want to achieve higher profits in the short term or in the long run? It is a matter of weighing up relatively secure short-term profits against uncertain long-term profit opportunities. The goal of the penetration strategy is to achieve the fastest possible market penetration through a low initial price. Arguments for a penetration strategy include (Jensen & Henrich, 2011, p. 96; Pastuch, 2018; Simon, 1992; Homburg & Totzek, 2011): • Achieve high overall contribution margins through rapid sales volume growth (despite low margins). • Establishment of a dominant market position.

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• Exploitation of economies of scale through a rapid increase in the cumulative quantity. • Reduction of the risk of failure in the course of the market launch at a low price. • Deterring potential competitors from entering the market. • Low marginal costs (e.g., for digital goods). • High price elasticities. For many products, manufacturing costs change over time. Particularly in the case of technically sophisticated products—but also in the case of information goods—costs often fall significantly with output volume. The central premise of the penetration strategy is that unit costs fall sufficiently sharply with increasing sales volume due to economies of scale. In this case, the supplier who can secure a more favorable cost position early in the product life cycle through attractive prices and high sales volumes will be the most successful in the long term. For digital goods, the penetration strategy is of high importance. The main reason: the enormous importance of reaching a critical mass early on and the associated network effects. Internet platforms increase their value for the user with each additional user. With these business models, only a few dominant providers inevitably prevail on a global level. The rapid generation of new users via price incentives is critical to success. Penetration pricing can thus create significant barriers to market entry for competitors. At a later stage—after a critical mass has been reached—there is usually potential for price increases. The strategy can be justified by a long-term orientation of the company. A prerequisite for the consistent implementation of the penetration approach is the willingness to accept losses in the short term. A high level of financial strength and risk tolerance are essential. One form of implementation of the penetration strategy is high price discounts for firsttime customers. Case Studies Penetration Strategy The company Sigfox from France is a major pioneer of the Internet of things and offers an alternative to conventional cell phone networks. Based on efficient technology and cost advantages, Sigfox was able to take the price leadership. Scaling advantages are the core argument for the penetration strategy and the price advantage over its competitors. In the field of game consoles, Sony’s Playstation is a concise example of the implementation of a penetration strategy. The Playstation—as the most important product in Sony’s portfolio—has long been offered at strategic prices below manufacturing costs. In the skimming approach, products are introduced to the market at very high prices. The relatively high initial price is successively lowered over time. The strategy is particularly relevant for products with a high degree of novelty and low price elasticity. The following bullet-point arguments support the skimming strategy

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(Jensen & Henrich, 2011, p. 96; Simon & Fassnacht, 2019; Pastuch, 2018; Homburg & Totzek, 2011): • • • • • • •

Realization of high short-term profits. Profit realization in the time interval of a quasi-monopolistic market position. Limited production capacities in the market launch phase. Rapid amortization of research and development expenditures. Creation of a price reduction potential. Time-differentiated skimming of willingness to pay. Use of the positive prestige and quality indication of a high price.

The two main factors influencing the skimming strategy are the expected cost development and the predicted willingness to pay of different customer types. Cost structure: Technologically sophisticated products are associated with a high initial investment. The high investment costs and the significant cost advantages in the later phases of the life cycle are passed on directly to customers over time. In the skimming approach, the price development follows the cost development of the product over the life cycle. Willingness to pay: Customers with a high willingness to pay are served first. High entry prices appeal to the high-value expectations of customers who are the first to adopt new product ideas. The customer segment of innovators attaches a high importance to prestige effects. First-time buyers have a corresponding budget for products that support their social status. The willingness to pay in the early stages of the product life cycle is particularly high for lifestyle or technology products. At a later stage, consumers with lower price acceptance are served. Applications of the skimming strategy are very common, especially in the consumer goods and media sectors. For new smartphones, tablets, DVD players, cameras, etc., the skimming strategy is used regularly. For many digital products, topicality and novelty are the key value drivers. Examples include economic and stock market information, new music titles and computer games, and software versions. All of these information products generate attention and a high level of buying interest simply by being introduced. In the case of these innovations, a high price can be charged initially, which is successively reduced as soon as the topicality wears off. Once the target group with the highest willingness to pay has been covered, prices are lowered as output volumes increase. The successive price reduction means that the broad market can now also be served and increased sales potentials can be tapped. In this way, customers’ different willingness to pay is monetized in a targeted manner. Case Studies Skimming Strategy The skimming strategy can only be successful if the new product has superior benefits and is the first to market (Roll, 2018). It is especially true for innovations that the price must always reflect the relative value of the product. (continued)

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A skimming strategy and an undifferentiated, interchangeable offering are mutually exclusive. In some industries, price trends can be identified that appeared to follow skimming approaches but ultimately proved to be unsuccessful. The companies concerned were forced to lower the price further and further without realizing any profits. They often ended up with large losses or had to take the product off the market. One of several examples is Nokia with its Lumia 900 smartphone in the USA in 2012. A particularly succinct case study of an unsuccessful pricing strategy is provided by Microsoft with a version of its Xbox game console. In the spring of 2002, Microsoft started with an introductory price of USD 479 in the USA. The most important competitor product, Sony’s Playstation 2, was priced at USD 299 at the time of the Xbox launch. After only a few weeks, Microsoft drastically lowered the price to USD 299. In September 2002, relatively shortly after the launch, the price was again lowered to the level of the Playstation. The Sony product was offered at a price of 249 USD at that time. The price drop could not be stopped in the following period either. In August 2004, the Microsoft console was already at a level of USD 149. When the model was discontinued at the end of 2006, the price of the Xbox was only 99 USD. The cumulative losses over the 5 years amounted to USD 4 billion (Jurran, 2002; Kolokythas, 2002). A combination of both approaches is realized with the “penetration skimming” strategy. In this case, the initial price is at a very low level. Prices are successively raised in the later course of the life cycle. In the late 1990s, eBay launched its auction platform with a commission that depended on the sales price achieved. The offer presentation was not priced. After the launch phase, the management expanded the Price model to include another revenue stream—a fixed component. eBay also asked auction participants to pay a bid placement fee from that point on. Toyota—with its premium brand Lexus—offers another example of a successful “penetration skimming” strategy. The introductory price in the USA in 1989 was USD 35,000. Within just 6 years, the price was increased to more than USD 51,600 against the backdrop of high acceptance (Simon & Fassnacht, 2016). In 1995, the Lexus was sold at an average list price of 51,680 USD. The opportunities and risks of the penetration skimming strategy must be weighed specifically for each offering. Low initial prices can trigger major sales growth of a new product. In growth markets, it is very important to occupy market positions early on. If the competitors who follow suit do not offer a significantly better value-price ratio, many customers will remain loyal to the pioneer. However, a positioning that is too low is very dangerous. Whether price increases can be implemented in later lifecycle phases remains an open question. The risk is that customers quickly get used to low prices. Subsequent increases will then face a considerable resistance.

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Incorrect entry pricing can rarely be corrected upward. It can take years to reach a satisfactory level again if you started too low. Implementation difficulties are to be expected, especially when prices are negotiated with individual customers. If the price is seen as a quality indicator, low prices are also counterproductive and can destroy profit potentials. The risk of prices that are too low is that the user will suspect low quality. Conversely, this means that price increases are associated with sales volume increases. There are several examples of companies in both B2C and B2B sectors that have been able to resolve a negative price-quality indication by raising prices.

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digital/smartphones/iphone-x-verkauft-sich-schlechter-als-gedacht—und-stellt-samsung-vorprobleme-7869276.html Mansholt, M. (2018b). iPhone X. Warum nur Apple sich ein Smartphone für 1319 Euro leisten kann. Accessed April 22, 2022, from https://www.stern.de/digital/smarter-life/iphone-x– warum-nur-apple-sich-ein-smartphone-fuer-1319-euroleisten-kann-7618822.html Mengen, A. (1993). Konzeptgestaltung von Dienstleistungsprodukten: Eine Conjoint-Analyse im Luftfrachtmarkt unter Berücksichtigung der Qualitätsunsicherheit beim Dienstleistungskauf. Schäffer-Poeschel. Obermeier, L. (2018). Kaum jemand will das Galaxy S9–warum sich Samsung und Apple verzockt haben. Focus Online. https://www.focus.de/digital/handy/schlechte-verkaufszahlen-beismartphones-kaum-jemand-will-das-galaxy-s9-kaufen-warum-sich-samsung-und-appleverzockt-haben_id_8609160.html Pastuch, K. (2018). Pricing-Lexikon. Prof. Roll & Pastuch Management Consultants. Accessed April 22, 2022, from https://www.roll-pastuch.de/de/unternehmen/pricing-lexikon Postinett, A. (2018). Das nächste Netflix? Was Sie zum Börsengang von Spotify wissen müssen. Handelsblatt Online. Accessed April 22, 2022, from https://www.wiwo.de/finanzen/boerse/dasnaechste-netflix-was-sie-zum-boersengang-von-spotify-wissen-muessen/21019612.html Ramanujam, M. (2018). Pricing excellence beginnt in F&E. In Umsatz. Gewinn. Wachstum. An die Spitze mit TopLine Power. Simon, Kucher & Partners. Roll, O. (2018). Pricing-Lexikon. Prof. Roll & Pastuch Management Consultants. Accessed April 22, 2022, from https://www.roll-pastuch.de/de/unternehmen/pricing-lexikon Roll, O., Pastuch, K., & Buchwald, G. (Eds.). (2012). Praxishandbuch Preismanagement. Strategien–Management–Lösungen. Wiley. Roll, O., et al. (2010). Innovative approaches to analyzing the price sensitivity meter. Planung & Analyse, 2010(2), 27–30. Rottwilm, C. & Lange, K. (2022). Diese Corona-Gewinner werden jetzt abgestraft. Accessed April 22, 2022, from https://www.manager-magazin.de/finanzen/boerse/biontech-netflix-pelotonsartorius-investoren-lassen-corona-gewinner-fallen-a-427f752f-c3b7-47f4-8b9f-306c9c397c82 Salden, S., Schaefer, A., & Zand, B. (2017). Der Kunde als Gott. Der Spiegel, 2017(50), 12–19. Schlieker, K. (2018, February 23). Smartphones werden teurer. Wiesbadener Tagblatt, 25. Schütte, C. (2017). Kampf gegen Monopole: Geht es Amazon und Google an den Kragen? Manager Magazin Online. Accessed April 22, 2022, from http://www.manager-magazin.de/magazin/ artikel/monopole-trustbusters-ii-a-1178562.html Schwab, K. (2017). Digitaler Wandel. So machen Sie Ihr Unternehmen zukunftsfähig. Capital Online. Accessed April 22, 2022, from https://www.capital.de/wirtschaft-politik/ digitalisierung-so-machen-sie-ihr-unternehmen-zukunftsfaehig Schweikl, H. (1985). Computergestützte Präferenzanalyse mit individuell wichtigen Produktmerkmalen. Simon, H. (1992). Preismanagement: Analyse–Strategie–Umsetzung (2nd ed.). Gabler. Simon, H. (2015a). Preisheiten. Campus. Simon, H. (2015b). Confessions of the pricing man. Copernicus. Simon, H., & Dahlhoff, D. (1998). Target pricing und target costing mit conjoint measurement. Controlling, 10(2), 92–96. Simon, H., & Dolan, R. J. (1997). Profit durch Power Pricing: Strategien aktiver Preispolitik. Campus. Simon, H., & Fassnacht, M. (2009). Preismanagement. Strategie–Analyse–Entscheidung– Umsetzung (3rd ed.). Gabler. Simon, H., & Fassnacht, M. (2016). Preismanagement. Strategie–Analyse–Entscheidung– Umsetzung (4th ed.). Gabler. Simon, H., & Fassnacht, M. (2019). Strategy, analysis, decision, implementation. Springer Nature. Simon, H., Laker, M., & Mengen, A. (1993). Im Focus–Der zielgenaue Preis. Absatzwirtschaft Sondernummer, 1993(10), 86–89. Tacke, G., & Vidal, D. (2014). 72% of all new products flop. Press release., 2014.

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Theuerkauf, I. (1989). Kundennutzenmessung mit Conjoint. Zeitschrift für Betriebswirtschaft, 59(11), 1179–1192. Weisenfeld, U. (1989). Die Einflüsse von Verfahrensvariationen und der Art des Kaufentscheidungsprozesses auf die Reliabilität der Ergebnisse bei der Conjoint Analyse. Duncker & Humblot. Welbers, G. (2018, April 19). Bewertung des digitalen Reifegrades–Tec, Web und App? Vortrag European Sales Conference SKP 2018. Wirminghaus, N., Kreimeier, N., & Langenberg, B. (2018). Machine learning. Künstliche Intelligenz–Aller Anfang ist schwer. Capital Online. Accessed April 22, 2022, from https:// www.capital.de/wirtschaft-politik/kuenstliche-intelligenz-aller-anfang-ist-schwer

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Against the backdrop of increasing digitization, the assortment of offerings in many industries is becoming more diverse. The complexity of product and price optimization is increased by, among other things: digital business models, segment-specific combinations of different revenue sources, and the management of different revenue partners. Against this backdrop, understanding pricing structures from the customer’s perspective is becoming more important. Exploiting all differentiation opportunities is not advisable. This is because the costs of implementing intelligent price structures do not include only direct expenses for fencing. The opportunity costs of excessive pricing complexity must also be taken into account. End customers and sales agents are simply deterred by complicated rate structures. Value transparency and simplicity can prove to be decisive success factors— depending on the industry and customer segment (Frohmann, 2008). Apple Case Study In the case of digital products, the perception of value to customer is much more difficult to quantify than in the case of consumer goods. In the case of online music, the value of the digital offering for the user depends on numerous criteria. These include the number of units used, topicality, and compression quality. In addition, platform providers face the challenge of optimizing a broad portfolio of music simultaneously. Apple faced both constellations in its music division in 2003: – Very broad portfolio. – Challenging benefit quantification for digital music tracks. (continued)

# Springer Nature Switzerland AG 2023 F. Frohmann, Digital Pricing, Management for Professionals, https://doi.org/10.1007/978-3-031-24591-6_10

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The technology group deliberately opted for a simplified, heuristic pricing. In the case of Apple iTunes, there were three arguments in favor of a simple pricing structure: 1. Internal efficiency aspects. 2. The goal of rapid market penetration. 3. Price-psychological interrelations. The influence of the price structure on customer perception was empirically demonstrated in an experiment. The initial question of the study was as follows: Is it profit-optimal to vary prices for online music tracks based on their popularity? Or does it make more sense to sell music tracks at a uniform online price in view of the goal of customer loyalty? In an experiment with users of online music, two variants were tested for their acceptance: Variant 1—Price differentiation: Current hits were offered at USD 1.29, soundtracks at a price of USD 1.19 and all other tracks at a significantly lower price of USD 0.89. Variant 2—Unit price: Each download was charged a uniform amount of USD 1.29. The specific characteristic of this test constellation is that the unit price corresponds to the highest differentiated price of variant 1 (USD 1.29). The result of the price experiment is surprising at first glance: in the case of variant 2—with a unit price of USD 1.29—sales turned out to be significantly higher than in the first test variant. The higher average price—compared to the first option—led to significantly higher spending on music. The pricepsychological explanation for the success of the higher unit price is: 1. Customers perceive a uniform price structure as particularly fair. Perceived fairness increases price acceptance. 2. At the same price, customers pay significantly more attention to quality aspects. The importance of price—and thus elasticity—drops significantly in the course of uniform pricing. The provider’s portfolio is valued significantly more as a benefit driver when prices receive less attention. Conclusion: Offering different options for the same amount is a suitable strategy for reducing the importance of price in the purchase decision. Apple’s Steve Jobs deliberately chose this path of heuristic pricing for his music offering. Swiss watch manufacturer Swatch took the same approach with its lifestyle products. In the 1990s, Swatch’s unit price of USD 40 (CHF 50) remained unchanged for a long time.

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In the case of a simplified pricing logic, hitting the right price point is critical. Put another way: Especially in the case of uniform prices, users’ willingness to pay must not be overstrained. When Apple set its prices, it took particular account of the findings on the effect of price thresholds (see Chap. 13). In the USA, Apple opted for a unit price of 99 cents across the board for downloading a piece of music from iTunes. Looking at the cost structure, it is relevant: A music provider like Apple had to pay 75 cents per download to the rights holders of the music. The uniform price of 99 cents was optimal with respect to the main price determinants (cf. 11 C, Chap. 5): – – – –

Customers’ willingness to pay and perception of benefits. Competitor prices. Costs. Context: Price-psychological influences (esp. threshold price effects).

The case study is a concise illustration of the fact that the smallest price differences in the context of a very large assortment can have a considerable effect in total. In view of the relatively high variable costs as well as the striking price threshold of USD 1.00, the unit price of USD 0.99 was a major contribution to the profitable growth of Apple’s online music division. A fundamental dilemma arises from the increasing complexity of offerings and the price dynamics. On the one hand, pricing decisions should be prepared with the utmost care. Comprehensive data analyses are required for this purpose. On the other hand, it is necessary to make a large number of different pricing decisions in the shortest possible time. Agility in pricing is critical to success, especially for companies in highly competitive industries and with a large number of products. Price structures or individual prices have to be adjusted again and again in order to react to new competitors, upcoming customer trends, and variations in internal specifications (strategy, cost changes, capacities, etc.). The value created with the offering must be captured as comprehensively as possible in terms of price. The challenge comprises three dimensions (Fig. 10.1): • Pricing must be standardized and automated to a greater extent. This is the only way to cope with the increasing complexity of pricing. • Pricing structures must be consistent. The point is to be comprehensible to the customer and to be perceived as fair. • Pricing must become more flexible. Agile price management is necessary in order to adapt to constantly changing market situations and to be able to exploit the potentials of digitization. In the course of optimization, determinations must be made regarding the price level for new or existing products. Tool-supported optimization is often only possible for the most important lead products and innovations. A price optimization problem arises, for example, with the launch of a new car model, an innovative pharmaceutical product or a complex machine tool (cf. Chap. 9). The core of optimization is an intensive examination of the individual product and its

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Goals of portfolio pricing

Standardization of the pricing process

Consistent price structures

Agile pricing

Fig. 10.1 Challenges of portfolio pricing (Source: Own representation)

parameters. This includes, in particular, the measurement of price elasticity. However, exact elasticity measurement and detailed optimization for each individual offer are not possible in many companies. There are two main reasons for this: 1. The product programs of large companies and the assortments of retailers often include millions of items. However, the product is only one of a total of six price dimensions. Across all customers, regions, and sales channels, as well as across different quantity classes and times, a significantly higher number of price points results. As a consequence, big consumer and industrial goods manufacturers work with price points in the clear double-digit millions. The same applies to international service companies such as airlines and hotels. 2. The amount of input data relevant for price management is growing progressively every year. In particular, demand and competitive conditions will continue to become more dynamic in the future. Ever faster decisions and level adjustments are a logical consequence of this development. At the same time, it should be noted: Established products are much more difficult to reposition than innovations. Once an image has been established, it is difficult to change it. Practical Example: Long-tail Business Model and Price Structure From a pricing perspective, the long-tail business model (see Chap. 3) offers opportunities for differentiation. Many online retailers appeal to new customers in particular with very attractive prices for popular items. Rarely purchased niche products are used to exploit profit potentials. From a profit point of view, pricing for the long-tail model basically corresponds to the (continued)

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phenomenon of the loss leader offering (familiar in stationary retail). The assortment consists, among other things, of price leader items—products on which the company earns nothing or hardly anything. Even if the margins are close to zero or negative: Decoy products are very important for the portfolio. This is because they attract customers to the store. The ultimate goal of the supplier is to sell profitable complementary products. The philosophy of crosssubsidization is gaining in importance on the Internet, not least against the background of the enormous portfolio sizes of online retailers. On the Internet, in particular, customers are interested in reducing their search and procurement costs. They concentrate purchases of different items on one supplier (one-stop shopping). The assortment network of a large online retailer is an independent value lever for the customer; it serves the core need of “convenience”. With a price reduction for a focus item, the online store attracts new customers. These often buy additional products out of the portfolio. The online retailer's objective is to achieve the highest possible contribution margin for the product range as a whole. For the individual product, this means: 1. The price should be set lower the more an article contributes to the contribution margin of the assortment. Lower contribution margins on the main product can be accepted if the decline is more than compensated for by higher margins on ancillary products. 2. The optimal price of an item can even lie below the marginal cost. This fundamental principle of “loss leadership” applies, among other things, to special offers. Here, a low or negative contribution margin is deliberately accepted for the special offer item in order to attract customers to the platform. 3. In online stores with strong economies of scope, sales of products below marginal cost can be justified from a business point of view. The decisive factor for the price monitoring of the decoy products is that the realized cross margin corresponds to the company targets. For the overall assortment, this mixed calculation results in an interesting effect from a price-psychological point of view: a few low-priced items in the assortment (objective dimension) are sufficient to make an entire store or online store appear reasonably priced (subjective perception dimension). Online retailers can certainly enter into active price competition on comparison portals with a few items, as long as adequate margins are earned on the additional items purchased. Given their assortment sizes and the diversity of input data, many companies rely on simplified rules for making pricing decisions. Optimizations of the price architecture are made at most for the most relevant customer segments, product variants, regions, and channels. All remaining price points are derived via a standardized process. From an efficiency point of view, pricing must concentrate on the segments

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and product groups that are significant both strategically and from a profit point of view. These success-critical price points define the brand images of companies and determine their long-term survival. Examples include Apple’s iPhone, Lufthansa’s particularly lucrative business travel services, and Mercedes-Benz’s E-Class and S-Class model ranges. In many other cases—with slight product modifications, with low-value products, etc.—not very much time can be invested in each individual pricing decision. In all these constellations, companies need precisely defined processes that lead to a successful “value extraction”. These processes are more or less standardized depending on the industry. In global companies, the regional coordination of prices is of high importance. Typical for mobile companies is a two-step approach that combines standardization, transparency, and flexibility in the best possible way: 1. The structure of the tariffs is the same across countries. 2. Regional differences in the factors which are determining pricing are reflected in the parameters of the price structure and the terms of the contract. Automated decision-making processes represent the highest form of standardization. Retailers and numerous service providers (such as hotels, fastfood chains, and airlines) work with clearly defined processes for price setting. Airline revenue management was developed over 50 years ago. Against the backdrop of market liberalization and intense price competition, airlines in the USA were virtually forced to become much more professional: in terms of processes and tools. The importance of professional portfolio pricing became transparent at the beginning of 2018 as well as at the beginning of 2022 in the fiercely competitive market for food consumer products in Germany. Conflicts between large retail companies and food manufacturers are not uncommon there. For both parties—retail groups (e.g., Edeka) and manufacturers (e.g., Nestlé)—the smallest price differences of a huge assortment can have a considerable impact in total (Hielscher, 2022). Against the backdrop of the increasing penetration of information technology, the potential for creative pricing for large assortments is growing. Real-time pricing based on artificial intelligence supports all three challenges outlined: standardization/automation, consistency, and agility. Retail companies (such as Otto, Bonprix, or Kaufland) use specialized software that helps them to optimize assortment prices. External data is dynamically integrated into the forecasts. Purchasing-relevant influencing factors such as public holidays, seasonal factors, or the weather are integrated, as is price information from competitors. On this basis, the retailers’ CRM systems submit automated purchase recommendations to the customer.

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Methodical Derivation of Price Structures

The pricing logic for an assortment results as a direct consequence of the strategic positioning of lead products. Once the optimum price has been set for a basic offering, price points for individual product variants and customer groups are derived in a further step (Homburg & Totzek, 2011). This is based on the longterm decisions on positioning (product dimension) and segmentation (customer dimension). The logic of the price structure results from answering the following questions: 1. 2. 3. 4.

How many price-performance combinations are offered in total? What are optimal entry prices for lead products? How many price alternatives are there per product? What price increments should there be within a product line? The total number of price points to be set for a product line is equal to the sum of:

– – – –

The base prices The price alternatives per offer variant The number of variants offered within a product line The number of price-relevant customer segments.

Determining the number of versions is an optimization problem. The following arguments have to be considered: – Increasing the number of product variants leads to a greater exploitation of market and profit potentials. – Too large a number can mentally overwhelm the customer. In addition, the effort on the part of the supplier is increased across the entire pricing value chain. The decision on the complexity of the price structure is also an optimization problem. Between the extreme positions of “radically simplified rates” and “very complex differentiation”, each company must find the appropriate compromise. Often, both strategy variants are used in parallel for different product lines (e.g., premium offers vs. low-cost versions). Determining the price intervals within the product line is another optimization problem. There are various options for the price structure within a portfolio. Two logics of practical relevance are based on the following premises:

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Storage capacity (GB) 256

Variant 3

128

64

Variant 2

Variant 1 499

599

699

Price (USD)

Fig. 10.2 Price structure Apple iPad in the USA 2010 (Source: Own illustration)

• Logic 1: The price increments are designed to be linear continuously. The main reasons for this structural logic are customer transparency and simple traceability. Within the selected pricing logic, the prices of the individual products are comprehensibly differentiated. The increments from the entry-level product to the premium product are coherent. • Logic 2: While the price for the different variants increases linearly, the quality increases disproportionately across the price tiers. The value-price ratio is increased across the individual product versions. This can increase the demand for the expensive premium offerings. The launch of the Apple iPad in the USA in February 2010 is a prime example of this strategy. The logic of Apple’s pricing structure, in key points, is: a. Offer of three variants with a price gap of USD 100 each (USD 499, 599, 699). b. Strong focus on the most important product feature “storage capacity” in the communication campaign. c. Disproportionate increase in capacity performance across all three variants. The gradation was 64 GB, 128 GB, and 256 GB. d. Creating strong incentives for customers to choose the most expensive variant (256 GB; USD 699). e. The surcharge of the most expensive version compared to the cheapest variant is 200 USD. The additional cost for Apple is only USD 88 in comparison. With its iPad, Apple offers a concise practical example of a three-tier structure (Fig. 10.2). The most expensive version offers a significantly higher storage capacity of 256 GB. This creates a strong incentive to use the most profitable version. The

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significantly better value-price ratio of the most expensive version overrides the customer tendency “toward the middle” (see Chap. 13, trade-off effect). The success of tiered pricing is based on the following assumptions: • There are economies of scale on the production side! The additional costs of performance improvements must not be proportional, but must necessarily be degressive. This is one of the structural peculiarities of information goods. • Exactly those product features are varied which are particularly important for the customer! These elements of the offering must then also be clearly differentiated from each other. In other words: services are enhanced where the customer is able to perceive it. Only then does performance differentiation lead to an increase in demand. • Customers perceive a significantly higher additional value in the next higher price level than they have to pay in addition! This gives them an incentive to switch to the higher-value product. However, if too few additional benefits are offered at the higher price levels, most customers choose products at lower price levels. The up-sell effect desired from the manufacturer’s point of view then fails to materialize. Depending on the product category, value to customer means: making phone calls, playing games, downloading, storing, and much more. In the case of the Apple iPad, it was the significantly increased storage capacity that was the most important value driver for the customer. Under no circumstances should the differences in price demands be greater than the differences in benefits. • The product variants are also effectively differentiated from each other by their labeling! The branding of the variants is a central challenge. The designation of the individual versions provides the user with initial indications of the quality level and the expected price positioning. Here, too, the aspect of perception is crucial. If customers cannot recognize the differences between product lines or versions, in the worst case they will not perceive premium products as such. The necessary willingness to pay more for higher-quality offerings is then lacking. Netflix’s pricing structure for new customer business in the US market was based on three versions and price levels at the end of 2022. The same content was available for all three subscription plans. The differences were in the resolution and in the number of devices that can play content simultaneously. (a) Basic package: USD 7.99/month—only one device can stream the content. Resolution is available in HD. (b) Standard package: USD 12.99/month—up to two devices can stream content simultaneously. Full HD is available. (c) Premium package: USD 17.99/month—up to four devices are streamable. Ultra HD is available. A basic package with ads at USD 4.99/month has been added in the last quarter of 2022.

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Project Outline: Product Line Pricing for Information Goods

Since the variation of digital products generates hardly any additional variable costs, an increase in the product range can lead to more sales volume, revenue, and thus profit. Many digital media providers (such as Apple or Amazon) have expanded their portfolio to include a large number of niche products (long-tail business model). Apple offered over 12 million songs for download in its iTunes Store 5 years ago. Many of these were niche products with low usage (Salden et al., 2017, p. 12). The number of listed items (SKU; stock keeping units) on Amazon’s marketplace was approximately 576 million in 2018. Price optimization for niche products should be as automated as possible for reasons of efficiency. A decisive criterion is the intensity of competition. Availability in a competitive comparison can be used as an essential parameter for relative price positioning within the niche portfolio. The questions are: Which niche products are offered by competitors? And which ones do we offer exclusively in our own store? Higher markups can be realized for products that are subject to low competition or are difficult to obtain. The willingness to pay for niche products is higher than for fast-moving offerings. The lower number of suppliers leads to increased search costs for the customer. These search costs result in a lower price elasticity. Due to the complexity of the offering, a system-technical support of the pricing logic is mandatory. In practice, the “value score” approach has proven its worth. This method takes different market potentials in the product portfolio into account in a differentiated manner. In value score pricing, customers’ willingness to pay is derived in a standardized way. The starting point is various price-relevant criteria. Important criteria include: • • • • • • •

Competitive intensity. Price transparency in the market. Product turnover rate. Importance of the brand from the customer’s point of view. Product type involvement of the customer. Brand loyalty of the customer. Competitive strength of the product.

A value score is derived per product from the case-specific parameter value of the criteria. The perceived value of the product is an indicator of price acceptance. The correlation between the customer’s willingness to pay and the value criteria is of decisive importance for “value score” pricing. The competitive strength index plays a significant role in the “value score” logic—the perceived relative performance of the product is one of the decisive levers of willingness to pay. Purchase frequency is also a significant price criterion within the overall logic. Customers are more sensitive to the price of frequently purchased products than of infrequently bought items. The methodological steps in “value score” pricing are in brief as follows:

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• The respective value criteria are determined for each product group or individual item. • Based on this, the products are evaluated with regard to the value factors. • A value score is calculated for each product from the evaluation of the criteria. • A mathematical function is used to determine a markup logic from the value score. For higher stock turn rates (or more frequently purchased products), for example, lower markup rates are calculated. • The price of product results from the variable costs plus the calculated value markup. • The lower the value score or the higher the elasticity, the lower the markup on the marginal costs. The customer’s appreciation determines the level of the markup! Only with the help of appropriate software can a consistent system for portfolio pricing be set up. The development of the model, the simulation, and the calibration of the data can be fully digitized. Artificial intelligence methods are available for this purpose. The “value score” approach follows a logic that runs as a common thread through the individual chapters of the book: Prices must be directly related to the customer’s perception of value. Simple mass products with little potential for differentiation justify only comparatively low markups. High-quality special products or branded articles, on the other hand, can be sold at significantly higher margins. The second major challenge in addition to optimizing product range prices is the digital presentation of offers. Complexity and lack of transparency in the presentation of offerings represent a massive barrier to purchase for potential customers. Studies show a direct correlation between the perceived complexity of the product presentation, the resulting search costs, and the likelihood of purchase. In the case of online solutions, the rejection rate increases by 50% with every click that a potential user has to go further in depth on a platform. In terms of price psychology, search costs represent a perceived loss for the customer. One instrument to reducing search costs and perceived complexity from the customer’s perspective is modular offerings. There are two basic options when designing a modular system: 1. An “all inclusive” solution that covers all potential needs. The customer can reduce this total package online by modules that are superfluous for him. 2. The offer of a basic version at a relatively low price. This basic offering can be expanded to include certain product features and services at additional cost. Regardless of the basic principle chosen, it is crucial that these technological solutions significantly reduce complexity from the customer’s point of view. A simple, digitized product and price presentation become a decisive value lever. It influences the customer’s purchase decision independently of the objective offering. In conclusion, “value score” pricing enables structured pricing for a product or service portfolio. Through the standardized and comparable consideration of the value drivers, a consistent pricing logic in the product range can be achieved. The

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level of detail of the information on the pricing criteria can be varied between single product and product group level. The portfolio system provides a concrete guideline for pricing. Chapter 11 outlines how the portfolio approach can be extended to include customer-specific criteria. By combining the dimensions of product and customer, guidelines for price negotiations can be derived.

10.4

Method Innovation: Analysis and Steering of Price Elasticity

The importance of price elasticity has already been emphasized several times. A reduced price sensitivity of customers increases the potential for enforcing higher prices. A particular challenge of strategic pricing is to proactively influence the price elasticity of customers. The logic behind this is as follows: 1. A maximally price-sensitive customer will buy an available competitor’s product as soon as it is only marginally cheaper. In the context of “reverse auctions” on the Internet, orders are often won exclusively on the basis of price. Differences in performance fade completely into the background for price-focused customers. The other extreme pole is occupied by a customer who buys a product regardless of how expensive it is in a competitive comparison. 2. For most customers, what counts is an optimal value-price ratio. Their decisionmaking process is based on a trade-off logic. The prerequisite for weighing up between alternatives is that their available budget is not exceeded. 3. The best pricing strategy is to reduce the price sensitivity of users. This is the most direct route to higher “pricing power” and thus to higher margins. 4. Proactive management of price elasticity is all the more important the more intense the price competition and the greater the apparent interchangeability of suppliers. Commodities often arise precisely in those sectors where too little energy is invested in managing perceptions. 5. Price sensitivity basically corresponds to the relative weight of price in the customer’s purchase decision. There are various ways to measure it. In addition to professional methods such as conjoint Measurement, price sensitivity can also be approached indirectly: by assessing criteria (cf. Berz & Dörner, 2010). Valuation Logic: Indirect Derivation of Price Elasticity In the first step, the interrelations between price elasticity and the criteria influencing it are made transparent. Operationalization begins with the derivation of hypotheses and trend statements. The logic is as follows: The price sensitivity of a customer tends to be lower, the: • More differentiated the product compared to the competition. • Less transparent the prices of competitors in the market. • More importantly the price-quality signal in the purchase decision of the customer.

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Lower the storability of the product. Higher the switching costs for the customer. More fair prices are perceived by the customer. Lower the share of the product price in the customer’s total budget. Better the price can be divided among different parties. Higher the time pressure of the customer. Higher the benefit of the offering in the perception of the customer. Lower the frequency of purchase/use.

All criteria relate to the two essential dimensions of price: the product and the customer. The criteria result in various levers for reducing price sensitivity. These can be applied differently depending on the market situation, the industry, and the product (cf. Berz & Dörner, 2010). Most of the effects are directly steerable by the company: in the product specification, via communication measures, the price structure, or via the price model. The evaluation process is based in detail on the following steps: 1. 2. 3. 4.

Definition of all relevant factors with an influence on price elasticity. Operationalization of the relationship between the criteria and price elasticity. Prioritization of criteria (on an interval or cardinal scale). Evaluation of each product with regard to the characteristics of the criteria (interval scale). 5. Calculation of an overall score for each product (linking of Steps 3 and 4). 6. Derivation of elasticity categories. 7. Definition of measures to reduce price elasticity.

Some of the criteria and their influence on price elasticity are outlined as examples: • Transparency of prices in the market. – Logic: the more difficult it is to compare prices, the less price-sensitive customers are. According to the “difficult comparison”-effect, poor comparability leads to increased search costs. Increased search costs in turn reduce price sensitivity. If prices are only comparable to a very limited extent, price differences tend to be underestimated. Increased price transparency from the customer’s point of view reinforces price effects. Price reductions then have a stronger positive effect. Price increases have a stronger negative effect. – Challenge: Avoid comparability through creative price models! • Product differentiation. – Logic: The more differentiated the product is compared to the competition, the greater the customers’ willingness to pay (Berz & Dörner, 2010). – Challenge: Create unique selling propositions! • Switching costs.

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– Logic: The higher the costs for a possible change of a supplier, the lower the price elasticity. Customer loyalty programs (e.g., airline mileage programs, loyalty programs such as Amazon Prime or Payback) are an indirect contributor to higher prices. They increase customer loyalty, make it more difficult for users to potentially switch to competitors, and reduce price sensitivity. – Challenge: Increase switching costs! • Share of the product price in the total budget. – Logic: Price elasticity increases with the absolute level of the price. The higher the share of expenditure in the customer’s total budget, the more price sensitive the customer reacts. Thus, spare parts are often procured in the context of a repair service. When optimizing the price of spare parts, complex high-value parts can often be reduced in price. Conversely, less expensive parts can bear significant price increases. – Challenge: Higher markup rates at absolutely low prices! • Urgency of the need. – Logic: The customer’s price elasticity decreases with increasing time pressure or his urgency of solving a problem. In many B2B sectors, availability is one of the key customer requirements. High costs for spare parts and services are more readily accepted there. The reason for the low-price sensitivity for service components is obvious: For high-quality and very expensive machines, fast maintenance services are critical to success. A machine downtime is associated with considerable opportunity costs.

10.5

Implementation Tip: Price-Related Measures to Influence Price Elasticity

Customers can be influenced with creative measures to take a closer look at the value of company offerings. Among the numerous possible levers are those that focus on price as an instrument. This may sound contradictory at first, but a wide variety of examples can be used to prove it: With the help of specific price measures, the importance of price as a purchasing criterion can be reduced from the customer’s point of view. The following four levers can help to reduce the importance of prices in the course of a purchase decision: 1. 2. 3. 4.

Change in the basis of the price structure. Intentionally very high price positioning to draw interest to the quality. Division of the price into individual components. Offering of different product options at a single price.

The potential of uniform pricing has already been described using the example of Apple’s music division. Swatch offers another successful example of the unit pricing strategy. Watches of the most varied designs were offered at standard prices from

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1983 onward. The Swiss watch brand—Swatch for short—became the world’s most successful wristwatch. With a consistent strategy, the innovative Swiss watch manufacturer succeeded in beating the Japanese competition in its core segment— low-price watches. The cornerstones of the strategy from the very beginning were: • Attractive, but very simple design. • Simple pricing that draws customers’ attention to quality aspects. • A continuous expansion of the portfolio to include brands and models in all price categories. The core of Swatch’s strategy was a unit price of CHF 50 (USD 40).

10.6

Dynamic Pricing

Dynamic pricing adjusts the prices of products, services, and information goods to the current market situation according to defined time cycles. The concept of constantly changing prices is becoming increasingly professionalized and more closely timed as technological change progresses. Due to the rapid development of information technology, Dynamic Pricing has become one of the most important levers within the price management process (Frohmann, 2014; Fisher et al., 2017; Anonymous, 2019; Pena, 2017; Salden et al., 2017). Dynamic Pricing can be defined, categorized, and delineated in keywords as follows.

10.6.1 Definition of Dynamic Pricing • • • •

Time-dependent pricing. Form of intertemporal price differentiation. Real-time price adjustment at product and customer level. Time-varying prices take into account fluctuations in demand and supply (supply bottlenecks, excess capacities). Prices rise systematically when demand is expected to increase. They are lowered when demand is expected to decline. • In addition to the supply–demand relationship, numerous other factors are included. A core element of dynamic pricing is the definition of appropriate factors that determine the “time-dependent variation of prices”. • The list of criteria is diverse: competition, sales, inventory/availability, perishability of goods, season/time of the year, time of the day, day of the week, weather, customer profile (purchase history), urgency of customer need, end device, search engine/browser, user location, other time aspects (workday vs. weekend, vacation dates). • Prices are adjusted automatically—based on the factors outlined. Prices vary depending on the time of purchase or use.

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• The creative combination of factors is the key to a differentiation from the competition. Example: Tanq4you—a gas station chain in the USA—bases its dynamic prices on current crude oil price indices and weather conditions. Weather-oriented pricing systems are also known from funiculars (Heim et al., 2019).

10.6.2 History and Development • A special form of dynamic pricing—revenue management—was developed by airlines 55 years ago. • American Airlines introduced a largely automated capacity and price management in 1967 (Dirlewanger, 1969; Doganis, 1991; Pompl, 1991). This proves that the digitization of pricing in some industries began decades ago. Dynamic pricing is not a new phenomenon. • Other tourism sectors (hotels, car rental companies, cruise ships) and the entertainment industry (including theaters) have also been applying revenue management systematically for a long time. Parking providers and sports organizers are among the other examples. • Tour operators have been dynamizing their offerings and prices for many years. The components of a trip are not put together until the booking request is made. The price of the trip fluctuates depending on demand—it is recalculated for each request (Frohmann, 2007). • Dynamic pricing depending on the development of supply and demand plays a steadily increasing role, especially in e-commerce (B2C). • Amazon—the e-commerce market leader in Germany—introduced dynamic pricing back in 1999. In 2014, product prices on the “Amazon Marketplace” were adjusted up to eight times per day. The adjustments were differentiated according to the product category, the competitive situation, sales, and time of the day (Salden et al., 2017; Anonymous, 2018a). • On e-commerce platforms, price adjustments are made mechanically using defined algorithms. The most frequent price changes are observed in the consumer electronics and clothing/shoes sectors (Salden et al., 2017). • Dynamic pricing is of particular importance for platform business models in the B2C sector (e.g., Airbnb and Uber). • Airbnb uses a dynamic pricing engine for its Business2Consumer platform. Prices are recommended to accommodation providers (business) with potential users (consumer) in mind. Among the parameters used are seasonality, day of the week, and special events. More sophisticated factors are added—including photos of the property to be used or the price structure of overnight accommodations in the neighborhood. • Dynamic pricing is driven not only by digital transformation but also in part by changes in the legal framework.

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Case Study Gas Stations Since 2013, service stations in Germany have had to communicate their updated prices to a central reporting office. Since then, the daily price level adjustments have increased significantly in terms of number and extent. A study from 2017 refers to four cities in Germany that were examined: there, the mineral oil companies changed their prices more than six times a day on average. Within one city, price fluctuations of more than 30 cents per liter were observed over the course of the day. Depending on the gas station, prices differed by up to 10 cents per liter throughout the day. Based on technology support (special apps) and the legally required supply transparency, consumers took greater advantage of the price differences than in the past. Refueling transactions were specifically shifted to the cheapest times (e.g., late in the evening). By taking advantage of price declines, motorists were able to save an average of more than 3 cents per liter, according to the study (Anonymous, 2018b, 2018c). • In the wake of increasingly professional price management software, dynamic pricing can no longer be found only in the B2C segment, but to an increasing degree in B2B sectors as well. • When implementing automated pricing, companies can use specialized software vendors (Blue Yonder, Prudsys, etc.) and their decision support tools. • However, its application is limited in the industrial goods sector. Dynamic pricing is less relevant in all sectors/companies in which the following conditions apply: few, large transactions; high proportion of price negotiations; and complex transactions with customer-specific quotations. • There has been a significant increase in the speed at which companies are considering numerous factors. The volume of data is growing progressively. The granularity of information is also increasing steadily. Current data on supply capacity and demand constellations can be combined with detailed information on user preferences (Salden et al., 2017; Pena, 2017). • In commodity sectors (e.g., online retail of secondhand clothing and re-commerce), intelligent pricing algorithms are a prerequisite for success. • Re-commerce companies (Momox, reBuy) change the price points for their product portfolio every 30 min. Speed is a competitive differentiator in the context of pricing in this sector.

10.6.3 Delineation: Dynamic Pricing Versus Revenue Management • Revenue management goes beyond dynamic pricing. It is based on an interplay of inventory management and price differentiation (capacity allocation and pricing). The focus of inventory management is on the availability of different booking

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classes. The decisive factor for pricing is the forecast of the utilization of different price classes. • The higher the demand forecast in terms of available capacities, the more limited low-priced booking classes will be. • Prices tend to rise with the short-term nature of the booking. This logic applies to numerous tourism sectors such as rental cars, hotel accommodations, longdistance bus travel, rail, and air travel. Case Study Air Traffic In domestic German air traffic, Lufthansa's enormous increase in market power led to a drastic rise in average prices over the course of 2017. On domestic flights, Lufthansa and Eurowings controlled a share of almost 90% of the market. Pricing works for Lufthansa’s passenger business as it does for almost all airlines: if the flight date is still far away and only a few seats have been sold, tickets are offered at low prices. Last available seats shortly before departure, on the other hand, tend to be sold at high prices (Dirlewanger, 1969; Doganis, 1991; Pompl, 1991). As a result of reduced industry capacities in the wake of the Air Berlin bankruptcy, Lufthansa aircraft were much more heavily utilized than usual as of May 2017. Against the backdrop of the revenue management system, airline tickets became up to 50% more expensive at peak times. The capacity gap was closed again by competitors at the beginning of 2018. In March 2018, stable price development became apparent again for the first time.

10.6.4 Requirements and Fields of Application • Dynamic pricing (digital pricing, level 3) requires massive investments in the “operating model” (digital pricing, level 1). Advanced artificial intelligence (AI) is critical. Investment in technology (algorithms, machine learning, electronic price tags) is a core challenge. Changing business processes is another requirement. • The application of dynamic pricing is economically viable under two conditions: 1. Demand (as well as price elasticities and willingness to pay) of different customer segments varies significantly. 2. Supply capacities are limited (or fixed).

10.6.5 Objective of Dynamic Pricing With a view to the two main influencing factors (demand; supply), three main objectives of dynamic pricing result.

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• Goal 1: Balance supply and demand. Create incentives to enhance value to customer through increased supply capacity. Practical example: In situations where transportation capacity is in short supply, the ridehailing company Uber increases its prices. Surge pricing creates an incentive for increased supply or reduces demand. In this way, Uber generates acceptable waiting times for customers who are willing to pay a price premium. • Goal 2: Create incentives to monetize limited supply capacity. Supermarket assortments consist of perishable inventory. Many products have an expiration date. Revenue potentials are to be exploited if the waste of perishable products with expiration dates can be minimized. AI solutions for dynamic pricing predict sales volumes per product based on the latest transaction data (clicks, shopping carts created, purchases, etc.). Depending on the target sale date, the price for each item (SKU) is updated daily. The algorithm re-evaluates each product on each day—top priority is given to the sale date. If the target sale date is close and the sales forecast is below expectations, the price is lowered. Practical example: The Dutch supermarket chain Albert Heijn initiated a computer-controlled, dynamic price reduction in May 2019 to reduce the number of products that already passed expiration dates (Roll & Loh, 2019). • Goal 3: Effective monetization of seasonal peaks in demand. Urgency of demand means that price sensitivity decreases—customers are more willing to accept a higher price. Companies can leverage this effect, especially in product categories that are rarely purchased. In US online retail, Amazon has repeatedly used the seasonal effect on pumpkin pies. For example, a temporary price increase from USD 4.49 to USD 8.49 in the run-up to Christmas (around the Thanksgiving holiday).

10.6.6 Technical Variants and Forms of Dynamic Pricing Variant 1: Rule-Based Pricing • There are several ways to implement dynamic pricing. Variant 1 is rule-based pricing. There are two options for defining the price via a rule. Option A: The price is defined directly. Option B: Specifying the increase (or decrease) to be applied to a reference price or base value. In rule-based pricing systems, different base values can be used (e.g., supplier price, break-even price, last year’s price, and competition price). The competition-oriented approach is the most commonly used variant in the retail industry. • Rule-based systems can be significantly improved by propensity models. Propensity models enable companies to differentiate customers in a better manner. The rules use propensity parameters. These criteria reflect the propensity of potential customers to buy or the expected economic value of their shopping cart. • The quality of dynamic pricing stands and falls with the level of detail at two levels: Customer and Product. In the travel industry, pricing is potentially based on an almost infinite number of product attributes: Round trip, origin and

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destination, trip duration, airline, fare category, etc. Customer attributes may include number of passengers, time of the day and day of the week of the booking, operating system and device used, geographic location, etc. The more product and customer attributes are combined in rule-based pricing systems, the more profound is the level of granularity—this makes the pricing strategy almost impossible for competitors to understand. The level of granularity can become so fine that the system approaches the status of personalized pricing (Pena, 2017). Variant 2: AI-Based Models Machine Learning (ML) is used to determine the rules in a data-driven manner. The methodological advantages are in short: ML models – – – –

Do not need to be programmed. Learn patterns from data. Adapt themselves to new data. Allow algorithms to train and recognize patterns based on inputs (transactions, external data). – Provide a price optimization solution to maximize an objective function (For example, “Maximize profit—but ensure that the sales volume does not drop more than 5%”).

10.6.7 Personalized Dynamic Pricing • The technical and conceptual development of dynamic pricing leads to automated personalized pricing. Numerous synonymous terms are used for this: one-to-one pricing, dynamic personalized pricing (DPP), individual automated pricing, and personalized dynamic pricing (PDP) (cf. Krämer et al., 2017). • The dynamic setting of individual consumer prices for products or services corresponds to price differentiation of the first degree in Pigou’s sense. • Personalized dynamic pricing (PDP) results from factors that identify the consumer, including IP address, location data, browsing history (browsing behavior), device type used for a search query (smartphone, PC, laptop, tablet), purchase behavior (transactions of the customer in the past), age and gender of the customer, and distance of the customer to a retail store. From all these input criteria, insights and forecasts on the willingness to pay are derived. • Example 1: Companies use the user’s location as a proxy variable for the willingness to pay. This results in higher prices for people living in cities with a higher GDP per capita. This price differentiation based on location information is based on GPS data. • Example 2: Companies use the brand purchased, including the price range of smartphones, to show users different prices (for example, Samsung vs. Apple; Apple iPhone 13 vs. an older version, e.g., iPhone 7).

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10.6.8 Dynamic Pricing Case Studies The application of dynamic pricing does not guarantee success in pricing. The example list of companies that failed is long (Fisher et al., 2017; Dakers, 2016; Cox, 2017). • Example 1: Sony Music. The price of Whitney Houston’s “Ultimate Collection Album” was drastically increased (from a starting price of USD 4.99) to USD 7.99. The massive price increase on Apple’s iTunes distribution channel came in 2012, just a few hours after the artist’s death. The problem with this: the price increase was driven by algorithms that referred to the increase in demand as the primary trigger. The potential impact of the price change on customer perception and acceptance was completely neglected. • Example 2: Uber. Uber’s algorithmic pricing has repeatedly provoked massive criticism and resentment among users in recent years. This was prompted by several drastic price increases—perceived as unfair—in local crisis situations (including the hostage taking in Sydney in 2014, bombing in New York City in 2016, terrorist attack in London in 2017, and mass shooting in Seattle in 2020). • Example 3: Coca-Cola. In 1999, the beverage company introduced a vending machine in Japan that was intended to implement temperature-dependent prices. The price logic: a price increase for beverages on hot days; the higher the outside temperature, the higher the price. This measure was heavily criticized by consumers and the media. • Example 4: Amazon. The online retailer tried to differentiate prices according to customers’ Internet browsers unsuccessfully. • Example 5: MyTaxi. In 2014, the mobility service failed in its attempt to assign mandates to cab drivers by an auction system. The aim of the passenger auction: the cab driver with the highest commission bid receives the assignment. The dynamization of the commission was met with fierce resistance. A short time later, the transition to a fixed commission took place. The example proves: auctions are a variant of dynamic pricing.

10.6.9 Risk Factors in Dynamic Pricing • Excessive prioritization of algorithms – Logic: Pricing algorithms have a crucial weakness—they only take into account the fluctuations of supply and demand (and the other factors) in real time. The exclusive focus on algorithms—and the neglect of the business experience of pricing experts—is one of the main reasons for the failure of dynamic pricing in numerous cases. An example of the negative consequences of pure automation is provided by the pricing of a textbook. The work “The making of a fly” was listed on the online platform Amazon. Two booksellers distributed the book—both referred to an automatic reaction to any potential competitive action in their algorithms. A price spiral evolved, which was not noticed or checked for a long time. The consequence of the excessive automation: the price for a book copy reached an amount of 23.7 million USD.

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– Challenge: The integration of human intelligence is a prerequisite for the success of dynamic pricing! Continuous monitoring is important. This is the only way to ensure that dynamic pricing is implemented in line with corporate objectives. • Neglect of customer acceptance (fairness!) – Logic: A failure of dynamic pricing is mainly due to the fact that customers perceive the pricing as unfair. Negative perceptions of fairness are accompanied by a decline in consumer satisfaction. Importantly, the more familiar users are with the pricing system, the fairer they perceive it to be. Other findings in this context are: personalized dynamic pricing (PDP) is rated as less fair than segment-based pricing (third-degree price differentiation) or quantity-based (tiered) pricing. – Challenge: Fairness aspects limit the potential for price differentiation, especially with regard to PDP. Transparency is important for acceptance and perception of fairness. The basic principles of dynamic pricing should be comprehensible to consumers. However, transparency does not mean having to disclose all calculation mechanisms completely (Cox, 2017; Pena, 2017). • Competitor behavior – Logic: In the course of automatic price adjustments, price retaliation by competitors is not uncommon. A negative price spiral can be triggered. In the worst case, a price war is threatening. This leads to one of the core statements of this book—and back to the business model as the starting point: lack of differentiation (value to customer) cannot be compensated by the best algorithm! In the case of complete comparability (and thus interchangeability), dynamic pricing must be critically evaluated. E-commerce is an example. In contrast to many tourism offers (including flights) and mobility services, there is often no supply restriction in the online sale of products. However, dynamic pricing can increase cross-price elasticity and lead to shifts in demand. – Challenge: Dynamic pricing should be used selectively. The price model portfolio (see Chap. 8) offers incredible potential for competitive differentiation. For example, Addison Lee (a competitor of Uber) has differentiated itself in the USA ridehailing sector using a particularly simple model—a single price that applies to all rides regardless of the time of the day or the day of the week. This simplified price model addressed key customer needs: transparency and fairness. • Short-term effect – Logic: Dynamic pricing is purely transactional. Long-term relationships or partnerships between buyer and seller are not fostered by automated pricing. In the airline industry, the widespread adoption of revenue management contributed to a weakening of brand values and a reduction in customer loyalty. – Challenge: Innovative price models should—wherever possible—be applied in parallel with dynamic pricing. Customer loyalty concepts (such as loyalty programs and bonus programs) also help to improve the perceived price fairness.

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References Anonymous. (2018a). PREISKAMPF AN DER ZAPFSÄULE. So sparen Sie bis zu 30 Cent pro Liter. Bild Online. Accessed April 22, 2022, from https://www.bild.de/geld/wirtschaft/ benzinpreis/30-cent-pro-liter-sparen-55115990.bild.html Anonymous. (2018b). Lufthansa will ihren Höhenflug weiter treiben, Wirtschaftswoche Online. Accessed April 22, 2022, from https://www.wiwo.de/unternehmen/dienstleister/profit-steigernlufthansa-will-ihren-hoehenflug-weiter-treiben/21069664.html Anonymous. (2018c). Passagierzahl gestiegen, aber Lufthansa enttäuscht Anleger mit stabilen Preisen. Manager Magazin Online. Accessed April 22, 2022, from http://www.managermagazin.de/unternehmen/industrie/lufthansa-aktie-schmiert-ab-wegen-stagnierenderticketpreise-a-1197369.html Anonymous. (2019). Top-Studie: Kunden akzeptieren dynamische Preise–unter bestimmten Voraussetzungen. Accessed April 22, 2022, from http://www.absatzwirtschaft.de/top-studiekunden-akzeptieren-dynamische-preise-unter-bestimmten-voraussetzungen-148722/ Berz, G. & Dörner, J. P. (2010). Höhere Preise durch gezielt reduzierte Preissensitivität der Kunden. https://www.batten-company.com/wp-content/uploads/2010/12/BC13_Insights13_4_ Preissensitivitaet.pdf Cox, J. (2017). London terror attack: Uber slammed for being slow to turn off ‘surge pricing’ after rampage. The Independent. Accessed April 22, 2022, from https://www.independent.co.uk/ news/uk/home-news/london-terror-attack-uber-criticised-surge-pricing-after-london-bridgeblack-cab-a7772246.html Dakers, M. (2016). Uber knows customers with dying batteries are more likely to accept surge pricing. The Telegraph. Accessed April 22, 2022, from http://www.telegraph.co.uk/ business/2016/05/22/uber-app-can-detect-when-a-users-phone-is-about-to-die/ Dirlewanger, G. (1969). Die Preisdifferenzierung im internationalen Luftverkehr: Eine empirische Studie. Lang. Doganis, R. (1991). Flying off course: The economics of international airlines. Routledge. Fisher, M., Gallino, S., & Li, J. (2017). Competition-based dynamic pricing in online retailing: A methodology. Science, 64(6), 2496–2514. Frohmann, F. (2007). Der Preisfindungsprozess (Lektion 6). Strategisches Preismanagement. In Schriftlicher Lehrgang in 13 Lektionen. Management Circle. Frohmann, F. (2008, February). Simplify your price. Vortrag im Rahmen der “Banking World” von Management Circle. Frankfurt. Frohmann, F. (2014, March 14). In Gewinn maximieren: Big Data für kleinere Shops. i-Business. V. Gründel-Sauer. Accessed Apr 22, 2022, from http://www.ibusiness.de/aktuell/db/1914 65veg.html Heim, N., Müller, S. & Grob, L. (2019). Akzeptanz von Dynamic Pricing. Eine Untersuchung am Beispiel von Schweizer Skigebieten. In Marketing Review St. Gallen, 2019/5, pp. 40–47. Hielscher, H. (2022). Preiserhöhungen. Aldi-Einkäuferin verrät ihre Verhandlungstricks. Accessed April 22, 2022, from https://www.wiwo.de/my/unternehmen/handel/preiserhoehungen-aldieinkaeuferin-verraet-ihre-verhandlungstricks/28264944.html Homburg, C., & Totzek, C. (2011). Preismanagement auf Business-to-Business-Märkten: Zentrale Entscheidungsfelder und Erfolgsfaktoren. In C. Homburg & C. Totzek (Eds.), Preismanagement auf B2BMärkten (pp. 15–69). Gabler. Krämer, A., Dethlefsen, A., & Baigger, J. F. (2017). Der PSM-Ansatz neu überdacht–Der Schritt von der Preispunktanalyse zur Zahlungsbereitschaft. Planung & Analyse, 45(6). Pena, N. (2017). Ecommerce pricing strategies–The good, the bad and the ugly, paymotion, in ecommerce, marketing, pricing. Accessed April 22, 2022, from https://www.paymotion.com/ ecommerce-blog/pricing-strategies-good-bad-ugly/ Pompl, W. (1991). Luftverkehr: Eine ökonomische Einführung. Springer. Roll, O. & Loh, P. (2019). Dynamic Pricing in der Kundenwahrnehmung, In: Marketing Review St. Gallen, 2019/5, 32–39. Salden, S., Schaefer, A., & Zand, B. (2017). Der Kunde als Gott. Der Spiegel, 2017(50), 12–19.

Pricing Process Part 4: Implementation

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Introduction: Condition System and Sales Management

The optimization steps within the framework of the price structure (cf. Chaps. 7–10) are followed by the implementation of prices in the market. Both core processes (price structure and price implementation) must be seen in their interrelationships. Any optimization is worthless if prices set for the first time or changed rates cannot be implemented with the customer. List prices are the starting point for price negotiations (Voeth & Herbst, 2011). The actual price parameter in the discussion with customers is the discount—or more comprehensively formulated—the condition system. Target segments and negotiating partners differ in many ways. Purchasing volume, size, and market power as well as customer behavior differ across segments. The intensity of the business relationship with the customer is also relevant. The condition system must reflect these customer differences (Roll et al., 2012). Therefore, a list price is defined with a view to the target positioning (value to customer). This gross price is the starting point for a discount differentiation, in which attractive customers receive targeted incentives (gross-minus calculation). The value of customer—i.e., the importance of the customer for the company—is rewarded with corresponding discounts. This chapter looks at the basic principles of condition system optimization and sales management. This part of the pricing process focuses on the customer dimension—and, in particular, the value of the customer. The customer value for the company—together with the logic of product pricing already outlined—is transferred into a target price system. Digitization enables significant improvements in efficiency and consistency in this process phase of price management also. When deriving the target price, three central criteria—and associated objectives—play a decisive role: 1. Product value. Goal: create a pricing logic for the offering portfolio that is comprehensible to the customer.

# Springer Nature Switzerland AG 2023 F. Frohmann, Digital Pricing, Management for Professionals, https://doi.org/10.1007/978-3-031-24591-6_11

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2. Service value. Goal: calculate additional services in order to extract customer benefits as well as cover service costs. 3. Customer value. Goal: manage differences in user behavior as well as customer value.

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Basics of the Condition System

In many industries, official price lists have only an orientation function. Prices are negotiated individually between customers and sales. Apart from regular price level adjustments, list prices are often structurally constant. The necessary adjustment to changing market situations is made by negotiating prices (i.e., granting discounts). This is particularly true for product categories and industries in which corporate customers are not the end users of the services. A significant supply category with high negotiation relevance is industrial goods (Simon & Fassnacht, 2019). This sector includes capital goods and industrial consumer products and services, among others. However, terms and conditions are also agreed for consumer goods and numerous services. The annual negotiation sessions between manufacturers and retail chains can be cited as an example. The position of power of suppliers and customers is of decisive importance here. The oligopolists Edeka, REWE, Aldi, and the Schwarz Group (Lidl) can exert great negotiating pressure on the large number of consumer goods manufacturers in Germany. The four largest retail chains in Germany accounted for a market share of 74% in 2020, compared with 60% in 2018 (Schlesiger & Haseborg, 2018, p. 24). The focus of negotiations is on conditions. The delisting of a significant share of the Nestlé assortment at the retail market leader Edeka in spring 2018 shows the high importance of condition systems. The position of power of the retailer (Edeka) vis-à-vis the consumer goods group (Nestlé) was decisive for the escalation of the negotiation process (Reiche, 2018; Giersberg, 2018). The condition system contains all price-related agreements of a company with its customers (sales agents; end customers). It includes: 1. Invoice-relevant price reductions (discounts) 2. Ex post price reductions (rebates) 3. Other price-related agreements (e.g., payment term agreements and advertising grants) Discounts are price reductions that are listed as a separate item on the invoice at the time of purchase (Buchwald, 2018). The most frequently used discount variant is the volume discount for larger purchase volumes. Other discounts, which are already granted at the time of invoicing, include early booking discounts and functional discounts for specific customer services (such as storage, presentation, and consulting). In contrast to discounts, rebates are not paid immediately at the time of purchase (Roll et al., 2012). Rebates are based on agreements that do not affect invoicing. The sales volume bonus is the most frequently used of all the variants of a

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rebate. It presupposes a concrete performance in return. The customer’s performance is rewarded at the end of a period with a previously agreed repayment (Roll et al., 2012). The condition systems agreed between manufacturers and retailers in the consumer goods sector are similarly complex to the discount and rebate structures in B2B sales. By cleverly structuring price agreements, the manufacturer can influence the behavior of the retailer. Through conditions, producers set targeted incentives to steer the product range, to increase sales, and to enhance customer loyalty among retailers. Loyal sales agents are rewarded for basic capacity utilization, for example. An assortment bonus is a special form of a rebate that takes into account both total sales and the customer’s assortment mix. The rebate level increases with the breadth of the portfolio sourced (Pastuch, 2018). Volume discounts are the best-known form of non-linear pricing (see Chap. 7). They are used in different variants (Pastuch, 2018; Miller & Krohmer, 2011). In the case of the fixed quantity discount, larger purchase quantities result in higher price reductions. The actual average price to be paid decreases with increasing volume. The discount rate in each case relates to the user’s total purchase volume. A simple example of a fixed discount rate (“all units discount”) is as follows: The price of an article is 10 EUR. From a purchase quantity of 100 units, the unit price for all 100 units is reduced by 10%. In the case of the tiered quantity discount, the price reduction applies only to a defined interval in each case. For example, the price of an item is 100 EUR. From a purchase quantity of 100 units, the price decreases by 3% for each additional unit, from a purchase quantity of 200 units by 4%, and so on. Thus, the discount applies only to the additional volume—and not to the total quantity purchased. The increasing discount levels create an effective incentive to increase the purchase volume. With this form, the average discount—overall—is significantly lower than the perceived additional discount. The structural difference of the discount, which is insignificant from the customer’s perspective, can lead to a sharp increase in profit for the supplier—compared with the fixed discount. Wherever possible, tiered volume discounts should therefore be applied. Price concessions can be granted in the form of cash discounts or discounts in kind. In both cases, the average price decreases. In the case of cash discounts, the price paid is reduced with an effect on the invoice. Discounts in kind include non-linear price structures such as “buy 5 and pay 4”. Discounts in kind have the following advantages over cash discounts (Roll et al., 2012; Roll & Laker, 2004; Schumacher, 2017): 1. 2. 3. 4.

Discounts in kind promote capacity utilization. Their profit effect is more advantageous. Discounts in kind ensure price continuity. They can be used in the short term (and are easier to take back later than a direct price discount). 5. In view of behavioral economics, they are more highly valued by customers than cash discounts (cf. Chap. 13: Ownership effect).

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effective allowance Seasonal discount Cash discount Assortment rebate Annual bonus Rebates + Boni

Discounts List price

Invoice price

Transaction price

Fig. 11.1 Condition system: from list price to transaction price (Source: Own representation)

Two of the biggest profit risks for many companies are the lack of systematics and transparency in the allocation of conditions. Discounts, rebates, and boni as well as payment terms are often allocated in an unstructured manner. The resulting proliferation of unsystematic net prices is difficult to manage. The consequences of an opportunistic conditions policy encompass four key dimensions: 1. 2. 3. 4.

Customer perception Competitor response Irritation of sales agents Internal inconsistencies.

In addition to the negative external implications (dimensions 1–3), the internal effect of an unsystematic discounting (point 4) is particularly fatal. An optimized price structure is simply distorted by a lack of discount guidelines. The objectives of the central price managers and the behavior of the sales staff then do not match. Management has effectively no influence on actual price levels (Tacke, 2012). The end result of an unsystematic allocation of conditions is a price level that proves to be too low compared to the targeted positioning (Fig. 11.1). Pricing strategy and implementation are not consistent. In some industries, negotiated transaction prices are up to 80% below list prices (Homburg & Totzek, 2011). The core causes of this value erosion are unprofessional processes and a lack of price discipline.

11.3

11.3

Performance-Oriented Condition Systems

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Performance-Oriented Condition Systems

Sales staff need quantitative guidelines for price negotiation and discounting. Only in this way can the management enforce the desired strategic positioning in the market (Homburg & Totzek, 2011). There should be a comprehensible connection between the negotiated price level and the quantitative reference variables (sales, volume of the customer, etc.). The logic behind this: Discount levels are aligned in a structured way with customer value—the value of customer. The value of customer results from the actual performance or behavior of a customer. There are several reasons for awarding discounts based on performance and behavior: 1. Performance-oriented discount structures reflect the basic principle of an intelligent pricing strategy: a benefit rendered by the company (the price reduction) requires a counter-performance by the customer (e.g., a higher purchase quantity)! 2. A performance and behavioral reference motivate customers to increase their loyalty: – Higher purchase quantities or an indented behavior are rewarded in terms of price. – Incentives for additional purchases are set. – Purchasing behavior is controlled in a way that is comprehensible to the customer and perceived as being fair. The aim is to reward a customer behavior that promotes the implementation of the corporate strategy. 3. A performance and behavior orientation provides support for sales: – Sales staff receive quantitative arguments for a discount differentiation. – Negotiating power can be significantly promoted. – Consistency in discounting across customers, products, regions, and sales channels is promoted. In this way, the condition system supports the pricing strategy. – The basis for a quantitative monitoring of the target achievement is laid. 4. Key customers have a strong bargaining power; they demand higher discounts: – Customer value-oriented discount structures make pricing comprehensible for buyers. The relationship between the price demanded and the performance as well as the behavior of the customer is clearly recognizable. – Quantitative benchmarks structure the negotiation and reduce the price pressure in the discussion. – Objective arguments reduce the share of emotions in the negotiation process. Structured discounting is a form of price segmentation based on objective criteria. The following logic suggests itself:

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1. Small customers pay the list price. In the retail trade, this corresponds to the recommended retail price. 2. For core customers, discount differentiation takes place according to two dimensions: a. Importance of the customer for the company. – Price-relevant indicators of importance include customer size, purchase volume, customer revenue, growth potential, customer’s industry, stability of the business relationship, and credit ratings. b. Customer behavior. – Price-relevant indicators of behavior include structured exchange of information, disclosure of customer data, and payment behavior. Discount structures must go beyond a simple price–volume relationship. Professional condition systems reflect the company’s pricing strategy. They serve to achieve a company’s market-related goals but also integrate internal criteria. Possible goals of a condition system include (Roll et al., 2012; Roll & Laker, 2004): 1. 2. 3. 4.

Preventing the customer from switching to the competition. Optimize capacity allocations. Achieve sales targets. Increase customer loyalty via creative discount structures.

This necessarily means that discounts should not be based solely on the volume purchased by the customer. Sales volume is only one of numerous indicators of customer value. Price-determining factors include behavioral patterns of customers that have a favorable impact on the manufacturer’s processes and cost structures. For example, a discount can be granted if the buyer provides a service in turn that translates into an economic advantage for the supplier (Buchwald, 2018). This is common in B2B business. Customer performances that justify price discounts include: a. b. c. d.

Shorter payment terms Advance payments Guaranteed purchase quantities New business.

With increasing digitization, customer data is becoming more important as a resource for pricing (Meckel, 2018). This value driver can also be promoted via the discount structure. B2B companies such as machine builders can reach agreements with their customers on the scope and extent to which data may be used. The use of data can be incentivized via discounts or boni. All of the examples outlined are based on a core principle of pricing: price discounts are always based on something the customer does in return (such as a specific behavior).

11.4

11.4

Target Price Systems and Peer Pricing

293

Target Price Systems and Peer Pricing

Portfolio pricing was outlined in Chap. 10. This offers concrete guidelines for the systematic pricing within an assortment. A target price system is created when portfolio pricing (with its product reference) is extended to include the customer perspective. In this approach, customer-specific criteria are used to derive an individual target price (Artz & Schröder, 2011). By combining the two dimensions of product and customer, operational specifications can be derived for sales negotiations (CPQ; configure price quote). In this evaluation, too, an optimal compromise must be found in the typical area of conflict that pricing faces: • Depth of detail and accuracy of the assessment • Efficiency of data analysis • Agility in the competitive environment. At the core of the target price system is differentiated—and at the same time consistent—implementation of pricing strategies based on quantitative criteria (Roll, 2018). The price level can be controlled in a differentiated manner according to the most important dimensions (product and customer segment). Decisive for the degree of price adjustment for products are their competitive strength and profitability. Among the most important factors in defining the customer-specific price are the importance of the customer and the customer’s behavior. The target price system combines product (product group) and customer (customer segment) figuratively in the form of a matrix. Ancillary costs and service aspects must be included. Further dimensions that determine the price level (regions, sales channels, etc.) can be added to the two-dimensional view (Roll et al., 2012; Artz & Schröder, 2011). “Peer pricing” is a machine learning method that supports the implementation process. Optimized price proposals are determined automatically during the bidding process. The target prices essentially take two core data into account: comparable offers; prices that could be successfully enforced on the market. The core of this artificial intelligence method is a classification algorithm. This uses a similarity measure to determine which transactions from the past are closest to the current request. The similarity score takes into account all the key characteristics of the transaction (and thus four dimensions of price): product, customer, quantity, and time. A company’s CRM platform is a suitable system for the IT implementation of the target price tool. In companies with digitized pricing processes, target price systems are integrated into the workflow of quotation. System integration also provides efficient support for the subsequent controlling process (see Chap. 12). An outstanding requirement in the development of a target price system is price consistency, both internally and toward the customers (Roll et al., 2012). Consistent prices are ensured by an identical pricing logic. The effects: 1. Transparency for all employees who take an active role in the pricing process. 2. Comprehensibility of the price structure from the point of view of sales agents and customers.

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The customer-related analyses in the context of the target price development are based on two main pillars: historical data and forecasts. One dimension is the analysis of historical sales prices and volumes. This is supplemented by data on won and lost orders per customer. This requires a correspondingly professional monitoring. The second pillar consists of the expected market potential and future willingness to pay. The combination of the two perspectives results in concrete specifications for operational price management.

11.5

Price Enforcement

11.5.1 Value Selling A successful price enforcement in the market is the goal of “value selling”. Valuebased selling involves supporting sales in implementing target prices in the market (Buchwald, 2018). Value-based price enforcement places the company’s own competitive advantages at the center of the sales process. It is explained in detail which customer benefits are associated with the various product features. This principle applies to B2B business as well as to B2C sectors. The level of detail of the information and the design of the tools differ depending on the business model. In B2B markets, value selling is much more quantitative than in the consumer goods business. Especially in the case of industrial goods, methods of benefit quantification are used to prepare price negotiations. The advantages and disadvantages of the company’s own offering for the customer are quantified as part of a profitability calculation. Two important concepts for the implementation phase are the “total cost of ownership” analysis and the “total benefit of ownership” calculation. In terms of data, both must be linked to the value driver analysis (cf. Chap. 9). In order to better grasp the application of the two methods, the framework conditions of price enforcement in a B2B business model are briefly outlined. The most important price-relevant characteristics of industrial goods are (Homburg & Totzek, 2011): 1. The value to customer can be measured comparatively well. The resulting benefit for the customer is more quantifiable than for consumer goods or digital products. 2. Industrial markets often consist of only a few suppliers and consumers. Bilateral oligopoly situations are very common. 3. The purchasing decision of industrial customers is often made by a buying center. Heterogeneous interests of the people involved from different functional areas such as technical management, application engineering, and purchasing shape the decision. 4. Many products are created on a one-off basis to customer specifications as part of comprehensive projects. The volume per project tends to be very large and the number of projects tends to be small. Very often, a specific calculation is set up for each new offer. The goal is to ensure profitability at the project level.

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5. B2B customers assess potential suppliers much more rationally than consumer goods buyers do. 6. Customers of industrial goods comprehensively examine the economic benefits of offers before making purchasing decisions. Benefit transparency has increased significantly as a result of new information technologies, especially for industrial products and services. 7. Conclusions of contracts are preceded by extensive negotiations. Technical solutions, prices, payment terms, etc. are the subject of negotiation discussions (Hake & Krafft, 2011). 8. Industrial goods and services are often systems business. Contracts are awarded by competitive bidding. In competitive bidding, price plays a prominent role. In contrast to auctions, only one binding price bid is submitted in the bidding process. 9. Price transparency in industrial goods markets is usually lower than in industries where actual prices are publicly visible. 10. A price-psychological finding is that as the procurement process becomes more professional, the customer’s expectation of the supplier’s price fairness increases (Simon & Fassnacht, 2009, p. 179). This leads directly back to the starting point of the chapter, the principle of value selling.

11.5.2 Total Cost of Ownership Approach Companies’ investment decisions can be quantified using the total cost of ownership (TCO) approach (Homburg et al., 2006, p. 262). The method is becoming increasingly important in purchasing departments. The expected costs cover the complete life cycle of an industrial good (e.g., a machine). From the customer’s perspective, five phases can be distinguished: machine purchase, installation, maintenance/servicing, extraordinary repairs, and disposal. These differ significantly with regard to the following price influencing factors (Homburg & Totzek, 2011; Homburg et al., 2006): 1. 2. 3. 4.

Price sensitivity of customers Competitive intensity Customers’ perception of benefits Contribution to customer satisfaction

The criteria of competitive intensity and price sensitivity are closely related. Both parameters are very different in different phases of the cycle. In the new machine business, the focus of customers on price is high. In contrast, the after-sales area is less price-sensitive than other customer contact points. One explanation for this is the high cost of a machine downtime and the importance of a reliable performance. High urgency on the part of the customer reduces the importance of price in this process phase. Price sensitivity for spare parts and repair services is accordingly

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rather low (Roll et al., 2012; Artz & Schröder, 2011). High margins can be realized in particular in those phases in which: a. The intensity of competition and price sensitivity of customers is low. b. Overfulfillment of customer expectations is possible. Urgent and extraordinary repairs offer potentials for exceeding customer requirements. Especially where expectations are exceeded, high margins can be enforced more easily. While the core business of machine manufacturers used to be the sale of new machines, the proportion of value added is shifting toward services as digitization increases. Sales revenues are increasingly being generated with services related to the machine. The numerous examples include, among others: a. Rentals of machines and monetization via subscription models. b. Predictive maintenance. c. Plant optimization for business customers (Lietzmann, 2018). The development of 3D printing is changing value creation processes and the interaction with customers further in the direction of automation. The physical distribution of spare parts will then no longer be necessary. Facilities will independently initiate print jobs for spare parts when a need for repair is detected.

11.5.3 Total Value of Ownership Bonus System This method for the systematic assessment of suppliers is used primarily by the purchasing departments of B2B companies. Automotive manufacturers, telecommunications companies, as well as mechanical and plant engineering firms use the approach regularly. Typical applications are in project and tender business. The comparison of the different bidders is based on a quantitative evaluation pattern (Mecke, 2011). Different performance levels of the suppliers are included in the purchasing decision by means of a bonus-penalty evaluation. Quantification takes place either in price units or as a relative comparison (in percent). Intangible benefits (such as service, consulting, system integration, brand, and design) are also systematically included. For example, the total value of ownership (TVO) system not only evaluates product quality but also, among other things, the image of the supplier. TVO quantification makes it possible to compare the prices of individual competitors in terms of supplier performance. The core objective is to quantify the price-performance ratio of all competitors. The starting point is the bid prices communicated by the bidders. Boni for features with a better performance is deducted from the starting price. Performance advantages increase the value to customer—in this respect, they are calculated like a credit in the price comparison. Penalties for negative product features (disadvantage compared to the competition) must therefore be added to the bid price. Based on this bonus-penalty quantification, a comparative price is calculated for each bidder. Even a much higher bid price can

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be overcompensated with performance benefits as long as the customer’s budget limit is not exceeded. Thus, a supplier that initially appears to be too expensive may prevail in the end. The TVO system is used in numerous e-bidding processes. There, boni and penalties are awarded in the preparation phase, which are quantitatively linked to the price in the negotiation phase. To be successful in an e-bidding process, an integrated procedure is required: additional services for the customer must be clearly worked out and formulated in the form of boni. Quantifying one’s own performance advantages is the most efficient way out of pure price competition. If all the competitors involved only meet specifications, it is ultimately all about price. The same quantification logic is also practiced by the supplier’s sales department in the run-up to a negotiation. However, the perspective is reversed to the TVO method (Hermenau, 2009). A frequently used approach in the context of negotiation management is the “price walk.” The aim is to objectively evaluate the economic advantages of one’s own offer for the customer (e.g., the operator of a machine in B2B business). Advantages can be traced back to two causes—cost reductions or quality improvements. In addition to the purchase price of the machine, quality differences compared to the competition as well as cost implications are included in the systematics of the “price walk” (Hermenau, 2009). Thus, a more efficient machine is reflected in a higher sales price for the final product produced. Cost savings for the customer are deducted from one’s own offer price. These include, for example, reductions in plant downtime, reductions in wastage, or shortened maintenance work. The results of these calculations are made available in the form of a sales management tool. The added value of the company’s own offering compared with the competition is also reflected in “battle cards”—a negotiation support tool for sales. Digitization allows customer-specific data to be integrated much more efficiently into a value-selling approach of this kind. Value-based negotiations can be conducted on this professional basis. Price aspects recede into the background in the sales conversation (Fig. 11.2). In addition to the quantitative preparation of price negotiations, the targeted use of negotiation tactics is recommended. These include the very helpful interest matrix. This takes into account the mindset of game theory already outlined for the best tactics in the bidding process. At its core, it is about reflecting on the motives and negotiation power of both parties. The dimensions of the matrix are “interest of the customer in the supplier” and “interest of the supplier in the customer”. The positioning in the matrix results in immediate consequences for the negotiation (Voeth & Herbst, 2011). The supplier’s sales department is optimally supported with the help of this simple strategic approach—in combination with the quantitative calculations presented above. Added to this are qualitative tools such as argumentation guidelines and question-answer documents.

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+5% - 4%

+6%

-10 % 100%

-3%

Current Price

Target price

Cost increase A

Cost reduction B

Cost reduction C

Cost savings for the customer

Fig. 11.2 Value selling and benefit quantification (“price walk”) (Source: Own representation)

11.6

E-Bidding

In B2B markets, e-bidding as a procurement process has steadily gained in importance in recent years. Technically, the electronic auction typically takes the form of a reverse auction. Customers publicly request a product using an electronic platform. The requirement is specified down to the last detail. The application is made by potential suppliers who compete on price in a bidding process with the help of the electronic auction. The framework conditions of this pricing mechanism are defined before the auction process in the form of a tender. The tender conditions include, among other things, possible lower and upper price limits, stipulations regarding quantity, levels of price variation, time-related framework conditions, etc. Tendering services are sometimes outsourced by the customer to commercial providers of Internet platforms. These determine the most cost-effective provider of the respective service within the framework of the reverse auction. For the supplier, the question arises as to the optimum bid price. In the course of the price optimization in tenders, two aspects are basically relevant for the supplier (Homburg et al., 2006, p. 82): 1. The probability of receiving the order 2. The expected profit. Both criteria show an opposite development:

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299

1. The lower the price demanded, the greater the likelihood of a tender acceptance. 2. The higher the price, the higher the profit in the event of a tender acceptance. When calculating the optimum bid price, it is advisable to use a quantitative decision support method. This supports the trade-off between the two aspects. The probability of winning the bid and the resulting profit are calculated for different target prices. The aim is to determine the bid price that maximizes the expected profit (Homburg et al., 2006). E-bidding processes are associated with several advantages for purchasing departments. These are in key points: 1. 2. 3. 4.

Increased price competition on the supply side. Rationalization of the price negotiation. Process efficiency through standardization of the purchasing process. Provider evaluation based on comparable, quantitative facts (e.g., via scoring models).

In the future, customers will be increasingly interested in tendering via the Internet. Companies must find clear answers to these challenges. The decision as to whether a company participates in invitations to tenders on the Internet is of great strategic importance (Homburg et al., 2006, p. 83). It reflects the following influencing factors, among others: 1. How price-sensitive is the customer? 2. What goals are our competitors pursuing? 3. How do we prioritize? What do we focus on—margins or volumes?

11.7

Incentive System

Another lever of price enforcement is the design of the incentive system (Roll, 2018; Homburg & Totzek, 2011). One of the reasons for the outlined value erosion is the compensation system of sales employees. Sales compensation is often still focused on revenue. The commission paid increases with the sales force’s revenue. Professional incentive systems are based on the company’s strategy, which usually prioritizes profit generation over maximizing sales. Individual incentives for sales consequently include a profitability measure in addition to revenue (Roll et al., 2012). There are various options when it comes to the technical design of incentives. Commission and bonus can be used equally. A commission serves to incentivize sales. Bonus payments, on the other hand, secure further target variables. Here, criteria such as price stability, profitability, or customer satisfaction can be used. A useful measure of customer satisfaction is the net promoter score. A payment proportional to the contribution margin provides a suitable incentive for a better price implementation by the sales department. Setting a relative contribution margin

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reward significantly improves the quality of price negotiations (Roll, 2018). The target systems of the sales force correspond to the strategy of the company.

11.8

Tactical Pricing

In addition to the structural approaches to sales management, there are also numerous levers for increasing earnings in the area of tactical measures. One example is an increase in list prices. However, it is not uncommon for companies to fail in their attempt to increase their average net prices by raising list prices in the short term (Giersberg, 2018). According to a study by Simon, Kucher & Partners, less than 40% of the planned price increase is actually realized in 50% of companies (Tacke, 2018). The price acceptance of customers is not sufficient to implement the planned increases. The failure of price increases is potentially due to numerous reasons (Simon & Fassnacht, 2016): 1. 2. 3. 4. 5.

Poor preparation. Unprofessional distribution to customers and products. Hasty implementation. Too high level of price adjustment. Lack of justifications with relevance to the customer.

A typical problem in this context is the determination of price increases across the board. The question of what extent of price adjustment is realistic for individual product groups, customer segments, distribution channels, or regions is ignored. Planned price increases must be carefully prepared and enforced. Increasing gross prices is not always the best solution for realizing higher net prices. The following other options come into question: 1. 2. 3. 4.

Reduction of discounts, rebates, and boni. Shortening of payment terms. Introduction or optimization of cost-based surcharges and fees. Surcharges for special services.

Irrespective of the preparation and implementation, industry peculiarities play a particularly important role. For example, the bargaining power of customers in certain sectors is so high that suppliers have to cope with high resistance to implementation. Food retailers with their strong position vis-à-vis consumer goods manufacturers offer striking examples. The delisting of Nestlé products by Edeka is a particularly drastic example of the consequence of conflicts in price negotiations (Reiche, 2018). In the automotive industry, industrial customers also put up high resistance to price increase attempts by suppliers. The company’s interests with respect to price changes are asymmetrical. Price reductions should be perceived as strongly as possible by customers. In the case of price increases, it is in the interest of the company that these are not recognized as

11.8

Tactical Pricing

301

such as far as possible. This is highly relevant in the current situation of cost inflation. Numerous tactics can be used to enforce higher prices. The list is as follows: 1. Long-term preparation of the price increase: Early communication of the price increase is recommended. The necessity of the price increase must be justified in a comprehensible manner. 2. Select the time of the price increase in such a way that the link to the cause is particularly credible. This recommendation applies above all to cost changes. A situation which is of outstanding relevance in 2022. Possible causes are increases in raw material prices, energy costs, or price-relevant tax increases. 3. Customer-specific differentiation of price increases: A case study for linking the three tactics outlined is provided by the technology company Amazon in its video streaming division. In January 2018, Amazon made an early announcement of a price increase for streaming services in the USA. For the premium segment of Prime members, the monthly subscription was increased from USD 10.99 to USD 12.99. All customers with an annual subscription plan continued to pay USD 99 for the time being. The 18% increase was justified by long-term investments in streaming services. New licenses for high-quality movies and series worth several billion USD were secured to increase the quality of the service. The price increase was easily implemented by linking it to a benefit argument for the customer (Harengel, 2017). At the end of April, an increase was also announced for the annual subscription—as of May, an adjustment was made from USD 99 to USD 119. 4. Linking price increases with product modifications: If the product is modified in parallel with the price, buyers re-evaluate price and performance in each case as part of their value-price assessment. A pure price focus is avoided. Attention is focused on the trade-off between performance and price. 5. Surcharges: This instrument can be used primarily in commodity industries. Surcharges are particularly effective in the event of strong and volatile cost changes. Surcharges for cost increases in energy, oil, and steel are a common practice in B2B industries. Changes in commodity costs can be flexibly accommodated by varying the surcharge factors. Car rental companies or airlines also work with this pricing tactic (Frohmann, 2006). Temporary markups on price are independent of the product price. They offer more flexibility because they can be adjusted to fluctuating raw material or energy costs without any time lag. Surcharges are also easier to implement than direct changes in price. It is sufficient to specify an absolute or percentage markup. In the event of cost reductions, the surcharges are reduced accordingly. Surcharges are also interesting from a price psychology perspective: they are perceived less strongly by customers than increases in list prices. According to price psychology studies, customers react only half as sensitively to surcharges compared to price increases of the same amount. The reaction to surcharges corresponds to only about 50% of the price elasticity.

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Last but not least: Price increases must be carefully prepared and implemented, not only with regard to customers and sales agents. Sales teams must not be ignored as a target group under any circumstances.

References Artz, M., & Schröder, M. (2011). Durchsetzung von Zielpreisen in dezentralen Landesgesellschaften über Transferpreise. In C. Homburg & C. Totzek (Eds.), Preismanagement auf B2B-Märkten (pp. 237–261). Springer Gabler. Buchwald, G. (2018). Pricing-Lexikon. Prof. Roll & Pastuch Management Consultants. Accessed March 22, 2022, from https://www.roll-pastuch.de/de/unternehmen/pricing-lexikon Frohmann, F. (2006). Internationale Preisstrategien. Management Circle Seminar. Giersberg, G. (2018, April 28). Nestlé bekommt den Edeka-Boykott zu spüren. Frankfurter Allgemeine Zeitung, 99, 24. Hake, S., & Krafft, M. (2011). Delegation von Preissetzungskompetenz an den Verkaufsaußendienst. In C. Homburg & C. Totzek (Eds.), Preismanagement auf B2B-Märkten (pp. 181–203). Springer Gabler. Harengel, P. (2017). Streaming und Fernsehen. Radikaler Umbruch: Wie wir TV-Sender, Netflix und Amazon zum Umdenken zwingen. Accessed March 22, 2022, from https://www.focus.de/ digital/experten/mediennutzung-streaming-wie-nutzer-tv-sender-netflix-und-amazon-zumumdenken-zwingen_id_7751533.html Hermenau, B. J. (2009). Besonderheiten beim Pricing von Industriegütern (Lektion 5). Strategisches Preismanagement. Schriftlicher Lehrgang in 13 Lektionen, 2nd edition, Management Circle. Homburg, C., & Totzek, C. (2011). Preismanagement auf Business-to-Business-Märkten: Zentrale Entscheidungsfelder und Erfolgsfaktoren. In C. Homburg & C. Totzek (Eds.), Preismanagement auf B2B-Märkten (S. 15–69). Springer Gabler. Homburg, C., Schäfer, H., & Schneider, J. (2006). Sales excellence. Gabler. Lietzmann, P. (2018). Digital-Chef Klaus Helmrich. Siemens testet Fabriken vorher am Computer– und verzehnfacht dadurch die Produktion. Accessed March 22, 2022, from https://www.focus. de/finanzen/news/unternehmen/mindsphere-in-der-cloud-siemens-testet-fabriken-am-com puter-und-verzehnfacht-dadurch-produktion_id_8786168.html Mecke, J. (2011). TVO: Total value of ownership–geld oder Wasser? Accessed Mar 22, 2022, from www.silicon.de; https://www.silicon.de/blog/tvo-total-value-of-ownership-geld-oder-wasser/ Meckel, M. (2018). Die echten Handelskriege werden längst um Daten geführt. Accessed March 22, 2022, from https://www.wiwo.de/politik/ausland/schlusswort-die-echten-handelskriegewerden-laengst-um-daten-gefuehrt/21142272.html Miller, K., & Krohmer, H. (2011). Ausgewählte Entscheidungsfelder des Preismanagements auf B2B-Märkten. In C. Homburg & C. Totzek (Eds.), Preismanagement auf B2B-Märkten (pp. 105–126). Gabler. Pastuch, K. (2018). Pricing-Lexikon. Prof. Roll & Pastuch Management Consultants. Accessed March 22, 2022, from https://www.roll-pastuch.de/de/unternehmen/pricing-lexikon Reiche, L. (2018). Kein Frieden im Preiskampf. Edeka weitet Boykott gegen Nestlé aus. Manager Magazin Online Accessed March 22, 2022, from http://www.manager-magazin.de/ unternehmen/handel/edeka-nestle-boykott-ausgeweitet-und-trifft-jetzt-30-prozent-derumsaetze-a-1201491.html Roll, O. (2018). Pricing-Lexikon. Prof. Roll & Pastuch Management Consultants. Accessed March 22, 2022, from https://www.roll-pastuch.de/de/unternehmen/pricing-lexikon Roll, O., & Laker, M. (2004). Rendite durch Rabattsteuerung. Acquisa, 2004(9). Roll, O., Pastuch, K., & Buchwald, G. (Eds.). (2012). Praxishandbuch Preismanagement. Strategien–Management–Lösungen. Wiley.

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Schlesiger, C., & Haseborg, V. T. (2018, April 27). Sie lieben Leidensmittel. Wirtschaftswoche, 18, 19–24. Schumacher, O. (2017). Preise durchsetzen (3rd ed.). Gabal. Simon, H., & Fassnacht, M. (2009). Preismanagement. Strategie–Analyse–Entscheidung– Umsetzung (3rd ed.). Gabler. Simon, H., & Fassnacht, M. (2016). Preismanagement. Strategie–Analyse–Entscheidung– Umsetzung (4th ed.). Gabler. Simon, H., & Fassnacht, M. (2019). Strategy, analysis, decision, implementation. Springer Nature. Tacke, G. (2012). Simon-Kucher expert talk: Pricing power–how you get what you deserve. Accessed Mar 22, 2022, from https://www.youtube.com/watch?v=CrghO0q6C1Q Tacke, G. (2018, April). Digitalisierung: “Think big, start smart”. Vortrag European Sales Conference 2018 SKP. 19. Voeth, M., & Herbst, U. (2011). Preisverhandlungen. In C. Homburg & C. Totzek (Eds.), Preismanagement auf B2B-Märkten (pp. 205–235). Springer Gabler.

Pricing Process Part 5: Monitoring

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Price Monitoring: Challenges

Price controlling draws relevant insights for price management from the incredible variety of data from a wide range of sources, formats, and systems. Data procurement, analysis of the information, and sound decision preparation are the foundations of modern price monitoring (Buchwald, 2018). Of crucial importance is the separation between quantity and quality of information. The challenge in many companies is not to increase the quantity of available information. The quantity of existing data is more than sufficient. However, valuable information is not always used across departments, evaluated in a target-oriented manner, and translated into concrete measures (Sprenger, 2018; Meckel, 2018). In addition, technology-driven competitive dynamics place completely new demands on agility and responsiveness in decision-making. Drawing the right conclusions quickly is critical to success. The more data available, the more important systems and metrics become to identify relevant patterns. Outstanding data quality is a necessary condition for greater agility (Müller-Jung, 2018; Lindinger, 2018). This can be described using the example of online retailing or on the basis of commodity industries. There, correct assessments of the market situation and fast price adjustments are crucial for success. Against the backdrop of increasing inflation in the course of 2022 and sharply rising input costs, this insight is more relevant than ever. A delayed reaction to trend variations leads to an erosion of profit potentials. Digital support in these sectors means more reliable price forecasting and appropriately optimized price adjustment (in terms of timing, direction, and intensity). To ensure effective price monitoring, integrated software solutions are imperative. The goal is to make better and faster decisions by reducing complexity and increasing data quality. The main objectives of price controlling are (Homburg & Totzek, 2011; Roll et al., 2012): 1. Transmission of price-relevant controlling information (such as profit, turnover, profitability, and contribution margin) to all relevant functions. 2. Monitoring of all phases of the price management process. # Springer Nature Switzerland AG 2023 F. Frohmann, Digital Pricing, Management for Professionals, https://doi.org/10.1007/978-3-031-24591-6_12

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3. 4. 5. 6.

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Analysis of the implemented prices. Measuring the profitability of customers, products, sales channels, and regions. Control of the pricing process via key figures (key performance indicators). Measuring the overall success of price management in the company.

Controlling the management process of “value extraction” absolutely requires that the efficiency of the individual steps and their effects on the market can be measured. Only that which is quantitatively recorded and measured can be controlled professionally. Monitoring the pricing success using key performance indicators (KPIs) requires that the overriding goals in pricing are operationalized. Operationalization means clearly defining what is to be achieved to what extent and by when (see Chap. 6). The content of the targets, the extent of the targets, and the times at which the targets are to be achieved must be defined for all relevant aspects of pricing. The measurement of success is differentiated in detail for the following two dimensions: 1. Hierarchical levels (company, business unit, product line, product) 2. Price dimensions (product, customer, region, distribution channel, etc.).

12.2

Pricing Cockpit

The management cockpit is a highly aggregated presentation of the most important key figures of price controlling. In the sense of a pricing audit, the aim is to achieve the greatest possible transparency with regard to performance in price management. The measurement of the pricing index closes the circle to the target definition. The integrated performance measurement based on an overall indicator comprises three levels: 1. Financial goals 2. Market targets 3. Professionalism of the pricing process. The pricing index is composed of criteria and measures relating to three dimensions (Fig. 12.1): 1. Level 1 (finance) is about standard metrics such as sales volumes, contribution margin, profit, profitability, and cash flow. 2. The market dimension (level 2) refers to customer- and competition-oriented criteria such as image, customer satisfaction, value-price ratio, market share, and pricing power. 3. The process dimension (level 3) comprises the internal “excellence” of pricing. The success in pricing is all the higher, the:

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Pricing Cockpit

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Pricing performance index

• • • • •

Value extraction

Pricing excellence

Market power

Financial targets

Process-related goals

Market-related targets

Profit Turnover Cash flow Liquidity Index of value capture

• • • •

Target consistency Professionalism of the process Competencies/ skills Software solutions

• • • • • • •

Customer satisfaction Price fairness Price-performance ratio Competitive advantage Sales volume Market share Brand value Index of value generation

Fig. 12.1 Three pillars of price monitoring (Source: Own representation)

1. More positively the development of the financial targets defined as part of the strategy development (e.g., return on investment, contribution margin, profit, cash flow, and customer lifetime value). 2. More positively the development of market-related goals such as customer satisfaction, customer retention, market share, and revenue retention rate. 3. Higher the internal performance across the pricing process.

12.2.1 Financial Monitoring The pricing strategy defines, among other things, which of various paths for realizing higher net prices the company wishes to pursue. The following options are possible: 1. 2. 3. 4. 5.

Increase in gross prices. Reduction of discounts, rebates, and boni. Shortening of payment terms. Introduction or optimization of cost-based surcharges and fees. Surcharges for special services.

Monitoring must address all five dimensions. Financially oriented monitoring is based on various basic analyses. The standard analysis procedures include “price

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waterfall” and “profit waterfall”. The price waterfall visualizes the condition system at a glance. The different types of price reductions and their levels are processed in the figurative sense of a waterfall (Pastuch, 2018; Homburg & Totzek, 2011; Miller & Krohmer, 2011, p. 122). The graphical structure is the result of the individual price adjustments of the sales department. The structural development of price reductions from the base price (list price) to the transaction price (net-net price) is shown at the segment level. All discounts, boni, and other rebates are to be recorded via the price staircase. The price waterfall supports management in identifying hidden costs, among other things. It serves as an initial indication of the levels at which countermeasures by sales can open up financial potential. For example, bonus agreements with customers and budget payments can conceal substantial discounts without reference to performance. The greatest challenge in terms of data is the allocation of customer-specific boni to individual products. The profit waterfall visualizes the measurement of the profitability of products or customers. Subtracting all relevant costs (unit costs of production, sales and service costs, etc.) results in the contribution margin as the central objective of pricing. The greatest challenge from a data point of view is the allocation of sales and service costs to individual customers. Additional standard analyses are recommended for price controlling: (a) Price range: Visualization of all transaction prices at different end customers. (b) Price corridor analysis: Extent of price differences between customers, products, regions, and sales channels. The measurement of price enforcement is of particular importance in the context of price controlling: It is checked whether the planned prices have been realized on the market. Customer-specific agreements without a performance-based logic have negative consequences for: (a) The products (the level of contribution margins, the implementation of positioning targets, cannibalization effects within the portfolio, dilution of the brand image, etc.). (b) The results of the sales regions. (c) The implementation of target group strategies. The customer-related risks are manifold. Customers are negatively conditioned by unstructured discounts. The company literally trains its customers to be discount buyers. Regionally inconsistent discounting leverages value arguments with international customers. Users can no longer perceive a discount equity in the overall structure. Inconsistencies in pricing across sales channels have similarly negative consequences, as illustrated by the example of Media Markt in Chap. 7 (Mitsis, 2018). Pricing inconsistencies of a company damage the price trust and orientation of customers. The following questions, among others, are answered as part of price monitoring (Buchwald, 2018; Homburg & Totzek, 2011, p. 558 ff.):

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– How profitable are which company offerings (e.g., products, services, and software)? – How widely do the prices achieved vary? – How do net prices evolve over time? – How profitable are which customer relationships? – What is the achieved price level of different sales units or regions? – How profitable are which orders won? – How can the most important key figures of pricing be visualized in one tool? It is important to have full transparency about which causes are responsible for deviations. Price developments can be made transparent on the basis of various KPIs. The selection of indicators should always be tailored to the specific requirements of a company. Three important indicators and the associated questions are as follows (Buchwald, 2018): 1. Net prices. – How do absolute net prices develop in a competitive comparison? – What is the net price level enforced per product? – Does our net price increase in relation to competition? – Does our net price follow the market trend? 2. Performance orientation of the discount structure. – Which customers receive which discounts? – Is there a systematic relationship between the discount level and the importance of the customer? – Are the price gaps between customers justified? 3. Deviations from target prices. – What are the deviations (absolute or relative) of actual prices from targets? – Why were orders and customers lost in the past? – To what extent was price potential given away when winning orders?

12.2.2 Monitoring Market Effects The effects of pricing measures must not be reduced to key figures with financial relevance alone. Customer-oriented metrics must be an integral part of a monitoring system. They capture the long-term effects of product and price measures that go beyond short-term financial effects. KPIs relevant for price management are: – – – – – –

Market share Net promoter score (as an indicator of customer satisfaction) Customer loyalty Number of price-related complaints Price image Perceived price fairness

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Pricing Process Part 5: Monitoring Criteria • Market share • Negotiating power of customers • Number of competitors • Index of value generation • Brand image • Importance of the brand • Relevance of the price

high Market share

Pricing Power

low low

Margin

high

Fig. 12.2 Pricing power and pricing strategy (Source: Own representation)

– Customer benefits – Perceived price-performance ratio – Pricing power. Pricing power is the potential of a company to enforce price increases. It includes the ability to pass on costs (e.g., due to inflation) to customers without affecting sales volumes. Quantifying the pricing power of a company, a business unit or a product is of crucial importance. An assessment tool is a basic requirement for measurement. A pricing power score is based on the following criteria, among others (see Fig. 12.2): 1. 2. 3. 4. 5. 6.

Index of value generation (see Chap. 3). Brand image. Market share. Capacity utilization (supply-demand ratio). Number of competitors and market share distribution of competitors. Relative importance of price compared to value criteria. The results of the value driver analysis can be integrated here (cf. Chap. 9).

The KPIs for monitoring the market impact of price management need to be supplemented with metrics that can be used to measure the company’s innovative strength: • Share of sales from newly launched products. • Time-to-market for new product launches. • Hit vs. flop rate of launched products. Market and customer-oriented KPIs are often much better suited as early indicators than financial data. Declining market shares, a significant reduction in perceived value to customer, and a weakening of “pricing power” can lead to a medium-term erosion of profits. It is counterproductive in the long term to focus too much on profit optimization. Goals such as customer satisfaction and price image

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must not be neglected (Polleit, 2018). In the context of dynamic pricing, concrete examples have already been used to prove that the exaggerated exploitation of willingness to pay can have a counterproductive effect. Negative developments must be questioned and traced back to their causes. The importance of the price image as a success factor can be described using the example of Amazon. The price image is understood to be the buyer-individual evaluation of a retail company or online store as a low-priced place to shop (Simon & Fassnacht, 2019). In the case of a favorable price image, the price levels perceived by the customer are below the actual prices. The price-performance ratio perceived by the customer is decisive. Particularly in online industries, with increasing diversity of information, the price attitude toward the provider often determines whether a company is considered in the digitized selection process. Customers very often start their search for offers at the online retailer Amazon. The reason: In analogy to Aldi in the German retail trade, many customers consider the global market leader in Internet sales to be the lowestpriced provider (Anonymous, 2018c). Case Study of User-oriented Monitoring: Amazon and Netflix Amazon has developed an innovative measurement method for its video streaming. The aim is to use indicators to check the success of series. The “first streams” measurement approach records which video content new customers use first on Amazon Prime Video. Including the production costs of the series and the total number of “first streams”, one calculates the acquisition costs of each new Prime member (Anonymous, 2018a, 2018d). The level of the “first stream” score serves, among other things, as an indicator of the value to customer. The most important metric at the streaming service Netflix is the “consumer screen time”. The success metric of the world market leader in streaming records the time users actually spend with the Netflix offering.

Case Studies: Target Prioritization in Selected Industries Companies pursue several goals that are very often in conflict with each other. Profit maximization, for example, is often in conflict with the objectives of sales or market share maximization. Target prioritization depends on the sector, the business model, and many other criteria. Selected examples of a target prioritization in the year 2021 are, in bullet point form, as follows: the food delivery service Delivery Hero was focusing on market share growth in the German market—profitability targets were subordinate. The mobility provider Bolt was also primarily aiming for growth rather than profitability. In the case of Tesla, the prioritization is diametrically opposed. The focus on profitability also applies to the portfolio strategy and new product (continued)

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development. Mercedes-Benz focuses on cash flow instead of high sales figures (Ziesemer, 2021).

Method Tip: Integration of Financial and Market Objectives The pricing target matrix is an excellent tool for monitoring the effects of pricing measures. The monitoring method shows the effects of pricing on profit and quantity. It visualizes possible target conflicts. The target matrix combines the financial perspective (profit) and the market dimension (sales volume or market share). Price changes can influence both goals positively or negatively. In sum, there are four possible scenarios (Hofer & Ebel, 2002; Simon & Fassnacht, 2016, p. 35; Simon & Dolan, 1997). An ideal price constellation leads to both positive volume growth and an increase in margins (Fig. 12.3). This scenario is found in the upper right quadrant of the pricing target matrix).

Profit

Market share

=

Market share

increase

Market volume

x

x

Margin

Trade Off I

Win-Win

Increase market share at the expense of margin

Increase margin and market share

Dilemma

Trade Off II

Reduction in margin and market share

Increase in margin at the expense of market share

reduce reduce

Margin

increase

Fig. 12.3 Pricing target matrix, extended (Source: Own representation; Simon & Dolan, 1997)

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313

Case Study Pricing Power: Sea Freight In the recent past, the products of numerous companies have been manufactured in China and transported by containers. This has led to an enormous demand for container freight. Supply has not come close to keeping pace with this enormous increase (see 11 C of Pricing; Chap. 5). The consequence for the price level: on the sea route from China to Europe, freight rates have increased more than sevenfold in the past two years (until the end of the third quarter of the year 2022). Freight rates from China to Europe have in some cases exceeded USD 14,000 per container. From the supplier's point of view, this is an ideal price constellation, as positive volume growth is accompanied by an increase in margins (Reiche, 2022). Consequently, the sea freight industry is found in the upper right-hand field (quadrant “Pricing Power” in Fig. 12.2). In most cases, however, a prioritization is required: A clear decision has to be made for either volume growth only or margin growth. In the trade-off area—in the quadrants trade-off I and II—there are positive and negative effects in both directions. How margins and sales volumes (or market shares) develop can only be assessed if the price elasticities of customers are known. In the case of a high elasticity, price increases lead to failure. In Apple’s case, it became increasingly obvious over the course of spring 2018 that a significant proportion of customers were no longer willing to accept further price increases (Eisenlauer, 2017; Fröhlich, 2018a, 2018b; Hohensee, 2018; Jacobsen, 2018; Kharpal, 2016; Mansholt, 2018a, 2018b; Obermeier, 2018; Anonymous 2018b, 2018f, 2018g; Schlieker, 2018). The price for the successor model of the iPhone X was set even higher. This was despite the fact that the launch of the X model series in November 2017 proved to be a risky strategy in view of market targets (sales volume, market share, customer satisfaction, etc.) (Anonymous, 2018e; Fröhlich, 2018b; Schlieker, 2018; Jacobsen, 2018). With the iPhone X, Apple was assigned to the lower right quadrant (Trade-off II) in the adapted pricing target matrix for a few months (Fig. 12.3). Looking at two important pricing KPIs (market share and profit), Uber’s recent development is dramatic. The net operating profit margin after tax (as a key indicator of a company’s profitability) was minus 29.9% in 2021. In 2018, it was still minus 19%. In the USA, Uber’s strongest market for ride services, the company has been losing market share for four and a half years. At the beginning of 2017, it still dominated 82% of the market. In 2021, the share was only 69% (Rest, 2021). Strategic price monitoring must not be limited to the effects of pricing measures on customers. In addition, all those price aspects that are relevant for the management of digital business models must also be included. Controlling must also include business model aspects such as necessary resources, revenue models, and value creation partners. It is important to pay close attention to changes in order to be able to secure strategic advantages. Sample questions in this context relate to:

314

– – – –

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The control of resources. The influence on the value creation system (ecosystem) The motivation of the value creation partners. The company’s sources of revenue.

Supplementary metrics for measuring the success of the digital transformation are indispensable. Chapter 3 presented a comprehensive method for deriving a business model. Numerous KPIs result from the business model map. However, the status quo in a large proportion of companies is sobering: over 80% of digital transformation projects end in failure. In over 50% of companies, there are not even any metrics for measuring the success of digital transformation!

12.2.3 Monitoring Pricing Excellence Professionalism in pricing refers to the process and the consistency of the individual steps. Consistency encompasses two dimensions. Horizontal: Across the pricing process. Vertical: The interaction of business model, revenue model, and pricing process. The following exemplary questions are used to assess the internal “excellence” in pricing: 1. How good is the quality of information on costs, competitors, and customers? 2. How extensively and in detail do we include all necessary information in the strategy definition? 3. Do we document our strategy? 4. How systematic is the process for the pricing of new products? 5. How do we communicate prices for new products? 6. Do we use modern methods for price optimization? 7. Are we developing creative methods? 8. Do we systematically derive price models from the higher-level revenue model? 9. How structured are we in planning price adjustments in the market? 10. How professionally do we prepare for price negotiations? 11. Do we support our sales team with tools for negotiation? 12. How comprehensive and systematic is our price controlling? The 12 items outlined represent selected success criteria. Depending on the company, sector, and business model, these questions must be expanded and detailed accordingly. While the items of pricing excellence can be adapted to specific sectors and companies, the evaluation process should be standardized. Simple maturity models are not sufficient for corporate practice. Typical maturity models have major limitations (e.g., inaccurate measurement via an ordinal scale, insufficient delimitation of the maturity level, lack of operationalization of the maturity level via practical criteria, and non-existent implementability for pricing). The evaluation process that I developed is based in detail on the following steps:

References

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1. Definition of all relevant steps of the price management process. 2. Operationalization of performance levels for each process step. The basis is a cardinal scale for performance. This ranges from 0 (“non-existent” or “not observed”) to 100 (“very professional” or “no further improvement possible”). 3. Prioritization of the steps (optional). 4. Evaluation of each step along the pricing process in terms of performance. 5. Calculation of an overall score for the professionalism of the pricing process. This brings us full circle back to the core statements made at the beginning: digitization is neither a strategy nor a goal. It serves to achieve strategic goals. Digital pricing is about optimization in the area of conflict between revenue, profitability, market share, and customer satisfaction. The following applies to both digitization and pricing: prioritizing goals is the first step in any initiative. Digital pricing is a systematic process for achieving strategic corporate goals and meeting new customer needs. The focus is on the customer and thus on the two central influencing factors of digital pricing: value to customer and value of customer.

References Anonymous. (2018a). Interne Dokumente belegen erstmals, wie Amazon mit Prime Video massiv neue Kunden gewinnt. Absatzwirtschaft Online Accessed April 22, 2022, from http://www. absatzwirtschaft.de/interne-dokumente-belegen-erstmals-wie-amazon-mit-prime-video-massivneue-kunden-gewinnt-128255 Anonymous. (2018b). iPhone X ist ein Flop–Und trotzdem wird Apples nächstes Modell wohl noch teurer. Focus Online. Accessed April 22, 2022, from https://www.focus.de/digital/handy/ iphone/analysten-prognose-iphone-x-ist-ein-flop-und-trotzdem-wird-apples-naechstes-modellwohl-noch-teurer_id_8772182.html Anonymous. (2018c). 90% aller Amazon-Verkäufe finden in der BuyBox statt. Wir geben 100 Prozent, damit Sie die BuyBox gewinnen. Accessed April 22, 2022, from https://www. sellerlogic.com/de/amazon-repricing-tool/ Anonymous. (2018d). Geheime Messinstrumente zu Amazon Prime Video enthüllt: So weiß Amazon, ob sich eine Serie lohnt. Finanzen.net. Accessed April 22, 2022, from https://www. finanzen.net/nachricht/aktien/first-streams-geheime-messinstrumente-zu-amazon-prime-videoenthuellt-so-weiss-amazon-ob-sich-eine-serie-lohnt-6046608 Anonymous. (2018e). Künstliche Intelligenz vor dem Durchbruch: Roland-Berger-Chef prophezeit: Facebook und Google droht ähnliches Schicksal wie Nokia. Focus Online. Accessed April 22, 2022, from https://www.focus.de/finanzen/news/kuenstliche-intelligenzvor-dem-durchbruch-roland-berger-chef-prophezeit-facebook-und-google-droht-aehnlichesschicksal-wie-nokia_id_8329799.html Anonymous. (2018f, February 3). Der Preis des iPhone X rettet Apple die Bilanz. Frankfurter Allgemeine Zeitung, S. 23. Anonymous. (2018g). Einnahmen mit Cloud-Anwendungen. Wie Apple mit Ihrem iPhoneSpeicherplatz Geld macht. Manager Magazin Online Accessed April 22, 2022, from https:// www.focus.de/digital/computer/apple/apple-das-geschaeft-mit-zu-geringem-speicher_id_84 56497.html Buchwald, G. (2018). Pricing-Lexikon. Prof. Roll & Pastuch Management Consultants. Accessed April 22, 2022, from https://www.roll-pastuch.de/de/unternehmen/pricing-lexikon

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Eisenlauer, M. (2017). Kein Supercycle. Schadet das iPhone X apple? Bild Online. Accessed Apr 22, 2022, from https://www.bild.de/digital/smartphone-und-tablet/apple/iphone-x-hype-542 76620.bild.html Fröhlich, C. (2018a). Phil Schiller–Dieser Mann soll den Apfel glänzen lassen. Stern Online. Accessed April 22, 2022, from https://www.stern.de/digital/smartphones/phil-schiller-inter view%2D%2Des-gibt-keine-preis-obergrenze-fuer-das-iphone-7776804.html Fröhlich, C. (2018b). 1000 Euro für ein Telefon. Wozu braucht man überhaupt noch ein teures Smartphone? Stern Online. Accessed April 22, 2022, from https://www.stern.de/digital/ smartphones/smartphones-fuer-1000-euro—wozu-braucht-man-die-eigentlich-noch–7894046. html Hofer, M., & Ebel, B. (2002). Alles eine Frage des Preises. Auto-Marketing Journal, 2, 18–21. Hohensee, M. (2018). iPhone X: Apple enttäuscht und begeistert zugleich. Accessed April 22, 2022, from https://www.wiwo.de/unternehmen/it/iphone-x-apple-enttaeuscht-undbegeistert-zugleich/20919524.html Homburg, C., & Totzek, C. (2011). Preismanagement auf Business-to-Business-Märkten: Zentrale Entscheidungsfelder und Erfolgsfaktoren. In C. Homburg & C. Totzek (Eds.), Preismanagement auf B2BMärkten (pp. 15–69). Gabler. Jacobsen, N. (2018). Apple: Das iPhone X ist eine Verkaufsbremse–Trotzdem soll das Nachfolgemodell noch teurer werden. Handelsblatt Online. Accessed April 22, 2022, from http://www.handelsblatt.com/technik/it-internet/apple-das-iphone-x-ist-eine-verkaufsbremsetrotzdem-soll-das-nachfolgemodell-noch-teurer-werden/21178402.html Kharpal, A. (2016). Apple captures record 91 percent of global smartphone profits: Research. Accessed April 22, 2022, from https://www.cnbc.com/2016/11/23/apple-captures-record-91percent-of-global-smartphone-profits-research.html Lindinger, M. (2018). Digitale Flut. Frankfurter Allgemeine Woche, 6, 60. Mansholt, M. (2018a). Smartphone-Konkurrenz. iPhone X verkauft sich schlechter als gedacht– Das stellt Samsung vor Probleme. Stern Online. Samsung vor Probleme. Stern online. Accessed April 22, 2022, from https://www.stern.de/digital/smartphones/iphone-x-verkauft-sichschlechter-als-gedacht—und-stellt-samsung-vor-probleme-7869276.html Mansholt, M. (2018b). iPhone X. Warum nur Apple sich ein Smartphone für 1319 Euro leisten kann. Accessed April 22, 2022, from https://www.stern.de/digital/smarter-life/iphone-x%2D%2 Dwarum-nur-apple-sich-ein-smartphone-fuer-1319-euro-leisten-kann-7618822.html Meckel, M. (2018). Die echten Handelskriege werden längst um Daten geführt. Accessed April 22, 2022, from https://www.wiwo.de/politik/ausland/schlusswort-die-echten-handelskriegewerden-laengst-um-daten-gefuehrt/21142272.html Miller, K., & Krohmer, H. (2011). Ausgewählte Entscheidungsfelder des Preismanagements auf B2B-Märkten. In C. Homburg & C. Totzek (Eds.), Preismanagement auf B2B-Märkten (pp. 105–126). Gabler. Mitsis, K. (2018). Kein Preis-Chaos mehr bei Media Markt: Elektro-Riese plant langersehnten Schritt. Accessed April 22, 2022, from http://www.chip.de/news/Kein-Preis-Chaos-mehrMedia-Markt-und-Saturn-planen-langersehnten-Schritt_134574723.html Müller-Jung, J. (2018). Daten im Blut. Frankfurter Allgemeine Woche, 10, 61. Obermeier, L. (2018). Kaum jemand will das Galaxy S9–Warum sich Samsung und Apple verzockt haben. Focus Online. Accessed April 22, 2022, from https://www.focus.de/digital/handy/ schlechte-verkaufszahlen-bei-smartphones-kaum-jemand-will-das-galaxy-S9-kaufen-warumsich-samsung-und-apple-verzockt-haben_id_8609160.html Pastuch, K. (2018). Pricing-Lexikon. Prof. Roll & Pastuch Management Consultants. Accessed April 22, 2022, from https://www.roll-pastuch.de/de/unternehmen/pricing-lexikon Polleit, T. (2018). So unterscheiden Sie gute Gewinne von schlechten. Wirtschaftswoche. Accessed April 22, 2022, from https://www.wiwo.de/finanzen/geldanlage/intelligent-investieren-sounterscheiden-sie-gute-gewinne-von-schlechten/20805752.html Reiche, L. (2022). Containerreeder im Gewinnrausch. Accessed April 22, 2022, from https://www. manager-magazin.de/unternehmen/handel/containerreeder-im-gewinnrausch-fuer-den-

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Focus Topic Pricing Process and Pricing Psychology: Introduction

Behavioral economics studies the psychology of consumers. Human behavioral tendencies provide companies with numerous clues for designing and communicating prices. Psychological, cognitive, emotional, cultural, and social factors influence two central dimensions which determine price management fundamentally: 1. The perception of individuals (e.g., potential customers) and groups (e.g., buying centers) 2. Decisions based on perceptual processes. From the user’s perspective, key findings of behavioral economics are as follows: – A price point is always perceived depending on the context in which it is placed. – Different customers perceive objectively identical price information differently. – The perception of prices and price changes depends on numerous factors. Particularly important determinants of perception are the price level, the product type involvement, and the type of payment method. – Prices and price differences are systematically over- or underestimated. – Customers use cognitive shortcuts (heuristics) to make decisions. – Heuristics are rules of thumb that reduce complex tasks to mentally simpler processes (cf. Tversky & Kahneman, 1974, p. 1124). Examples of simplification heuristics are provided by the customer behavior at self-service gas stations and in restaurants. In many cases, the refueling process is controlled in such a way that the invoice price ends in a round amount. Many customers apply the same logic in the course of their decision on a tip in a restaurant: the tip brings the total bill to a rounded number. – Cognitive biases result from the application of heuristics. # Springer Nature Switzerland AG 2023 F. Frohmann, Digital Pricing, Management for Professionals, https://doi.org/10.1007/978-3-031-24591-6_13

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– Biases simplify the decision-making process! Customers often reduce their decision to a small amount of available information (e.g., “Halo effect” and “Primacyrecency effect”). Bounded rationality is the key term in this context (Krämer, 2018, p. 102). From a business perspective, key insights from behavioral economics are as follows: – Psychological pricing tactics can be used to influence the customer’s buying decision. – Willingness to pay is relative. Price acceptance can be influenced by reference values. – The context in which a price is presented can be changed without significantly altering the price itself. – Innovative pricing structures increase profits by mitigating the perception of the price paid. Price acceptance increases. The demand curve is shifted outward. Sales volumes and customer loyalty increase. – Companies have numerous “cues” as instruments at their disposal. Cues are informational stimuli that can influence consumers’ perception processes. – Nudges (in the sense of “nudging”) can lead customers to make a decision that contributes to achieving the company’s goals. – Behavior-based pricing techniques are product-, customer-, and contextdependent—and therefore hardly generalizable. Psychological insights are of central importance for price management. The following integration of pricing process and pricing psychology is one of the numerous methodological innovations of the book. Eleven pricing psychology principles are assigned to the point in the pricing process where they can be applied in terms of process sequence and content. The focus is on the essential decision fields in price management that relate to the interactions with customers—the process challenges “structure” and “implementation” as well as their detailed steps (cf. Fig. 13.1).

13.2

Price Psychology and Structure (1): Mental Accounting

Psychological insights are of central importance for price differentiation. Customers assign their transactions to different mental account categories (Thaler, 1985; Kopetzky, 2016, p. 30). The total expenditure for products, services, and digital offers is mentally booked to thematically different accounts. A classic example in the B2C sector is vacation trips. For example, all expenditures for air travel as well as accommodation, rental cars, etc. are booked to the thematically appropriate “vacation” account. In B2B business, capital expenditures for an industrial good are distributed over various points in time and different services (initial purchase, extraordinary repairs, spare parts, etc.). Mental accounts are used for self-control:

13.2

Price Psychology and Structure (1): Mental Accounting

Pricing process (stage)

Challenge

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Price psychology

B.

Structure

•

Price differentiation

Bundling vs. unbundling?

1. Mental accounting

•

Price models

What time-price structure?

2. Price level effect 3. Anchoring

What price level? •

Price optimization

4. Value effect Odd vs. smooth prices?

•

Portfolio pricing

C.

Implementation

•

Condition systems

•

Tactical pricing

How many value-price alternatives?

5. Price threshold effect 6. Compromise effect 7. Decoy effect

Form of price presentation?

8. Price figure communication

What discount frequency?

9. Tiered discounts

Price discounts or discounts in kind?

10. Endowment effect

Price adjustments

11. Loss aversion

Fig. 13.1 Pricing process and psychology (structure) (Source: Own representation)

customers assign budgets to individual accounts and monitor the consistency of their own behavior. Separate accounts are maintained for different categories of transactions—and managed independently of each other. The creation of mental categories helps to make spending decisions (Thaler, 1985, p. 207 f.). The cognitive effort is reduced. The customer only has to refer to one category and not to the entire financial portfolio. The budgets for different accounts are not interchangeable. As soon as the allocated budget on one account is used up, further expenses are avoided. For example, A user’s mental budget for entertainment includes EUR 800 per year. After buying a concert ticket in the amount of EUR 50, there is EUR 750 left to spend on entertainment. Over time, a mental deduction of expenses occurs. Details of the individual accounts are forgotten. Under three specific conditions, the individual amounts are not mentally added up by the customer: • The prices for a main product and additional ancillary costs are communicated separately. These can be, for example, shipping costs or service charges in online retailing. • The payments are staggered. • Expenses are assigned to different categories (e.g., pleasure, security, and transportation for a trip). This overall constellation significantly influences both dimensions of customer perception—price and value (see Chap. 9).

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With regard to the price effect: customers often underrate ancillary costs. Or additional costs and parts of the offering are perceived independently of the price of the core service. The consequence of this price-psychological effect applies equally to products and services: If customers only consider a part of the costs, they perceive the products in question to be cheaper. The total cost of a purchase is underestimated. In extreme cases, ancillary costs are perceived as being completely independent of the price of the main product. The following results from the sunk cost fallacy (sunk cost effect): Bundled prices (flat prices) are perceived more strongly than partitioned (split) prices (Kopetzky, 2016, p. 11). Debundling (price partitioning) is recommended—due to the comparatively lower customer sensitivity. With regard to the value effect: The separate price presentation of individual components of a product bundle can increase the value of the individual services. This also increases the perception of the benefits of the product package. The overall effect of debundling can be summarized as follows: lower price elasticity and increased value transparency! Other findings in this context are: • The temporal sequence of purchase, payment, and consumption influences the customer’s purchase decision and intensity of use. If payment takes place before consumption, an interesting effect arises: subjective price perception during consumption decreases with increasing temporal distance from the payment. The greater the temporal distance between payment and use, the higher the psychological depreciation effect. Prices are forgotten (Kopetzky, 2016). • The recall of prices paid also depends on the satisfaction of a customer (Homburg et al., 2005). As customer satisfaction increases, the exact recall of the price paid decreases. Dissatisfied customers tend to pay significantly more attention to current and paid prices. The recommended action points for companies in such constellations are: – Break down the product into components and price the services individually! – Set the individual prices for the components optimally! – Actively manage customer satisfaction!

13.3

Price Psychology and Structure (2): Price Level Effect

Psychological findings are of central importance for the design of price models. The following findings from brain research are particularly relevant for price metrics (Simon, 2015a, p. 106): 1. An annual fee is perceived differently by customers than a payment per quarter or month.

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Price Psychology and Structure (3): Anchoring

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2. Customers tend to prefer shorter terms with lower amounts per time unit. Consequently, a monthly fee of EUR 1 is perceived more favorably than an annual price of EUR 12. 3. A price metric of EUR 1 per day is clearly preferred by consumers over the alternative of EUR 365 per year. The objectives of companies are diametrically opposed to the requirements of users. Effective customer retention requires the longest possible run times of contracts (e.g., subscriptions). Consequently, to encourage customers to sign contracts with longer durations, the average price must fall significantly. A non-linear price scale for time units describes Sky Sport’s offer in 2017. The price scale was: EUR 24.99 per month, EUR 14.99 per week, and EUR 9.99 per day. There was a strong incentive to increase the duration of the contracts. The average price per day decreases significantly as the term increases.

13.4

Price Psychology and Structure (3): Anchoring

Psychological insights are of central importance for product pricing. Behavioral economic aspects are highly relevant, especially when launching a new product. Three theses on the pricing strategy for new products are: 1. Initial pricing is a key determinant of product success. 2. Underestimating product benefits costs companies a great deal of profit. 3. If the initial price is set too low, it is difficult to compensate for the loss of profit over the life cycle. What explains the influence of price psychology? According to the aspect of anchoring, customers pay close attention to the first available information in the course of their purchase decision processes (Simon, 2015a, p. 91, Simon, 2015b, p. 31; Kopetzky, 2016, p. 21). A price anchor plays an important role in the selection decision. Customers often decide subconsciously with a reference price in mind— the decision process is no longer reduced to every detail (focalism). In the case of a new product category, however, the buyer has no price anchor. There is no reference point for an orientation in the assessment process. There is no benchmark for the evaluation of whether a price is appropriate. From the company’s point of view, the following applies: new products in a category set a unique anchor price when they are launched on the market. With the help of a single figure, the supplier anchors the benefits of its product in the customer’s perception. With this anchor, the new product price sets a reference point for all subsequent price movements. All products in the category introduced are subsequently compared with the anchor price. Apple’s iPhone 8G was launched in June 2007 at USD 599 in the USA—2 months after its release, the price was reduced to USD 399.

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Anchoring is also one of the most important price-psychological influencing factors for the existing product portfolio of companies and retailers. To understand this, one must apprehend how customers behave. Customers – Have difficulties to evaluate prices according to their absolute level. – Perceive a price relatively (i.e., with respect to an internal and/or external reference price). – Rely heavily on the first piece of information (the number considered first) in comparative assessments. The first information influences all subsequent judgments. This explains the metaphor of the anchor and the notion of anchoring for reference evaluation. What happens when a buyer considers the price of one product versus another? He is influenced by the first product he sees. The reference price comes about as follows: 1. Internally (customer): Based on the price experience from past purchases or price expectations of future transactions. The reference price is embedded in the consumer’s mind. The intrinsic value corresponds to the price that customers define as normal or appropriate. 2. Externally (company): Reference prices are actively presented to the customer by companies. Suitable formats (cues) are: recommended retail prices, the previous sales price, or the price of comparable products in the same category. Cues can have a great effect in the sense of “nudging”: Nudging is a price-psychological principle of behavior control. Nudging encompasses all controlling measures that lead to a decision being made in a certain direction. It is literally about “nudging in the right direction”. For example, an article sold at a price of EUR 25 is perceived as being cheaper when the customer learns that the product normally costs EUR 35. Legal framework conditions (“compliance”) must necessarily be taken into account. Some tactics of “nudging” are not permitted under competition law. An example of this: A higher price point is visually crossed out as part of a promotion and replaced by a lower one (e.g., on a price tag or in a price list)— however, the higher price never factually existed. Implementation as a psychological anchor is not legally permitted in this form. Another aspect of price psychology is relevant in the context of these considerations: Framing! Framing is about presenting information in a context that is intended to steer its interpretation in a certain direction. Framing classically works through the different formulation of texts with the same content. The framing principle states: the attractiveness of a price changes depending on the context in which it is presented. When a customer sees the most expensive (of five) prices first, he perceives the other prices as cheaper (option A). When the customer sees the cheapest (out of five) prices first, all other price points appear to be more expensive. For companies, this means: option A is used for price presentation. The product with

13.6

Price Psychology and Structure (5): Price Threshold Effect

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the highest price and the best quality is placed on top. The customer is then more likely to choose one of the higher-priced products than with option B. Companies that want to achieve more sales should present the most expensive price (or higher quality offers) first. Option A leads to a higher average sales price than option B.

13.5

Price Psychology and Structure (4): Value Effect

“The higher the price, the better the product”: this price-psychological effect has been proven by many experiments in numerous industries. One experiment in the product category of wine is particularly striking. The test involved the brand Cabernet Sauvignon and various versions offered for trial. Price information was available to the subjects—brand and quality level were concealed. The test subjects did not know that they were offered two completely identical products in terms of quality. Customer selection focused on the most expensive of the versions. The psychological principles of the value effect are as follows: (a) In certain constellations (unclear quality assessment, time pressure, etc.) customers apply a simple heuristic. (b) A high price is perceived as an indication of a high-quality offering—customers assume a positive correlation between price level and quality. (c) The price is an indicator of quality—and thus a signal of quality. The price itself is what signals and generates value in the eyes of the customer. (d) Higher prices may signal a better quality to consumers without sufficient information in certain constellations. These situations include (in bullet points): Product attributes can only be assessed through practical experience; potential customers know nothing about the product or service; consumers have a high degree of uncertainty about what to buy; information search on offerings and prices is difficult; there are only a few available data sources. For companies, these psychological findings mean in consequence: (a) Price can be used as a cue for assessing the value-price ratio. (b) A high price can help a company build the image of a premium product. (c) A higher price can therefore lead to an increase in sales volumes.

13.6

Price Psychology and Structure (5): Price Threshold Effect

Psychological insights are of central importance for the optimization of price levels (product pricing). Price thresholds are price points which, if exceeded, can result in significant sales volume losses for reasons of customer psychology (Simon, 2015a, p. 81 f.; Kopetzky, 2016). For this reason, many companies work with broken (odd) prices—especially in consumer goods industries, for services and digital offerings. Fractional price points are values just below a round EUR or USD amount or ten cent

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amount. Suppliers in numerous industries expect this type of price communication (e.g., EUR 0.49, EUR 4.99, or EUR 499) to boost sales volumes. The logic is: odd prices such as EUR 9.90 are perceived as significantly cheaper than smooth prices (e.g., EUR 10). In food retailing, this type of price labeling is used for a large proportion of products. The hypothesis is—simplified—as follows: price acceptance for a chocolate bar in the medium price segment for less than EUR 1 tends to be high. As soon as the price exceeds EUR 1, willingness to pay drops significantly. Tour operators, airlines, rental car companies, ferry companies, or cable car operators also hardly ever work with smooth amounts in their price lists. It is not uncommon for companies to deliberately undercut striking thresholds with price points of EUR 99, EUR 499, and EUR 999. Price threshold effects are used especially in subscription price models. Fitness studios are an example of this. Even for products whose purchase is preceded by an intensive decision-making process (e.g., cars and highend consumer electronics), manufacturers often use threshold prices. Two examples show the great importance of odd price points in growth industries: A. Nokia—Smartphones in Germany: Launch of five different versions, each with prices below a price threshold (Eisenlauer, 2018). – Nokia 8810: EUR 79 – Nokia 1: EUR 99 – Nokia 6: EUR 279 – Nokia 7 plus: EUR 399 – Nokia 8 Scirocco: EUR 749 B. Amazon—Echo product line in online retail in Germany: Launch of five different versions, each with prices just 1 cent below a price threshold (source: Amazon website, March 4, 2018, 1 p.m.). – Echo Dot: EUR 59,99 – Amazon Echo: EUR 99,99 – Echo Plus: EUR 149,99 – Echo Spot: EUR 129,99 – Echo Show: EUR 219.99 The importance of price thresholds is to be explained by a variety of effects (Stiving & Winer, 1997, p. 65). Customers – Group prices into rough ranges and apply patterns to help them make decisions. – Convert numbers so fast that the size of the number is encoded before the reading process is complete. – Read prices from left to right. – Perceive the digits of a price with decreasing intensity. – Are most influenced by the first digit in the price perception (i.e., a price of EUR 9.95 is perceived as “9 and something”).

13.6

Price Psychology and Structure (5): Price Threshold Effect

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– Compare two numbers digit by digit from left to right; the comparison is aborted as soon as one digit differs. – Place the focus on the first alternating digit from the left. – Tend to remember only the first digit and round off the rest. – Perceive mainly numbers ahead of the decimal point. – Pay more attention to EUR or USD amounts than to cent levels. – Divide a price scale into discrete categories (EUR 2.98 is coded as “between EUR 2 and EUR 3”; EUR 4.95 is perceived as “below EUR 5”). Let us analyze two price pairs (EUR 0.89/EUR 0.75) and (EUR 0.93/EUR 0.79), respectively, which are presented to customers for an evaluation (list price/ discounted price). The question is which offering is perceived as being more favorable? The majority of respondents will perceive the discounted price of “EUR 0.79” (option 2) as a more advantageous offering. Yet the price difference is exactly the same in both cases: 14 cents. The explanation for the customer perception is as follows: in the digit-by-digit comparison from left to right, the difference of the first decimal place in the second price pairing (9–7 = 2) exceeds that of the first (8–7 = 1). Two psychological reasons explain the phenomenon of odd prices: A. Profit effect: When evaluating an odd price point (e.g., EUR 2.99), the customer considers the nearest round price (EUR 3.00) as the reference price. The difference between the odd and the smooth price is perceived as a gain. B. Left digit effect: 1. Price comparisons are primarily made on the basis of whole-number amounts. An amount of EUR 1.01 is consequently perceived as being significantly more expensive than a price of EUR 0.99—which is only slightly lower. The transition from EUR 2.99 to EUR 3.00 is significantly different in terms of price assessment compared to the difference from EUR 3.59 to EUR 3.60. 2. The impact of a digit on the price perception is smaller the further to the right it is positioned. 3. The digits to the right of the decimal point are less important than the digits to the left of the decimal point. A lower first number at the beginning of the price (e.g., EUR 3.99 vs. EUR 4.00) has an enormous psychological effect, even if the price is more or less the same. 4. Changes in the digits before the decimal point are perceived more strongly by consumers than (absolutely equal) changes after the decimal point. 5. If the left digits are the same, customers use the right digits for evaluation. The right digits are completely ignored if the left digits are different. 6. Price thresholds with changing front digits of the price (EUR 0.99 vs. EUR 1.00; EUR 9.90 vs. EUR 10.00, etc.) have a stronger psychological impact than thresholds where the first digit of the price remains unchanged (EUR 2.49 vs. EUR 2.59).

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7. The difference of one cent between 6.80 and 6.79 EUR does not have a big impact on customer perception—but a difference of 1 cent between 10.00 and 9.99 EUR is significant. 8. Price thresholds mark the boundaries of perceptual categories. Particularly striking boundaries are amounts such as EUR 1000, EUR 100, or EUR 1. For example, Apple’s exceeding of the EUR 1000 price threshold for smartphones was viewed very critically by many users (Kuhn & Berke, 2018). Customers do not perceive milk prices of 98 cents as being too expensive. At a price of EUR 1.09, on the other hand, milk has crossed an important threshold. Budget aspects are added to the overall consideration. In the case of consumer goods, maximum amounts are very often set before the purchase decision is made. In industrial purchasing processes, too, a buyer often has only a certain investment amount at his disposal. Users and professional decision makers prefer to define their subjective maximum prices in round numbers (e.g., “price should be less than 3 EUR”, “I would pay a maximum of 5,000 EUR”). Most odd prices end with “99”. However, the “99” price effect does not work identically everywhere (Kopetzky, 2016). The effect depends on the price level, the region, and the sector, among other factors. Competitive behavior also explains the effect of price thresholds: 1. Price level: For low-priced offers, a “99” ending causes a positive volume effect compared to a price digit ending in 95. However, this favorable volume effect turns as the price level increases. The explanation lies in the customers’ perception of quality. In the case of high-quality offers, odd price points jeopardize the quality indication of the price. This explains, among other things, why BMW and Mercedes abandoned price points ending with a 9 a few years ago. 2. Region: The odd price effect works differently in different countries. Example China: price points ending in 4 and 8 stimulate the sales volume more than endings in 9. Socio-cultural aspects are responsible for this. 3. Sector: In B2B segments, purchasing decisions are made with greater rationality. The solution to the problem is much more in the foreground. The price perception differs significantly from an end consumer in a B2C purchase example. In addition, price points that end in zero (or have no decimal places at all) are seen as an indicator of quality. With smooth prices (e.g., “EUR 1000”), customers tend to assume that the price is negotiable (Kopetzky, 2016). 4. Competitive dynamics: A convergence at certain price points can be observed in numerous B2C sectors. Three influencing factors have contributed to this: (a) the widespread use of the price threshold technique (“charm pricing”); (b) a strong competitive orientation in B2C pricing (“competitive pricing”); (c) algorithms and associated pricing logics (price matching, price beating, repricing, etc.). The consequence for companies: increase in price transparency for customers; complete interchangeability. In order to differentiate themselves from competitors,

13.7

Price Psychology and Structure (6): Compromise Effect

329

some companies have resisted this trend toward an “one-size-fits-all” pricing (including Walmart).

13.7

Price Psychology and Structure (6): Compromise Effect

Psychological insights are of central importance for portfolio pricing. The price position of an offering in relation to other prices and product versions influences customer behavior. A customer’s perception and willingness to pay are always relative. They can be strongly influenced by reference points or anchor prices. For the versioning in the context of a product portfolio, this means: the same price level (e.g., EUR 10) causes completely different reactions, depending on whether the price point represents the highest, lowest, or middle level within a product line. Customers: – Tend to choose a middle option when presented with three options. – Tend toward a price in the middle of the range. – Feel that the middle option is a reasonable compromise between the smallest (or cheapest) option and the largest (or most expensive) option. This interrelation is described in behavioral economics by the “comparison set” effect: • Low-priced alternatives lead to reduced price expectations. • Alternatives with a higher price positioning shift the reference price upward. Two major consequences result from this: • Purchases can be controlled by the price range of an assortment. • The addition of higher-value alternatives. – Influences the product selection. – Promotes the purchase of higher-priced products. – Can be used to increase sales volumes. Using telecommunications as an example, this can be proven mathematically with a simple case study. The premises are: • • • •

In the initial situation, two tariffs are offered. The basic fees are EUR 25 and EUR 60. 78% of customers opt for the low-cost rate (EUR 25). The resulting average price (“average revenue per user”, ARPU) is EUR 32.70.

The introduction of a third variant at EUR 50 causes a significant proportion of customers to choose the medium-priced product. Price psychology describes this customer behavior with the terms “extremity aversion” or compromise effect

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(Kopetzky, 2016, p. 26). Customers avoid extremes (Buxmann & Lehmann, 2009). A significantly smaller proportion of users choose the cheapest rate. Depending on the exact percentage distribution of users among the three offer variants, this results in a significantly higher average price. In order to calculate the ARPU effect, we assume a percentage share of the cheapest rate of only 44%. The average price is now EUR 41.20. Price-psychological findings speak in favor of an optimal number of three versions (“good-better-best” offer), especially for digital offers. This is based on three structural interrelations of behavioral economics: 1. The paradox of choice: The number of product-price options has a significant effect on the customer’s choice. It also determines the user’s satisfaction with his decision-making process. Hick’s law states: the number of options (choices) increases the time and effort required to make a decision. This in turn leads to churn! The insight of the choice paradox is that reducing the number of choices leads to both a more efficient decision and greater user satisfaction (Pena, 2017). Too much complexity in choice reduces customer satisfaction (overchoice effect)! 2. The magic of the middle (“extremeness aversion”): Customers’ aversion to very cheap and very expensive solutions is the core motive for a medium-priced version. According to this logic, the customer will avoid the two extreme poles and choose the middle version as a compromise solution (Buxmann & Lehmann, 2009; Kopetzky, 2016). 3. Control over the shopping experience: Comfort aspects also speak in favor of a preference for the center. The center-stage effect applies particularly to decisions that seem insignificant from the customer’s point of view. A manageable number of choices gives the customer the good feeling of being able to make a selection. In the case of a monoproduct, they can only accept or reject the offer. By means of a product versioning, the provider takes the binary decision (yes/no) away from the purchase. The trade-off effect is a particularly striking example of how influenceable customers’ perceptions can be and how companies can steer this. Introducing new (and better quality) versions of the product at higher prices significantly increases sales volumes and profits. Small contextual changes can be enough. Let us look at another example from the wine industry. The premises are: • In the initial situation, two versions of wine are offered. • The prices are EUR 4.99 and EUR 9.99. • 80% of customers opt for the cheap rate (EUR 4.99). • The resulting average price (“average revenue per user”, ARPU) is EUR 5.99. • The introduction of a higher-value variant at EUR 14.99 causes a significant proportion of customers to gravitate toward the medium-priced product. The new distribution is as follows: Half of the buyers choose the EUR 9.99 bottle. 40% buy the low-price variant. 10% choose the high-end wine. The new average price is EUR 8.49 (increase: 41.7%). • The increase in sales is more than 15%—only due to an additional offer.

13.8

Price Psychology and Structure (7): Decoy Effect

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For a B2B industry (mechanical engineering), the recommendation is simplified as follows: If you want to sell a medium-quality category (“gold” version) and not the low-priced version (“silver” variant): Offer a third version with higher quality (“platinum”). Implementation tip: analyze the customer selection (the allocation to the various alternatives) in detail to optimize your price level. Excessive relative usage of the “good” variant is a clear indication for a repricing. Only a change in the pricing structure can ensure that customers use the higher-value offers.

13.8

Price Psychology and Structure (7): Decoy Effect

The choice effect can be used in a variety of ways to optimize the price structure. In one extreme form, two different product versions are offered at the same price. The core of this effect is the direct comparison of an attractive offer with a recognizably inferior product at exactly the same price. By means of this technique, the purchase decision of all users is influenced (Kopetzky, 2016). The attractive variant is preferred by all customers. This special form of the compromise effect is known as the decoy effect (attraction effect; bait effect). The following case study on the decoy technique refers to an advertisement in the magazine “The Economist” a few years ago. It was about price advertising for media content. The offer of three subscription versions was designed as follows (Pena, 2017): – Online version: USD 59 – Printed version: USD 125 – A package of both services: USD 125 The printed version makes no sense from the customer’s point of view in this extreme price constellation. The decoy product offers the perceptibly worse priceperformance ratio. The obvious question is: What customer would choose the printed version if they can buy both versions together as a package for the same price? The crucial question, however, is a different one. How would the customer decide if the printed version was not offered as a single package? Let’s assume an offer with two subscription versions (Bauer, 2015): – Online version: USD 59 – A package of both services: USD 125 In the reduced product line, the bundle is not nearly as attractive as in the first case. For the majority of users (68%), the surcharge of 66 USD for the printed version is too expensive. This segment consequently chooses the online variant at a price of USD 59 (Bauer, 2015). The effect of the—seemingly useless—middle variant in the decoy offer is as follows: The middle version (dummy option):

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1. Sets a very high price anchor. 2. Significantly increases the perceived benefit of the third package variant. 3. Leads to a significant increase in sales volume and profit for the product line. The price psychology doctrine, in summary, is: • An integration of additional product alternatives changes the price perception of existing items (Pena, 2017; Trevisan, 2015; Kopetzky, 2016). • Adding a third product changes people’s preferences between the other two options. The unattractive third option is dominated by one of the two existing products (consequently asymmetrically). This explains the term “asymmetric dominance effect”. • The added product is called “bait” (decoy). • The lure product is dominated by the target product: It provides a clear rationale to buy the target product. • While the bait products are not purchased, they do increase the demand for other products in the portfolio.

13.9

Price Psychology and Structure (8): Price Figure Communication

Different forms of price presentation can influence the perception of products from the consumer’s point of view. Single figures, the sequence of numbers, and visualizations lead customers to associations that go beyond the numerical value of a price. The size of the price presentation is also relevant. Numerical information (e.g., prices) can be presented and encoded in three different forms: – Visual-numeric (e.g., 25) – Verbal (e.g., 25) – Analog (between 20 and 30) The same price point (e.g., EUR 1000) is perceived differently depending on how it is communicated. Price figures that are labeled with a currency amount (e.g., EUR) and decimal places are perceived differently than whole-number amounts without a currency. The two price points EUR 1000.00 and EUR 1000 serve as an example. Although the amounts are exactly the same, the second price point (EUR 1000) is interpreted as lower by the majority of customers due to its presentation without decimal digits. Customers associate a larger number of digits with a higher price amount. One particular finding of perceptual psychology relates to price communication in restaurants. There is a correlation between the presentation of the price and the loss perceived by the customer. The smallest negative effect has a price, whose amount in letters is written out (EUR 10). On the other hand, a presentation with numbers is more noticeable (EUR 10). Commas promote the attention of the price most

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Price Psychology and Implementation (1): Tiered Discounts

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strongly. Amounts with numbers and commas (e.g., EUR 10,00) thus have a negative effect on willingness to pay from the supplier’s perspective. The perceived loss decreases—based on the following simple numerical example—from the first to the third price amount: – EUR 17,00 – EUR 17 – Seventeen. The advertised price amount without a currency is associated with a lower price level than the other two examples. Currency symbols trigger a subconsciously negative reaction in the brain. They activate the human center of pain (the “pain of paying”). The price threshold effect results in another behavioral management tool—the figure effect. Descending (EUR 3.21) or constant digit sequences (EUR 2.22) are perceived more strongly. Creative price figures can attract attention. The organization of the Olympic games in London in 2012 is considered “best practice” here: – Goal: To prominently feature the year of the event in all price figures. – Strategy: The price figures for all tickets reflect the year 2012—the price communication determines the price level. – Implementation: The cheapest ticket costs GBP 20.12. The most expensive ticket costs GBP 2012. Discounts are not granted. – Monitoring: The simple pricing structure resulted in a very positive response from users and in the media.

13.10 Price Psychology and Implementation (1): Tiered Discounts Price-psychological insights are particularly relevant in the implementation phase of the pricing process (Fig. 13.2). Customers’ price perceptions can be actively controlled through payment modalities and discount structures (cf. Tversky & Kahneman, 1974). In this area of the process, too, creativity in price management leads to sustainable increases in earnings: 1. An important finding of price psychology for the discount policy is the “asymmetry of profit utility”. Several small profits are perceived more positively by the customer overall than a profit that is just as large in total. Expressed in a numerical example: the perception of the profit utility is higher in the case of 12 payments amounting to EUR 10 than in the case of a one-off payment of EUR 120. From a customer psychology perspective, profits include discounts, boni, or other gratuities (e.g., vouchers and coupons). Two individual discounts (e.g., 10% promotional discount plus 10% discount on all items purchased) have a higher impact than a total discount of 20%. If the company presents discounts separately,

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Pricing Process and Pricing Psychology

Challenge

Price psychology

B.

Structure

•

Price differentiation

Bundling vs. unbundling?

1. Mental accounting

•

Price models

What time-price structure?

2. Price level effect 3. Anchoring

What price level? •

Price optimization

4. Value effect Odd vs. smooth prices?

5. Price threshold effect 6. Compromise effect

•

Portfolio pricing

C.

Implementation

•

Condition systems

•

Tactical pricing

How many value-price alternatives?

7. Decoy effect

Form of price presentation?

8. Price figure communication

What discount frequency?

9. Tiered discounts

Price discounts or discounts in kind?

10. Endowment effect

Price adjustments

11. Loss aversion

Fig. 13.2 Pricing process and psychology (Source: Own representation)

the total discount given will be perceived as higher by the customer. The recommendation for the company is that a bonus, rebate, or discount is better given in several smaller amounts and increments—rather than as a larger one-time amount. Price reductions should be separated rather than aggregated! 2. With a view to users’ loss aversion, discounts have an additional positive effect on customer benefit. The perceived additional gain from the customer’s perspective explains the advantageousness of a discount compared to a reduction in the list price. A list price of EUR 200 in conjunction with a discount of EUR 20 granted is perceived more positively than a total price of EUR 180 (Kopetzky, 2016, p. 25). 3. Stacking multiple discounts is more effective than offering a single overall discount (flat-rate discount). Stacked discounts are discounts offered to consumers one after the other. An example of a cumulative discount: a “first time buyer” discount (of 20%), in addition to a 15% “holiday weekend” sale.

13.11

Price Psychology and Implementation (2): Endowment Effect

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4. In the case of stacked discounts, the order is relevant for the customer perception: stacking discounts from bottom to top (e.g., “15% discount plus additional 30%”) is more effective than the reverse order (e.g., “30% discount plus additional 15%”). The price-psychological explanation for this asymmetric effect: consumers evaluate the first discount as “standard”. The amount of the second discount is evaluated in relation to this first discount—i.e., relatively. One explanation is the Weber-Fechner law from psychophysics. The core thesis is: The greater the initial level of a stimulus, the greater the change in the stimulus must be in order for the variation to be perceived (Hagendorf, 2011, p. 49). 5. For payments to be made by the customer (e.g., membership fees in the case of services), recommendations can also be derived from psychological findings. Let us assume two options: In the case of an identical total price, the choice is between: a. Higher but infrequent payments b. Lower but more frequent payments In the service sector, the second alternative is more preferred by users. Monthly payments of contributions lead to a more frequent use than annual payments, with identical total costs. Customer satisfaction is also positively influenced. A subscription fee of EUR 10 per month is perceived as more favorable than an annual price of EUR 120 (Kopetzky, 2016).

13.11 Price Psychology and Implementation (2): Endowment Effect Psychological insights are of central importance, among other things, for the way in which discounts are allocated. Owning something is of a distinct value from the customer’s point of view. Greater value is attached to objects if the customer can use them more than just temporarily. The endowment effect is very strongly associated with status and prestige—luxury cars, high-end watches, etc. are well-known examples. With the iPhone, Apple also offers an example of the outstanding importance of the endowment effect for high-value products. This can be used in various phases of the customer relationship. For example, companies can trigger emotional reactions if they get customers to touch the purchased object or try out an offer. Two constellations apply to different segments: (a) Being able to literally “take a physical object in one’s hand” increases the probability of a purchase. Haptic perception also tends to increase the price a buyer is willing to pay. The explanation: haptics triggers an emotional response. Nucleus accumbens (the center of the human reward system) is addressed. Free samples are a tool that gives customers the opportunity to test the product before buying it. (b) Additional mechanisms can be used for digital goods. These include free trial versions of software and trial periods for digital services. Implementation tip: If

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price discounts are planned—discounts in kind (free material) are significantly more effective than price discounts.

13.12 Price Psychology and Implementation (3): Loss Aversion The eleventh and final price-psychological insight has central importance for several aspects of the price management process (cf. Tversky & Kahneman, 1974)—“loss aversion” is particularly relevant for short-term price adjustments (implementation). Customer perception is asymmetric: profits and losses are perceived differently. Price increases (losses) are more negatively evaluated than price decreases are positively perceived. In the course of the initial launch of the iPhone, Apple systematically exploited these insights. The original price of USD 599 (for the 8 GB variant) was deliberately set very high. The market entry price in the USA in June 2007 was used as phantom bait. Two months later, Apple granted a very high discount of 200 USD. Since all devices purchased in the future were measured against the phantom lure of USD 599, the now significantly lower price appeared to be a very attractive offer. The sharp reduction from the anchor price level was perceived as an additional profit benefit by subsequent buyers. The effects outlined are based on loss aversion theory. The main findings of this subfield of behavioral pricing are in keywords: 1. The payment of a price is associated with a loss for the customer. The purchase and use of a product, on the other hand, results in a profit benefit for the customer. 2. Losses and disadvantages have a greater impact on preference than gains and advantages. 3. Purchase decisions are motivated by the desire to avoid losses and risks—this is more important to customers than achieving gains (making profits). 4. From the perspective of user psychology, there is an asymmetry between profit and loss benefits. Losses (“pains”) are perceived and weighted differently by consumers than gains (“profits”) of the same size. 5. Losses are perceived as more negative than gains are perceived as positive (Kopetzky, 2016, p. 23; Tversky & Kahneman, 1981, 1991). 6. Discounts are perceived by customers as a gain, while markups and surcharges are considered a loss. 7. Customers are more likely to want to avoid late payment fines than to take advantage of early discounts, even if the value is the same. 8. Consumers are more likely to perceive price increases than price reductions. 9. Losses or price increases are weighted twice as heavily as gains (or price reductions) of the same size. 10. Consumers perceive multiple minor prices more strongly as a loss than a price point equal to the sum of the minor prices.

References

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11. Minor price changes have only a disproportionately small effect. Only when price variations exceed certain perceptible percentages does a stronger, disproportionate sales effect result. The conclusion from this is: profit and loss benefits run asymmetrically. The findings of loss aversion and risk aversion theory have important implications for price management: 1. Price points, but also payments, should be combined (bundled) as much as possible. 2. Customers’ loss aversion argues for asymmetric price adjustments (Simon, 1992). 3. Price reductions have a more positive effect in several small steps than in one big one. The recommendation is: “Unbundle Gains”! 4. Price increases should be implemented in one step because they are less noticeable then. Spreading them over a few small steps is perceived more strongly and consequently has a more negative effect. The recommendation is: “Bundle Pains”! Losses are to be bundled. 5. The introduction of a subscription price model accommodates customers with a high aversion to risk.

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