Getting Price Right: The Behavioral Economics of Profitable Pricing 9780231549073

Citation preview

GETTING PRICE RIGHT

Praise for Getting Price Right “Gerald Smith shines light on the psychological biases that have long made pricing as much art as science. Drawing the link between behavioral economics and pricing theory, he explains how to take advantage of the quirks that motivate consumers to, for example, pony up for a subscription to shave ‘butter’ instead of buying cheaper shaving cream at the drugstore. At the same time, Smith uncovers the competing magnetic forces that explain why finance, marketing, and accounting managers each seem to approach pricing problems from a different planet and how to optimize among their competing influences in the organization. Getting Price Right steers new managers through the foundations of profitable pricing while offering new tricks for experts in readable prose with intuitive examples.” — SAMUEL

ENGEL , SENIOR VICE PRESIDENT , ICF

“There are introductory books available for people new to pricing and higher-level books for people who want to get into the theory of pricing and analytics. What has been missing is an advanced pricing-strategy book that connects practical everyday pricing approaches with academic scholarship. Smith has done just that. This is an excellent read for all pricing stakeholders looking to better understand how pricing fundamentals like customer value and willingness to pay intersect with the evolving science of behavioral economics.” — KIRK

JACKISCH , PRESIDENT , IRIS PRICING SOLUTIONS

“This book makes a strong contribution to pricing knowledge by bridging the bodies of pricing and behavioral science with a unique focus on pricing orientation. It proposes a comprehensive and structured review of critical concepts with practical recommendations and examples on how to use them in real business life. This is a must-have book for any pricing practitioner’s library.” — STEPHAN

M . LIOZU , FOUNDER OF VALUE

INNORUPTION ADVISORS AND AUTHOR OF TEN PRICING BOOKS INCLUDING B 2 G PRICING

Columbia University Press Publishers Since 1893 New York Chichester, West Sussex cup.columbia.edu Copyright © 2021 Gerald E. Smith All rights reserved Library of Congress Cataloging-in-Publication Data Names: Smith, Gerald E., 1953- author. Title: Getting price right : the behavioral economics of profitable pricing / Gerald Smith. Description: New York : Columbia University Press, [2021] | Includes index. Identifiers: LCCN 2021011180 (print) | LCCN 2021011181 (ebook) | ISBN 9780231190701 (hardback) | ISBN 9780231549073 (ebook) Subjects: LCSH: Pricing. | Economics—Psychological aspects. Classification: LCC HF5416.5 .S583 2021 (print) | LCC HF5416.5 (ebook) | DDC 658.8/16—dc23 LC record available at https://lccn.loc.gov/2021011180 LC ebook record available at https://lccn.loc.gov/2021011181

Columbia University Press books are printed on permanent and durable acid-free paper. Printed in the United States of America Cover design: Noah Arlow

contents

Preface PART

1 2 3 4 PART

5 6 7 8 9

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IBehavioral Economics of Everyday Pricing Decisions Pricing Orientation | Pricing Strategy

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Framing and Strategic Frames of Reference

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Psychological Pricing Orientation: Psychological Price-Setting Biases and Skills

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Social Pricing Orientation: Cultural Price-Setting Biases and Skills

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IIBehavioral Economics of Cardinal Pricing Orientations Cost-Driven Pricing Orientation Biases and Skills

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Customer Value–Driven Pricing Orientation Biases and Skills

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Customer Willingness-to-Pay–Driven Pricing Orientation Biases and Skills

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Competition-Driven Pricing Orientation Biases and Skills

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Balanced Pricing Orientations, Profitable Pricing Strategy

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Notes

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Index

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pre fac e

Over a half century, strategic pricing has become broadly accepted as one of the most influential and important paradigms of business management. Yet pricing strategy remains elusive for many companies, everyday managers, and entrepreneurs. Despite extensive training and experience in business, many firms fail to formulate, articulate, or design pricing strategy. One reason, it turns out, has little to do with economics and business and much more to do with psychology—the behavioral side of human decisionmaking. Behavioral economics has surged into the consciousness of policy makers, business managers, and medical professionals with concepts such as framing, confirmation bias, nudging, loss aversion, the sunk cost fallacy, and many others. Even Daniel Kahneman, awarded the Nobel Prize in Economics in 2002, was not an economist but a Princeton University psychologist who applied groundbreaking psychological insights to economic theory. We know a lot about strategic pricing but much less about behavioral pricing and how managers approach price-setting. This behavioral aspect is largely hidden in the journals of academia and typically studied from the perspective of consumer behavior, not managerial behavior—but my focus here is managerial price-setting. For example, price framing is a ubiquitous behavioral price-setting skill using comparative pricing information stored

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in memory, such as setting the sale price of your home using the recent sale price of a neighbor’s home as a convenient frame of reference called price framing. Or consider the auto industry’s successful reference framing strategy to reframe selected “used” cars as “certified pre-owned” (CPO) cars that offer almost “new” car dependability (backed by an extended warranty, an inspection, and the option to return the car if you are unsatisfied) at a premium used-car price—CPO cars typically sell for $500 to a couple of thousand dollars extra, all due to framing. Behavioral economics especially illuminates the mental shortcuts, heuristics, and biases that each of us infuses, unconsciously, into the process of price-setting. Those who have pricing authority and responsibility—division leaders, managers, proprietors, and entrepreneurs—subconsciously form attitudes about pricing during their career journeys, perhaps in finance, accounting, marketing, selling, or production. They consequently form beliefs about what price-setting is and how it should be managed, and they subconsciously assume that they are generally right. Usually, however, their attitudes are narrow and short-sighted, leading to imperceptible cognitive biases that go unnoticed or seem inconsequential at the time but that grow over time and subtly undermine pricing effectiveness and the pricing morale of employees, team members, managers, and leaders. Instead of narrowing our focus, we need to diversify our thinking about pricesetting—to debias pricing by inviting new insights and fresh views of the same pricing problem with conflicting and contradictory perspectives. The key to exploring these ideas is a new conceptual approach grounded in behavioral economics termed pricing orientation, which relates to how pricing actually happens and how it gets done. It is usually manifested in intuitive and behavioral pricing practices—sometimes spontaneous and extemporaneous, but other times long-established behavioral pricing patterns that become well-worn ruts that are hard to steer out of. In this new paradigm, pricing orientation invites managers to ask, “how does pricing get done around here?” With training and insight, informed by behavioral economic theory, your biases can become behavioral pricing skills that strengthen and shape smart pricing strategy. This book has been years in the making, and I have benefited by many along the way. First, I want to thank my colleagues and students at Boston College with whom I have shared pricing research findings, strategies, and stories. I have learned so much from our discussions in and out of the classroom, in independent study projects, and in “pricing in the news” that students continue to share with me years after their graduation. I am grateful

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to clients who have opened their pricing experiences to discuss, explore, and experiment with new and better pricing practices and outcomes; Ray Mancini and the Mancini family have been especially helpful. I wish to acknowledge Tom Nagle, Samuel Engel, and Reed Holden for generously reading and commenting on earlier drafts of the book; their insights were invaluable. I appreciate the hard work of John Elder as development editor during the earlier phases of the manuscript. I especially appreciate the work of Rose Treon, my graduate assistant, whose capable management of the final manuscript’s preparation has been so valuable— copyright relationships, chapter drafts, charts, figures, and interfacing with Columbia University Press. I am grateful to Myles Thompson and Brian Smith, my editors at Columbia, for their steady guidance, support, and direction. I am also grateful to fellow travelers in pricing and marketing, including Tom Nagle, Hermann Simon, Reed Holden, Mike Marn, Richard Harmer, Gene Zelek, Kay Lemon, Richard Hanna, Samuel Engel, Kirk Jackisch, Craig Zawada, Kent Monroe, Chris White, Gagan Chawla, Lisa Thompson, George Cressman, John Hogan, Mick Kolassa, Stephan Liozu, Andreas Hinterhuber, Arch Woodside, Navdeep Sodhi, and Takaho Ueda. And I especially acknowledge the late Dan Nimer, father of modern pricing, to whom I and my colleagues dedicated a volume of influential papers on the advancement of pricing, Visionary Pricing: Reflections and Advances in Honor of Dan Nimer. I have presented this material to a variety of audiences that have led to beneficial conversations, encouragement, and recognition, including the American Marketing Association, the Pricing Institute of the Institute for International Research, the Academy of Marketing Science, and the Emerald Literati Club, in addition to client presentations and venues. I want to thank Boston College, which has given me the resources and support for this research, especially dean of the Carroll School of Management, Andy Boynton; Senior Associate Dean Ronnie Sadka; and Associate Dean Carla Boudreau. Finally, I want to thank my family—my wife Betsy and my children and their spouses, Bryant and Heather Smith, Caranine Smith, and Lindsay and Alfredo Quezada. All have sacrificed to enable me to bring this work to fruition, and I am truly grateful.

GETTING PRICE RIGHT

I Behavioral Economics of Everyday Pricing Decisions

Framing, frames of reference

Psychological pricing orientation

Social pricing orientation

Emergent pricing strategy

1 Pricing Orientation | Pricing Strategy

How does pricing get done around here? The question gets at the behavioral economics of pricing and how price-setting can sometimes seem irrational. When I asked this question of pricing professionals during my field research, one corporate pricing manager responded: “Pricing gets caught between the cracks. Everybody wants to be a part of it. Yet, nobody really owns it. Pricing is ad hoc.” In fact, pricing is often a contentious activity within corporate business units and within small companies. At one corporation, “A pricing analyst described how at one of the early pricing strategy meetings, a representative from the marketing group and one of the members of the sales force ‘were shouting back and forth, . . .’ and the argument became so heated that ‘I thought they were going to throw punches.’ ”1 There is a psychological, but also a social, orientation to the behavioral economics of pricing. Why would price-setting be so irrational? First, it is rare that a single person or functional department “owns” pricing; instead, different persons or departments have adjunct responsibility for smaller pieces of the pricing process and usually compete vigorously to influence pricing outcomes—and they bring their own unique biases to the task. Finance and accounting personnel own responsibility for costing and financial profitability; field sales personnel own revenue generation and customer

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relationships; engineering owns product design and customer experience; and marketing owns competitive positioning, customer value, and market demand. In your pricing situation, who is tasked with owning pricing, and what biases do they bring? Is pricing shared among different leaders or departments within the business? Second, pricing decisions have significant leverage to drive competing dimensions of business success that are often tactically and short-term oriented. For example, higher prices increase unit margins and short-term profits—a financial accounting goal. Lower prices stimulate short-term sales volume—a field sales goal. Lower prices influence market share and retaliate against competitors; and higher prices signal quality and prestige or create exclusivity—both marketing goals. These competing pricing dimensions, driven by competing departmental factions, show the potential for different behavioral biases to distort the price-setting process. Pricing is a persistently thorny area for business management. Few managers have any training in pricing, leading to broader pricing illiteracy. According to a recent survey by Bain and Company, “most CEOs and Owners do not have a formal methodology when it comes to pricing their products and services. . . . Most companies call pricing a high priority, but 85 percent say they have significant room for improvement in pricing.”2 Consider your own experience. Did you take a pricing class in college or business school? How many of your professional colleagues involved in pricing your company’s offerings received professional pricing training? How does pricing get done in your business—is it strategically planned and executed, or more fluid and ad hoc?

Toward Another Paradigm in Pricing

This book presents the research foundation for another paradigm for pricesetting. The paradigm rests on two conceptual foundations. The first is the field known as behavioral economics, which by now has accumulated over a half century of research findings. The second is my own concept of pricing orientation, which is based on three decades of research and field consultation. As you will see, these conceptual foundations make it possible to build an actionable, profitable, and organizationally healthy pricing strategy building on the useful price-setting skills you already have but without the hindrance of the price-setting biases inherent in your current pricing orientation.

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The Behavioral Economics of Price-Setting

There has been a flurry of scholarly and popular interest in behavioral economics and the psychology of decision-making, with highly regarded books from Dan Ariely (Predictably Irrational), Daniel Kahneman (Thinking Fast and Slow), Richard Thaler (Nudge), Karl Weick (Sensemaking in Organizations), and others. Two of these authors are Nobel Prize winners in economics: Kahneman in 2002 and Thaler in 2017. The New York Times published a long piece celebrating Thaler’s prize in October 2017, entitled “Why Surge Prices Make Us So Mad.”3 Behavioral economics helps us understand how human actors make decisions and predicts the types of decision biases we can expect in various situations. In business, behavioral economics helps us see how managers act predictably, even if not necessarily rationally, as viewed through the lens of traditional normative strategy or economic theory. Scott Huettel, a leading researcher at the intersection of behavioral economics, functional brain imaging, and decision science and a professor of psychology and neuroscience at Duke University, does a good job summarizing the contribution that has been made by behavioral economics: Over the past half-century, decision scientists have identified anomalies, or biases, in people’s behavior that can’t readily be explained with traditional economic models. This research has sparked a new field of inquiry now called behavioral economics that integrates economics and psychology—and, recently, neuroscience—toward the goal of better explaining real-world decision making.4

The book you are reading now is the first to explore in depth how behavioral economics applies to price-setting. The connections are fascinating, illuminating, surprising, and often counterintuitive. They will be explored in depth in chapters 2 through 4 and in application to cardinal pricing orientations most often observed in pricing practice in chapters 5 through 8, which examine four key drivers of pricing success—and pricing bias: costing, customer value, customer willingness to pay, and competition. Each of us makes hundreds of routine decisions every day, usually following intuitive and predictable “rules,” such as choosing to not buy milk or meat too near the expiration date. Such behavior on the part of buyers is well documented in behavioral economics—where it is termed

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risk aversion—and it seems rational. However, when we engage in pricesetting, we adopt an entirely different orientation, with surprisingly counterintuitive behaviors that contradict what traditional economists might have predicted. We oversimplify our decision-making, we fall back on familiar price decision rules, and we often consistently set prices lower than they should be. Whatever your decision-making orientation might be for other routine decisions, when it comes to price-setting, that orientation is often transformed into something decidedly different, characterized by decision biases that appear unique to the context of price-setting. (We will see research about this in chapters 2, 3, and 7.) A consistent finding of behavioral economists is that people tend to follow two models of thinking when making judgments and decisions. Sometimes decisions are made instantaneously and unconsciously. When you’re driving and suddenly see a ball rolling into the street, you instantly swerve to avoid the child you expect will come running heedlessly in front of you, even if you see the ball only in your peripheral vision and haven’t seen the child at all. Behavioral economists call this type of intuitive decision-making System 1 associative processing. It is driven by memory-based associations that take place automatically, instinctively, and intuitively and is therefore fast and easy to do. Other decisions involve much greater effort, attention, calculation, and deliberation. When investing your year-end holiday bonus, for example, you must decide how much to allocate to different financial asset classes— equities, fixed income, cash investments—and then how much to invest in individual company stocks, bonds, or mutual funds. It takes considerable time and effort to determine the historical and prospective financial returns of these assets, the level of risk associated with each, and your own risk preferences. This type of methodical, effortful decision-making is referred to as System 2 analytic processing. It is driven by deliberate calculation and estimation and is therefore slow and challenging to do. Consider now the task involved in pricing, whether undertaken by a corporate pricing department or an individual manager or proprietor. A price-setter must make cognitively complex predictions in five pricerelevant domains; that is, he or she must estimate (a) customer value—the value, or worth, of the product or service to customers; (b) customer willingness to pay—reflected in customer or market demand at different price points; (c) competitive pricing—the impact on demand of the relative price and performance of competitive substitutes; (d) costing—the incremental cost to produce and deliver the product or service; and (e) the product’s

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profitability at the price that will be set. Predicting profitability, in particular, involves significant cognitive strain, as it requires integrating the four preceding tasks—with predictive uncertainty. All of this implies that pricesetting requires methodical, deliberate calculation and estimation and therefore is usually performed using System 2 analytic processes. However, often this is not how pricing is actually carried out. Instead, the predictive calculation, estimation, and integration required of price-setters is deemed too challenging, either because of a deficit in price-setting skills or because the price-setting situation itself is so complex. In such situations, price-setters routinely default to using less effortful System 1 associative decision heuristics that simplify the decision to more cognitively manageable proportions. By “heuristics,” I mean mental short-cuts to a solution, counterintuitive behaviors that often contradict what traditional economic theory would predict. As Kahneman noted, when people go about solving a difficult problem, they intuitively “fall back on a simpler assessment that is made quickly and automatically and is available” from memory.5 At a recent antique fair, for example, I found an attractive cast-iron green model car. It had no price tag, so I asked the seller. He appeared startled—clearly, I had put him on the spot—but then he quickly looked at a neighboring yellow car with a price tag. “This one is $28,” he said, “and yours is larger, so its price is $40 or $45. Let’s say $40.” I offered $35 and he accepted. Given the pressure of the moment, he had quickly defaulted to System 1 associative price-setting using memory-based heuristics, assuming that the bigger the model car, the greater should be its price. That was the easiest and fastest heuristic he could call up from memory in the moment. This type of price-setting happens all the time, as we shall see throughout this book. The complexity of the price-setting task is often so great that it compels you to adopt easy and fast System 1 heuristic price-setting to make the task more manageable. These are not comprehensive or complex pricing strategies that drive individual instances of price-setting; rather, they are practical micro-cognitive pricing decisions that—though they are not optimal—are deemed satisfactory to get price-setting done. When it is impossible or impractical to find an optimal pricing solution, using a heuristic often seems to yield a satisfactory solution. However, take caution: System 1 associative price-setting is vulnerable to System 1 heuristic biases, which can undermine pricing effectiveness if not managed and used to your advantage. The model car seller at the antique show, for example, displayed a bias in assuming that a bigger model was worth more than a smaller one. This might often—even usually—be true; yet the smaller car

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could have been a particularly rare model worth much more in true value than the larger one. The first key to this new pricing paradigm is to understand the heuristic biases of System 1 associative price-setting and then to debias, and in the process to learn better behavioral soft skills to use in price-setting. For example, psychological price-setting biases are characteristic of individual price-setting and problem-solving relating to personal cognition; we will discuss these in chapter 3. Social price-setting biases are characteristics of price-setting and problem-solving relating to team, or group, decision-making, involving others whose powers, opinions, and biases influence the outcome. We discuss these social price-setting biases in chapter 4. Examples of psychological bias that we will study in pricing include pricing goals and goal-framing bias; rules of thumb and truisms that get rehearsed over and over in price-setting situations; canonized formulas, templates, and algorithms that are broadly accepted as true and correct; reliance on easily accessible price metrics (such as price per hour for the services of a consultant or attorney) because those are the dominant metrics used by other competitors in the industry; anchoring and adjustment; or availability heuristics, relying on information that is most quickly and easily accessed from memory (at the antique show it was the price of the nearby yellow car that provided a convenient price anchor for the seller). Examples of social bias emanate from the six cultural “nations” of pricing influence, discussed in chapter 4: Finance, Accounting, Sales, Marketing, Production, and Pricing. The biases we see in these various cultural nations come from their professional origins, schools of learning, and traditions. They influence the price-setting that gets done in the organization, with subtle group biases such as overconfidence bias, confirmation bias, and narrow framing bias—cited often with Finance and Accounting Nations; action-oriented biases such as excessive optimism bias and loss aversion with Sales Nation; and ambiguity aversion in approaching price-setting with Marketing Nation.

Pricing Orientation

The second pillar of this book is pricing orientation, a conceptual approach I developed in 1995 that is grounded in behavioral economics.6 The key to this approach is asking, How does pricing get done around here? How is

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price framed for price-setting? Who is involved on our price-setting team? Who is influential? What kinds of calculations do we usually make? What decision rules do we regularly use? By repeatedly setting and resetting prices, a firm’s managers who are involved in price-setting tend to follow particular patterns and processes that can be observed, analyzed, and then categorized. Having done that, one is ready to ask, “How should pricing get done here?” That is, what prices should be set, or how should prices be configured, designed, and structured to maximize long-term profit contribution to strengthen your differential advantage in the market? Notice that in this approach, the “ideal” depends very much on the “real.” What a firm ought to do might depend on reshaping and renovating what it is already doing. Rather than insist that a firm establish a pricing strategy, the gold standard, this approach identifies the strengths and weaknesses of the firm’s current price-setting, then uses those strengths as the foundation for improvement while using debiasing to free the firm’s pricesetting from its weaknesses—to therefore evolve a pricing strategy. Figure 1.1 illustrates these key points. Pricing orientation is descriptive— diagnostic of how things are—whereas pricing strategy is normative— specifying how things should be. Sometimes, an intelligent, practicable approach is to base the normative—the optimal strategy—on the descriptive— the existing pricing orientation.

How pricing gets done

Pricing orientation

Pricing strategy What pricing should be

Figure 1.1 

Twin pillars of everyday pricing.

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Behavioral skills, frames

Behavioral, intuitive

Analytic, systematic

Soft behavioral price-setting skills

Hard analytic price-setting skills

Types of skills: • Memory-driven • Relational • Tacit • Heuristic • Improvisational

Types of skills: • Data-driven • Procedural • Methodical • Structural • Deliberate

Analytic skills, expertise

Figure 1.2 

Managerial pricing skill sets.

Two managerial pricing skill sets are usually found in a given firm’s or individual’s pricing orientation (see figure 1.2). Soft behavioral price-setting skills are typically more behavioral and intuitive: for example, price framing to reflect and influence customer willingness to pay (discussed in depth in chapters 2 and 7); or margin leverage, to intuitively sense how to best leverage profitability given the cost and margin structure of the price-setting situation (chapter 5); or competitive moves that interpret competitors’ prices in competitive context (chapter 8). These skills are sometimes organically driven; that is, they are spontaneous and extemporaneous behaviors that, over time, become established behavioral pricing patterns—and sometimes become ruts. Soft pricing skills are usually memory-driven, relational, tacit, and heuristic. They involve automatic thinking and often begin as improvisational. They can also be biased, heuristic, and shortsighted. However, they can—with training, insight, and focus—be adapted into soft skills that sustain and refine pricing and lead to more holistic behavioral outcomes such as employee productivity, employee satisfaction, and customer satisfaction.

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Kahneman described these soft skills as the learned intuitions of experts: [The] accurate intuitions of experts are better explained by the effects of prolonged practice . . . . We can now draw a richer and more balanced picture, in which skill and heuristics are alternative sources of intuitive judgments and choices. The psychologist Gary Klein tells the story of a team of firefighters that entered a house in which the kitchen was on fire. Soon after they started hosing down the kitchen, the commander heard himself shout, “Let’s get out of here!” without realizing why. The floor collapsed almost immediately after the firefighters escaped. Only after the fact did the commander realize that the fire had been unusually quiet and that his ears had been unusually hot. Together, these impressions prompted what he called a “sixth sense of danger.” He had no idea what was wrong, but he knew something was wrong. It turned out that the heart of the fire had not been in the kitchen but in the basement beneath where the men had stood.7

Hard analytic price-setting skills are typically more systematic, such as customer value modeling, breakeven sales calculations, or conjoint analysis. They are thus the opposite of soft pricing skills, but the two are complementary when used in combination. Hard analytic price-setting skills are datadriven, procedural, methodical, and structured and involve slow deliberate thinking (see figure 1.2). Sophisticated price modeling systems and enterprise pricing platforms are often found in large corporations that can afford MBA-trained pricing consultants and are used to formulate economicsdriven pricing strategy. Hard pricing skills can be challenging to learn, and many price-setters with little or no formal business education just assume these skills are out of reach and therefore ignore them. However, most businesses need to make hard skills more accessible to the personnel involved in pricing. With training, insight, and accessibility, such skills can add structure and guidance to people’s pricing orientation, leading to better longer-term economic outcomes. Some of these hard pricing skills are in fact accessible and easy enough to work with; they simply require focus and effort. For example, customer-focused pricing analytical methods such as price band analytics and price waterfall analytics require slowing down to systematically track and identify outlier customer pricing transactions that cause profit erosion and customer relationship distortions; we discuss these in chapter 7.

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Objective customer value models and profit pools are used all the time by many professionals such as real estate professionals and wholesale distributors, to assess the value and attractiveness of different customer segments. Even dynamic pricing—such as surge pricing, which adjusts with changes in demand, supply, and the economics of the pricing situation—is becoming increasingly accessible to many firms, even small and mid-sized ones. We discuss these in chapters 6 and 8.

Pricing Orientation Research

In this book we cite various examples from my research and field consulting and from firms in the public domain. My field research consisted of face-to-face in-depth interviews and focus group interviews with managers from a diverse set of manufacturing and services industries that ranged in size from fewer than twenty employees to many tens of thousands. Firms and industries included computers and software, wholesale and retail, health care, financial services, telecommunications, consumer goods, basic industries (e.g., steel, cement, pulp, and paper), automotive, and various consumer and business services. Respondent job titles included president, chief executive officer, vice president, senior VP, executive VP, general manager, director, manager, analyst, and others with professional jobs in finance, accounting, strategy, purchasing, pricing, marketing, general management, sales, customer service, engineering, operations, and more.

The Significance of Pricing Strategy

Over the past half century strategic pricing, or pricing strategy, has become the most influential and successful thought paradigm in pricing. Some corporations have built sophisticated pricing or revenue management departments that own pricing strategy, staffed by PhD economists and professional price-setters with MBA degrees who have access to leading-edge big data, analytics, and enterprise pricing systems. The Walt Disney Parks and Resorts Division has a revenue and profit management team that includes a primarily PhD-level Decision Science team and several Management Science and Integration business consulting teams tasked with ensuring analytic [pricing] solutions are successfully integrated into Disney’s

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business units. The Science teams’ extensive analytic capabilities include customer choice modeling, stochastic models, price optimization, machine learning, and demand modeling, among many others.8

According to Disney’s senior vice president for revenue and profit management, After initially proving the value of analytics in areas such as hotel and theme park pricing, the team was asked by Disney’s CEO to extend the application of science and analytics to other areas outside of Disney Parks and Resorts, including the movie studios, television networks, on-line media channels and Broadway shows. As a result, the [revenue management and pricing] organization has experienced phenomenal growth.9

Airlines, hotels, and car rental companies have similar pricing and revenue management divisions that dynamically set prices strategically based on the time of the year, time of the month, day of the week, and hour of the day. This pricing strategy is overlaid by customer price sensitivity indicators such as loyalty program status, frequency of purchase, time before purchase, amount of purchase, and response to various promotional offers. Still, this kind of sophisticated strategic pricing is found mostly among the very elite of business corporations; it seems inaccessible to everyday price-setters in other settings. Only “a small percentage of independent hoteliers use revenue management [pricing] strategies and thus limit their revenue-generating potential,”10 said one hotel consultant. Even among Fortune 500 companies, the elite of the corporate world, 78 percent do not have a dedicated pricing organization, according to data compiled by Stephan M. Liozu.11 Moreover, 37 percent (184) have no pricing titles in their organization at all. Of those firms that do have pricing titles in their organizations (316), only 25 percent are led by a vice president of pricing with influence in the C-suite. Of the remaining, 41 percent are led by a director or head of pricing, and 34 percent delegate pricing leadership to a pricing manager, pricing analyst, or pricing specialist.12 Scholars studying one U.S. industrial corporation found that its total investment in pricing (including management time and expenses) amounted to 1.23 percent of its annual sales revenue, whereas marketing merited 25 percent, operations 20 percent, and R&D 8 percent. The largest price management expenditure in that company was for customer negotiation costs (43 percent of the total 1.23 percent), then customer communication costs (30 percent), internal decision-making costs (23 percent), and

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out-of-pocket expenses (4 percent).13 In this industrial corporation, pricing received a fractional investment of the firm’s resources, and even then, the vast majority of its actual pricing investment was spent communicating or negotiating price with customers. Price-setters need better access to the best-in-class skills and capabilities that enable a firm to profitably develop long-term pricing strategy. As you will see, the key to leveraging those skills starts with this fundamental question: How does pricing get done around here? The behavioral tactics in this book reveal insights that might prove useful in one-off, ad hoc price negotiations or communications. But the full value comes from adopting a systemic pricing orientation that drives a consistent pricing strategy based on these behavioral principles. The research from this book should be especially useful for those of you for whom price-setting and pricing strategy are challenging to get right, due to either the misaligned pricing skillsets you bring to the task or the challenges of a dynamic, competitive business environment. A coherent and consistent long-term pricing strategy is rightly considered the gold standard and should be your aspiration—not just an afterthought. The key is how to get there. Price-setting can be approached behaviorally—and often very effectively—by observing the price-setting that is already being done in your company and then refining it into an actionable, profitable, and organizationally healthy price-setting strategy.

Balanced Pricing Orientations

So, what does it mean to get price right? What is optimal price-setting, and what, therefore, would be an optimal pricing orientation? Most pricing thought leaders stress the importance of customer value in setting price; chapter 6 shows how. In addition, however, effective price-setting requires a complete and balanced view of those influences that drive the success or failure of price-setting. At its essence, a balanced pricing orientation requires two things: data diversity and decision diversity. Huettel, in his work on behavioral economics, described diversity as “the degree to which different people approach a decision in different ways. . . . [For example, for] many sorts of judgments and decisions, we care about intellectual diversity, where people bring different information and different approaches to the same problem.”14 The importance of balance and diversity arises from one of the foundations of pricing theory: the profit-maximizing rule of economic theory,

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Price

Marginal cost Average cost P*

Profit Demand (average revenue)

Marginal revenue

Cost Q1

Q*

Q2

Quantity

Figure 1.3 

Profit-maximizing rule of economic theory. Adapted from Intelligent Economist.

MC = MR. This rule states that to maximize profits, the firm should sell its output until its marginal revenue (MR) from selling the next unit is equal to its marginal cost (MC) (see figure 1.3). Built into this formulation are the various internal and external forces—the influence drivers—that influence profit maximization. Marginal cost represents internal drivers: costs and related financial measures such as margins, return on investment, and asset efficiency. Marginal revenue represents external drivers relating to the market: customer perceptions of value, customers’ willingness to pay, and competitive influences. These market drivers are often difficult to measure but nonetheless vital to a balanced, complete, and strategic view of pricing. Thinking on the margin is itself important for a balanced and complete pricing orientation. It focuses price-setting decisions on the profitability of incrementally selling one more unit. It leads you to ask this fundamental question: Should you sell the next unit of your product or service at this price?

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In this economic moment, the answer is yes if the marginal revenue generated by selling this unit at this price exceeds the marginal cost of producing and delivering it. In figure 1.3, this is true for output levels to the left of Q∗, where the marginal revenue associated with the demand curve is greater than the marginal cost. For example, a customer order at quantity Q1, considered incrementally, would yield a net addition to profit contribution, whereas a customer order at Q2, considered incrementally, would yield a net subtraction from profit contribution. Historically, airlines routinely applied this principle when overbooking their flights. They asked lower-paying non-loyal passengers to give up their seats in exchange for compensation (a marginal cost) and then offered those same seats to higher-paying customers (marginal revenue), yielding on the margin a more profitable customer seat and a more profitable flight. As you will see, the same principle is followed in price-setting for hotels, car rental firms, Broadway theaters, sports venues, amusement parks, and many other firms and industries. This profit-maximizing rule is useful for another reason. It points to the four potential building blocks of a firm’s pricing orientation. These are cardinal influence drivers of pricing orientation, illustrated in figure 1.4, and will be central to the exploration of price-setting in this book.

Marginal Revenue

Marginal Cost

=

MR

Customer value

Customer willingness to pay

Customer valuedriven pricing

Customer WTPdriven pricing

MC

Competitor prices

Incremental cost to serve

Competitiondriven pricing

Costdriven pricing

Figure 1.4 

Profit-maximizing rule of economic theory: influence drivers of price-setting.

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Cost influence drivers relate to the cost of delivering the next unit of product or service to the next customer—the incremental cost. These drivers include variable costs, incremental fixed costs, semi-variable costs (that vary with step-levels of volume), opportunity costs, and the capacity costs of adding or leasing additional production capacity to service incremental customer sales. As shown at the far right in figure 1.4, they affect the marginal cost component of the profit-maximizing equation. Customer willingness-to-pay (WTP) influence drivers relate to prices that customers are willing to pay and the marginal demand associated with those prices. As shown on the left side of figure 1.4, these affect the marginal revenue component of the profit-maximizing equation. Customer value influence drivers reflect the economic, psychological, and experiential value that customers get from using the product or service, which in turn influences prices they are willing to pay and customer demand, which in turn influence marginal revenue. Competition influence drivers relate to prices of competitive substitutes. They reflect the impact of competition on the firm’s ability to set prices and protect the differential value it delivers to customers from the potential harm of competition. Chances are, your price-setting—how pricing gets done around here— is oriented toward one or two, but not all, of these four pricing influence drivers. This leads to a suboptimal—possibly harmful or detrimental— pricing orientation, for two reasons. First, overemphasis on any given influence driver brings with it certain biases that are endemic to that pricing point of view. For example, a largely cost-driven pricing orientation usually suffers from cost-averaging biases that arise from standardizing costs across different product lines, across many different types of customers, and across different types of costs (such as fixed and variable costs), in an attempt to synthesize them into a common, easy-to-compare costing system—the essence of standardized costing. As a consequence, any notion of marginal cost is sacrificed to average costing heuristics. This might be acceptable for historically focused cost reporting to evaluate past profit performance but not for forward-looking price-setting that evaluates the potential profitability of future profit opportunities to maximize incremental profit contribution. Second, an out-of-balance, incomplete pricing orientation (that is, largely or exclusively cost-driven, customer-WTP-driven, competitiondriven, or even value-driven) suffers biases resulting from missing data and missing decision perspective. Because price-setting is inherently complex,

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for most firms the key to achieving profitable pricing at the margin is by inviting data diversity and decision diversity and bringing them together into price-setting, which means continuously tapping the influence perspectives from the four cardinal pricing orientations. Doing so exploits the price-setting skills—both soft and hard—of each of the different pricing nations while debiasing the price-setting process with a constant awareness of the strengths and weaknesses of each—a system of checks and balances, if you will, that leverages decision diversity. This is the essence of a balanced pricing orientation and is the focus of the final chapter in the book, chapter 9, as we pull these themes together.

What Is at Stake?

Two firms approached pricing orientation differently. One ended in bankruptcy, the other grew to become a highly successful technology firm. MoviePass was an innovative subscription service startup founded in 2011. It offered moviegoers the opportunity to see one movie a day for just $9.95 a month, a very good value. MoviePass hoped to use low price and subscriber growth to drive profitability by monetizing a large subscriber base, and, indeed, by mid-2018, it had 3 million subscribers, with another 2 million forecasted within six months. However, MoviePass paid theaters full price for the tickets its subscribers used, causing it to burn through cash at a high rate. For its first six years, price-setting was especially vexing. MoviePass went through a series of highly visible pricing trials to drive customer acquisition. They tried $50 for six movies per month, then $99 for unlimited movies per month, then $15 for two movies per month in small markets and $21 in large markets. It finally settled in 2017 on $9.95 per month for one movie per day but then changed yet again in 2018 to $9.95 for three movies per month. On September 14, 2019, MoviePass finally shut down, well in advance of the coronavirus epidemic that brought the entertainment industry to a standstill.15 What went wrong for MoviePass? The startup company tried to reframe the price of moviegoing by overlaying a creative new subscription price model onto a well-established frame of reference—cinema tickets—a potentially brilliant price-framing strategy. But frames of reference can be challenging and costly to construct or change in the marketplace; witness Amazon’s twenty-year investment in its e-commerce model before realizing a sustained profit. MoviePass focused on growing its customer base with an

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attractive price point, driven relentlessly to find the right price that customers would be willing to pay that would optimally drive the growth of the subscriber base. It pursued a narrow customer willingness-to-pay–driven pricing orientation, spending years and all of its venture capital testing price, finally running out of time and money. In the end, the movie theater chains MoviePass hoped to disrupt instead set up their own rival movie subscription offerings. (AMC’s Stubs A-List offers three movies per week for $23.95 per month.) By contrast, Adobe deployed a more balanced pricing orientation to pursue a similar subscription price reframing in 2012, with a very different outcome using a more compelling, though still risky, reframing of the value proposition. The box documents Adobe’s experience in detail, including the dramatic increase in the company’s stock market price and the market capitalization that resulted.

Adobe’s Balanced Pricing Orientation Amid a Disruptive Price-Reframing Strategy

In April 2012, Adobe reframed its pricing with the announcement of Adobe Creative Cloud, a SaaS (software as a service) subscription pricing bundle that offered a suite of Adobe’s popular software products to customers via cloud subscription for $74.99 per month—subscription price framing. Historically, Adobe sold its popular software products such as Photoshop, Illustrator, InDesign, and Acrobat Pro, which ranged in price from $699 to $2,599, using product price framing—a “software product” price paid in exchange for “ownership” (a perpetual license) of a shrink-wrapped copy of the software in a box, such as Photoshop. However, Adobe discovered that its high product prices invited piracy and cheating by users and priced many customers out of the market, and its revenue growth was limited. Mark Garrett, Adobe’s CFO said: The number of units we shipped under the old perpetual-licensing model was about three million units a year, and it remained flat for a long time. We were driving revenue growth by raising our average selling price— either through straight price increases or through moving people up the product ladder. That wasn’t a sustainable approach . . . people were saying things like, “I’m happy with what I have, I don’t see the need to ever buy another one again.”16

Adobe’s price reframing was bold and risky in the early days of subscription pricing, reframing the way its customers paid for value received: price metrics, price bundle, and segmented prices. The change required a significant disruption of the revenue generation model for the Adobe enterprise, requiring accounting and finance to redesign revenue recognition “from managing upfront revenue recognition and a few large contracts to billing more than four million individuals every single month in addition to enterprise customers.”17 But Adobe’s rollout was gradual and measured, going through continuous testing iterations for five years. “We offered, side by side, similar products under both a subscription [pricing] model and our traditional perpetual-licensing [pricing] model,” said Dan Cohen, vice president of corporate strategy.18 This side-by-side transition strategy lasted until 2017, enabling Adobe to refine its subscription price frame while transitioning and onboarding new customers while maintaining the loyalty of customers who preferred the old perpetuallicensing price frame. Garrett said, “There wasn’t any one point where we just flipped the switch and everything changed; it happened over time. During the period that we were actively selling both perpetual and cloud versions, our finance team did an analysis and found it would cost us twice as much to offer perpetual and subscription products side by side.”19 Adobe’s pricing orientation was balanced, broad, and inclusive, with an emphasis on enterprise-wide decision diversity and data diversity from various stakeholders. Garrett remarked, There were a lot of discussions among Adobe’s management, finance, and strategy teams and among business-unit leaders. We spent many hours talking about risk. A lot of people didn’t buy into the idea at the beginning. . . . We literally covered the boardroom with pricing and unit models, predictions for how quickly perpetual licenses would fall off, and how quickly online subscriptions would ramp up.

Early on, 30,000 Adobe customers resisted the change, creating a petition on Change.org, a platform for social causes, that demanded Adobe abandon the price-reframing strategy. Yet marketing remained closely connected to customers: “We conducted monthly customer studies which informed how to shape marketing efforts for every customer segment, including the biggest resistors. By looking at the data and understanding the customer insights that were driving their concerns, we leveraged blogs and community forums to own the change and educate customers on the benefits they would reap.”20

Price-sensitive photographers were particularly alienated by the expensive new monthly subscription price. In response, Adobe refined its segmented pricing by introducing a price bundle with Photoshop and Lightroom—separate from the full Creative Cloud suite—at an affordable subscription price of $9.99/month. Adobe’s reframing created lower-price access for new price-sensitive customers, reduced piracy, and forged a closer cloud-based relationship with its customers. Since the introduction of Adobe’s subscription price framing with Creative Suite, its share price has increased 1,213 percent, compared with 181 percent for the S&P 500 Index over the same period (April 2012–April 2021, see the box figure) and outpacing rivals.21 Its market capitalization grew from $16.5 billion in 2012 to $212 billion in 2021—including acquisitions during that period of about $8.4 billion.

ADBE 490.43

^GSPC 3363.00

×

+1,381.6% Adobe

1,000.00%

Stock price growth % 500.00%

250.00%

S&P 500

+196.9% 37.01M

– + 2013

2014

2015

2016

2017

0.00%

2018

2019

2020

2021

Box Figure 1.1 

Adobe’s stock market performance, 2012–21. Source: Verizon Media and Yahoo Finance for instructional or illustrative purposes. Data sourced on April 30, 2021.

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Pricing Orientation and Pricing Strategy

My thesis, then, is that reframing, reshaping, and refining pricing practice— by growing and evolving a more effective and better pricing orientation in response to changes in your market environment—leads to more profitable and more rational pricing outcomes and to a more successful long-term pricing strategy. A recent Deloitte study found that compared with average companies, “companies with effective pricing programs” achieved 17 percent higher average market capitalization relative to net income, 16 percent higher average return on equity, 9 percent higher return on assets, and 23 percent better net profit margins (see figure 1.5).22

Average net profit margin

115 123

Average market cap/net income

111 117

Average return on equity

122 116

Average return on assets

101 109 70

Average company

89

90

100

Companies that actively pursue pricing programs

110

120

130

Companies with effective pricing programs

Note: The metrics are indexed on base 100 for relative comparison Source: Pricing Effectiveness Benchmark Study, Deloitte LLP, Deloitte Analysis

Figure 1.5 

The financial payoff for effective pricing. Source: Julie Meehan, “The Price of Pricing Effectiveness: Is the View Worth the Climb?”, Journal of Professional Pricing (Third Quarter 2019): 20.

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Normative driven pricing strategy, “Normative pricing strategy”

Pricing strategy

Pricing orientation

Behavioral driven pricing strategy, “Emergent pricing strategy”

Pricing orientation

Pricing strategy

Figure 1.6 

Two models for approaching pricing strategy.

However, this book offers another paradigm for pricing strategy (see figure 1.6). One model is to make large up-front investments to formulate pricing strategy, which then drives carefully scripted pricing implementation going forward (“Normative Driven Pricing Strategy” in figure 1.6, left)—a model found more frequently in mature, stable growth environments with sophisticated corporate price-setters. But there is an alternative model. A business can reframe, reshape, and refine its current pricing practice—its pricing orientation—which can evolve into a better pricing strategy (“Behavioral Driven Pricing Strategy” in figure 1.6, right). I have termed this way of forming pricing strategy emergent pricing strategy,23 similar to Henry Mintzberg’s theory on emergent strategy.24 It is a bottom-up approach: innovative pricing practices that might be effective get tried, repeated, expanded, and refined into successful pricing practices that, over time and across situations, evolve into emerging pricing strategy. The result is not only better financial outcomes but also better behavioral outcomes, such as improved employee productivity, employee satisfaction, and customer satisfaction—dimensions that are missing from the traditional strategic pricing perspective. This new model is accessible to the many everyday price-setters in a variety of small, mid-sized, and large price-setting contexts. A business can implement this model by leveraging the soft psychological skills of behavioral economics—never before addressed in a managerial pricing book—and adding accessible versions of the hard skills of value economics and differential pricing to create a stronger pricing orientation. That, for most businesses, is the key to not only pricing orientation but everyday pricing strategy.

2 Framing and Strategic Frames of Reference

Let’s begin with a foundational concept of behavioral economics: framing and strategic frames of reference. Framing is especially important for pricing orientation and strategy as firms choose to deliberately frame and reframe their product or service in the marketplace. Framing is sometimes called “positioning” by advertisers and marketers, but their conceptualization is limited. It is informed primarily by consumer research (relating to consumer attitudes, cognitive thinking, and affective feelings) rather than behavioral economics (relating to decision judgment and decisionmaking). Positioning further fails to account for the impact of reference price and reference value in a frame of reference, both of which are foundational constructs of framing purchase decisions. More broadly, framing is an important component of pricing orientation as firms fall into pricing patterns that reflect framing choices made implicitly, often with little or no deliberation. Of the many predictable biases and effects documented by behavioral economists, framing is among the most elemental of managerial decision-making. The digital economy has swept away many old competitive barriers, offering new and disruptive opportunities for engaging customers and creating customer value. Technological disruption itself is only part of what transforms a brand’s value in the marketplace. The other, often larger, part centers on how marketers and price-setters frame new offerings in

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market context, or reframe existing ones, with new and transformational customer value propositions versus existing alternatives to encourage trial and adoption. Successful innovators, such as Uber or Airbnb, disruptively framed their new innovations—Uber framed as “ride sharing,” Airbnb as “homestays”—with transformative new benefits and models of value-price exchange that accelerated customer adoption, even as they disrupted old established market categories like taxis or hoteling. But framing in business is not an exclusively digital economy strategy; it is a basic soft skill available to managers that enables them to create new value, new value propositions, and new models for price-setting. Most managers can see framing in hindsight but lack the ability to see framing opportunities as they arise, construct new frames or manage existing frames, or leverage framing to achieve superior differential value and prices. In this chapter I define what framing is, how it is influential in human cognition, and how it applies in marketing strategy and pricesetting. I begin with the building blocks of framing in business based on value-based pricing theory. I then explore the theory behind how framing biases and influences human cognition in business and price-setting. I then apply framing to three value-based marketing and pricing domains: price framing, reference framing, and benefit framing. The chapter concludes with recommendations on how to achieve successful framing and reframing in practice.

Building Blocks of Value-Based Framing

Framing in value exchange and price-setting is anchored in core building blocks of value-based marketing and pricing theory. For customers, the net value of a purchase decision is determined by adding the value of benefits received—that is, the worth of what customers get—and subtracting the price paid—what and how customers give in exchange, shown in figure 2.1. Let’s focus first on price. Companies can frame or reframe price— called price framing—by structuring or restructuring how customers pay, or give, in exchange for the product or service they receive. For example, airlines traditionally framed price as coach fare paid per seat to a destination; beginning in 2008, they began framing price as core fare paid plus fees and upgrades (for baggage, pets, unaccompanied children, extra legroom, aisle or window seats). This new framing has been more effective at segmenting customers by price sensitivity—and more profitable. Airlines can offer competitively priced fares in reservation systems with highly visible

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Price

Benefits

Value

=

Worth of the benefits the customer receives, or gets

–

What and how the customer pays, or gives

Figure 2.1 

Value to the customer.

low core prices, appealing to price-sensitive buyers, while charging fees and offering upgrades to less price-sensitive buyers, who cost more to serve or seek additional value. Under the old price framing, most airlines struggled with profitability; under the new price framing, most of their profits come from fees and upgrades. What about framing benefits? Companies can frame or reframe benefits by structuring or restructuring what customers get. However, the benefit part of the value equation offers not one opportunity for framing but two, based on how the total worth of benefits is estimated. According to economic value theory, which underlies value-based pricing, total economic value (or total worth) to the customer of what the customer gets is estimated as the sum of two components: reference value, what customers would get from competitive substitutes, measured in terms of the price of competitive substitutes; and differentiation value, the unique differential value customers get from using their preferred brand over and above baseline competitive substitutes.1 Consequently, the two opportunities for framing the get part of the equation are • Reference Framing, which defines how customers cognitively frame, or categorize, the brand in memory—the brand’s purpose and meaning, what it is, what it does, how it is used—and consequently the competitors with competitive prices that are considered to be alternative substitutes to the brand; and • Benefit Framing of the differential benefits and unique differential value that customers get from using their preferred brand over and above the reference value offered from competitive substitutes.

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=

Value of differential benefits

–

Reference value of competitive substitutes Reference framing

Get

Total price

Value

Total worth

Benefit framing

Price framing Price per unit

Give

Figure 2.2 

Building blocks of value-based framing.

Together with Price Framing, these define the three building blocks of value-based framing, illustrated in figure 2.2. Especially new here is the idea of reference framing. Most marketing and brand managers focus on differentiating from competitors within an existing frame of reference, which is obviously important. However, much less understood—and consequently overlooked—is reference value and the opportunity to reframe the brand by redefining category identity, brand purpose and brand meaning, and thereby associating with new and different competitors that have new and different reference prices. At their inception, Uber and Lyft framed their new services not as taxis (a well-established frame of reference with established prices and fares exchanged hand-to-hand) but as ridesharing, a new frame of reference with a new reference value. Their apps connected riders by “e-hail,” similar but still quite different than “street hail,” and handled payments and receipts automatically, electronically, and cashlessly via one’s personal mobile device—a touchless transaction. Traditional firms from the old taxi frame of reference struggled to compete against ridesharing companies in the new frame because many customers had embraced the new frame with its newly framed benefits, price, and value proposition. Two corollaries are useful to point out. First, framing and frames of reference are psychological; they exist cognitively in the minds of customers in

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the marketplace. Marketers and price-setters build sustainable frames of reference in the minds of customers. Frames are associated with brand identity, purpose, and meaning, “what the brand is perceived to be or do,” categorized in a customer’s memory with respect to the unique needs that the frame addresses. Second, frames of reference for buying and selling are associated in customers’ minds with reference prices, the price that buyers typically pay or expect to pay for competitive substitutes in the frame; economic value theorists call this reference value. Estimating reference value requires business capabilities that are especially customer- and market-focused, usually involving depth-oriented customer research. This requires a knowledge of existing price frames in the market space, a business orientation toward customer value, and competitive intelligence to facilitate insights on how to create or modify framing to achieve better competitive framing results. Discovering and defining frames of reference is a soft strategy skill grounded in the science of behavioral economics, discussed further in chapter 3. Let’s now explore what we know about framing from the research of behavioral economists. To learn framing skills, it will be helpful to understand precisely what framing is and how it affects human cognition.

What Framing Is and How It Is Influential in Price-Setting

Framing arises when people make different choices based on how decision information is presented or portrayed. With framing, the core information relating to a decision does not change; people still have the same decision  options and same potential decision outcomes. But how the information is presented and described leads to different choices.2 For example, would you prefer ground beef described as “25 percent fat” or “75 percent lean”? Researchers at the University of Iowa found that positive  frames (75 percent lean) yielded more favorable evaluations than negative frames (25 percent fat). Even after tasting the meat, those shown positive frames rated the meat as leaner, higher quality, and less greasy compared with those exposed to negative frames.3 Underlying framing influence is reference dependence; that is, the human tendency to form reference points, or frames of reference, as a basis for making decision judgments. One of the most important behavioral advances of the last century is prospect theory, proposed by Daniel Kahneman and Amos Tversky in 1979. In recognition of the impact of this and related work, Kahneman was awarded the Nobel Prize in 2002; Tversky

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More value

Reference point Losses

$100 loss

Gains $100 Gain gain

A $100 loss is more painful than a $100 gain is pleasurable

Less value

Figure 2.3 

Prospect theory: “risk averse for potential gains, risk seeking for potential losses.” Adapted from Daniel Kahneman and Amos Tversky, “Prospect Theory: An Analysis of Decision Under Risk,” Econometrica 47 (1979): 263–91; and Dave Rothschild, “How People Think About Buying New Products,” JTBD.info, April 21, 2015, https://jtbd .info/getting-consumers-to-switch-to-your-solution-fa292bb29cea.

had passed away six years earlier. According to prospect theory, people use a reference point to frame prospective alternatives as either potential gains or losses and judge how much pleasure is associated with gains or how much pain is associated with losses. When people consider prospective gains, they usually make risk-averse decisions; that is, they prefer certain gains that might be smaller rather than risk uncertain gains that might be larger (see figure 2.3). When people consider prospective losses, they usually make risk-prone decisions; they prefer to risk the possibility of larger but uncertain losses rather than smaller but certain losses. Have you ever postponed making a dental or medical appointment when encountering a surprise medical symptom for fear of a negative prognosis, even though your situation might become worse by delaying? In order to avert an immediate loss, you are actually willing to accept even greater risk.

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While testing prospect theory in price-setting, pricing researchers at Notre Dame and Ohio State Universities made important framing discoveries about how managers frame the task of price-setting.4 To create a baseline for comparison, they gave a sample of students and retailer managers the classic Kahneman and Tversky prospect theory gamble, which you can do for yourself by choosing among the options below (the bracketed data were not shown to respondents). Would you choose Option A offering an 80 percent chance of getting $4,000 and a 20 percent chance of getting $0? [an expected value of $3,200]

or would you choose Option B offering $3,000 for sure? [an expected value of $3,000]

Chances are, you were probably risk averse and chose the sure Option B rather than the more valuable but risky Option A, which would be consistent with prospect theory. Indeed, the researchers reported that 79 percent of respondents chose B, the sure outcome, and 21 percent chose A, the risky option. However, preferences reversed when the offer was reframed as a pricing problem. Here study participants were told, “Your firm is considering a price decrease for your commodity product in the coming period. In the current period, unit price is $8.00, unit cost is $5.00, and sales are 1,000 units.” Would you choose Option A: Cut price to $7.40 with an 80 percent chance that you’ll sell 1,400 units (a 40 percent gain) and a 20 percent chance that you’ll sell 1,000 units (no gain)? [expected value of $3,168]

or would you choose Option B: Maintain price at $8.00 with a 100 percent certainty that you’ll sell 1,000 units (no gain)? [expected value of $3,000]

Chances are, you were probably risk-prone and chose to cut price, the risky Option A, rather than maintain price, the sure Option B, contradicting

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prospect theory (even though the expected values of the outcomes are roughly the same as the prospect theory gamble shown earlier). The researchers reported that 87 percent of respondents chose Option A, the risky price cut, whereas only 13 percent chose Option B, the sure outcome that maintained the $8.00 price. In a related study comparing cutting versus raising price with manufacturing managers involved in their firm’s pricing decisions, the researchers reported that 78 percent chose the risky price cut option rather than the sure option of maintaining price. For the price increase, only 45 percent chose the risky price increase option, and 55 percent chose the sure option of maintaining price.5 The bottom line: when it comes to price-cutting, price-setters seem to behave with capricious rationality. Like gamblers riveted to a game of chance, they consistently choose to cut price with a chance for a risky gain—risk proneness; but they avoid raising price to avoid the chance of a risky loss—risk aversion. What’s going on? When making pricing decisions, managers frame pricesetting outcomes, not in terms of profits or potential value, but in terms of customers and sales to customers, contradicting prospect theory. One retailer who chose to cut price said, “I believe it is sometimes wise to cut [price] to help increase the possibility of new customers.” Another said, “You must gain customers at all times, that is your purpose.”6 The researchers concluded, “The potential gain of customers and the opportunity loss of not gaining customers [produces] risk-seeking in a price cut context,” and the potential loss of customers produces risk aversion in a price increase context.7 Citing this research, the Harvard Business Review concluded, “This and many other studies indicate that pricing managers routinely set prices too low, sapping their companies’ profits.”8 This is a seminal finding: unlike typical economic decision-making, which focuses on the expected value of profit outcomes (in business), pricesetters reframe pricing decisions in terms of expected customer sales gains or losses, a bias that persistently undermines pricing profitability. Let’s now broaden our discussion beyond prospect theory to address different types of frames of reference in the marketplace. Framing and reframing price and value are risky strategic choices, with the potential for profitable gains in both customers and profits but also potential losses, depending on how customers and competitors respond. We will look at how managers can build or construct new frames, revise (reframe) existing frames of reference, or simply rely on existing frames of reference in the marketplace, based on behavioral economic theory. Let’s begin

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with three useful soft skills for applying framing in the marketplace: (a) recognizing framing bias; (b) recognizing narrow versus broad framing; and (c) recognizing framing opportunities in the marketplace. The box, “Chuck E. Cheese Adapts Its Pricing Orientation,” gives a good example of broad versus narrow framing in practice.

Chuck E. Cheese Adapts Its Pricing Orientation

When the Chuck E. Cheese Pizza Time Theatre restaurant chain was founded in 1977, founder Nolan Bushnell (also founder of Atari video games) wanted to make video games accessible to price-sensitive families with younger children. His executive team wondered how to price the new pizza parlor/arcade game concept and whether to price some arcade games higher than others. “You know,” said Bushnell, “this was a subject to debate. We felt that since most of our revenue really came from kids under eight, I felt that it would be confusing.” Instead, they decided to offer a bundled experience of pizza and arcade games, typically for a family of four, at a price they felt customer families could afford to pay— starting at about $30 (for pizza, four soft drinks, and thirty game tokens). Additional game tokens could be purchased at twenty cents per arcade game play. Strategically, Chuck E. Cheese used a broad frame of reference in which the brand was framed as a bundled “pizza + arcade family destination” for a family price—using bundled price framing. However, though well intended at the outset, this framing led to harmful behavioral biases. Over time, Chuck E. Cheese gained a reputation for fights and violence at some of its restaurants, reportedly as parents argued over players that might overuse any one arcade game for hours. Why did this occur? Free game tokens included as part of the family bundled price unwittingly encouraged what behavioral economist Dan Ariely calls the “zero cost” heuristic, in which free goods become “irresistible” and “incredibly appealing.” This led to what economists call rivalry among customers (one party’s consumption of a good prevents or reduces its consumption by others) for limited capacity on highly popular games. To address these problems, in 2014, Chuck E. Cheese first reframed price using a new segmented price-framing strategy that unbundled arcade game prices and set different prices based on the level of usage of different arcade games—narrow usage price framing (per game) instead of broad bundled price framing. Higher usage prices for more popular games helped manage excess demand, and lower prices for less popular games encouraged their trial and greater usage, leading

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to better capacity utilization of all games and a more consistent customer experience. Price reframing leveraged principles of System 1 behavioral decisionmaking for price-setting using soft intuitive memory-focused skills: holistic sensing and making sense (sensemaking) of what was going on with customers and usage, leading to creativity, strategizing, and strategy making. In addition, Chuck E. Cheese switched from using game tokens for purchases to plastic RFID cards, enabling more sophisticated pricing analytics of customer purchase and game usage. This pricing innovation employed principles of System 2 analytic price-setting with hard, systematic, data-driven, and methodical analytical skills. RFID also enables algorithmic dynamic pricing—raising price during peak usage and lowering price during slack use to manage demand and achieve better total outcomes such as improved capacity utilization and customer satisfaction and greater profitability.

Recognizing Framing Bias

Economists argue that framing effects are irrational biases, that decision choices should be influenced by information about choices and outcomes, not how information is presented or viewed. However, research of the last half century by behavioral economists has documented more than one hundred behavioral biases in which people make seemingly irrational but predictable decisions. Herbert Simon from Carnegie Mellon University received the Nobel Prize in Economics in 1978 for his theories of bounded rationality and satisficing. The Royal Swedish Academy of Sciences said, “What is new in Simon’s ideas is, most of all, that he rejects the assumption made in the classic theory [of economics] of an omniscient, rational, profit-maximizing entrepreneur.” Instead, decision makers must work with limited knowledge and bounded rationality. “Individual companies, therefore, strive not to maximize profits but to find acceptable solutions to acute problems”9—or satisficing. Four behavioral biases especially relate to framing: loss aversion, the status quo effect, the ownership-endowment effect, and the sunk cost fallacy. According to Kahneman, loss aversion occurs when the disutility of giving up something is greater that the utility associated with acquiring it.10 One implication of loss aversion is that individuals have a strong tendency to remain at the status quo, because the disadvantages of

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leaving it loom larger than advantages [of staying with it. William] Samuelson and [Richard] Zeckhauser have demonstrated this effect, which they term the status quo bias.11

In marketing and price-setting, we see this bias especially when managers are overly influenced to prefer well-established existing frames of reference and find it difficult to imagine value or price-setting opportunities from other new or different frame perspectives. With the ownership endowment effect, “people often demand much more to give up [something] than they would be willing to pay to acquire it,” said Kahneman and coauthors Jack Knetsch and Richard Thaler.12 Rather, people place greater value on things in which they feel a sense of ownership. (We discuss ownership bias further in chapter 4.) Managers feel ownership of current strategies, programs, contracts, or relationships; competitors feel ownership of markets or market segments in which they have significant shares or positions; and customers feel ownership of products or services they have in their possession. A final behavioral bias is the sunkcost fallacy, in which people allow the unrecoverable costs of the past to influence future courses of action.13 Let’s illustrate framing bias with a real-world example in residential real estate. A couple decided to sell a ranch property they had owned for many years It consisted of a ten-acre hayfield and an adjacent five-acre house lot with a ranch house; in total, a lovely fifteen-acre ranch in the western United States. When they considered selling, they asked a broker for a property value appraisal. He reviewed a handful of “sold competitive listings” and concluded that the “value for the Home on 15 acres is $1.5 million” (the figures are disguised). But the sellers noted that a neighboring ten-acre undeveloped vacant lot had recently sold for about $1 million, which meant that the value of the home and five-acre house lot was only $500,000, much lower than they had expected. The realtor insisted that getting a higher price on the fifteen-acre ranch would be unsuccessful. The sellers called another realtor with a creative proposal: market the two properties separately as two distinct real estate listings. The new realtor agreed, adding that the two parcels would sell differently, one faster than the other, as they appealed to different customers with different needs (and they framed the purchase differently). For the two new listings, the sellers framed the house and lot as a five-acre “country home” with its associated benefits for $1.2 million. The hayfield lot was framed as a ten-acre “buildable estate” with its unique associated benefits for $1.1 million. The owners

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quickly received six offers for the country home and sold it at full price. Months later they sold the buildable estate property at nearly full price. The total sale value of both properties was almost $2.3 million, over 50 percent greater than the original appraisal estimate from the first realtor. A useful framing skill is to learn to recognize framing bias, mental shortcuts, and heuristic thinking. In this situation, the original broker was subtly influenced by framing bias, heuristically estimating the worth of the total property as a single frame of reference rather than seeing two different frames appealing to two types of customers. What appeared to have been this broker’s typical pricing orientation for property appraisals—viewing the property “as is” and looking for recent comparative sales—would have led to suboptimal and shortsighted pricing that would have cost the sellers three-quarters of a million dollars. However, simple thoughtful queries often can lead to debiasing and useful framing insights. For example, could this fifteen-acre property be framed differently to different types of buyers? How? Are reference prices (what buyers would expect to pay) different for buyers of “country homes” than for those of “buildable estate” properties? How? With a little extra work by the second realtor, the answer was “yes.” By contrast, even when challenged, the original realtor could not break free from the biases of his original framing, the fifteen-acre “ranch” property; he was hindered by status quo bias and incapable of ignoring the sunk costs of the efforts he had already expended to construct his original appraisal, all of which reflected his standard, but biased, pricing orientation for his clients.

Recognizing Narrow Versus Broad Framing

Nobel Laureate Daniel Kahneman wrote, The great Paul Samuelson—a giant among the economists of the twentieth century—famously asked a friend whether he would accept a gamble on the toss of a coin in which he could lose $100 or win $200. His friend responded, “I won’t bet because I would feel the $100 loss more than the $200 gain. But I’ll take you on if you promise to let me make 100 such bets.”14

Most of us never recognize that framing can be conceived, or constructed, in broad terms as an aggregate bundle of multiple individual decision

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judgments combined into one, as in the willingness of Samuelson’s friend to accept an offer of a bundle of 100 coin-toss bets; or in narrow terms, as disaggregated separate decision judgments considered individually, as in the friend’s refusal to accept a single coin-toss bet. As seen with Samuelson’s single bet, narrow framing can often evoke loss aversion. The expected value of the outcome of the coin toss is +$50, calculated as .5 × −$100 + .5 × $200, and any rational person should accept the bet. But many people do not. We see this same type of narrow framing influence on loss aversion in various settings: for example, novice investors avoid investing narrowly in individual stocks because of anxiety over a stock’s price volatility, but they readily invest in broader mutual funds, which comprise many individual stocks. Broad versus narrow framing applies in many market-framing and price-setting situations. Always ask, what is the broad frame here? And what narrow framing opportunities exist that might enable more effortful and sometimes riskier but also more profitable pricing opportunities? A vivid example of broad and narrow framing appeared in the art world in 2017 with the price-setting at auction of the painting Salvator Mundi (“The Savior of the World”) by Leonardo da Vinci, dated to around 1500 and called “The Last da Vinci” by art collectors and dealers (see figure 2.4). Christie’s auction house, representing the sellers, had scheduled an auction in New York City for November 13 for “Old Masters” artworks. Pieces were listed for sale by famous artists such as Matisse, van Gogh, Renoir, and Monet. The average estimated price was $8 million per painting. Christie’s had also scheduled another auction in New York just two days later, November 15, for “Post-War and Contemporary Art,” listing for sale pieces by famous contemporary artists such as Rothko, Twombly, Bourgeois, and Basquiat. The average estimated price in this contemporary auction was $13.6 million. The Salvator Mundi hardly qualified as contemporary art, yet its sellers hesitated to commit to the Old Masters auction, fearing an undervalued sale. Meanwhile, Christie’s learned of a late addition to the contemporary art auction: Andy Warhol’s Sixty Last Suppers, his 1986 silk-screen riff on Leonardo’s famous The Last Supper; the presale estimate for the Warhol was $53 million. Salvator Mundi’s sellers quickly saw a narrow framing opportunity: market their iconic Old Masters da Vinci painting alongside Warhol’s Sixty Last Suppers in the contemporary auction linked by their common da Vinci legacy. The presale estimate for Salvator Mundi: $100 million. Highlighting the thematic framing link between the two works, Loïc Gouzer, cochairman of postwar and contemporary art at Christie’s, said, “The work

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Figure 2.4 

Salvator mundi, “The Savior of the World.” Leonardo da vinci (1452–1519). Source: Wikimedia Commons.

of Leonardo is just as influential to the art that is being created today as it was in the 15th and 16th centuries.”15 The outcome: Warhol’s Sixty Last Suppers sold for $61 million. Leonardo’s Salvator Mundi sold for $450 million, a record. In value-based marketing and price-setting for highly intangible products such as fashion, residential real estate, interior design, or destination

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cruises—and, in this example, art—framing is extremely important, which Salvator Mundi’s sellers clearly sensed. The obvious existing framing for the da Vinci was within the Old Masters category, a classic broad frame in a very well-known art category accompanied by broadly published statistics of composite prices, average prices, and price indexes that were widely followed in the art world. There were many works that clearly were fair, competitive Old Masters comparative referents for the price-setting that would take place at auction—it would have been a low-risk sale. However, Salvator Mundi’s sellers waited, seeking a more profitable, though riskier, framing strategy among the frothier contemporary art buyers who were willing to pay average prices 70 percent higher than Old Masters buyers. The entry of Warhol’s Sixty Last Suppers into the contemporary art auction provided the perfect framing opportunity: market the two famous Leonardo and Warhol works alongside each other, creating a new and narrow frame of reference. They thus framed Salvator Mundi not as Old Masters art, nor as contemporary art, but as what Christie’s framed “Leonardo’s influential art,” a new narrow framing with just one reference price created in the moment for this auction. The framing bias present in this situation would have been to accept the simple heuristic assumption that Salvator Mundi was an Old Masters work and therefore that its reference price for price-setting should be the composite price of Old Masters artworks for sale; this might have been a likely pricing orientation for many art dealers. However, once again, a few simple queries can lead to debiasing and useful creative framing insights. What are the obvious broad frames of reference that appear in the present market situation? Often, they are well-defined and well-worn industry categories, such as Old Masters or contemporary art; their composite reference prices are clearly catalogued, published with various summary statistics, highly visible, and well known to buyers, sellers, and their agents. However, are there other conceivable narrow framing possibilities that might exist with imagination and exploration? Or, sometimes a narrow frame is the obvious frame that presents itself in the current market situation. In this case, are there other possible broader possibilities for framing that might be considered? The key is to try to see beyond the existing frame of reference. This applies especially to products or services that are experiential or psychological in nature. It requires real effort to break free from the obvious way in which most people in the marketplace view the buying and selling of a product, service, or experience. Think beyond the obvious existing frames to other new framing possibilities.

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Recognizing Framing Opportunities in the Marketplace

Frames of reference evolve and change over time. In new and emerging markets differentiation is significant, diverse, and heterogeneous among different brands, marked by various competitors seeking to establish unique frames of reference (figure 2.5). Consider music-streaming services like Amazon Music, SiriusXM Internet Radio, Slacker Radio, Deezer, Tidal, Spotify, Google Play Music, iHeartRadio, Apple Music, and Pandora. Among these companies are at least four different emerging frames of reference competing for survival, each with a differentiated and unique frame of reference: premium high-fidelity music streaming, curated music streaming, content library music streaming, and live talk-sports-music streaming. In earlier stages of emerging markets, there is opportunity to strategically define new and creative frames of reference and to invest and establish the acceptance of the frame of reference. However, over time, frames of reference evolve, eventually showing signs of strain and misalignment with customer expectations. Why? On the supply side, competitive entry and imitation, increasing customer

Perceived value

Earlier phases

Later phases Customer expectations

Differentiation value

Reference price

Competitive entry and imitation

Commoditized framing bias Differentiated heterogeneous frames of reference

Increasing customer knowledge

Opportunity gap More price sensitive buyers

Converging frames of reference

Category commoditization

Commoditized dominant frame of reference

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Figure 2.5 

Frame of reference evolution and opportunity.

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knowledge, and more price-sensitive buyers eventually lead to standardization, category commoditization, and less differentiated alternatives in a dominant frame of reference. Large or dominant competitors raise prices within the dominant frame, increasingly pricing some customers out of the market. On the demand side, customer needs evolve with exposure to new technologies, prices, social sharing, word of mouth, and updated expectations, but current options increasingly seem limited or outdated as customers feel trapped in old commoditized frames with worn competitive alternatives. The difference between the two—evolving customer expectations and commoditized competitive alternatives—creates an opportunity gap with prime openings for new framing or reframing strategies. But opportunity gaps are not obvious to most managers; due to human behavioral biases—status quo, sunk cost, loss aversion, and ownership endowment—they are sometimes barely noticeable, nascent, and difficult to imagine when viewing the world from within the dominant frame. For example, consider the reframing impulse in the last decade to sell shaving accessories using customer subscriptions through e-commerce channels. For a century, Gillette had dominated the razor and blade market, controlling retail channels with dominant worldwide marketing budgets and superior shaving product innovation. In 1972, Gillette replaced old single-edge safety razors with cartridge blade razors of varying types, establishing a new frame of reference at premium prices that would last a half century, including Trac II twin-head razors, Atra pivoting-head razors, Sensor with independently moving twin blades, Mach3 Turbo, M3Power, and Fusion Power Phantom. Each new generation offered a new benefit framing (discussed in the next section) at higher premium prices reflecting Gillette’s dominance. It also reframed price. What had long been framed using simple product price framing—a price for a razor—Gillette reframed with a reverse two-part price framing: give away the razor at substantial price discounts (or free) but then set high prices for replacement cartridges. Gillette’s dominant framing strategies were highly successful, culminating in 70 percent market share at the turn of the twenty-first century. However, by 2010, the cartridge blade razor frame of reference showed signs of category commoditization as more customers became price sensitive and increasingly knowledgeable about competing and common claims of premium cartridge blade razors. Meanwhile, Gillette’s premium razors had approached $5 per replacement cartridge (for the Gillette Fusion ProShield 5) and $30–$40 for a multi-pack, pricing price-sensitive buyers out of the market. In 2011, upstart internet competitors sensed a framing

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opportunity gap to leverage e-commerce technologies that had evolved beyond Gillette’s now dated dominant frame of reference. Dollar Shave Club disruptively reframed razor and blade purchases, creating a new differentiated framing (see figure 2.5, left). Its strategy: a subscription frame of reference, first reframing the benefits customers get—including automatic monthly blade replenishment shipped to the customer’s door on a set schedule to save replacement hassle—then reframing price that customers give using subscription price framing with a simple fixed price, $1 per month. For five years Gillette executives disparaged the new online subscription competitive framing, blind to their own status quo and sunk-cost framing biases that were deeply engrained in their long-established pricing orientation, even as market share fell from 70 to 54 percent by 2016. Finally, Gillette adopted subscription framing in 2017, called Gillette On Demand, followed a year later with broad price cuts. “You told us our blades can be too expensive and we listened,” it confessed.16 Recognize framing opportunities in the marketplace. Look for signs of declining differentiation and category commoditization—declining or flat growth, share losses, sales losses, declining category prices, price sensitivity, persistent margin and pricing pressure, loyalty erosion, customer frustration and dissatisfaction, and new or surprising competitive entry. For example, smartphone unit sales declined 4 percent worldwide in 2018; Apple’s iPhone sales, with five models priced at over $1,000, declined 17 percent even as Chinese competitors introduced feature-rich lookalike products, all early signs of maturation and then commoditization. The number of people buying their first iPhone declined 63 percent, from 129 million in 2016 to 48 million in 2019. Financial analysts had already concluded, “the market for iPhones will largely become a replacement market . . . [and] successive generations of iPhones will likely become less differentiated (i.e., new iPhones will become ‘good enough’ to forestall further upgrades), resulting in the elongation of replacement cycles,”17 now at three to four years due to high prices. These signs, evidence of category commoditization and evolving customer expectations, suggest an impending opportunity gap for framing or reframing (see figure 2.5). An idea suggested by some financial analysts is to reframe the iPhone using subscription framing. We could imagine Apple implementing a subscription plan of its own [like Netflix, Spotify, or Microsoft Office 365]. In such a plan, customers could lease iPhones, iPads, Macs, and services such as iCloud

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and Apple Music for one “low” monthly fee, and have their hardware upgraded [regularly and automatically]. By moving to a subscription model, Apple would be able to lock in recurring revenue streams and freeze the length of replacement cycles.18

Apple’s introduction of Apple One in fall 2020 reframes the purchase of six Apple subscription services with bundling as Apple continues to creatively consider reframing opportunities to strategically drive growth. Let’s now apply the aforementioned framing principles to the three building blocks of value-based marketing and pricing and explore price framing, reference framing, and benefit framing, highlighted earlier in figure 2.2.

Framing to Transform Perceptions of Value

Firms use many strategies to compete and communicate the value of their brands in the marketplace: advertising campaigns to maintain top-ofmind brand awareness, promotional campaigns to stimulate trial or repeat purchase, and digital marketing strategies to maintain engaging customer relationships. These marketing strategies are essential for maintaining the brand’s position vis-à-vis competitors in the existing frame of reference in the marketplace. However, framing and reframing strategies are different. Reframing transforms the brand’s core baseline value proposition to the customer by redefining the brand’s value, what customers get and what they give in exchange for new transformed value, reframing the frame of reference.

Price Framing

Price framing achieves transformative changes in perceived value by recasting or redefining—reframing—the give side of the value-price exchange, what and how customers pay for the value they receive in the frame of reference. Figure 2.6 shows a handful of familiar possibilities. Of course, there might be many others, depending on competitive context. How customers pay refers to the structure and metrics of the value-price exchange; and what customers pay refers to the price level paid relative to the value received. Price reframing changes the competitive landscape so that competitors no longer appear to compete in the same way with the brand, or it might eliminate competitors from the customer’s evoked set of competitive

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Cost to serve +

+

Price paid for product ownership

+

Product price framing

Core + price +

Value adds

Price paid per membership period

+

Price paid per bundle Price paid per usage, or per user

Usage Price Framing

Bundled price framing

Price paid for unlimited use Flat-rate price framing

Subscription price framing

Core + fees price framing

Initial price + price per Two-part use price framing

Price paid per oz, per ml, per GB, per . . .

Price paid for limited trial

Metric price framing

Freemium price framing

Figure 2.6 

Price framing: reframing how the customer gives, or pays—price structure, metrics.

alternatives, meaning that customers no longer consider former competitors as viable substitutes. For example, for years, leading U.S. mobile wireless firms built a lucrative dominant price-framing strategy based on mobile contracts pricing, a broad bundled price framing (see figure 2.6, middle). They offered substantial incentives, called “mobile handset subsidies”—a free or highly discounted new smartphone—and in exchange required customers to commit to long-term contracts of up to twenty-four months, resulting in highly profitable monthly service revenues. Additional lucrative profits were generated by extra fees: early termination fees of hundreds of dollars to lock customers in for long periods with high margin usage and overage fees of, say, $15 per gigabyte for exceeding the contractual data usage limit. By 2012, growth in mobile wireless customers plateaued, sparking intense competitive rivalry to retain market share, straining the dominant mobile frame of reference. Customers became increasingly frustrated with higher prices, penalties to prevent switching, and category commoditization within the dominant frame, which created an opportunity gap for reframing. T-Mobile, ranked fourth in the United States, called itself the “Uncarrier”

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and broke apart the old contract price framing by unbundling price with the introduction of basic product price framing (compare in figure 2.6, left). In that case, customers purchased the mobile phone outright or paid in monthly installments. Overage charges were eliminated; now there was just one price for an unlimited data plan. Verizon and AT&T executives, biased by the status quo effect and loss aversion over the prospect of losing their profitable dominant price framing, at first ignored and then retaliated against T-Mobile’s new individual product price framing, but eventually they had to relent. Virtually all mobile players adopted individual product price framing, with unbundled product pricing and unlimited data plans, which were sold separately. Within months of its disruptive price reframing strategy (figure 2.5, right to left), T-Mobile’s subscriber base increased by nearly a third; its growth in revenues and operating income outpaced market leader Verizon by a wide margin, resulting in a T-Mobile stock price/earnings multiple nearly double that of Verizon’s.19 Figure 2.7 compares T-Mobile U.S’s stock market performance from 2013, the period of its price reframing strategies, to 2020, with Verizon’s performance.

TMUS 127.780

VZ 55.640 ×

+430.5% 400.00%

T-Mobile

300.00%

Stock price growth % 200.00%

100.00%

Verizon +3.2%

– + 2014

2015

2016

2017

2018

2019

2020

2021

Figure 2.7 

T-mobile U.S. versus Verizon stock market performance, 2013–20. Source: Verizon Media and Yahoo Finance for instructional or illustrative purposes. Data sourced March 16, 2021.

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Amazon Prime is a simple and accessible price-reframing example that could have been created by any number of firms. For years Amazon offered free shipping, called “Super Saver Shipping,” to customers with a $25 minimum order and delivery in eight to ten business days. But one perfectionist Amazon engineer noted the frustration of the repetitive clickintensive order process of online orders, pointing to a possible opportunity gap for reframing. He suggested, “Wouldn’t it be great if customers just gave us a chunk of change at the beginning of the year and we calculated zero for their shipping charges the rest of that year?” Another team member cited a competitive bookseller’s $30 membership program, which offered upgraded customer benefits. Within four months, in February 2005, Amazon Prime was launched. For an annual membership payment of $79, customers received unlimited two-day delivery on their orders plus other selected benefits—a new broad bundled subscription price framing. Amazon CEO Jeff Bezos said, “I want to draw a moat around our best customers  .  .  . [and] change the psychology of people not looking at the pennies differences between buying on Amazon versus buying somewhere else.”20 Amazon took a commoditized forgotten corner of the value proposition, shipping, and created a new price framing that evolved into highly profitable customer loyalty (figure 2.5, right to left). According to Morgan Stanley, Amazon Prime has penetrated 51 percent of U.S. households, with members spending on average $2,486 in the prior twelve months versus $544 for non-Prime members.21 Competitors in many e-commerce settings have broadly adopted Amazon’s price framing for shipping, because customer expectations have changed to embrace the new frame of reference. The point of price reframing is to frame prices for customers using innovative price structures that differentiate from those of competitors in ways that intuitively enable customers to perceive that the price they pay aligns with the unique value they get. “Each time a company discovers a better metric than its competitors, it gains margin from existing customers, incremental revenue from customers formerly priced out of its markets, or both,” concluded Nagle and Müller.22 We discuss price metrics more in chapter 7.

Reference Framing

Another transformative framing strategy is reframing reference value, based on the price that customers expect to pay for competitive alternatives in a new frame of reference. Reference framing frames or reframes the meaning

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Total worth

of the brand in the customer’s mind. Brand strategists call this brand meaning. It includes the brand’s purpose and identity; the categorical affiliation of the brand’s product or service in the customer’s mind; what the brand is perceived to be (what it is); what it does; how it is used; and the real needs it addresses. With reframing, the brand gets associated with new purpose and new meaning affiliated with a new product or service category; what it does and how it is used change. Consequently, it gets associated with new and different competitive alternatives and, most important for pricing, new and different reference prices that customers typically expect to pay in the new frame of reference. Reframing strategies might involve neighboring category framing into a perceptually adjacent product or service category with different competitive referents, or new category framing into a perceptually new product/service category space that has no competitive referents in the customer’s mind—called by strategists a “blue ocean strategy”; these strategies are depicted in figure 2.8.

Value of differential benefits Value of differential benefits Original competitive reference value

Original category reference frame

Neighboring competitive reference value

Neighboring category reference framing

New competitive reference value

New category reference framing

Figure 2.8 

Reference framing: reframing reference value and what the brand is perceived to be or do.

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For example, a bottle of Evian Natural Spring Water (330 ml, 11.2 ounces) sells at retail for about $0.88 ($0.03/ounce). However, a 5-ounce bottle of the same Evian water in a small spray bottle with a fine mist spray nozzle, called “Evian Facial Spray,” sells for $12.50 ($2.50/ounce), 83 times the price of Evian bottled drinking water. Evian reframed the meaning of its water—its purpose, what it does, and how it is used—to appeal to customers seeking a different benefit—skin hydration—thus using neighboring category reference framing to a perceptually different product category that still evokes Evian’s brand heritage in quality water (see figure 2.8, middle). The competitors and competitive reference prices of the neighboring frame were now different as well; instead of competitive referents like Poland Spring ($0.01/ounce) or Dasani ($0.02/ounce), Evian facial spray for skin hydration now competed with Hampton Sun Continuous Mist Hydrating Aloe for $28.00 per 5-ounce bottle ($5.60/ounce) or SuperGoop! SPF50 Defense Refresh Setting Mist for $28.00 per 3.4-ounce bottle ($8.24/ounce). Why such high reference prices? Because Evian Facial Spray belongs perceptually to a different category with different customer benefits and greater inherent value. Cosmetics are expensive, time consuming to apply, and worth considerable self-esteem and self-expression. A fine water mist protects that customer investment, so that customers feel prepared for personal and professional engagements throughout a busy day. Many bookstores were driven to near bankruptcy by large online booksellers such as Amazon, which had already disrupted the traditional bookstore frame of reference. In response, A Cappella Books in Atlanta, Georgia, reframed a new part of its business not as a bookstore but as an “event marketer” using neighboring category reference framing (see figure  2.8, middle). It booked speakers and signing events featuring high-profile authors and such figures as Senators Al Franken and Bernie Sanders, best-selling author Malcom Gladwell, and entertainer Harry Belafonte. These events were not staged at the bookstore but at comedy clubs, city hall, museums, libraries, churches, and synagogues. In this new reframing strategy, A Cappella’s brand meaning (what the brand is perceived to be, what it does, how it is used) encompassed a broader entertainment identity, with speakers, alluring themes, popular venues . . . and books. Contrast this with its original “bookstore” framing in which it simply offered books at discount prices in the store or online. Now, its event marketing sells books at local events for full cover price, or higher than cover price for popular events where ticket buyers get a copy of the book as part of the price of admission. With 200 events per year, A Cappella’s reference reframing strategy enabled it to triple its revenue from

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about $300,000 with pure bookstore framing to nearly $900,000 with event marketer reframing, making it highly successful among local book retailers’ businesses.23 Another form of reference framing is new category reference framing, depicted in figure 2.8, right. A good example is shave cream, which sells for about $2.60 for an 11-ounce can ($0.24/ounce) in an old, traditional commoditized frame of reference. Dollar Shave Club’s new entry, however, was framed not as shave cream but as “shave butter”—Dr. Carter’s Easy Shave Butter, described as “buttery smooth . . . for effortless delightful shaving.” It seems silly that a simple word change (“cream” versus “butter”) in its categorical meaning would make a difference, but it affects how customers frame the product. Shave butter sells for $8.00 per 6-ounce tube ($1.33/ounce), nearly six times the traditional reference price of shave cream ($0.24/ounce). By reframing, Dollar Shave Club created its own reference price with no direct competitors (figure 2.5). Framing the product as shave butter changed what the brand is perceived to be, what it does, and how it is used, appealing to younger men who were attracted to Dollar Shave Club’s edgy YouTube marketing campaign. Its new category reference framing was a blue ocean strategy, enabling it to achieve high premium prices. Pharmaceutical companies routinely target new disease categories, called “indications,” for early regulatory approval to establish new frames of reference in the minds of customers, with little or no competition while the drug is under patent protection. For example, Paxil received original government approval as an antidepressant but was challenged when competing against Prozac, the dominant competitive antidepressant brand. When Paxil received government approval for a new indication, social anxiety disorder, or SAD, GlaxoSmithKline, Paxil’s owner, quickly pivoted, reframing Paxil as a pioneering antianxiety drug to treat an illness in which there were no competitors, allowing it to set its own reference price in a new frame of reference. Harvard Business School researchers summarized: “Paxil’s product director noted, ‘Every marketer’s dream is to find an unidentified or unknown market and develop it.’ Social anxiety disorder presented just such an opportunity—it was estimated that only 5 percent of those affected by SAD [had] ever sought treatment. Paxil [was] the ‘first and only’ medication to win U.S. approval for SAD.” It went on to achieve sales of more than $12 billion. The point of reference framing is to change the brand’s frame of reference; in other words, changing what the brand is perceived to be, what it does, and how it is used categorically in customers’ minds and consequently

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changing the competitive landscape or the competitive brands with which it competes—even achieving blue ocean strategies with little or no competition.

Benefit Framing

Total worth

Benefit framing transforms perceptions of value by framing or reframing the brand’s differential benefits and the value that it delivers versus competitive alternatives in the marketplace. One way to achieve transformational differentiation is by forward-integrating across the value chain to offer more complete benefit bundles or systems, or total customer solutions, illustrated in figure 2.9. “Solutions are far more valuable to customers than generic packages of goods, services, and information that then

Use value of differential benefits Individual product

Use value of differential benefits Integrated bundles

Use value of differential benefits

Total solution

Reference value

Reference value

Reference value

Individual product framing

Bundled benefit framing

Customer solution benefit framing

Figure 2.9 

Benefit framing: reframing differential value, what the customer gets.

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have to be turned into solutions by the customer,” said Esko Penttinen and Jonathan Palmer.24 SKF, the world’s largest bearing manufacturer, traditionally offered customers high-quality bearings and related services on a transactional basis, selling to OEMs (original equipment manufacturers) in automotive, aerospace, power generation, and other industries. They competed in old, established frames of reference typified by the later right phases of figure 2.5. To separate from intense price competition, SKF strategically reframed benefits by bundling bearings products and services into integrated systems: bundled benefit framing. In the last decade, however, SKF has reframed further forward in the value chain, offering customers complete solutions with a performance-based contract guarantee that the bearings will always function optimally in the customer’s machinery, thus achieving total customer solution benefit framing, see figure 2.5. The CEO of SKF Finland said, The aim of SKF Integrated Maintenance Solution (IMS) is to offer customers a service [in] which . . . SKF would take the full responsibility of the bearing maintenance including condition monitoring, lubrication, replacement, and logistics needed to get a new bearing for replacement. The customers of SKF [would] outsource everything related to bearings to SKF. This would mean that the customers of SKF would no longer have to purchase the bearings, they would in fact purchase simply comprehensive maintenance functions and runnability for their machinery.25

SKF’s new model has been acclaimed by industry professionals for its “asset performance management [that] can yield [savings] in maintenance cost reductions, increased production, extension of asset life, and streamlined inventory management.”26 Similarly, General Electric reframed its commercial aircraft engine business using total customer solution benefit framing to break away from the price competition of its commoditized old frame of reference: selling engines (individual product framing) at low prices to sell more profitable replacement parts. With GE’s new frame, airlines purchased “power by the hour—in essence, purchasing as a package engines, parts, and MRO services (maintenance, repair, and overhaul), and paying on the [metric] of uptime, or per hour of use. The strategy [enabled] the company to

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make the most of its competitive advantages (its technical skills, diagnostic capabilities, financing expertise and scale, and end-to-end MRO services) against major competitors.”27 GE’s customer solution benefit framing became highly successful, consistently achieving the highest margins in the industry. Apple did not offer the first digital watch or the first wearable fitness device; however, it successfully reframed the smart watch category with Apple Watch using bundled benefit framing. At its launch in 2014, Apple CEO Tim Cook framed Apple Watch as a bundle of three key benefits: Apple Watch is “a precise timepiece, a new intimate way to communicate from your wrist, and a comprehensive health and fitness device.”28 Note that it was tastefully more than a wearable fitness device and elegantly more than a functional digital watch (a long-commoditized category); plus, it was an innovative intimate extension of the iPhone accessed from your wrist. To support this bundled benefit framing, Apple marketed the smart watch as a fine intimate piece of jewelry, with a twelve-page spread in Vogue magazine that featured gorgeous photos of wristbands and elegant timepieces. Like fine jewelry, it came in a long, elegant white box. Following jewelry industry protocols, the company required appointments at local Apple Stores for “demonstrations and fittings.” The price ranged from $349 for the base mass-market model to $17,000 for the Apple Watch Edition, which was made of 18-karat gold. By 2018, Apple was the top wearable device company in the world, with annual growth of 38 percent, compared with the category average of 5 percent. In smart watches, Apple Watch had a 56 percent category share compared with second-place Samsung with its 14 percent share; Apple sold more watches than the entire Swiss watch industry combined. Figure 2.10 shows Apple Watch’s category share since its introduction in 2015. Benefit framing frames perceptions of the brand’s differential value by transforming what the customer gets beyond the perceived competitive referent product or service, to encompass benefit bundles or total customer solutions that substantially reframe the customer’s relationship with the brand, the light areas of figure 2.9. Benefit framing is especially effective in transforming value for customers who seek the benefits of  loyal brand relationships. Look for customers who seek benefit bundles, systems, and customer solutions rather than simple price or product performance.

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100%

Share of shipments

Others

75%

Garmin Samsung

Fitbit

50%

Apple

25%

'17 2Q '18 3Q '18 4Q '18 2Q '19 3Q '19 1Q '19 1Q '20

'17

4Q

'16

Samsung

3Q

'16

4Q

'15

1Q

'15

Apple

4Q

'15

3Q

'15

2Q

'14

1Q

3Q

2Q

'14

0%

Fitbit

Garmin

Others

Figure 2.10 

Smartwatch category market shares, 2014–20. Source: Statista, “Market Share of Smartwatch Unit Shipments Worldwide from the 2Q’14 to 1Q ’20∗, by Vendor,” accessed March 1, 2021, https://www.statista.com/statistics /524830/global-smartwatch-vendors-market-share/.

Steps to Framing in Practice

UCLA strategy professor Richard Rumelt related a conversation with Steve Jobs, who had just returned to Apple. “Steve,” I said, “this turnaround at Apple has been impressive. But everything we know about the personal-computer business says that Apple will always have a small niche position. The network externalities are just too strong to upset the de facto ‘Wintel’ standard. So what are you trying to do? What’s the longer-term strategy?” . . . He just smiled and said, “I am going to wait for the next big thing.” . . . He was waiting until the right moment for that predatory leap, which for him was Pixar and then, in an even bigger way, the iPod.29

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Baseline framing

Framing bias

Understand customer framing of the brand

•

Current value proposition. What customers get, what they perceive the brand to be or do categorically, the needs it addresses?

•

Referent competitors. Which competitors do customers consider substitutes for the brand in the frame?

•

Competitive reference price. How and what do customers expect to pay for competitive substitutes?

•

Differential value drivers. The unique differential value customers get from the brand vis-à-vis competitors.

Framing opportunity

Recognize signs of framing bias

•

Status quo bias. Do managers, or competitors, seek to preserve the status quo?

•

Loss aversion. Are managers, or competitors, driven to avoid possible losses—in customers, market share, revenue, profit?

•

Sunk-cost fallacy. Are managers, or competitors, subtly influenced by sunk costs already incurred?

•

Ownership endowment effect. Do managers, or competitors, feel ownership of strategies, programs, relationships, or segments leading to inertia?

Framing innovation

Recognize signs of framing opportunity

•

Convergence and commoditization. Are there signs of competitive convergence, category commoditization, or dominant frames of reference?

•

Broad framing/Narrow framing. Are frames broad (narrow), and are there narrow (broad) frames that are obscured, hidden, or overlooked?

•

Customer expectations. Are customer expectations evolving with new technology, social sharing, and word-of-mouth?

Imagine reframing the value-price exchange

•

Price framing. How could price be framed/reframed to redefine how customers pay, or give?

•

Reference framing. How could products/services be framed or reframed to a neighboring category, or new category, with new competitive referents and new competitive reference prices? • Benefit framing. How could products/services be framed/reframed to redefine or transform what customers get?

•

Opportunity gaps. Are there potential opportunity gaps between old commoditized frames and evolving customer expectations?

Figure 2.11 

Four steps to framing/reframing value and price.

Jobs was a master at seizing framing opportunities and building successful frames of reference—the Macintosh computer, iPod, iPhone, iPad, iTunes, and Apple Watch. So, how can you, like Jobs, frame or reframe in the marketplace to achieve strategic frames of reference? I suggest four steps, summarized in figure 2.11. 1. Baseline Framing: understand customer framing of the brand. First, based on customer insights, understand the brand’s current frame of reference for baseline comparison and ideation. How do customers frame what the brand is categorically, its meaning and purpose, what it does, how it is used, and the needs it addresses? Which competitors are perceived to be substitutes? How and what do customers pay for competitive substitutes? Why do customers prefer the brand over competitors, and how do these advantages create differential value for customers vis-à-vis competitors? The sellers of the Salvatore Mundi knew well how customers traditionally framed Old Masters artworks and their reference prices at auction. But they sensed that this broad established framing

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was inadequate to capture the true value of this rare painting, “the last da Vinci,” his last known painting still in a private collection. 2. Framing Bias: recognize signs of framing bias. Look for signs among decision makers—within your decision team and among competitors—of framing bias that inhibits managers from making decisions rationally: status quo bias, loss aversion, sunk-cost fallacy, and ownership endowment effects. Recall the real estate agent who couldn’t see beyond the original assumptions about the value of a ranch property in the western United States, constrained as he was by status quo bias and the sunk-cost fallacy. Before the iPod, Steve Jobs knew that executive decision makers in the established music industry at record companies and music retailers were intimidated by the threat of digital music downloading due to their behavioral biases from their old, dominant frame of reference: loss aversion over profitable cash flows, status quo bias to protect entrenched market positions, and sunk-cost effect to preclude risky new investments. Their mistaken biases enabled an outsider like Jobs to overtake them as the largest U.S. online music retailer with Apple’s iTunes platform. 3. Framing Opportunity: recognize signs of potential framing opportunity. Look for signs of increasing competitive imitation and category commoditization, more knowledgeable customers, increasing price sensitivity amid higher prices, and dominant frames of reference—broad framing that obscures or overlooks possible narrow framing, or vice versa. T-Mobile sensed customer frustration as they chafed at lucrative contract pricing plans that locked in customers with high prices and switching penalties. The gap between customer expectations and commoditized competitive alternatives opened the door for T-Mobile’s simple price-reframing strategy to break apart the old bundled price framing, leading to strong gains in financial performance. 4. Framing Innovation: imagine framing and reframing the valueprice exchange. The first three steps open the door to imagination and ideation of new and different possible framing strategies that might define or redefine the value-price exchange—what customers get and what they give in exchange. This might be a simple

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but profound change in how customers pay for shipping with one annual membership fee for unlimited two-day delivery with Amazon Prime, a compelling subscription price-reframing strategy. Or it might be similar to Hewlett Packard’s change in how customers purchase printer ink, by digitally connecting printers to HP’s factory and automatically shipping replacements when ink is depleted, a digital customer solution benefit framing strategy. Or consider the example of a change in how and where a small book retailer sells its books, not so much at the bookstore as in the old narrow frame of reference but as an event marketer at popular venues like city hall with popular speakers and provocative themes—and, of course, books sold not at a discount but at full price or more, a simple but successful broader referencereframing strategy.

Conclusions

Framing and frames of reference in value-based marketing and pricesetting are ubiquitous and psychological. They exist in the minds of customers, requiring qualitative research such as depth interviews, customer observation, sensing, and ideation to discover. For managers, framing skills are often strategy skills: broad versus narrow framing; sensing opportunity gaps for framing; sensing framing bias; and creating new and differentiated price framing, reference framing, and benefit framing. These are soft skills that relate less to spreadsheets and more to recognition of value-based cognitive structures that exist or get constructed in the minds of buyers in the marketplace, how they evolve, and how they get disrupted, displaced, and supplanted. Framing can lead to transformative market performance (recall Adobe’s subscription price reframing from chapter 1), which Rumelt said can be achieved in two ways: One, you can invent your way to success. Unfortunately, you can’t count on that. The second path is to exploit some change in your environment—in technology, consumer tastes, laws, resource prices, or competitive behavior—and ride that change with quickness and skill. This second path is how most successful companies make it.30

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Framing skills enable managers and decision makers to leverage framing strategy. Rumelt continued, “Doing this kind of work is hard. A strategic insight is essentially the solution to a puzzle. Puzzles are solved by individuals or very tight-knit teams.”31 Such is the work of framing and strategic frames of reference. See the following templates to assist with framing skills in your pricesetting: • Template 2.1: Framing/Reframing Innovation Template • Template 2.2: Reference Framing Assessment Template • Template 2.3: Benefit Framing Assessment Template

Templates

Baseline framing

Framing bias

Framing opportunity

Framing innovation

Recognize signs of framing bias.

Recognize signs of framing opportunity.

Imagine reframing price and value.

Look for:

Look for:

Ideate:

Status quo bias

Evidence of commoditization

Price framing, how customers pay, or give

Broad versus narrow framing, hidden, obscure, overlooked frames

Reference reframing, to neighboring or new frames of reference

Current value proposition. How do customers frame your product/service? What do they say it is, what it does, how it is used?

Referent competitors. Who do customers say are competitive substitutes in the frame?

Loss aversion Sunk-cost fallacy Ownership endowment

Approximate reference price. What do customers expect to pay for competitive substitutes in the frame?

. . . among your price-setting team, among competitors

Differential value drivers. What do customers say are the unique differential value drivers of your product/service?

Template 2.1 

Framing/reframing innovation template.

Evolving customer expectations— technology, trends Opportunity gaps— old frames versus customer expectations

Benefit framing to bundled benefit frames or customer solution frames See Template 2.2 Reference Framing Projection Template and Template 2.3 Benefit Framing Assessment Template

Current reference frame Current value proposition. How do customers frame your product/service? What do they say it is, what it does, how it is used?

Referent competitors.

Potential neighboring category reference frames

Potential new “Blue Ocean” category reference frames

Assess the current reference frame—the get (value proposition) and the give (reference price), then project potential neighboring or new reference frames No competitors

Who do customers say are competitive substitutes in the frame?

Value of differential benefits

Approximate reference price.

Value of differential benefits

What do customers expect to pay for competitive substitutes in the frame?

Original competitive reference value Original category reference frame

Neighboring competitive reference value

Neighboring category reference framing

New competitive reference value

New category reference framing

Template 2.2 

Reference framing assessment template.

Current product/service framing

Potential bundled benefits frames

Potential total customer solution benefit frames

Current value proposition. How do customers frame your product/service? What do they say it is, what it does, how it is used?

Referent competitors.

Assess current benefit framing—the get (value proposition) with unique differential value drivers. Then project potential new benefit frames with their unique differential value drivers

Who do customers say are competitive substitutes in the frame?

Differential value drivers. What do customers say are the unique differential value drivers of your product/service?

Template 2.3 

Benefit framing assessment template.

Use value of differential benefits Individual product

Use value of differential benefits Integrated bundles

Reference value

Reference value

Individual product framing

Bundled benefit framing

Use value of differential benefits Total solution

Reference value

Customer solution benefit framing

3 Psychological Pricing Orientation Psychological Price-Setting Biases and Skills

Framing and frames of reference are at the center of behavioral economics with fundamental framing biases and soft skills, as we saw in the last chapter. In this chapter we expand our view of broader psychological pricesetting biases and skills that define your psychological pricing orientation: how pricing gets done around here psychologically. In chapter 4 we will turn to social price-setting biases and skills that define your social pricing orientation. Then, continuing through chapter 8, we examine behavioral biases and skills specific to each of the four cardinal pricing orientations: customer value-driven, customer willing-to-pay–driven, cost-driven, and competition-driven (see figure 3.1). Price-setting is especially fertile soil for soft decision-making biases, partly because the task is multifaceted and complex. It requires forecasting future outcomes, often under considerable time pressure and social influence. Add to that the tendency of many managers to approach price-setting with their own special brew of native business skills learned over the years in school or on the job. Most of these biases have been hard-baked into the managerial psyche through years of making pricing decisions, then reinforcing them with subsequent decisions, so that the biased methods come to seem true and correct merely because you or your company have been applying them for so long. Price-setting biases are almost always

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Your pricing DNA: Patterns, processes, orientations Framing, frames of reference Biases and skills

Psychological pricing orientation

Cardinal pricing orientations

Customer valuedriven pricing orientation

Customer WTPdriven pricing orientation

Biases and skills

Biases and skills

Cost-driven pricing orientation

Competition-driven pricing orientation

Biases and skills

Biases and skills

Biases and skills

Social pricing orientation Biases and skills

Figure 3.1 

Pricing orientation: Framing, psychological and social influences.

subconscious and subjective and are rarely noticed. Understanding your pricing orientation—the approach presented in this book—requires you to take notice, to call out your price-setting habits and biases and recognize them for what they are. Although they can be harmful as long as they go unnoticed and unchecked, they can—with debiasing and thoughtful guidance—be harnessed into soft behavioral price-setting skills.

Theory: System 1 versus System 2

Let’s begin with theory behind the biases and soft skills of price-setting. In his seminal book, Thinking Fast and Slow, Nobel Prize winner Daniel Kahneman describes two modes of decision processing—known as System 1 processing and System 2 processing—which underlie recent findings of behavioral economists, psychologists, and sociologists. I will present these two, in reverse order, in terms of how they relate to price-setting.

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A System 2 approach to decisions can be exemplified with a relatively simple multiplication problem: 17 × 24. As Kahneman observed, “You knew immediately that this is a multiplication problem, and probably knew that you could solve it, with paper and pencil, if not without. A precise solution did not come to mind [however], and you felt that you could choose whether or not to engage in the computation.”1 The answer to this problem is 408, which you might have quickly computed in your head in any of several ways. Whichever way you approach it, the computational nature of this problem requires deliberate, focused effort and demonstrates what Kahneman and other behavioral researchers call System 2 processing: analytic intelligence, algorithmic processing, cognitive control, and high demands on cognitive capacity. This relatively laborious process is the “slow” in Kahneman’s book title, Thinking Fast and Slow. A System 1 approach to decisions relies more on memory than on calculation. It is marked by automatic intuitive thinking using holistic cognition—which makes it much quicker and easier than System 2 processing. We see something and automatically access our memory to make memory-based intuitive judgments about what it is and is not, what it is like, what it is likely to do, how it feels, and how to respond. “Imagine yourself at the wheel of a car that unexpectedly skids on a large oil slick,” said Kahneman. “You will find that you have responded to the threat before you became fully conscious of it.”2 This is System 1 processing; its tasks are usually automatic, intuitive, and often heuristic (that is, they make use of a mental shortcut). System 1 processing is a matter of quickly jumping to solutions, which makes it fast—sometimes an efficient way to deal with an immediate situation—but not always accurate. Relating to value and pricing, we see System 1 processing being evoked by marketing symbols, icons, brand names, and prices. For example, when you see the image below, what immediately comes to mind?

Most of my students instantly say “Mercedes.” When I ask what ideas come to mind with this symbol, they quickly say “quality,” “luxury,” or “expensive.” Much of branded advertising relies on System 1 memory-based processing. Many brands set their prices to end in 9, such as $9.99, rather than $10.00, because buyers in Western cultures process digits from left to right and perceive a three-digit price with a leading $9 as less expensive than a four-digit price with a leading “$10.” (See the related box “Digit Bias as a Soft Skill of Price-Setting.”)

Digit Bias as a Soft Skill of Price-Setting3

A recent study from a researcher at Georgia State University discovered a fascinating form of digit bias in price-setting at auto dealerships. While analyzing 35 million auto loan transactions, the researcher, Zhenling Jiang, discovered that the monthly payment schedules of most auto loans end in digits of either 9 or 0 rather than in 1 through 8. Usually consumers enter an auto showroom and negotiate an overall price on the vehicle and then meet with the dealership’s financial manager to determine the car’s monthly payment—which itself is a separate price-setting negotiation between the auto buyer and seller because auto dealers obtain their financial loans from local banks and then add an interest rate markup and other fees, which they pass along in the loan package to the buyer. Her research found that loans with monthly payments ending in 9 carried higher interest rates, especially among low-income buyers, and loans ending in 0 carried lower interest rates, especially among high-income buyers. What was happening? Low-income buyers had less bargaining power and would accept incrementally higher monthly payment amounts but then stiffened their resistance when considering payments that might exceed a figure ending in 9—an apparently natural psychological digit barrier for low-income buyers. High-income buyers were better negotiators and had more bargaining power. They pushed for lower monthly payment amounts, but then they met stiffer resistance from the financial manager, who exhibited increasing concern when payments might go lower than an amount ending in 0—a seeming natural psychological digit barrier to dealers. Of course, this digit bias of just a dollar more or less in a monthly payment might seem like a small matter in price-setting; economists would say that the behaviors of buyers exhibiting a psychological barrier at 9-ending amounts, and sellers exhibiting a barrier at 0-ending amounts, are simple illustrations of price-setting irrationality. Price endings should have no effect on the utility associated with a car loan, only on the actual amount paid relative to the budget constraints of the buyer. However, buyers clearly perceive that their budget feels more strained as a monthly payment approaches a 9-ending figure; and financial managers clearly perceive that achieving their sales goal feels more challenging as the customer’s monthly payment approaches a 0-ending figure. It’s irrational, perhaps, but predictable and important to know for price-setting. Understanding digit bias is a useful soft skill in price-setting that subtly influences psychologically how price-setters attempt to automatically set price using a barely noticed personal memory strategy. If you were the owner of an auto dealership, consider how useful it would be to train your financial

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managers to understand this soft skill of price-setting and its potential impact on profitability. Buyers are less sensitive to raising price endings until they approach the psychological digit barrier of 9; and financial managers are less sensitive to lowering price endings until they approach the psychological digit boundary of 0. Considering how important financing is for the profitability of a modern auto dealership, this digit bias soft skill adds valuable points of profitability to the dealer’s bottom line. How much could that be worth? At one auto group reported by Forbes magazine, finance and insurance represented about 3 percent of revenues but 20 percent of gross profits;4 it could be worth a lot.

Price-setting is by nature a quantitative task involving calculation, analytics, and controlled rule-based processing; that is, you naturally expect it to be a System 2 analytic task. However, in practice, price-setting is often approached not analytically but subjectively, more of a System 1 intuitive task that is driven by what you recall from memory quickly, such as a competitor’s price, or a quick pricing rule of thumb. The reason, according to Kahneman, is “that System 2 is lazy and that mental effort is aversive.”5 To demonstrate the effects of mental effort and cognitive complexity, he conducted an experiment giving subjects a task called “Add-1.”6 Imagine you are asked to read an index card with a four-digit number and add 1 to each digit and to keep doing that every two seconds. For example, if the card says 5294, your task is to transform that into 6305. Then, two seconds later, add 1 to each digit again, which should yield 7416, and so on. If you try it, you will see that it is challenging to do. Kahneman then elevated the difficulty with an Add-3 task—now you have to add 3 to each digit. Measuring task difficulty by using eye pupil dilation and heartbeat, he concluded, Add-3 . . . is the most demanding [task] that I ever observed. In the first 5 seconds, the pupil dilates by about 50 percent of its original area and heart rate increases by about 7 beats per minute. This is as hard as people can work—they give up if more is asked of them. When we exposed our subjects to more digits than they could remember, their pupils stopped dilating or actually shrank.7

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Does price-setting involve that kind of cognitive effort? In fact, pricing is a highly challenging task, cognitively on a par with Add-1 and even Add-3. The normative pricing process—setting price strategically—is so complex that in sophisticated corporate settings, its subtasks are delegated to highly trained subpricing experts or departments such as price modelers, pricing economists, dynamic pricers, pricing strategists, and promotional pricers. Because of the cognitive challenge and the stakes involved in price-setting, price-setters often default to automatic, intuitive, memory-based, and less effortful System 1 behavioral price-setting to achieve their business goals. So, let’s talk about psychological price-setting biases and soft skills.

Biases and Skills

First, let’s clarify several important terms. In this book I talk about biases and skills—soft skills and hard skills. McKinsey offered a useful starting point: Biases are predispositions of a psychological, sociological, or even physiological nature that can influence our decision making. They often operate subconsciously and by definition are outside the logical process on which decisions are purportedly based. While we may readily acknowledge their existence, we often believe that we ourselves are not prone to bias.8

For example, Malcolm Gladwell recently wrote of one subconscious bias, human beings are so bad at detecting lies. We’re terrible at it. And the psychologist Tim Levine argues that that’s because as humans we “default to truth.” That is: we automatically assume that anyone we speak to is telling the truth, and it takes a mountain of evidence of doubt for us to change our minds. . . . Why did Bernie Madoff fool so many people for so long? How did the pedophile Larry Nasser get away with abusing so many girls in his care for so long?9

In response, Gladwell developed what for him became an improvised skill as a personal interviewer—a soft skill—to be “way, way more cautious as a journalist . . . to honestly profile [people], for example, . . . painting very specific narrow pictures of [them as] subjects.10

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Soft and Hard Skills

Soft skills are increasingly in demand in today’s knowledge economy, pushing cognitive limits with automation and AI technologies. They include higher cognitive skills such as creativity, critical thinking, decision-making, and strategizing and strategy making. We saw applications of these skills in the framing strategies cited in chapter 2 and will see more in the chapters to come. Hard skills are also increasingly in demand in today’s digital economy and include basic digital skills, including IT and programming skills, data analytics, statistical analysis, data mining, and web site development, and user experience (UX) design, among others. You will see examples of hard skills in price-setting especially in chapters 5–8 relating to skill sets found prominently in the four cardinal pricing orientations: cost-driven, value-driven, customer willing-to-pay-driven, and competition-driven pricing. An effective pricing orientation will balance both soft and hard skills to create a successful price-setting culture, and such a balance is required for effective business management in most settings. For example, a recent global CEO survey by KPMG shows just 35 percent of executives highly trust their organization’s data. Two-thirds of CEOs ignored insights provided by data analysis or computer models [hard skills] in the past three years because it contradicted their intuition [a soft skill]. Likewise, Bill Belichick, the New England Patriots’ head coach, has said he always prefers to “evaluate what I see” over analytics. But, still, he relies fundamentally on “scouting reports, statistics, growth forecasts and market data.”11 Jeff Bezos of Amazon said: “If you can make a decision with analysis, you should do so.  .  .  . But it turns out in life that your most important decisions are always made with instinct and intuition, taste, heart.”12

Psychological Price-Setting Biases and Skills

Let’s now explore six primary psychological biases and related skills in price-setting, shown in figure 3.2.

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Goal framing & pricesetting Forecasting & soft probability estimation

Goal framing & nudging

Psychological pricing orientation Pricing rules-ofthumb, truisms

Price metric framing Canonized formulas, templates, algorithms

Figure 3.2 

Psychological pricing orientation.

Goal Framing and Price-Setting

We discussed framing strategies at length in chapter 2. Let’s explore one more related framing dimension, the psychology of pricing goals and goal framing. When setting prices, managers often have an implicit goal in mind; not necessarily a concrete statistical target but still a focal goal that drives attention and decision-making. Goal framing means that when activated in the mind, a goal is cognitively embraced as overarching. Such “goals ‘frame’ a situation by steering important cognitive processes in the service of [that] focal goal,” to the subordination of other goals, according to scholars at the University of Groningen and Copenhagen Business School.13 Thus, on a personal level, for example, a weight loss goal becomes a focal frame through which other daily goals and decisions—what to buy,

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where to eat, whom to spend time with, when to go to sleep—are considered as they arise. In chapter 2, we saw research findings by scholars at Notre Dame and Ohio State Universities showing that when managers consider a choice between raising price or keeping price constant, they usually choose to keep price constant, a risk-averse choice. However, when considering a price cut versus keeping price constant, they usually—and in contradiction to prospect theory—choose to reduce price, a risk-seeking choice. Pricesetting appears to be a special case of human decision-making; one reason relates to goal framing. According to these researchers, companies are usually driven by two opposing goal frame orientations for price-setting, either to grow sales volume (61 percent of firms in their study) or to maintain gross profit margin (39 percent of firms). For managers whose companies had a sales volume goal-framing orientation, nearly all respondents in this group (97 percent) decided to take the risky choice and cut price when presented the option. For those whose companies had a gross profit margin goalframing orientation, 46 percent opted for the risky price cut when given the option—still a significant figure. Nonetheless, managers guided by a sales volume goal-framing orientation were overwhelmingly risk-seeking—and remember, these were real manufacturing managers with real involvement in price-setting (48 percent were presidents or vice presidents, 37 percent various managers).14 Goal framing has a powerful effect on price decision–making. For example, part of the pricing experiment required that managers deliberately calculate the estimated profit potential of the pricing choices before them—requiring slow, effortful System 2 analytic calculations. Nonetheless, System 1 behavioral goal framing overrode their System 2 analytic thinking: “Many of those who did calculate the profit implications were [still] willing to cut price in the interest of gaining customers, even when the expected profit value of the price cut was lower than that of holding price.”15 Part of what explains the impact of goal framing is what Kahneman and Tversky call the availability heuristic, in which people tend to use information that is easily recalled from memory and might be recent, frequently observed, or dramatic. Of course, frequently stressed goals keep information primed in memory to guide decision-making. With pricing, the default information that often seems to remain top of mind—and therefore most influential—is that of customers and sales to customers

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(sales volume, or sales revenue). Changes in customer sales offer vivid and often memorable evidence to price-setters of the outcomes of their pricesetting decisions. The researchers summarized their goal-framing insights: [Sales] volume shifts can be a very vivid indicator of performance to business decisionmakers. An increase in store traffic, for example, is a much more concrete, immediate, and available indicator of success than profit (which is more abstract and often a lagged measure of success) . . . particularly in the small business context.16

Goal Framing and Nudging

Uber has been particularly adept at managing pricing while balancing among three types of goal frames that have been identified by goalframing researchers. It uses soft skills that nudge its drivers’ decision-making, based on research conducted by Uber’s in-house group of behavioral scientists. Behavioral economics is especially important to Uber because its drivers are independent contractors who set their own hours and make their own business decisions. For example, one finding from Uber’s behavioral research, noted in a New York Times article, is “that many taxi drivers work longer hours on days when business is slow and shorter hours when business is brisk, the opposite of what economic rationality, to say nothing of common sense, would seem to dictate.” This tendency was especially true for inexperienced new drivers: “Many of these drivers appeared to have an income goal in mind and stopped when they were near it, causing them to knock off sooner” so they could enjoy some fortuitous leisure time.17 Their behavioral tendency was driven by the first of three types of goal framing, a hedonic goal frame, meaning that drivers were mindful of “me and how I feel or want to feel.”18 Because of drivers’ propensity to adopt a hedonic goal frame, Uber therefore must persuade its many independent drivers to work in concert with the company’s strategic goal frame for pricing: to maximize its drivers’ total contributions to corporate profit. This is the second type of goal framing, a normative goal frame, meaning that one is mindful of “we and our organization,” of how “we should act” to achieve organizational goals. How does Uber get its drivers to cooperate with its corporate strategic normative goal frame? By continually nudging drivers toward new and more lucrative revenue opportunities and, in so doing, leveraging the appeal of the third type of goal framing, a gain goal frame,

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whereby one is mindful of “me and my personal resources” (such as my income, assets, and possessions) to seek to always be better off.19 To appeal to drivers’ gain goal frames, Uber uses surge pricing, which dynamically changes fares to reflect supply and demand. Surge pricing increases the fare for a particular ride to, say, three times the basic fare in a surge zone, where demand is greater than the current supply and more drivers are needed. Via the Uber driver app, an on-screen visual heat map highlights revenue opportunities geographically and constantly informs drivers of surge zones and the accompanying price multipliers so drivers will know where to go to pick up the most price-attractive customers. The heat map uses visual rather than simply textual information to encourage drivers to rely on simple System 1 behavioral cues—cognitively it is easier for drivers to process visual information (see figure 3.3). In addition, Uber used strategic micro gain goals: they cleverly created a series of simple app-based reminders to encourage drivers to drive longer to achieve a twenty-five-ride gain goal, reinforced with a monetary reward. “Concerned that many new drivers were leaving the platform before completing the 25 rides that would earn them a signing bonus . . . Uber officials in some cities began experimenting with simple encouragement: You’re almost halfway there, congratulations! While the experiment seemed warm and innocuous, it had in fact been exquisitely calibrated. The company’s data scientists had previously discovered that once drivers reached the 25-ride threshold, their rate of attrition fell sharply.”20 And nudging: as drivers approached an income goal (set behaviorally by Uber) they received a message on the Uber app—for example, “with the headline ‘Make it to $330.’ The text then explained: ‘You’re $10 away from making $330 in net earnings. Are you sure you want to go offline?’ Below were two prompts: ‘Go offline’ and ‘Keep driving.’ The latter was already highlighted”21 as the default option. This kind of nudging is a clever System 1 behavioral way to encourage you to automatically “opt in” (as the default) to do something without having to think about it.22 In addition, Uber uses a digital dispatch algorithm that proactively sends “drivers their next fare opportunity before their current ride is even over,”23 to maintain drivers’ mental frame of staying on the job for their full shift. And the app’s user experience design for drivers is peppered with principles adopted from video gaming that leverage motivations to continually play, win, and achieve rewards: At any moment, the app shows drivers how many trips they have taken in the current week, how much money they have made, how

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Figure 3.3 

Uber heat map, Chicago. Source: Harry Campbell, founder of The Rideshare Guy, https://therideshareguy.com /lyfts-heat-maps-vs-ubers-surge-pricing-who-wins/.

much time they have spent logged on and what their overall rating from passengers is. All of these metrics can stimulate the competitive juices that drive compulsive game-playing. . . . Uber drivers can earn badges for achievements like Above and Beyond (denoted on the app by a cartoon of a rocket blasting off), Excellent Service (marked by a picture of a sparkling diamond) and Entertaining Drive (a pair of Groucho Marx glasses with nose and eyebrows).24

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“When asked about the badges he earns while driving for Uber, [one driver] practically gushed. ‘I’ve got currently 12 excellent-service and nine greatconversation badges,’ he said. . . . ‘It tells me where I’m at.’ ”25 In your own pricing environment, consider how goal framing can motivate employees and team members to do smart price-setting by leveraging System 1 behavioral impulses. Hedonic goal frames (for example, being awarded a stay at a resort) or gain goal frames (e.g., a cash spiff for making a sale) motivate price-setters in different ways. Then make sure your incentives align well with corporate normative goal frames to achieve profitability and group performance goals. (we discuss incentives in detail in chapter 4.) Ask yourself how you can frame goals to leverage System 1 behavioral advantages to nudge your price-setters into making fast decisions, repetitively and automatically, to the benefit of both the employee and your firm. It requires thoughtful design but usually leads to more profitable and more satisfactory pricing.

Price Metric Framing

In chapter 2 we talked about price-framing and the structure and metrics of price, or how customers pay. Let’s drill down into metrics. Most people give little thought to price metrics. They pay for gasoline by the gallon, eggs by the dozen, deli meats and cheeses by the pound, and milk by the quart. But price metrics communicate subtly to customers and serve three key purposes for price-setters. (a) They are useful for smart price segmentation; (b) they succinctly enable quick judgments of value alignment (what customers pay for the benefits they get); and (c) they can create competitive advantage. For example, the metric for Fairlife’s new Ultra-Filtered Milk (with 50 percent more protein, 30 percent less sugar, and no lactose) is not dollars (or other currency) per quart or per half-gallon, as you might expect, but per odd-shaped (52 fluid ounce) container, hardly comparable to the popular standard two-quart (64 fluid ounce) bottle of milk priced at $2.59. You have to do some math to facilitate a price comparison. Fairlife’s price per bottle is $3.99, which translates to $2.45 per quart, compared with $1.30 per quart for regular milk. More important, its difficult-to-compare price metric communicates to customers subtly and psychologically that though it might be framed as milk, it nonetheless is superior to regular milk. The theory behind pricing metrics is based on frames of reference and range effects. Many people think of a frame of reference as a reference point

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against which comparisons are judged, as articulated in Kahneman and Tversky’s prospect theory.26 However, consumer pricing researchers have found that with respect to prices, the reference point is part of an acceptable price range, whereby some higher prices are deemed too expensive for buyers and lower prices signal a product of questionable quality.27 Range theory suggests that the range consists of a mental scale in which judgments “are assigned to equal segments of the contextual range,”28 like a ruler that enables judgments such as “Tide is usually much more expensive than Wisk,” or “a Kenmore dishwasher is a better value for the money than a Maytag.”29 The key here is that buyers adopt different mental scales with different contextual ranges, depending on the purchase context. And they can adopt new mental scales that replace and supplant existing scales, or create new range scales for a new product context—this is the point of price-framing and reframing. Scott Huettel, addressing the neuroscience behind range theory, said, “The range of some quantity is given by the span of its potential values. For example, fast-food prices vary over a small range (a few dollars); prices of jackets vary over a larger range (tens to hundreds of dollars); and televisions vary over an even larger range (hundreds to thousands of dollars).”30 A $5 price difference would be a lot for a sandwich purchase at a fast-food restaurant, where prices range from, say, $4 to $10. But a $5 price difference would be barely noticeable for a television purchase ranging in price from $800 to $2,000. “The basic principle of a range effect is [that] a meaningful difference in some quantity is inversely proportional to its range,”31 and we tend to mentally calculate differences within the range in proportional (or percentage) terms. This phenomenon, called “Weber’s law,” is well known in psychology.32 A $2 price difference on a $10 sandwich would be just as noticeable as a $200 price difference on a $1,000 television, a proportional, or percentage, difference of 20 percent. PRICE METRICS FOR SEGMENTED PRICING

Range scales and their associated price metrics might appear to be fixed in customer memory as a frame of reference, but they are subject to framing and reframing, as I noted earlier. People can encounter entirely differently framed price metrics, even for the same product purchase, and adopt the metric that seems to best fit their needs. For example, Amazon Web Services offers customers multiple pricing metrics with associated range

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Peak usage price metric “On-demand pricing” $0.0832 per hour (value-based price)

Amount paid

Flat fee-based price metric (physical server) “Dedicated host pricing” $2,312 per year (lower cost to serve customers)

Flat fee-based price metric (cloud) “Reserved instance pricing” $38.11 per month (lower cost-to-serve customers) Off-peak price metric “Spot instance pricing” $0.0251 per hour (price-sensitive customers) Freemium price metric “AWS free tier pricing” Limited storage, computing, etc. (new and potential customers)

Total usage

Figure 3.4 

Amazon web services “E2C” cloud pricing (for a “linux user: t3.large”). Note: Fares shown are for illustration only; Amazon offers payment options of per hour or second, up front, per month, or per year for most fares.

scales that neatly segment customers based on their cloud computing needs and price sensitivity, illustrated in figure 3.4. A peak usage price metric, “On Demand Pricing,” is charged purely as price paid per peak hour or second used. This demonstrates Amazon’s fundamental value proposition to customers; namely, that with Amazon’s cloud services, you pay only for computing and storage that you really need, when you need it, with broadly expandable scale and scope (see figure 3.4, high-slope diagonal arrow). It is the AWS value-based price. An off-peak price metric is charged as price paid per off-peak hour or second used. It is designed to appeal to highly price-sensitive customers who, for a 70 percent price reduction, can adjust their usage to Amazon’s off-peak periods when excess capacity is available (figure 3.4, low-slope diagonal arrow).

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Flat-fee–based price metrics—one to rent a physical server, the other to rent chunks of usage in the cloud—are charged as price per month or per year. They enable Amazon to offer stable lower prices to customers whose more predictable needs result in a lower cost to serve those customers (figure 3.4, rectangular box [physical server], flat arrow [cloud]). Freemium price metric—free Amazon cloud access with limited storage and computing—is free, but its appeal is limited to customers seeking mainly trial and initial exposure. They have the option to upgrade to the best value-price package on the pricing menu that aligns with their value preferences (figure 3.4, lower left bottom axis).

Amazon skillfully achieves segmentation pricing by offering customers various price frames with differently structured range scales and price metrics. In early 2021, Elon Musk announced that Tesla would begin accepting Bitcoin in payment for a new Tesla car, though he abandoned the policy two months later. Still, other local auto dealers do accept Bitcoin as payment, including dealers selling Lamborghini, BMW, MercedesBenz, Rolls Royce, Bentley, and Bugatti. It is a clever way to reframe price metrics to achieve better segmented pricing. Not all buyers will be drawn to a Bitcoin transaction. However, a select high-value segment will especially appreciate the utility and value of Bitcoin transactions—those who already trade with Bitcoin. The sheer novelty of the new Bitcoin price metric framing attracts attention. However, Bitcoin has appreciated considerably in recent years (as of May 2021 its value increased 536 percent year-over-year), creating 100,000 Bitcoin millionaires, up from 15,000 in early 2020.33 And, according to behavioral economists, windfall gains are spent more readily than non-windfall gains, influenced by their unanticipated status.34 Consequently, these Bitcoin buyers, flush with their recent Bitcoin asset gains, are more likely to view a new luxury auto purchase as much more affordable today than a year ago if they can pay with more valuable Bitcoin. P R I C E M E T R I C S F O R VA L U E A L I G N M E N T

Metrics can subtly influence and bias decision-making. A good example by researchers at Duke University appeared in the influential journal Science. They wondered which would be more effective: to frame vehicle fuel efficiency in terms of “miles per gallon” or “gallons per mile.” Follow their logic by answering the behavioral scientists’ metric math questions:

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If the objective of fuel efficiency is to maximize fuel savings, would you choose Option A, to replace old vehicles that get 15 mpg with new vehicles that get 19 mpg?

or would you choose Option B, to replace old vehicles that get 34 mpg with new vehicles that get 44 mpg?

If you chose Option B, you would have made the most popular choice, but the incorrect one. In fact, most study participants (75 percent) chose Option B, which offers a larger gain in miles per gallon from 34 to 44 mpg. However, the correct choice is actually Option A, because if you were to drive 100 miles, a 15-mpg vehicle would use 6.67 gallons of fuel, whereas a 19-mpg vehicle would use 5.26 gallons—a savings of 1.41 gallons. But with Option B, a 34-mpg vehicle would use 2.94 gallons of fuel, whereas a 44-mpg vehicle would use 2.27 gallons—a smaller fuel savings of 0.67 gallons. When the researchers posed the same choice to study respondents, but framed in terms of gallons per mile (gpm), most participants (64 percent) correctly chose Option B. In other words, “the percentage choosing the more fuel-efficient option increased from 25 percent in the MPG frame to 64 percent in the GPM.”35 This research shows “how successful debiasing may be accomplished more effectively by . . . restructuring the information in the [metric] environment. Reliance on GPM ‘nudges’ people to better decisions because it [helps do] the math [correctly] for them,”36 based on simple System 1 behavioral processing. Similarly, a key principle for pricing is to debias your metrics to help customers see intuitively how the price they pay is related to the value they get. That helps customers use System 1 behavioral processing to do the math when sensing the value they receive for the price they pay. For example, Google AdWords’ online key word advertising metric is based on an intuitive “costper-click” price metric; the more valuable the word, the higher its price—its cost per click. Google allows advertisers to bid on keywords that end users enter when they do a Google search, and the bidding naturally reveals the most valuable keywords. Researchers at the Wharton School explained the effect: Somebody searching for the term spine surgery on Google is very likely to have back pain. Spine surgery is a very profitable product line

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for hospitals and private practices alike, so knowing that Joe Miller in Chicago is looking for spine surgeries is something that health care providers are willing to pay for. How much? With the clearing price for most Google AdWords costing pennies per click, it is notable that those concerning medical needs currently stand at around forty dollars per click. As this example shows . . . advertisers, who can use the data to create more targeted and more effective advertising campaigns, [get more value by bidding on higher cost-per-click ad words].37

Patrick Campbell, a software as a service (SaaS) pricing consultant, studied the issue of value-based pricing metrics. “Value metrics are what you charge for—per user, per 100 videos, per something that theoretically aligns with where your customer ascribes value from your product. They’re the most effective pricing mechanism you can have in the subscription world, but not all value metrics are created equal.”38 His firm surveyed 3,800 subscription companies across multiple industries. They found that SaaS companies using a value metric reported, on average, 53 percent growth compared with 21 percent for those using nonvalue metrics—termed feature-differentiated metrics, such as pricing based on the data storage your SaaS site offers. He further distinguished between functional value metrics (such as price per number of users, or per 100 videos posted) and outcome value metrics (like price per “how many views a video received or how much money you made your customer”). Firms using outcome value metrics reported a median customer churn rate 67 percent lower than firms using functional value metrics, which in turn reported a median churn rate 42 percent lower than firms using feature-differentiated metrics. And firms using outcome value metrics reported median expansion revenue (revenue from existing customers beyond initial subscription fees) 114 percent higher than firms using functional value metrics, which in turn reported median expansion revenue 31 percent higher than firms using feature-differentiated metrics. These findings are summarized in table 3.1. Campbell summarized: Imagine two SaaS companies that each have 100 customers. The first charges on a per seat per month schema, but there’s little need for more than one seat for each customer [a feature-differentiated metric]. The other sells the exact same product but charges along a metric of particular usage in the app [a value metric] with a bare minimum

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Table 3.1  Testing framing metric effectiveness: Churn and expansion revenue

50th percentile 25th percentile (median)

Churn

75th percentile

Feature-differentiated metric

5.5%

6.9%

13.0%

Functional value metric

2.0%

4.0%

5.5%

Outcome value metric

1.0%

2.3%

4.5%

50th percentile 25th percentile (median)

Expansion revenue

75th percentile

Feature-differentiated metric

5.0%

9.8%

10.5%

Functional value metric

6.0%

12.8%

19.5%

Outcome value metric

13.0%

21.0%

32.0%

Source: Patrick Campbell, The Value Metric: Optimize Your Pricing Strategy for High Growth, May 21, 2019, accessed July 20, 2019, from https://www.priceintelligently.com/blog/bid/195287 /the-value-metric-optimize-your-pricing-strategy-for-high-growth.

per month charge. The former has an artificial ceiling on the MRR [monthly recurring revenue] potentially gained from their customers. The latter’s MRR will grow as their customers grow and/or use the product more [because of the value they get]. I’d much rather be in company number 2. If you’re strictly charging per user, per month, or per hour, you’re probably losing out already.39

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Price metrics have a final benefit: reframing price vis-à-vis competitive prices helps your firm create competitive advantage. As we saw in chapter 2,

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when industry categories mature and get commoditized in a dominant frame of reference, the opportunity is ripe to reframe, to differentiate one’s price frame and create competitive advantage—by changing the price metric. In the financial planning market, many investment managers bundle financial planning with investment management. Under the predominant price metric employed by most competitors, customers pay a percentage fee of total financial assets under management. This price metric is appealing to profitable wealthy clients, who can afford high total fees, but overprices small clients out of the market. With $500,000 in assets under management, the price for financial planning (bundled with asset management) might be $7,500 per year (1.5% × $500,000), a steep price for a small investor to pay. In 2019, Charles Schwab launched Schwab Intelligent Portfolios Premium, which offers unlimited access to financial planners and online tools. But the company reframed the price to be different from that of traditional competitive financial planners: an initial $300 setup fee, then $30  per month, a simple subscription price frame, or $360 annually after the first year. Schwab had previously set its price similar to other competitive financial planning services, at 0.28 percent of a client’s investment assets annually, but found that it priced many of its target customers out of its financial planning customer market. For example, a $1,400 fee would have been charged for a $500,000 portfolio (0.28% × $500,000). Instead, Schwab is now “using technology to provide planning at scale and reduce costs, often to investors who’ve not had access before.”40 And smart subscription price framing, that leverages simple System 1 behavioral processing principles, enables Schwab to differentiate its price from other traditional financial planning services and further strengthens Schwab’s competitive position as a large-scale, full-service discount financial advisory firm that appeals uniquely to small and midsized investors.

Price-Setting Rules of Thumb and Truisms

In chapter 2 we cited Herbert Simon’s theories of bounded rationality and satisficing: because people are limited in cognitive ability, they use simple behavioral heuristics, or mental shortcuts, to make decisions and solve acute problems, usually making satisfactory rather than optimal decisions most of the time.41 For example, Benjamin Franklin published many simple rules and truisms in his Poor Richard’s Almanack: “A penny saved is a penny earned.” “When the well is dry, they know the worth of water.” “Beware of

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little expenses; a small leak will sink a great ship.” Here is a truism from the world of stock market investing: There is a pithy maxim that investors should “sell in May and go away”—at least until November. There is some truth to this adage: The S&P 500 typically has struggled from May through October while accelerating from November through April. Indeed, since 1945, more than 80 percent of the annual gains for the stock market have been generated in the six months from November through April.42

Good rules of thumb and truisms should still help us make good decisions quickly and wisely; they tap the rapid-processing capabilities of System 1 behavioral processing. As Scott Huettel put it, Our brains aren’t designed to process everything. Doing so would be much, much too expensive. So, they simplify when they can. They preferentially focus on some information, and throw away other information, to keep [cognitive] energy costs as low as possible.43

Apple has long embraced a corporate rule of thumb to guide its pricesetting, “55 or die,” referring to Apple’s internal goal for managers to achieve 55 percent gross margins on its products. The slogan originated with JeanLouis Gassée, former president of Apple France, who became the company’s vice president of product development in the late 1980s (replacing Steve Jobs, who had been reassigned under then-president John Sculley). It remained pervasive and influential, though hidden within Apple’s culture, for decades. Even the original iPhone’s gross margin at its launch in 2007, according to my calculations, was 56 percent, consistent with Apple’s “55 or die” rule of thumb. A coincidence? Probably not. Today, Apple’s relentless focus on gross profit margins has led it to push iPhone prices and gross margins ever higher, the latter recently approaching 79 percent. During my field research with pricing professionals, I asked pricesetting managers this question: Are there any beliefs, decision rules, or “truisms” that managers always seem to come up with when making a pricing decision? The responses were often instantaneous and passionate, routinely memorized by rote because managers had heard them so frequently. Here’s the response from an industrial marketing manager: “You’ve got to have X% margins; every product, every division has to have X% margins. There’s no way you can live without that

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margin.” A high-technology financial manager replied, “Prices decline 10 percent per year, so your costs must decline 15 percent per year.” And I heard this from a computer systems pricing manager: “Whatever you do, don’t underprice and leave money on the table.” These rules of thumb and truisms are useful and memorable motivators—even if heuristic—that guide the decision thinking of price-setters. The key is to examine your rules of thumb and truisms and be aware of the kinds of bias they also introduce, then refine and adjust them to remove bias. This rule of thumb from one of my field research interviews shows how pervasive and even commoditized these rules can become: When I was starting out in this business some people who run [web development] companies told me certain rules of thumb of the business and they have turned out to be reasonably true over the years. . . . And one rule of thumb [is] that if you are going to use [a] subcontractor to do the [programming] . . . that you want to be taking that price you pay the subcontractor and marking [it] up two and one-half times. That’s a kind of rule of thumb, and if you do less than that its going to be tighter; and over the years that’s born out to be fairly true.  .  .  . I’ve found that if we sort of double the price that we are paying the subsidiaries we’ll kind of breakeven, but it’s that extra 10 percent where the profit will come if we [make] that two and one-half times . . . that gets passed down as sort of folklore.

Let’s look at a healthier rule of thumb, one that is not entangled with the biases of cost-based logic and that links profitability with different benchmark levels of sales volume. When asked about price-setting rules of thumb, the CEO of an employment agency told me he used several breakeven sales volume rules to quickly judge business profitability—which he tied directly to price-setting: I’m [satisfied] as long as we’re over the 200 [contracts per month]. We’re making [reasonable] money. . . . [We make] zero when we go below 160—we’re not making money . . . but, give or take, if we have about 220 on contract [for the month] we are making [good] money and we are happy.

Later, he discussed comparative performance: “One thing we [are] good at [is] pricing,” which, he said, led to 50 percent more revenue than his

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leading competitor. His rule of thumb is not precise but good enough for satisfactory business performance satisficing as Herbert Simon might have suggested for a rule of thumb. Another example from the CEO of an HVAC contracting firm shows how rules of thumb and goal framing guided the firm’s price-setting for new customer installation contracts. The CEO had different pricing decision rules for residential versus commercial customer jobs, two different customer segments: I’ll tell you how we [set our price]. Net profit on a residential [customer] is 15 percent. That’s [bottom-line profit] for [me, the owner]. That’s when everything’s [paid], all the [employees] paid, bills paid, and overheads paid and everybody is paid. . . . [We also have commercial jobs, like] the Ninety-Nine’s, TGI Friday’s, we do Chili’s, Boston Market, McDonalds, Burger Kings—restaurants are kind of our niche. [Our competitors] typically earn 3 to 4 percent [bottom-line] profit [on their commercial customers]  .  .  . 3–4 percent is industry standard.  .  .  . [However,] since we know these [commercial] jobs, it’s a forte [for us. So our goal is] 7–8 percent net [profit on commercial customers] and that’s if the job is brought in on time and run properly.

He then described how he used these rules of thumb to achieve the net profit goal for a prospective commercial customer. Remember, this company buys HVAC equipment from manufacturers and then installs it. Well, 3–4 percent is industry standard [profit for most competitors on commercial jobs]. . . . I just put in a price for [a commercial] job this morning at $61,000 [with a projected] 7 percent net profit. [But] I know there’ll be [other] companies [with lower competitive prices of] $55,000 to $52,000 on this job. . . . [So] there will be a negotiated bid [with the customer]. We’re at $61,800, and he says to me, “I’d like to buy this job for under sixty [$60,000].” . . . What I’ll do is go back to [my equipment supplier] and say, listen, your [price to me is] $19,000 [for this job]. I need to buy [it] for $16,000. I will go to the people who supply all the ceiling grills and say, you’re at $2,700, I need to buy it for $2,300. So this is what we do to work to our net [profit goal]. Actually, when that happens, my net profit will go to 8–10 percent. Just an hour on the phone, we can add 3–4 percent net [profit] to a commercial job in our favor.

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Of course, these rules of thumb are not perfect—in this example, encumbered with cost-driven pricing bias, discussed in chapter 5. But they provide a starting benchmark for this small business owner to build a better pricing orientation and improve pricing profitability, leveraging the power of System 1 behavioral processing.

Canonized Formulas, Templates, and Algorithms

Canonized formulas, templates, and algorithms can help with price-setting by adding System 2 analytical structure to the process. But they can also be an unrecognized source of price-setting bias if the pricing team has embraced, accepted, and canonized its favorite templates while ignoring their faulty assumptions and missing information. One popular pricing template is the cost+margin worksheet, which is designed to produce a profitable price output for each unit sold—a simple spreadsheet accounting method with its accompanying biases. In my field research, I asked a manager at a book publisher how his firm went about price-setting. Their process funneled pricing-relevant information into a pricing spreadsheet software program that had been designed in house and anointed by corporate executives as definitive. Actually, I have a computer program back at the office. It’s a program that we developed in our particular office. We have worksheets that the company provides us and basically all we have done is plug in the numbers from the [market forecast] worksheet into the PC.  .  .  . It breaks down, we have costs based on plate costs, based on paper costs, based on art costs, etc. . . . We balance that against the projected sales of our book with various kinds of production qualities; by that I mean number of illustrations, kinds of illustrations, or whether it’s going to be a full-color book.

This is typical of standard cost-driven pricing templates—and it is fundamentally biased. The structure and seeming sophistication of such templates seem impressive, thereby inviting System 1 heuristic bias. In fact, they are undermined by a directional forecasting bias that causes managers to approach price-setting as if driving in reverse. They first forecast demand and assume it is fixed, then vary the price to find an acceptably profitable one. But, of course, changes in price cause changes in demand. So, by fixing

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demand, they ignore the changes in demand that inevitably will determine the pricing strategy’s success. Another firm used a very different, and more effective, template to ensure that its value-driven price-setting process avoided missing information bias. This was a value checklist to help its product managers completely think through the unique differential value of a product while price-setting. Here’s how one of the pricing managers explained it: We have a checklist of things that when we meet with [the product manager], we want them to come to us with: what are the customer alternatives, what are the unique benefits, etc.? So, we ask them to consider it because they know their [product] line better than we do. We’re just there to keep them honest. So, we provide this checklist all the time. . . . What are some of the competitors? . . . What if the alternative is the customer doing it himself? . . . In other words, it’s not only the competitor, it’s [comparing] different customer alternatives.

Checklists are the most mundane of decision tools, but—like this one— they can be effective at debiasing; that is, at avoiding missing information bias. McKinsey proposed that “a simple, checklist-based approach . . . can help flag times when the decision-making process may have gone awry and interventions are necessary. Our early research . . . suggests that is the case roughly 75 percent of the time.”44 That is the point of Herbert Simon’s Nobel Prize–winning logic, that sometimes we can be predictably good at making reasonably profitable pricing decisions by using and enhancing satisfactory System 1 behavioral methods such as simple rules of thumb, templates, formulas, and algorithms to help guide price-setting.

Forecasting and Soft Probability Estimation

Among the most challenging of business tasks is to forecast the outcomes of a pricing decision. Some corporations hire teams of PhD-trained statisticians who are tasked with modeling price elasticity to forecast, with some precision, the future sales volume (quantity demanded) resulting from pricing decisions. But for many price-setters, these sophisticated modeling methods are inaccessible. They are expensive, requiring significant investments in highly specialized statistical modelers and their supporting analytical resources. They invite overconfidence bias in the accuracy of their future projections—their estimates are generated using hard data and statistical methods, making it easy to assume that their estimates are true. And they

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bypass the tacit expertise of field managers who are close to customers in the market—salespeople, account managers, and field support personnel—who sense firsthand the subjective probable impact of price on customer demand. Let’s consider, then, another way to leverage the strengths of System 1 behavioral skills. Price-setting using soft behavioral skills means relying on subjective estimates of the probability of pricing outcomes, which might differ from objective forecasts generated by statistical price modeling. What “objective” means here is based on a set of hard data. But subjective estimates have their own advantages. They offer the focus and flexibility to predict the profitability of pricing outcomes at the micro-customer level. One can estimate the probable impact of price on incremental sales and profit contribution; that is, for this customer, for this order, at this economic moment. The resulting assessment might be neither precise nor statistically rigorous, but it still might be reasonably accurate because it considers a broader range of forces that might impact the outcome of the pricing decision—including forces for which there are no hard data to feed into the modelers’ mathematical prediction. Such forces might include, for example, a salesperson’s personal sensing of an important customer’s slight shift in preference, or a cue of a competitor’s pending price-relevant action. This subjective approach invites decision diversity and data diversity, both key soft skills of a balanced pricing orientation, discussed in chapter 1. Thomas Nagle first introduced a simple back-of-the-envelope calculation, the breakeven sales change, which transformed how to approach judging the probability of pricing success. It leverages the cognitive power of System 1 behavioral thinking to quickly and intuitively judge the probability of a successful pricing outcome by establishing the breakeven response in sales volume required for a price change to be profitable. The simple formula is as follows: %BE = −ΔP/(CM +ΔP) where %BE is the breakeven sales change, ΔP is the change in price, and CM is the baseline, or current, contribution margin. For example, a business-to-business (B2B) field sales representative might return from meeting with a customer to report that the customer demands a price cut of 10 percent (ΔP = −10%) to win the order. The contribution margin on this customer’s business is, say, 40 percent (CM = 40%). The additional volume that must be realized from this customer to breakeven on the recommended price cut—the breakeven sales change (%BE)— is therefore (−10%)/(40% + −10%) = +33.3%. This is useful information for the sales representative and the price-setting team: you must sell 33 percent

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more to this customer to breakeven on the 10 percent price cut. The calculation is simple, but its power is in reframing how price changes should be evaluated by managers. For a pending pricing decision, rather than use precise statistical demand forecasts to predict one or more demand scenarios (a slow, expensive System 2 analytic process), instead, price-setters judge subjectively the likelihood that the resulting change in sales volume will exceed the breakeven; that is, that the pricing decision will be profitable or not (a fast System 1 behavioral process). The simplicity of this approach leverages automatic System 1 memorybased thinking to subjectively do soft probability estimation, which enables fast and holistic pricing judgments. It is also accessible to other price-setters with little statistical training yet invites the subjective insights of these customer-facing managers to bring their vital customer knowledge to bear on this important question: What is the likelihood that the pricing decision will be profitable? The approach is somewhat like over/under (O/U) sports betting, which enables everyday sports fans to “participate” in the outcome of a game based on their subjective knowledge of the teams involved. In the days leading up to Super Bowl LIII in 2019, for instance, the over/under for the game was bid up by bettors to 56 points (the combined points of both teams). The two 2019 Super Bowl teams, the New England Patriots and Los Angeles Rams, were high-scoring teams, even more so in recent playoff games; the scoring statistics were trending higher, and most were expecting a shoot-out, making the over bettors appear sure winners. But some knew that New England’s coach, Bill Belichick, was a defensive genius, and they had a hunch—a subjective probability estimate—that in this, the biggest game of the year, they should bet under. The final score was 13–3, a combined score of 16, in favor of New England, the lowest-scoring Super Bowl in history. Subjective probability estimation is prone to two fundamental biases: overestimating the likelihood of low-probability events, and underestimating the likelihood of high-probability events. This is shown graphically in figure 3.5, from research by Amos Tversky and Craig Fox, then at Stanford University, with actual probability on the horizontal axis and subjective probability on the vertical axis. For example, people play high-value lottery tickets thinking they have “a shot,” a slight chance to win (figure 3.5, lower left), despite the fact that the odds are extremely small—just 1 in 292,201,338 in a recent $350 million Powerball lottery.45 Similarly, about 5 percent of married people have a prenuptial agreement, even though more than 50 percent of marriages end in

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1.0

Subjective probability

0.8

“Might” probability events

Underestimating the likelihood of high-probability events

0.6

0.4

“Should” probability events

“Could” probability events

0.2 Overestimating the likelihood of low-probability events

0.0

0.0

0.2

0.6 0.4 Actual probability

0.8

1.0

Figure 3.5 

Probability bias. “We overweight low probability outcomes, and underweight high probability outcomes.” Adapted from Amos Tversky and Craig R. Fox, “Weighing Risk and Uncertainty,” Psychological Review 102, no. 2 (1995).

divorce (figure 3.5, upper right). The “majority of couples believe that in spite of the [divorce rate] statistics . . . it will never happen to them. They believe their love will overcome any possible obstacles that can occur.”46 Some types of information naturally invite greater probability estimation bias. For example, vivid information or vivid consequences get overweight emphasis in decision-making, typical of System 1 behavioral processing bias. Consider an important, loyal B2B customer account with a new purchasing manager who threatens to take the account’s business elsewhere if you don’t accede to her demand for a low price. The objective probability of losing the entire account might be small, given your close long-term relationship with key customer personnel at the account—this is probably a low-probability event. Nevertheless, in this situation with vivid consequences, price-setters will likely overestimate the probability that the purchasing manager’s threat will become a reality and concede on price rather than thoughtfully check the attitudes of other key customer personnel who may not want to imperil the vendor relationship.

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To counteract these biases, Huettel recommends a useful approach to forecasting using soft probability estimation, adapted here to pricesetting.47 Consider these examples of pricing-related probability estimation questions: What is the likelihood that your pricing decision will be profitable—that the actual change in sales volume would be greater than the breakeven change in sales volume? What is the likelihood that your pricing decision might result in a price war? What is the likelihood that competitors will follow your price increase? To address these and related pricing questions, ask price-setting personnel to think of their subjective probability estimation as falling into one of three possible outcome buckets: “Could Outcomes” have, say, less than a 25 percent chance of happening. These are outcomes that likely won’t happen, such as winning the lottery or being in an airplane crash. Here, for pricing, you might say that the pricing decision could be profitable but likely will not. “Might Outcomes” have, say, a 25–75 percent chance of happening. These are outcomes that might or might not happen—and wouldn’t be very surprising either way—such as having grandchildren or getting cancer. Here, for pricing, you might say that the pricing decision might be profitable, but it might not. “Should Outcomes” have, say, a 75 percent or better chance of happening. These are outcomes that might not happen but should happen, such as getting home safely from the grocery store or having memory problems as you get older. Here, for pricing, you would say that the pricing decision should be profitable.

We will talk more about breakeven sales change skills in chapter 5. But for now, keep in mind the power of making subjective probability forecasting accessible to many knowledgeable customer-facing personnel, regardless of their statistical training. Ask them, for a pending pricing decision, is it likely that the resulting market response could (low probability), might (intermediate probability), or should (high probability) enable your pricing decision to be profitable relative to a calculated breakeven sales change?

Conclusion

Understanding the cognitive psychology of price-setting requires a knowledge of how decision makers approach decisions. One way, System 1

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behavioral processing, leverages the intuitive, tacit, and behavioral impulses from memory that enable us to make decisions quickly and automatically. The other, System 2 analytic processing, requires more effortful deliberate calculation. For price-setting, System 2 skills usually provide guardrails to keep pricing on track with strategy and to leverage the power of data to scale and test price-setting hypotheses. Thinking about your pricing orientation causes you to recognize and diagnose your price-setting biases and harness the cognitive power within them so that they become useful soft skills for price-setting. Soft skills such as goal framing, nudging, price metrics, decision rules of thumb, canonized formulas, templates, and algorithms, as well as soft probability estimation, are all soft skills that especially leverage the powerful processes of human intuition to do more creative and successful price-setting. See the following template to help diagnose your psychological pricing orientation: • Template 3.1: Psychological Pricing Orientation Template

Template

How pricing gets done around here

Evident pricing biases

Pricing goals Frequently stressed informal pricing goals—volume, margins, market share

Nudging, goal framing How do you set/manage hedonic goals, gain goals, normative goals?

Diagnose your psychological pricing orientation

• Pricing rules of thumb, truisms

Canonized pricing formulas, templates, algorithms Pricing metric framing What are your pricing metrics, what customers pay for what they get?

Forecasting, soft probability estimation How do you tap the expertise of customer-facing personnel for pricing?

Template 3.1 

Psychological pricing orientation template.

• •

How does pricing get done around here—psychologically? List examples of pricing practice. List potential pricing biases that seem evident for each.

4 Social Pricing Orientation Cultural Price-Setting Biases and Skills

Behavioral biases and shortcuts have another layer of complexity in corporate and company settings that involve the group decision dynamics of pricing committees, pricing departments, or ad hoc pricing teams with personnel from various functional departments—finance, marketing, accounting, sales, production, pricing—each with adjunct responsibilities for price-setting. With many voices involved in price-setting, there is a greater imperative to understand how pricing gets done around here socially. That is, what is your social pricing orientation? Who is involved, who is influential, and what social biases seem to be present as your organizational team goes about price-setting? Some organizations enforce strict strategic pricing discipline with carefully scripted goal framing using controlled processes and clearly defined tasks and procedures that exhibit—at the organizational level—characteristics of rational System 2 processing. Yet, as we will see, many of these System 2 analytics derive from the professional world from which pricesetters were originally schooled. Most of the time, that is not from the world of pricing. Price-setting in most corporate settings also exhibits characteristics of System 1 processing with heuristics, rules of thumb, truisms, and mental shortcuts that also reflect the professional biases from which price-setters come. Let’s now explore the drivers of a social pricing

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orientation: cultural nation pricing bias, and ownership bias and their related price-setting skills.

Cultural Nation Pricing Bias

Pricing is influenced by the cultural biases of those making price-setting decisions. An important discovery from my field research is that cultural biases affect how price setters become experts at price-setting. Becoming an expert is a process that, through repetition and learning, converts slow System 2 calculations and analytics into fast System 1 behavioral patterns and tacit skills that can be called up quickly from memory to address a task; that is, becoming an expert transforms task learning into tacit knowledge. Chess masters, for example, have learned (from a lot of System 2 study and practice) to see a large number of chess positions on a chessboard and quickly evoke from memory better moves through visual recognition (System 1). “Their greater efficiency [comes] not from evaluating more outcomes, but from considering only the better options,” said Daniel Simons, a psychology researcher.1 Often, the price-setting “experts” in corporations are typically not trained in pricing per se. Instead, they learn to convert slow pricing calculations and analytics (hard System 2 analytic skills) into fast expert heuristics (soft System 1 behavioral pricing skills) based on the training and expertise of their particular business professions. When speaking of cultural bias, then, I don’t mean world cultures such as Asian, South American, Canadian, European, and British; rather, I mean business cultures in which managers and executives—especially those involved in price-setting— receive their professional training, mentoring, and development. When it comes to pricing, these professional cultures act as cultural nations that define and demarcate how price-setters approach—and normatively should approach—pricing problems and solutions, how they communicate about pricing in business discourse, and how they influence pricing decisions. As an analogy, we can view the cultural nations of price-setting similarly to Colin Woodard’s conceptualization of the cultural nations of North America. According to Woodard, America is not defined by “red states and blue states, [or] conservatives and liberals. . . . Rather . . . the United States  is a federation comprised of the whole or part of eleven regional nations, some of which truly do not see eye to eye with one another.”2 In Woodard’s view, “A nation is a group of people who share—or believe

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Figure 4.1 

The eleven nations of North America. Source: “The American Nations Today” from Colin Woodard, American Nations: A History of the Eleven Rival Regional Cultures of North America (New York: Viking, 2011). Copyright © 2011 by Colin Woodard. Used by permission of Viking Books, an imprint of Penguin Publishing Group, a division of Penguin Random House LLC. All rights reserved.

they share—a common culture, ethnic origin, language, historical experience, artifacts, and symbols.”3 For example, the eleven nations range from Yankeedom Nation, founded in Massachusetts and stretching from Maine to Minnesota, to The Left Coast Nation, “a strip from Monterey, California, to Juneau, Alaska, including four decidedly progressive metropolises: San Francisco, Portland, Seattle, and Vancouver.”4 See figure 4.1 for a pictorial map of Woodard’s eleven nations. In a similar way, when it comes to price-setting, a handful of professional cultural nations have their own unique culture, language, historical experience, norms, and measures. There are six professional cultural

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Finance nation

Pricing nation

Accounting nation

Social pricing orientation Sales nation

Production nation

Marketing nation

Figure 4.2 

The six cultural nations of price-setting: social pricing orientation.

nations, which typically have significant influence in price-setting and therefore on your social pricing orientation—how price-setting gets done around here socially (see figure 4.2). I present them in the following order, based generally on the influential pricing bias scholars have discovered they exert on the corporate price-setting process: • • • • • •

Finance Nation Accounting Nation Sales Nation Marketing Nation Production/Operations/Manufacturing Nation Pricing Nation

There are three important questions to consider as you assess the influence of cultural nations on price-setting in your firm: Who is influential in price-setting? What professional cultural nation do they hail from? And, what cultural

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biases does their professional cultural nation usually bring to bear on your social pricing orientation? Let’s address these questions, looking at each of the cultural nations with their norms and biases relating to price-setting.

Finance Nation’s Influence on Price Setting

At its core, Finance historically has been entrusted with the capital to finance the enterprise. Its historical legacy can be seen in the early institutionalization of financial capitalism, beginning in the early seventeenth century with the first organized stock exchange in 1602 in Amsterdam (see figure 4.3). That exchange was followed later by one of the first

Figure 4.3 

The origins of finance nation: the first stock exchange, in Amsterdam, established 1602. This image depicts the courtyard of the Amsterdam stock exchange, a powerhouse of Dutch capitalism in the seventeenth century. The birth of the world’s first formally listed public company (the Dutch East India Company) and first formal stock exchange (the Amsterdam stock exchange), in the seventeenth-century Dutch Republic, helped usher in a new era of financial capitalism. Source: Wikimedia Commons.

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central banks, the Bank of England, in 1694, and, later, the New York Stock Exchange in 1817. The Dutch East India Company, founded in 1602, was the world’s first formally listed public company on a stock exchange, with a fleet of five thousand ships yielding lucrative profits from the spice trade. The charter of the new company empowered it to build forts, maintain armies, and conclude treaties with Asian rulers. The [Dutch East India Company] was the original military-industrial complex.  .  .  . Merchants would invest in several ships at a time so that if one failed to return, they weren’t wiped out. The establishment of the [company] allowed hundreds of ships to be funded simultaneously by hundreds of investors to minimize risk. . . . The company paid dividends of 15 percent of capital in 1605, 75 percent in 1606, 40 percent in 1607, 20 percent in 1608, 25 percent in 1609 in money.5

Professionals trained in finance bring this deep institutional heritage with them, which helps to uniquely bias their worldview of pricing. They approach price-setting with a decidedly financial performance predisposition, emphasizing that price-setting must be managed for firm profitability, profit margins, and, like ships, individual product profitability. The skills they apply to pricing come from their college and apprenticeship training in finance—financial modeling, asset and expense management, margin management, and financial performance. They look to thought leaders in universities with leading reputations in finance such as the Wharton School at the University of Pennsylvania, the University of Chicago’s Booth School, the Stern School at New York University, Columbia Business School, and the Massachusetts Institute of Technology’s (MIT) Sloan School—the top five finance programs. In research on price-setting in the organization, Finance has been singled out as exerting the most bias (presenting “the greatest roadblocks to the development of pricing strategy”), according to Temple University researchers who studied a sample of 125 Fortune 1000 firms across 30 industries with respect to business-to-business pricing (see table 4.1). Overall, finance departments were considered to be the most difficult ones to deal with when trying to develop pricing strategies because of the departments’ general inclination to want to control the price setting and planning process.6

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Table 4.1  Perceptions of social influence bias on pricingAmong price-setting’s cultural nations

No. of firms saying exerts most influential bias*

% of firms saying exerts most influential bias*

Ranking of influential cultural nation bias

Finance

45

36%

1

Accounting

37

30%

2

Sales

20

16%

3

Production

12

10%

4

Marketing

6

5%

5

Customer Service

5

4%

6

Cultural nations (Major departments)

Total

125

*Read as follows: 45 (36%) of firms say finance presents the greatest roadblocks to the development of general pricing strategies in the firm.

Adapted from: Richard Lancioni, Hope Jensen Schau, and Michael F. Smith, “Intraorganizational influences on business-to-business pricing strategies: A political economy perspective,” Industrial Marketing Management 34 (2005), 126.

Deloitte’s CFO Program identifies three core biases that commonly affect Finance’s decision-making: overconfidence bias, confirmation bias,7 and narrow framing bias. Overconfidence bias means overconfidence in Finance’s beliefs that its recommendations, decisions, and actions are correct and must be followed to ensure the success of the enterprise. “In his book, Thinking, Fast and Slow, Daniel Kahneman recounts a multiyear study involving autopsy results. Physicians surveyed said they were ‘completely certain’ with their diagnosis while the patient was alive, but autopsies contradicted those diagnoses 40 percent of the time.”8 [A] long-term study asked CFOs to predict the performance of a stock market index fund, along with their own company’s prospects. When asked to give an 80 percent confidence interval (that is, provide a range of possible outcomes they are 80 percent certain results will fall within), only 33 percent of the results actually fell within their estimates—and most miscalculated in an overly optimistic manner. Interestingly,

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the same CFOs who misjudged the market, misjudged the return on investment (ROI) of their own projects by a similar magnitude.9

Finance professionals are prone to confirmation bias—the tendency to overweight evidence that is consistent with their finance-driven worldview and underweight evidence that detracts from it. They impose this worldview on pricing in the organization by orienting pricing language and social pricing discourse around financial norms, measures, and beliefs about pricing. During my field research, a leader of the pricing department in a large multinational corporation described a common argument with Finance over its relentless focus on product margins: People in finance, for instance, do believe that . . . [price] concessions are bad because they think that it comes off of the gross margin. Our response to that will be, “No, we wouldn’t have gotten that business if we didn’t make that price concession. . . . Do you care about gross margin percentages or gross margin dollars?” We never get a very clear response. I would think they would care about gross margin dollars, myself. [But] gross margin percentage is what they keep looking at because they have no perception that anything [i.e., sales volume] can change. [They assume that] all these other things stay constant, but they don’t. So, there’s this [constant] push back on gross margin and there’s a lack of clarity about whether their goal is percentages or dollars.

This narrow focus on unit percentage margin at the stock-keeping unit (SKU) level is symptomatic of narrow framing bias that we saw in chapter 2, which frames pricing options and outcomes in narrow terms—as defined by Finance. Finance Nation typically drives the standard of profit performance from the enterprise level to the division level to the very SKU level, represented by each product unit’s gross margin—a myopic framing of pricing that, as we explore in detail in chapter 5, invites deep-seated computational bias that ironically undermines the very profit-making financial standard imposed on price-setting. The university researchers summarized their findings on the nature of Finance Nation’s bias (see table 4.2): [Finance is driven by] its desire for all product lines  .  .  . to make a profit. [It] generally resists any price reductions by the marketing and sales groups for fear that they will lead to negative margins for individual products or service offerings. . . . [It tends] to have a short-term

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Table 4.2  Finance nation biased behaviors impeding the development of pricing strategy

Common biases

No. of firms ranking*

% of firms ranking

Rank order of bias

All products must make a profit

47

41%

1

Short-term time horizon

26

23%

2

No allowance for crosselasticity effects

14

12%

3

Limit market flexibility

12

11%

4

Resist pricing flexibility

9

8%

5

Limit product/service bundling

6

5%

6

Total

114

*Read as follows: 47 (38%) of firms say “All products must make a profit” is ranked the greatest obstacle presented by the finance department in the development of an industrial pricing strategy.

Adapted from: Richard Lancioni, Hope Jensen Schau, and Michael F. Smith, “Intraorganizational influences on business-to-business pricing strategies: A political economy perspective,” Industrial Marketing Management 34 (2005), 126.

time horizon when viewing product and product line profitability  .  .  . [and to] think on a quarter-to-quarter basis and not year to year when reviewing the margin contributions of a company’s product lines. [Finance] is often accused of limiting the overall flexibility of companies in  .  .  . their pricing strategies  .  .  . [preventing] firms from defending market share positions, gaining new customers, and increasing sales volumes. . . . [It resists] the use of product and service bundling  .  .  . [that is useful] in stimulating the demand for mature products that need the addition of new products to continue their sales growth and extend their life cycle.10

How should the biases of Finance Nation be addressed in order to build a more profitable pricing orientation? First, price-setting needs to be treated as strategic—not tactical or operational—which requires a change in pricing culture (cited first by 54 percent of study respondents), driving home the importance of long-term thinking when making pricing

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decisions that influence markets, competitive advantage, and profitability. Second, avoid framing pricing success narrowly in terms of per-unit product price or profitability (“all products sold must make a profit”), realizing that products have different roles in leveraging enterprise profitability. Nik Jhangiani, CFO at Coca Cola, said, The answer isn’t always about the absolute price the market will bear. Sometimes, it’s much more about what you can do from an overall revenue-growth perspective. In addition to cutting costs and increasing prices, how do you get the right mix of products to generate more transactions?11

The importance of Finance’s perspective on price-setting—but also its disproportionate influence—stems from its deep institutional heritage as a cultural nation, with disciplined stewardship of the organization’s financial performance, and thus is an authoritative voice in the price-setting process. Its leadership can be made more effective—less of a roadblock—by avoiding narrow framing and instead adopting a broader, more adaptive, more strategic framing for price-setting.

Accounting Nation’s Influence on Price-Setting

What the Accounting Nation brings to price-setting culture is a long tradition of internal cost management, a newer heritage than that of Finance, to be sure, but nonetheless compelling in social pricing discourse with a deep wellspring of corporate influence. Professor M. Shotter of the School of Accountancy at the University of Pretoria documented Accounting’s cultural heritage: modern cost accounting originated during the middle of the nineteenth century with the advent of the railways and later the chemical, steel and metal working industries in the United States of America. These organisations were growing in size and their processes were growing in complexity, creating a need for cost information to determine prices and evaluate the performance of the businesses.12

Oliver Williamson, awarded the Nobel Prize in economics in 2009 for his theory on transaction cost economics, proposed that accountants must

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Market paradigm

$

Hybrid paradigm

Transaction costs

k1

k2

Corporate paradigm

k Asset specificity

Figure 4.4 

Oliver Williamson’s Transaction Cost Economics. Transaction costs occurring within a corporate hierarchical arrangement (with high asset specificity) versus a spot market arrangement (with low specificity). Adapted from Jan Whittington and David Dowell, “Transaction-Cost Economic Analysis of Institutional Change Toward Design-Build Contracts for Public Transportation” (working paper 2006–09, University of California at Berkeley, 2006).

distinguish between two opposing paradigms for managing the incremental costs of the firm’s inputs: internal corporate cost management and external market cost management. In the corporate cost management paradigm, the “cost of coordinating internal transactions by means of management accounting is lower than the cost incurred when entering into these transactions [externally] through the market.”13 The corporate cost paradigm applies especially in mature and stable business environments with dedicated proprietary assets (termed high “asset specificity”) like a petrochemical plant; see figure 4.4, right side). Alternatively, in emerging, changing, and fluid business environments, firms are reliant on renting spot market resources and skills (low asset specificity), such as hiring a contract assembly firm or management consulting team; figure 4.4, left side. In these

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settings, the market cost management paradigm should apply. It is accounting’s commission to properly discern how best to measure the firm’s relevant costs for price-setting. Accounting is cited as exerting significant bias on price-setting, second only to Finance (see table 4.1). Today, professionals trained in Accounting Nation approach price-setting with a decided total cost bias, emphasizing total cost recovery, fully burdened overhead costs, per-unit costing, and full cost accountability for every unit sold. The skills they bring to pricing from their college and apprenticeship training relate to managerial accounting as well as cost reporting and analysis. They look to university centers of accounting excellence for thought leadership, such as the University of Texas at Austin, the University of Illinois at Urbana-Champaign, the Wharton School at the University of Pennsylvania, Brigham Young University’s Marriott School, and the University of Michigan’s Ross School—the top five programs in accounting. KPMG, a Big 4 accounting consultancy, identified four frequently occurring biases that most strongly influence the judgments of Accounting Nation managers. “Availability bias occurs when individuals’ decisions are unduly influenced by information that is most memorable or easily accessible. This occurs when accountants are influenced by the most easily retrieved data”14 sourced from accessible standard cost accounting systems, rather than making more effortful estimates of the true incremental cost to serve customers. Accounting Nation is behaviorally biased by anchoring and adjustment—anchoring price calculations on the product’s total cost. According to researchers, Accounting is biased in “insisting that price setting be based on the traditional cost-plus-profit pricing methodologies [ranked first by 66 percent of respondent managers; see table 4.3]. . . . Using these costs in price setting may result in prices being set too high or too low, [ignoring] market conditions.”15 Like Finance, Accounting Nation’s rigorous quantitative training yields overconfidence bias, as “individuals overestimate their abilities to perform tasks or make accurate decisions”16 based on long-standing traditional accounting methods. They further exhibit confirmation bias as they “seek or interpret evidence in ways that support preexisting beliefs or expectations”17 stemming from their accounting apprenticeship training. One example of the strategic impact of Accounting Nation’s behavioral bias comes from a McKinsey report of a German firm’s mistaken reliance on the wrong cost management paradigm. An executive at the German electric utility RWE spoke of the company’s debiasing postmortem in the wake of disruptive

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Table 4.3  Accounting nation biased behaviors impeding the development of pricing strategy

Common biases

No. of firms ranking*

% of firms ranking

Rank order of bias

Emphasize cost-plus price-setting

83

66%

1

Insist on full cost recovery

28

22%

2

Insist on including all labor burden costs for price-setting

9

7%

3

Insist on including all overhead costs for price-setting

5

10%

4

Total

125

*Read as follows: 83 (66%) of firms say “Emphasize cost-plus price-setting” is ranked the greatest obstacle presented by the accounting department in the development of an industrial pricing strategy.

Adapted from: Richard Lancioni, Hope Jensen Schau, and Michael F. Smith, “Intraorganizational influences on business-to-business pricing strategies: A political economy perspective,” Industrial Marketing Management 34 (2005), 126.

missed strategic market opportunities in “the green transformation of the German energy system, and the technological progress in renewable [power] generation.” Instead, they had continued to invest heavily in old conventional power plants whose costs were stable and more certain; they appeared more economical based on seemingly low-cost internal corporate costing using the traditional accounting systems (figure 4.4, right side). But those assumptions were strategically misleading in a shifting market that required a reorientation toward more external market costing arrangements that were better suited for a disruptive new market environment (figure 4.4, left): What became obvious is that we had fallen victim to a number of cognitive biases in combination. We could see that status quo and confirmation biases had led us to assume the world would always be what it used to be. Beyond that, we neglected to heed the wisdom of portfolio theory that you shouldn’t lay all your eggs in one basket. . . . We also saw champion and sunflower biases, which are about hierarchical patterns and vertical power distance. . . . When the boss speaks up first, the likelihood that anybody who’s not the boss will speak

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up with a dissenting opinion is much lower than if you, for example, have a conscious rule that the bigwigs in the hierarchy are the ones to speak up last, and you listen to all the other evidence before their opinion is offered.18

How should the biases of Accounting Nation be addressed to build a more profitable pricing orientation? First, convince “the accounting group to move away from cost-plus-profit pricing methods to direct cost pricing.” Stress that incremental costing and activity-based costing skills are “the best methodologies for setting prices” (39 percent of study respondents). Second, monitor and track the tactical implementation of pricing strategy (29 percent) across product lines, product families, selling territories and geographies, and corporate departments and divisions. Third, get accounting to back away from the mistaken embrace of “burdens and overhead costs [that] may not be fully recoverable in the pricing of a single product” (18 percent). The goal of costing for pricesetting should be to ensure that incremental costs are measured accurately to facilitate pricing decisions that maximize the incremental profit contribution of forward-looking customer selling opportunities, not the recovery of backward-looking total costs. As respondents noted, “provide continual accurate and current cost data to the pricing group for pricesetting” (14 percent).

Sales Nation’s Influence on Price Setting

Though celebrated in the storied successes of brilliant business leaders, Sales Nation never built historic professional guilds as Finance or Accounting did. In 1914, Thomas J. Watson became general manager of what became International Business Machines (IBM), developing a highly successful, well-trained, and well-educated selling organization. Time magazine and the New York Times called him the world’s greatest salesman when he died in 1956.19 E. K. Strong’s The Psychology of Selling, published in 1925, introduced “sales principles such as features and benefits, objection handling, and question type. He showed that sales was a hard skill that could be taught, learned, and studied.”20 Still, Matt Smith said, I was shocked about how little is written about [the history of professional selling]. Clearly, academia continues to discount the sales

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profession. The lack of hard academic research and formal university sales curriculums further highlights this.21

Sales Nation was ranked third behind Finance and Accounting in its propensity to bias pricing (see table 4.1), but in different ways. The pricesetting norms of Sales Nation have a decided customer bias, emphasizing that price-setting must be managed to reflect the price that customers are willing to pay, and focused on customer price acceptability and increasing sales volume. Training in personal selling, sales management, and sales leadership is a fragmented field, with relatively few formal college or university programs—and little or no focus on price-setting and price management. There are few MBA degrees in professional selling and only 120 professional undergraduate sales education university programs in North America. The leading programs (ranked by faculty size) are at Weber State University (20 faculty), DePaul University (18), Louisiana State University (15), the University of Cincinnati (15), and the University of Houston and Ferris State University (13 each).22 The Sales Management Association published a recent list of “sales manager development topics” that firms rated in importance, including topics such as sales coaching (5.8 on a scale of 1–7), pipeline management (5.5), forecasting (5.3), and technology (4.5). Missing is any reference to pricing.23 Forbes magazine touted “Five Topics to Cover for Effective Sales Leadership Training,” but pricing wasn’t among them.24 In a corporate “Advanced Professional Sales Skills Class” offered by The Sales Alliance, Inc., of 113 professional sales skills taught, only 2 related to pricing (dealing with price negotiations and dealing with price sensitivity), and none related to price-setting. Clearly, most sales professionals receive little guidance relating to setting price. Sales Nation truly needs theory-based training in price-setting. The most harmful behavioral biases of Sales Nation are found in what McKinsey calls “action-oriented biases,” which “drive us to take action less thoughtfully than we should.”25 These include, again, the availability heuristic, in which people give more weight to more recent information or information that is accessed easily from memory—recent statements about what customers are willing to pay, a recent competitive price quote, recent hearsay about customer satisfaction or dissatisfaction. Vivid stories and narrative accounts are more easily recalled from memory and are therefore overweighted in price decision-making. Excessive optimism bias also occurs in Sales, in which future outcome scenarios are cited that overweight the

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Table 4.4  Sales nation biased behaviors impeding the development of pricing strategy

No. of firms ranking*

% of firms ranking

Rank order of bias

Quick price cuts in face of competitive challenges

41

33%

1

Failure to follow overall pricing policy of the firm

33

26%

2

Individual deals with customers

25

20%

3

Belief in “we will make it up on volume”

14

11%

4

Quick to use priced discounts to close deals

12

10%

5

Common biases

Total

125

*Read as follows: 41 (33%) of firms say “Quick price cuts in face of competitive challenges” is ranked the greatest obstacle presented by the sales department in the development of an industrial pricing strategy.

Adapted from: Richard Lancioni, Hope Jensen Schau, and Michael F. Smith, “Intraorganizational influences on business-to-business pricing strategies: A political economy perspective,” Industrial Marketing Management 34 (2005), 127.

subjective probability of favorable outcomes and underweight the probability of negative outcomes. Pricing researchers at Temple University found that salespeople reportedly (a) cut price quickly when encountering competitive challenges (cited by 33 percent of respondent managers); (b) fail to follow overall pricing policy (26 percent); (c) cut individual deals with customers (20 percent); (d) embrace an overoptimistic truism that “we will make it up on volume” (11 percent, referring to cutting price now, expecting that greater sales will compensate later), and (e) are too quick to use price discounts to close deals (10 percent). These findings are summarized in table 4.4. The key to leveraging the tacit customer-facing skills of Sales Nation is to provide them with the System 2 tools and skills to support pricing in the trenches through a good corporate or public price training program. Also provide useful System 1 behavioral goal frames and rules of thumb to help make smart pricing decisions automatically. For example, an easily remembered rule of thumb was long advocated by the Strategic Pricing Group: “Sell on value, not on price.”

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Marketing Nation’s Influence on Price-Setting

Building valuable brands with loyal customers and competitive advantage, called “market-based assets,” has contributed to Marketing Nation’s influence as a strategic discipline, especially evident in successful corporate marketing firms with strong strategic pricing (e.g., IBM, Intel, Apple, Microsoft, Disney, Amazon). Marc De Swaan Arons summarized Marketing’s evolution in the latter twentieth century: In the 1950s and 1960s, brands like Tide, Kraft and Lipton excelled in marketing activities . . . where “winning” was determined by understanding the consumer better than your competitors and then getting the total “brand mix” right. The brand mix is more than the logo, or the price of a product. It’s also the packaging, the promotions, and the advertising, all of which is guided by precisely worded positioning statements.26

Marketing Nation is cited for remarkably little price-setting bias (see table 4.1). It is ranked fifth, with only 5 percent of industrial managers noting bias. Marketing Nation looks to thought leadership in universities with leading reputations in marketing, such as Northwestern University’s Kellogg School of Management, Duke University’s Fuqua School of Business, the University of Michigan’s Ross School of Business, the Wharton School at the University of Pennsylvania, and Columbia University— the top five programs in marketing. When Marketing Nation is criticized for price-setting, it is because of its tendency to move too slowly, to “postpone pricing decisions until all parts of the marketing plan are ready [36 percent of Temple respondents]. By responding too quickly, [Marketing worries that the firm] could precipitate a pricing war. [Yet,] acting too slowly, [the] company may not be able to take advantage of a market opportunity.”27 Other Marketing Nation biases cited are its “slow response to market competition” [23 percent], “failure to review and update existing pricing policies” [21 percent], and “lack of an overall price plan” [19 percent]. The study is summarized in table 4.5. These findings are consistent with my field research as well. A manager at a national consumer products franchise firm noted frustration

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Table 4.5  Marketing nation biased behaviors impeding the development of pricing strategy

No. of firms ranking*

% of firms ranking

Rank order of bias

Postponing pricing decisions

45

36%

1

Slow response to market competition

29

23%

2

Infrequent review of current pricing policies

26

21%

3

Lack of an overall pricing plan

24

19%

4

Common biases

Total

124

*Read as follows: 45 (36%) of firms say “Postponing pricing decisions” is ranked the greatest obstacle presented by the marketing department in the development of an industrial pricing strategy.

Adapted from: Richard Lancioni, Hope Jensen Schau, and Michael F. Smith, “Intraorganizational influences on business-to-business pricing strategies: A political economy perspective,” Industrial Marketing Management 34 (2005), 127.

among franchisee retailers with the corporate franchise brand organization’s price-setting: They don’t think we [corporate marketers] understand anything that’s going on in the real world, they will sit in a meeting; as a matter of fact, last week in Philadelphia they [franchisees] sat in a meeting and would just say, “you guys don’t even know what the real world is all about, you don’t know what’s going on out there. . . . You don’t understand what we are going through, what our profitability is,” and they are right, we don’t.

Probing further, however, these responses told only part of the story. First, Marketing’s role in price-setting was often more strategically driven, and consequently its price-setting activities were often done quite deliberately. My field interview with the national franchise firm manager continued.

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The last pricing recommendation which I wrote, which was in December . . . it came from the director of marketing at the time and the VP of purchasing. . . . I did the leg-work and the analysis, myself and a product [marketing] manager, but everyone had to sign, all key parties had to sign, the senior VP of marketing had to sign off.

Here price-setting is done methodically and hierarchically. This is what he said when asked what managerial attitudes are about pricing: Manager: Pricing is very critical to us except . . . I think they [corporate executives] are driven by corporate goals, corporate mandates, down from [corporate headquarters]. Moderator: Pricing has not been a big piece on the radar screen, instead they are focused more on advertising and driving volume into the stores, I guess? Manager: Yes, they are truly. With our products, there is a great belief that there is a strong price-value relationship. The customer loves [our brand].

What we see here, implicitly, is that the worth of the brand in the customer’s mind is the organization’s highest priority. Price, then, is set methodically and deliberately. Managers in Marketing Nation in this firm considered themselves good at advertising and new product innovation but not at pricing. One other subtle bias could explain Marketing’s slow and diffident reputation with regard to pricing, well documented in the behavioral sciences. It is ambiguity aversion—the tendency to eschew the unknown and instead favor the known. Ambiguity aversion gained early attention by Daniel Ellsberg in his work in 1961, before his career turned to antiwar activism. This is the now well-known Ellsberg paradox: “People prefer to bet on the outcome of an urn with 50 red and 50 blue balls rather than to bet on one with 100 total balls but for which the number of blue or red balls is unknown.”28 Because pricing strategy and price-setting are less familiar, or unknown, to many marketers compared to marketing strategy and marketing coordination, they tend to approach price-setting tepidly. Instead they invest in those activities for which pricing feels more like a marketing task, such as competitive price positioning, marketing plans, or mere marketing coordination of price promotions. Therefore, an important key to Marketing Nation’s role in pricing is to develop pricing strategy and price-setting skills within the marketing team.

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Among the Fortune 1000 companies of the Temple research study, respondents said that “for marketing, the best strategy for . . . price setting was in the development of an integrated marketing-pricing plan [44 percent of respondents]. This strategy drives [all] groups [Finance, Accounting, Sales, Marketing, Production] to work together in coordinating price setting with the development and implementation of the marketing plan.”29

Production Nation’s Influence on Price-Setting

Production Nation, like Marketing Nation, is perceived to exert some bias on price-setting, but not very much (see table 4.1). Production professionals approach price-setting with a decided capacity management view, focused on production process efficiency, sourcing strategy, capacity utilization, and operational management. They look to thought leadership in universities with leading reputations in production and operations, such as MIT’s Sloan School of Management, Carnegie Mellon’s Tepper School of Business, Purdue University’s Krannert School of Management, Stanford University’s Graduate School of Business, and the University of Michigan’s Ross School of Business—the top five programs in production and operations. Production Nation’s bias in price-setting is manifest in its insistence on limited product variations (ranked first by 33 percent of respondent firms), insistence on the recovery of all direct and variable costs (25 percent), requirements for large-quantity purchases (21 percent), limits on new product introductions (14 percent), and insistence on perfect forecasting (7 percent). These findings are summarized in table 4.6. The researchers reported, The production group often takes a parochial view of its own operations with little concern for how things are priced and how their operation will affect the pricing process in the firm. The group’s insistence on limited product additions and new products restricts sales revenue in many firms. The research showed that the production departments’ insistence on full cost recovery limits can limit a company’s ability to match competitive price moves.

There are, however, valuable behavioral soft skills that are especially native to production and operations that influence pricing profitability; namely, skills relating to capacity planning, capacity utilization, and sensing the marginal cost of potential incoming orders that fill capacity.

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Table 4.6  Production nation biased behaviors impeding the development of pricing strategy

Common biases

No. of firms ranking*

% of firms ranking

Rank order of bias

Limits product variations

41

33%

1

Insists on recovery of all direct and variable costs

31

25%

2

Quantity purchases

26

21%

3

Limited new product introductions

18

14%

4

9

7%

5

Perfect forecasting Total

125

*Read as follows: 41 (31%) of firms say “Limits product variations” is ranked the greatest obstacle presented by the production department in the development of an industrial pricing strategy.

Adapted from: Richard Lancioni, Hope Jensen Schau, and Michael F. Smith, “Intraorganizational influences onbusiness-to-business pricing strategies: A political economy perspective,” Industrial Marketing Management 34 (2005), 127.

From my field research, an executive vice president and chief production officer at a fast-growing startup related a typical situation, showing how her sensitivity to capacity utilization and marginal cost with rush jobs typically would lead to higher prices: It’s Friday night and the client wants something by Monday afternoon and maybe they only want to pay 40 cents a [unit] and that’s all they can pay. . . . Tom [vice president of sales] comes to me with this information and he says . . . “We’re slow, our revenue is low this year, this is 40 cents a [unit,] money is money. We can get [a] freelancer [contractor] who will work over the weekend.” . . . [This] would force either [the production manager] or me to stay later and this puts stress on the [other] production people because they feel like . . . the Indonesian log cutters who just keep  .  .  . making more and more lumber whether they can sell it or not. . . . [Therefore, because of] production [concerns]  .  .  . [Tom] still won’t turn the job down but he’ll charge higher for it, which is sometimes the same as turning it down.

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The voice of production planning and capacity allocation can be essential to price-setting. Intel uses a simple gross margin rule of thumb to guide contentious decisions about how to allocate capacity among different incoming customer order opportunities. Production Nation is trained with the expertise and skills to recommend among these profit contribution trade-offs skillfully and efficiently.

Pricing Nation’s Influence on Price-Setting

Pricing Nation is a new cultural nation, to be sure, and for the most part it is nascent in its formal role in the organization. Dan Nimer, founding thought leader of modern pricing, and I wrote in 2012, Pricing has gone through breathtaking innovation over the past half century—value-based pricing, activity-based costing, yield management pricing, pocket price waterfall, to name a few—with contributions coming from many neighboring disciplines—marketing, accounting, finance, psychology, and operations. Economics has always provided the theoretical foundation for pricing.30

Today, Pricing Nation’s thought leaders are “close to the practice of pricing through consulting, advanced research, and price leadership in industry— and they [are] close to great universities of thought. They [are] schooled in economic theory, trained in research methods, and committed to pricing’s application in real world companies, with real world managers, and realworld results.”31 Institutionally, one university, the University of Rochester’s Simon School, offers a “pricing track” of pricing-related courses within its MBA program. Other schools offer dedicated pricing courses: Harvard Business School’s Pricing Strategy: Monetizing and Growing the Business, and MIT’s Pricing: Using Data to Improve Pricing Performance. Boston College’s Carroll School of Management has two courses: Strategic Pricing Management and Pricing and Data Analytics. New York University and Columbia University both offer Pricing Strategies. Boston University has Pricing Strategy and Tactics. Many universities offer executive education or online programs in pricing, including the University of Chicago, Wharton School, Cornell, University of Virginia, and ESSEC Business School. The Professional Pricing Society, created in 1984, now with several thousand members, offers pricing training programs and certification as a Certified Pricing Professional.

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An important function of Pricing Nation is leading and coordinating price-setting within the organization and managing pricing intelligence. From my field research, one of two pricing leaders for a large software firm described how their pricing committee brought order to price-setting chaos. The committee was composed of C-level organization members or their delegates and coordinated by the two pricing leaders. Asked about how they handled objections and conflict, one price leader said, If the controller came in and had serious objections, it would mean that [Rachel] and I hadn’t done our work. . . . Like last week, we had a woman who proposed special pricing for associations. Came to the committee, they asked her what’s her definition of associations, because they were concerned that, you know, between the American Bar Association, the American Medical Association, the CPA Association, whatever it’s called, would cover, you know, two-thirds of the globe, and anyone would be able to get this discount in pricing. And she hadn’t thought it through, and it was given back to her, there was no decision made. And she actually sent us an email saying, “it’s too much work, I’m not going to pursue it.”

Richard Lancioni summarized: “Pricing committees tend to be small with no more than 10 members. . . . Politically, pricing committees have become very powerful in industrial organizations. The influence that each department has had on pricing decisions is important.”32

Who Should Lead Pricing in the Organization?

Price setting is rarely a solo task but instead is a political process involving power, conflict, and strategic cooperation. This process balances the competing views and motivations of external stakeholders—customers, supply chain partners, and distributors—and internal stakeholders— finance, accounting, sales, marketing, production, and pricing. The ability to harness interdepartmental data diversity, decision diversity, and rivalry is therefore a key soft skill with which to manage corporate pricesetting bias. Each of these departments has a mandate to contribute [unique] value added to the firm that may translate into objectives, activities, and

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Table 4.7  Most influential price-setting departments

Departments, cultural nations

Influence mean rank score

Influence rank

Finance

5.3

1

Marketing

4.5

2

Sales

4.4

3

Accounting

2.6

4

Production

1.5

5

Mean rank scores based on a 5-part importance scale: 1 = not important to 5 = very important.

Adapted from: Richard A. Lancioni, “A strategic approach to industrial product pricing: The pricing plan,” Industrial Marketing Management 34 (2005), 180.

procedures that may conflict with other departments. For instance, marketing may be interested in extending a product line to provide more price options for buyers. Production may be concerned with decreasing lot size cost consequences, as a result of providing more product options. Finance is often concerned with modifying prices to ensure an appropriate return on any new investment required to modify the manufacturing processes.33

Which cultural nation should take the lead in price-setting? Lancioni’s research identified three tiers of organizational influence on price-setting, shown in table 4.7. Finance alone occupies the top tier; it clearly is perceived by organization members as having the most influence on pricesetting, at least in industrial firms. Marketing and Sales occupy a second tier with nearly equal levels of influence (but less than that of Finance). Accounting and Production occupy a third tier, with Accounting having more influence than Production. In many consumer products firms Marketing occupies the top tier along with Finance, due to its role as leader of pricing strategy in the marketplace.

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McKinsey’s finance consultants offered their views on price leadership in the firm’s social pricing orientation: Cutting costs might get more attention, but improving pricing discipline can add more to the bottom line. . . . Our experience shows that CFO-led pricing projects are more successful in identifying opportunities, ensuring that their full value is captured, and creating an environment of continuous price improvements. CFOs have the clout to raise the issue among the broader management group, to ensure that the appropriate data and analyses are available, and to push for standardized metrics and reporting. . . . In short, the CFO is uniquely positioned to champion sound pricing behavior throughout the organization.34

Finance Nation’s heritage represents the owners of the firm, those investing the capital and expecting a return on their investment. But financial managers frame their price-setting mandate in narrow terms to maximize the profitability of every product managed within the walls of the enterprise. A better way is to frame price-setting in broader terms, to manage a portfolio of “many ships” with differing cost and revenue structures, to set prices in different ways in different market segments, to leverage all opportunities individually, but, collectively, to maximize the financial returns for the entire enterprise. Marketing Nation’s heritage represents the owners of the firm, but in a different way that is complementary to that of Finance: to find and frame the strategic opportunities in the marketplace that the firm should invest in to achieve competitive advantage and long-term profitability. Sometimes marketers frame their price-setting mandate in narrow terms—for instance, taking market share points away from competitors with a price cut in a category, or achieving revenue growth with an engaging digital platform or sales promotion. A better way is to frame price-setting in broader terms in ways that deliver differentiable value to customers with combinations of products, services, and customer experiences, which enables price-setting that maximizes incremental profit contribution and long-term competitive advantage for the firm. Sales, Accounting, and Production Nations are, of course, valuable partners with Finance and Marketing Nations in a balanced pricing orientation that is ever cognizant of the imbalances and biases that necessarily arise from overinfluence by any one nation’s views. Working together in a balanced, unbiased social pricing orientation with data diversity and decision diversity, noted in chapter 1, leads to the achievement of the firm’s long-term financial goals for owners or shareholders and

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pricing satisfaction for those employees and partners who deliver that value to customers.

Ownership Bias and Price-Setting Skills

At the center of social pricing orientation is a sense of ownership of pricing, which introduces another important type of decision bias—ownership bias. People behave differently when they feel ownership. They can feel ownership of ideas, job roles, tangible possessions, or brands that they embrace, consume, and affiliate with. Consumer marketers have mastered the science of using test-trial periods, guaranteed return policies, and other methods to “endow” consumers with a product or service prior to purchase. For example, after thirty days of possession and use, most consumers are reluctant to return a product; it has been integrated into their “owner” frame of reference. Behaviorists Daniel Kahneman, Jack Knetsch, and Richard Thaler offered a delightful example of ownership bias. A wine-loving economist we know purchased some nice Bordeaux wines years ago at low prices. The wines have greatly appreciated in value, so that a bottle that cost only $10 when purchased would now fetch $200 at auction. This economist now drinks some of this wine occasionally, but would neither be willing to sell the wine at the auction price nor buy an additional bottle at that price.35

This economist friend is affected by ownership bias and the endowment effect: to part with these old wines would feel like a real loss indeed. This idea of perceived ownership is powerful. You do not need to actually own something to exhibit ownership effects; simply touching or imagining that you own it is enough to create feelings of what scholars call “mere” ownership. But does ownership have an effect on people’s pricesetting behaviors? It does. In a lab experiment at Cornell University, every other student was given a Cornell coffee mug. Then both groups of students, those with mugs and those without, participated in a series of four experimental “market auctions” in which any student with a mug could choose to sell to a student without a mug. Only a small proportion of mugs actually traded hands—18 percent, 5 percent, 9 percent, and 9 percent across the four experiments. One reason was the price-setting aspirations of the sellers versus those of the buyers: the median mug owner stated he or she was unwilling to sell for less than $5.25, whereas the median mug buyer was

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unwilling to pay more than $2.25−$2.75. The average market-clearing price across transactions ended up at about $4.50; that is, owners demanded and settled at a price roughly twice what nonowners had said they were willing to pay, which explained why so few mugs were exchanged.36 Another example can be seen with Duke University students seeking highly coveted tickets to men’s basketball games. Even before the start of the spring semester, students who want to attend [basketball] games pitch tents in the open grassy area outside the stadium. . . . The oddest part is that for the really important games, such as the national titles, the students at the front of the line still don’t get a ticket. Rather, each of them gets a lottery number. Only later, as they crowd around a list of winners posted at the student center, do they find out if they have really, truly won a ticket to the coveted game.

Dan Ariely and Ziv Carmon, professors at Duke and INSEAD, respectively, wondered, “Would the students who had won tickets—who had ownership of tickets—value those tickets more than the students who had not won them even though they all ‘worked’ equally hard to obtain them?” Ariely and Carmon called one hundred students after the lottery, both those who had won and those who had not. In general, the students who did not own a ticket were willing to pay around $170 for one. . . . Those who owned a ticket, on the other hand, demanded about $2,400 for it. . . . They justified their price in terms of the importance of the experience and the lifelong memories it would create. What was really surprising, though, was that in all our phone calls, not a single person was willing to sell a ticket at a price that someone else was willing to pay. What did we have? We had a group of students all hungry for a basketball ticket before the lottery drawing; and then, bang—in an instant after the drawing, they were divided into two groups—ticket owners and non-ticket owners. It was an emotional chasm that was formed, between those who now imagined the glory of the game, and those who imagined what else they could buy with the price of the ticket. And it was an empirical chasm as well—the average selling price (about $2,400) was separated by a factor of about 14 from the average buyer’s offer (about $175).37

Ownership affects price-setting in the real world as well. Consider what happens when real estate agents sell their own homes. “A recent set of data

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covering the sale of nearly 100,000 houses in suburban Chicago shows that more than 3,000 of those houses were owned by the agents themselves.”38 Did real estate agents sell their own houses at higher prices than the houses they sold as agents for their clients? First, let’s understand their incentives as agents. The real estate commissions that realtors earned were 6 percent of the home’s selling price, split evenly between the buyer’s and the seller’s real estate agency. Half of each agency’s commission then goes directly to the personal real estate agent. So, on a sale of, say, $400,000, each agency will collect $12,000 and each agent will make $6,000. If the real estate agent could negotiate a slightly higher price for the sale of her own home—say, $10,000 more—she would keep all of it; that is, all $10,000 for herself. But she would get only $150 if selling (as an agent) a client’s home—that is, $10,000 × 6 percent = $600 incremental total commission, shared across two agency firms = $300 each, with half of this, $150, going to the agent. So what happens in real life? “The study found that [a real estate] agent keeps her own house on the market an average ten extra days, waiting for a better offer, and sells it for over 3 percent more than [selling a client’s house as an agent only]—[that’s an extra $12,000] on the sale of a [$400,000] house.”39 What’s going on here is that when the real estate agent sells her own house, she senses intuitively that because she owns her house, she can make more bottom-line profit—$12,000 more, in fact—with a little more effort and resourcefulness to negotiate a slightly higher price. It’s worth it. But the flip side of this is just as true: real estate agents are overeager to discount to make a faster sale on a client’s house, which they do not own, encouraging their client to take a lower price to ensure that the client doesn’t lose the sale and that they themselves don’t lose the commission— exhibiting risk averse behavior that is only marginally profitable. But when setting prices for houses that they do own, they show strategic patience, waiting for a higher price even while taking the risk of passing on a sure sale at a slightly lower price—exhibiting risk taking behavior that is potentially very profitable. We see anecdotal evidence of these findings elsewhere. Southwest Airlines, Microsoft, Starbucks, Intel, Whole Foods, and PeopleSoft all are part-owned by their employees. For these companies, pricing is essential to their core value proposition, and employees sense the impact of their price-defending activities on bottom-line profits and therefore on their own earnings. Many fast-growing technology firms pass out stock options to valued employees through employee stock ownership plans (ESOPs) because ESOPs lead to greater employee retention, employee productivity, and company growth. As an executive at a New York law firm noted,

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making workers into owners incentivizes them “because owners do act differently than regular employees. . . . By providing not only a psychological sense of ‘ownership,’ but also actual ownership, employees are motivated both to increase profits and stay to reap the rewards of growth.”40 However, usually observed in most firms is the other side of ownership bias, especially the downside bias by which nonowner employees or agents typically underperform, not working as hard to defend higher prices for value delivered. Many firms pay their sales representatives with incentive compensation plans that reward them for achieving sales volume goals, inadvertently incentivizing them to discount price in order to lower the risk of failing to achieve the goal and losing their commission—encouraging anemic risk averse behaviors. Instead, companies should incentivize their sales representatives to act like owners, even if they are not actual owners— encouraging healthy risk taking behaviors. That is, they should orient their incentive compensation plans around incremental profit contribution—the difference between price and incremental variable costs multiplied by sales volume. Hermann Simon and Martin Fassnacht said, The most popular form in practice is a commission [incentive] plan based on revenue. . . . This form of incentivization does not make sense when combined with price decision authority. Such authority should be tied to a profit- or contribution margin-based incentive system. But . . . revealing profit or contribution margin data to the salesforce runs the risk that such information gets into the hands of customers.41

To work around this, pricing experts recommend a revenue-based profit incentive commission that nudges price-setters to behave consistently with normative goal frames and margin-based goals, as discussed in chapter 3. One client firm smartly set up an incentive compensation plan that measured manager performance based on return on assets, encouraging company price-setters to consider the impact of price on incremental profit contribution relative to assets under their supervision. “ROA is a better metric of financial performance than income statement profitability measures like return on sales [or sales revenue growth],” according to Deloitte consultants writing in the Harvard Business Review. “ROA explicitly takes into account the assets used to support business activities. It determines whether the company is able to generate an adequate return on their assets rather than simply showing robust return on sales.”42 And it encourages managers to think like owners.

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Nagle and Müller recommend a “sales credit” incentive formula in which the firm sets a target price and gives sales credit to price-setters based on actual price relative to target price, with a profitability factor, or “kicker,” that is tied to the contribution margin (see box, “revenue-based incentives that tie to profit goals”). “Salespeople who sell while maintaining price discipline will be much better compensated than the notorious discounters,” said Simon and Fassnacht. “Our experience shows that such systems generate sustainable profit improvements.”43 Bain consultants Ron Kermisch and David Burns said, Managers often criticize sales reps for losing a deal but rarely for pricing a deal too low, so reps learn to concede on price until the deal closes. Moreover, companies rarely reward sales reps for exceeding price targets, which means few of them take risks to push for a higher price. Misaligned incentives push deals down to the minimum allowed price.44

Revenue-based incentives that tie to profit goals

Sales Credit = [Target Price – k(Target Price – Actual Price)] × Units Sold

In the above equation, k is the profitability factor (or “kicker”). The profitability factor should equal 1 divided by the product’s percentage contribution margin at the target price, in order to calculate sales credits varying proportionally to the product’s profitability. For example, when the contribution margin is 20 percent, the profitability factor equals 5 × (1.0/0.20). When a salesperson grants a 15 percent price discount, the discount is multiplied by the profitability factor of 5, reducing the sales credit by 75 percent rather than by 15 percent had there been no profitability adjustment. Consequently, when $1,000 worth of product is sold for $850, it produces only $250 of sales credit. But when $500 worth of product is sold for $550 (a 10 percent price premium), the salesperson earns $750 of sales credit ($500 + 5 × $50). Source: Thomas T. Nagle and Georg Müller (2018), The Strategy and Tactics of Pricing (New York: Routledge), 278.

Bain created a chart showing the distribution of realized deal prices confirming this bias, as shown in figure 4.5—notice that, regardless of order size, prices trend toward lowest allowed price rather than the firm’s target price. In response, their consultants came up with a simple System 1 solution to balance revenue and profit:

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Sales commissions thresholds tend to depress deal prices Net price as a percentage of list price

100% 90 80

Target price

70

Lowest allowed price

60 50 40 Order size

Figure 4.5 

Charting behavioral deal prices. Source: Ron Kermisch and David Burns, “Is Pricing Killing Your Profits?,” Bain & Company Brief, June 13, 2018, https://www.bain.com/insights/is-pricing-killing-yourprofits/. Used with permission from Bain & Company.

It created a pricing tool to make the commission on each deal visible to sales reps—for instance, “if I raise the price by $2,000, I earn an extra $700.” Sure enough, reps began to close higher-margin sales. These changes led to a 7 percent increase in prices, which added 95 basis points as part of a 350-point improvement in margin overall.45

Conclusion

One of the most important questions you can ask is, “how does pricing get done around here—socially?” That is, what is our social pricing orientation? Who is involved, who is influential, and what biases seem to be present as we go about price-setting. In this chapter we have seen that the biases we bring to price-setting are often a direct consequence of the professional cultures from which we come: finance, accounting, sales, marketing, production,

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and pricing. The price-setting for any one of us, left alone, would reflect the biases inherent in our chosen professional cultural nation. But a pricesetting team, whether ad hoc or a formal pricing committee or department, includes price-setters from various professions. Look at your team, see where biases exist, and debias them with data diversity and decision diversity. In chapters 5 through 8, we turn to the four core pricing orientations themselves: cost-driven, value-driven, customer-driven, and competitiondriven. Each offers its own opportunities to debias pricing and brings its own price-setting skills—both soft and hard. Taken together, they offer a robust pricing palette to support a more financially profitable and organizationally healthy pricing orientation and strategy. See the following template to help diagnose your social pricing orientation: • Template 4.1: Social Pricing Orientation Template

Template

Pricing nations

Personnel involved in pricing

Price leadership

Influence on pricing (Allocate 100 points)

Finance personnel Accounting personnel

• Marketing personnel

• •

Sales personnel

•

Production personnel

•

Pricing personnel

Template 4.1 

Social pricing orientation template.

Diagnose your social pricing orientation How does pricing get done around here—socially? How many personnel are involved from each pricing nation? Where does pricing leadership reside? Where does pricing influence reside? (Allocate 100 points among the pricing nations) List evident pricing biases

Evident pricing biases

5 Cost-Driven Pricing Orientation Biases and Skills

We now explore the behavioral biases and soft and hard skills associated with the four cardinal pricing orientations, and begin first with costing and cost-driven pricing (see Part II title page). Costs are vital inputs to pricesetting, but used incorrectly, they can lead to anemic and unprofitable pricing decisions. Pricing strategists and scholars have denounced cost-driven pricing for decades, yet it remains widespread, with popular methods such as cost-plus pricing, target return pricing, markup pricing, and breakeven pricing. This unwitting testimonial from a company president illustrates the seductive appeal of this biased approach: But, in spite of not always getting our costs right, [a] cost-plus pricing model is what we have been relying on for the last four years. I am comfortable with it. I also believe that cost-based pricing is backed by integrity. The costs may not be accurate but they are not imaginary. They are real, honest and transparent. Any form of pricing, other than cost-plus, could be perceived as price gouging, and less defensible with a customer, in my view.1

As will be evident in this chapter, this short-sighted view of cost-driven pricing is marred by hidden behavioral biases that few take time to notice,

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undermining its effectiveness, credibility, and integrity. With its heuristic focus on fairness, a cost-driven approach exhibits bounded rationality, discussed in chapter 3; it might not be profit maximizing but seems to be at least profit satisficing. And because costing data are quantitative, it offers a patina of legitimacy, of seemingly rigorous System 2 data analytics. But, in many cases, its pricing analytics are often misdirected and undercut by simplistic assumptions. Let’s unpack some of these issues.

True Costing Principles for Pricing Orientation

The theoretical maxim for a profitable pricing orientation is to set prices and sell units until the marginal revenue derived from selling the next unit is equal to its marginal cost; that is, MR = MC, as discussed in chapter 1 and summarized with respect to the four cardinal pricing orientations in figure 5.1. This axiom requires a forward focus—that is, on the next unit sold, its incremental cost and revenue, and consequently the incremental profit contribution from its sale. Most business accounting systems are designed to measure systematically the historical costs that determine

Marginal Revenue

Marginal Cost

=

MR

MC

Customer value

Customer willingness to pay

Competitor prices

Incremental cost to serve

Customer valuedriven pricing orientation

Customer WTPdriven pricing orientation

Competitiondriven pricing orientation

Costdriven pricing orientation

Figure 5.1 

Theoretical maxim for a profitable pricing orientation, focus on cost-driven pricing influences.

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the firm’s profitability. Their cost data are rearward- rather than forwardfocused, inviting managers to misuse historical costs as the basis for futurefocused pricing decisions. Instead, reported costs need to be checked for assumptions, then adjusted and used as a starting point to estimate the true incremental cost to serve the next customer order and its incremental contribution to profit. Also, most accounting systems are internally production-focused rather than market-focused; they are oriented toward the internal costs to produce the product or service, rather than the broader costs to serve customers in the marketplace. Consequently, price-setters must anticipate the hidden market-driven forces that constantly influence true cost to serve. For example, the San Francisco Giants were early adopters of algorithmic dynamic pricing among baseball teams using Qcue software. “This software is a multi-variable algorithm that allows tickets in each section to be changed up to 24 hours before the event. For baseball, depending on the demand factors such as pitching match ups, day of the week, team [winning record] and weather, the software highlights how many tickets are sold and recommends if sections are under or overpriced.”2 Thus, demand will be heavy for very popular games, requiring the ballpark to hire additional temporary workers and even turning away customers with higher willingness to pay because seating capacity is limited—an opportunity cost relating to serve those customers fortunate enough to get a ticket. These incremental costs are market-driven costs that influence the true cost to serve customers for this high-demand baseball game.

Costing Biases in Price-Setting

Let’s now look more closely at several common heuristic biases of costdriven pricing orientations: standardized costing bias, sunk-cost bias, and two forms of average costing bias. These concepts have been studied by accounting, marketing, and pricing scholars, but we are interested here especially in their behavioral economic impact on managerial price-setting.

Standardized Costing Bias

Common among many price-setters is the impulse to convert all costs into per-unit terms so they can be easily summed, compared, and interchanged

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at the product-unit level, leading to standardized costing bias. This is a pricing-specific manifestation of a System 1 behavioral bias that has been documented by behavioral economists. It is known as simplification bias, an intuitive way of reducing calculations based on heuristic mental estimation. For example, people pay “more attention to the number of times something has happened [the numerator] than to the number of opportunities for it to happen [the denominator], such as believing that 1,286 cancer incidents out of 10,000 indicates a higher likelihood of cancer than 24.14 incidents out of 100.”3 The typical result on decision-making is “denominator neglect.” Let’s see how this simplification bias works with standardized costing. When calculating costs, it makes sense to express variable costs, such as materials, packaging, and production labor hours, in per-unit terms because selling one more unit really does involve spending more on those inputs. But fixed costs—like rent, executive salaries, or plant and equipment—are another matter. To express fixed costs in per-unit terms usually means that they get divided by total sales volume—based on, say, recent historical data or a sales forecast—even though selling more units generally does not mean spending more on fixed costs. After all, fixed costs are fixed. But cost accountants and financial managers typically gloss over this definitional trait. This method treats future sales volume (aka market demand) as if it were already known; but, of course, a key role of pricing is to affect demand—whether pricing high with a skim pricing strategy and less demand or pricing low with a penetration pricing strategy and greater demand. Consequently, simplification bias with denominator neglect (ignoring changes in sales volume, or demand) leads to standardized costing bias that turns the price-setter’s perspective inside out: fixed costs get treated as though they were variable, whereas market demand— which truly is variable—gets treated as though it were fixed. In a variation of standardized costing—with similar simplification bias—fixed costs get tied to variable costs as a percentage, called in accounting an “overhead burden rate.” The idea is that if I spend one dollar more on labor, I will also spend, say, X% more on fixed-cost overheads. The HVAC firm from my field research, cited in chapter 3, provides a good example. We have [our] direct cost [of] material [and] we [have] our direct cost [of] labor [both of which are variable costs]. What we do is we take an average installer at [$75] an hour and an average [assistant installer] say at [$50]. We add those two numbers together and let’s say it’s [$125 per crew hour]. . . . We have a “burden” factor [on our labor] for every [labor] dollar we [spend] . . . and our burden is calculated

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roughly [at] 40 cents on a dollar. [Meaning for every $1 spent on labor, we incur 40 cents more for] things . . . we would pay [even] if [our workers] didn’t work. It’s like [insurance, leases,] workmen’s compensation, [and maintaining] the trucks.

In fact, there was little correlation at all between labor costs and fixed costs. The firm’s true labor cost per crew hour was really $125 per hour. However, its burdened labor cost, what labor costs incorrectly appeared to be (mistakenly including standardized fixed costs per unit of $50 [40% of $125 = $50]), led to a total estimated labor cost of $175 per hour. This fully burdened hourly labor cost was then used for pricing, in which pricesetters appended a so-called reasonable margin to arrive at the final price. You would be surprised how ubiquitous standardized costing bias is. It is found even among many elite consulting firms—including pricing consultancies. A consulting project price is calculated by multiplying the expected number of consultant hours by the consultants’ fully burdened hourly billable rates. How do they calculate the burden rates? By adding up all fixed costs associated with the payroll—salaries and bonuses, health and dental insurance, disability insurance, life insurance, retirement plan contributions, and various paid leaves—and dividing that by the consultants’ total hours annually. For example, let’s listen in on a conversation in an online forum for architects (here, the overhead burden rate is called a “multiplier”): [USER DDOT] I think that a billing rate 2.6 times the worker’s salary (broken down to an hourly rate) is a good number. However, I’ve also heard that 3.0 is a good rule of thumb. Can anyone give a good reason to land on or near either of these numbers? The 2.6 factor seems enough to cover overhead, employee expenses like 401K, healthcare, etc.; and profit. . . . Any experience with this? [USER LFLH] There are firms that use multipliers as low as 1.5 and others that approach 4.0. Your math may work for your firm—but there are so many variables that are specific to each firm. . . . A huge amount of overhead can be insurance: professional errors and omissions (liability), business property, employee dishonesty (theft), firm’s vehicles, etc. etc. If your firm omits some of these it may be able to bill less. If it has had recent insurance claims it may have to bill more.4

In other words, if an architect’s salary were $200,000 per year, her billable rate would be $400 per hour ($3,200 per eight-hour day) using an overhead

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Table 5.1  Calculating an architect’s billable rate

Overhead multiplier: 1.5

Overhead multiplier: 4.0

Salary (annual)

$200,000

Salary (annual)

$200,000

Salary (hourly)*

$100

Salary (hourly)*

$100

Overhead multiplier

1.5

Overhead multiplier

4.0

Overhead estimate*

$50

Overhead estimate*

$300

$150

Billable rate (hourly)

Billable rate (hourly) Billable rate (daily)

$1,200

Billable rate (daily)

$400 $3,200

A multiplier of 1.5 means that fixed overhead costs will be an additional 50% of the employee’s salary. A multiplier of 4.0 means that fixed overhead costs will be an additional 300% of the employee’s salary. *Assumes architects get paid for 40 hours/week × 50 work weeks per year = 2,000 hours annually

multiple of 4.0 times salary but only $150 per hour ($1,200 per day) using an overhead multiple of 1.5 (see table 5.1). Such substantial variation points to a capricious method that is exposed to gross System 1 behavioral memory-based bias. Many law, accounting, engineering, and architectural firms inject this type of standardized costing bias into their price-setting. Standardizing costs into per-unit terms is alluring to price-setters in fixed-cost-intensive services (such as law firms, advertising agencies, and software development firms) because prices are inherently difficult to calculate in per-unit pricing terms. What metric units should you charge for an advertising campaign designed by creative artists who earn fixed salaries? Setting prices per hour spent on the client’s ad campaign seems heuristically intuitive, but ultimately it commoditizes creative work by framing price based on the hours (or cost) spent by the creative team rather than on the value received by the client’s customers. For years, advertising agencies set prices for their creative services using agency commissions—typically 15 percent of the total advertising invested in various media (television, radio, online, outdoor)—enabling them to get compensated based on the incremental value they created for their client’s customers for great ad campaigns with large media investments. However, ad agencies today often set prices using cost-driven, labor-based fees and billing rates, like the allocation-driven methods shown earlier. Standardized costing bias matters. It causes firms to overstate total costs for very successful products while understating total costs for less successful

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ones, leading to distorted profit contribution comparisons and mistaken resource allocations. Imagine two varieties of sparkling waters—a lime flavored SKU and a strawberry flavored SKU. They cost the same to produce and deliver, but the lime SKU is more popular, selling more units than expected, and the strawberry SKU is less popular, selling fewer than expected. So, although their actual fixed costs are the same, the overhead burden per unit for the lime SKU multiplied by its higher-than-planned unit volume, overstates its total fixed costs when computing SKU profitability, making it appear less profitable than it really is. Consequently, high-performance products, like the lime SKU, get profit-penalized. But low-performance products, like the strawberry SKU, get profit-subsidized; they appear more profitable than they really are because their total fixed costs get understated. An important soft costing skill for price-setting, therefore, is debiasing for standardized costing bias. Look for standardized fixed costs that appear as per-unit fixed costs, or for fixed-cost overhead burdens. Ask yourself, what are the hidden assumptions behind the cost accounting data used for price-setting calculations?

Sunk-Cost Bias

Similar to the sunk-cost fallacy of behavioral economics—the tendency to allow unrecoverable past costs to influence future actions—sunk-cost bias in price-setting is the tendency to allow the financial costs to which a firm is already committed—such as the costs of R&D, real estate, or equipment, —to influence future-focused pricing decisions. Citing a classic example from behavioral economics, it seems irrational for a person who purchased a $100 concert ticket to risk his life driving through a blizzard to get to the convert venue just because he’s already spent $100 on it.5 Similarly, it seems irrational for a retail price-setter to set high prices today to recover costs for a decision made years ago to locate in a more costly high-traffic retail location, when it might actually be more profitable to set lower prices to draw more customers. In either case, when price-setters are called on to choose among options, they should be motivated to maximize long-term incremental profit contribution. If sunk costs are irrelevant, then which costs are relevant to pricesetting? There is a useful rule of thumb: costs that change when price is changed are relevant to pricing—and therefore costs that do not are irrelevant to pricing. This is illustrated in the simple profit-and-loss statement

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Price Unit Sales Volume Total Revenue Unit Variable Costs Total Variable Costs Unit Contribution Margin

$10

If price changes . . .

100,000 $1,000,000 $3 $300,000

. . . Relevant

$7

Total Profit Contribution

$700,000

Sunk Fixed Costs

$500,000

Net Operating Profit

$200,000

. . . Irrelevant

Figure 5.2 

Sunk-cost bias—costs irrelevant to price-setting. “What will be affected by a change in price?” (Δ means change, – means no change).

shown in figure 5.2, where the delta (Δ) symbol indicates which items change if price changes. Three insights are quickly evident. First, total variable costs will change if price changes because unit sales volume will change (even if per-unit variable costs are constant). Variable costs therefore are clearly incrementally relevant to price-setting. Second, the fixed costs that are already sunk are not changeable and are therefore incrementally irrelevant to forward-looking price-setting. Third, the change in total profit contribution due to the price change—that is, the incremental profit contribution—is equivalent to the change in net operating profit. Therefore, the best measure of the impact of a pricing decision is the change in total contribution, or incremental profit contribution. The profit-maximizing objective of price-setters should always be to set (or reset) price in order to maximize incremental profit contribution. One clear example of sunk-cost bias in price-setting was shown in the last section: converting sunk fixed costs into per-unit terms and including them in pricing calculations as if they were variable costs. Including sunk—and therefore irrelevant—fixed costs as if they were another variable cost makes margins seem smaller than they actually are, which then affects how managers set prices to maximize incremental profit contribution. Seemingly low margins appear to preclude marketing strategies to

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sustain or grow customer demand and sales volume—such as promotional discounting, loyalty programs, advertising, and responding to competitive price moves—because managers perceive that they have too little margin to work with. A good example of this, involving Progressive Insurance, is shown in the nearby box.

Progressive Insurance Costing and Margin Compression

In a popular business school case study, Progressive Insurance, like many auto insurance companies, used “a cost-plus pricing system, which helped guarantee the profitability of all its products and assisted navigating state [insurance] regulations.” Insurance companies use this computation: “the ratio of the combined costs of writing insurance to the premiums received, expressed in a whole-number percentage [called the ‘combined ratio’]. A combined ratio of 100 meant, in essence, that the amount the company paid out in claims and all other costs exactly matched the premiums collected—an example of pricing driven by cost recovery; thus, a combined ratio of 100 meant no profit was earned. Many automobile insurers had combined ratios in excess of 100 and achieved company profits only through investment income. Progressive, however, targeted a combined ratio of 96, making it one of the few insurance companies that set out to make money on insurance.6

Over time, Progressive increasingly invested in advertising to build market awareness and support selling, becoming, in 2019, the second-largest insurance advertiser ($1.7 billion) behind GEICO ($1.9 billion).7 How should those advertising fixed costs be accounted for in price-setting? Using what the company calls a “flat allocation method,” it calculated the policy price by summing all costs, including claims, processing, and underwriting costs that varied with policy sales volume, then adding what it called a “fixed allocation,” which included its large advertising expense. For a simple individual insurance policy, shown in box table 1, claims, processing, and underwriting costs per unit might be $200, plus the fixed allocation of $100 per unit, which sums to total costs per unit of $300. Applying the combined ratio of 96 (that is, dividing $300 by 0.96) yields a final price to the consumer of $313.

Box Table 1  Sample cost-based prices at progressive insurance

Flat allocation method

Policy A

Claims costs, processing and underwriting

$200

Fixed allocation

$100

Basis for markup

$300

Combined ratio

96

Final price to consumer ($300 ÷ .96)

$313

Adapted from: Acquisition Cost Allocation at Progressive Insurance, Darden Business Publishing, University of Virginia (UVA-M-0785).

However, look at the impact on margins of inaccurately treating fixed costs, shown in box table 2. Properly treating fixed costs as fixed and not as variable, called “CM-Based” (contribution margin-based) in that table, yields a contribution margin per unit of 36 percent. But treating fixed costs as variable, the “Allocation-Based” column in the table, yields a contribution margin of just 4 percent. Of course, the auto insurance market space is highly competitive, and Progressive had to ensure that its prices were competitive. However, not surprisingly, it perceived that it had little margin to work with, which showed up in its incentives to selling agents. Said one agent in an online discussion forum, “I have two main gripes with Progressive. Low commission rate (10 percent on commercial vs up to 17.5 percent with other commercial carriers), and it’s difficult to qualify for any profit sharing.”8 For Progressive, on a per-unit basis, there seemed little contribution margin from which to share due to its biased accounting assumptions—sunk cost bias. Box Table 2  Margin calculation comparative methods sample costbased prices at progressive insurance

CM based

Allocation based

Policy A

Policy A

Price

$313

$313

VC/unit

$200

$300

CM/unit

$113

$13

CM/unit %

36%

4%

Based on: Acquisition Cost Allocation at Progressive Insurance, Darden Business Publishing, University of Virginia (UVA-M-0785).

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A key soft price-setting skill, therefore, is to ensure that the contribution margins that price-setters use are not biased by the inclusion of sunk fixed costs. For price-setting, contribution margin shows the incremental profit contribution that will be realized per unit of revenue received from selling another unit. Many consumer packaged-goods manufacturers (e.g., breakfast cereal, laundry detergent, hair shampoo) have high contribution margins—say, 65 to 75 percent—which they use as a primary indicator showing that driving sales volume is the most effective way to leverage incremental profitability (discussed next). However, if sunk fixed costs are mistakenly included in the contribution margin estimation, then margins will be understated and managers will mistakenly believe that they have less leverage to drive sales volume than they actually have. Gross margins (price minus variable cost) can approximate contribution margins, although the variable costs used to calculate them are usually sourced from business accounting systems that report only average, rather than incremental, costs. However, a note of caution: many public companies, possibly including your own, define gross margin in a way that can mislead for pricing—as “the difference between sales and the cost of goods sold (COGS) divided by revenue. This represents the percentage of each dollar of a company’s revenue available after accounting for cost of goods sold.”9 The problem here is that in most companies, the accounting system calculates cost of goods sold by estimating revenues and then subtracting variable costs and operating costs—many of which are in the form of fixed cost per unit, thus bringing into play many of the standardized costing biases we discussed in the last section. One client firm used a measure they called “business gross profit,” defined as price minus variable costs and operating costs based on COGS (which included various overhead allocations of fixed costs). The margins they routinely judged as normal in their accounting reports were lower than they should have been had they removed operating costs (fixed-cost allocations)—an obvious case of sunk-case bias.

Average Costing Bias

Average costing is baked into cost accounting systems because costs from different sources—such as normal hourly pay versus overtime pay, or internal versus external procurement costs—are pooled and then averaged into

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per-unit terms for operational and pricing purposes. However, average costing fails to take into account that procurement from some sources will cost more than procurement from others for the same cost line item, leading to incorrect cost and profit signals for pricing, and consequently to average costing bias. For example, when a firm is operating at full capacity, the incremental cost of an incoming customer order will be higher than normal because the firm must pay overtime wages, procure materials at spot market prices from noncontract sources, or lease temporary production capacity. Often overlooked but increasingly built into digitally driven dynamic pricing models is the opportunity cost—the incremental profit contribution that is lost by turning away more profitable customer orders than you are currently serving during a peak demand period. Booking incremental customer orders in these situations without adjusting costs to reflect their true incremental cost can thus undermine profit contribution. Ride-sharing services such as Uber and Lyft smartly monitor cost to serve during peak service periods, charging surge prices and dispatching drivers to undersupplied locations to quickly increase supply. A related average costing bias that is built into the assumptions of most accounting systems is average customer costing bias; that is, ignoring differences in the cost to serve different customers. Some customers and customer segments are high cost to serve, and price-setters should know who they are and what drives their high costs. UPS traditionally imposed surcharges on retailers when their holiday shipments exceeded their contractual forecasts, but more recently it added fees if holiday shipments fall below the forecast—because UPS still must pay incremental costs for higher holiday shipping volume, whether or not its retail customers meet their forecast commitment. UPS’s holiday season cost swings could not easily be reported in its traditional cost accounting systems; instead, the company worked with individual customers in a forward-focused manner to adjust prices based on incremental costs. “If there are variations to the plan, let’s see what we can do, but we should be compensated accordingly,” said UPS chief executive David Abney. “We will handle [the price surcharges] on a customer by customer basis, we will look at our costs [with each] and that’s the way we’re going to address it.”10 Estimating such incremental costs usually requires exploratory methods to discover the subtle cost impacts of hidden or unknown cost drivers, which are beyond the reach of most cost accounting systems.

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Soft intuitive costing skills Cost-driven biases and debiasing

Cost discovery, cost sensing

Hard analytic costing skills Incremental costing, analytics, modeling

Standardized costing bias

Exploratory cost discovery

Activity-based costing (TDABC)

Sunk-cost bias

True cost-to-serve indicators and attributes

Price waterfalls analysis

Average costing bias Average customer costing bias

Margin leverage based on true contribution margins

Pricing breakeven sales calculations

Figure 5.3 Soft and hard skills of costing in a pricing orientation: a checklist inventory.

Soft Costing Skills for Price-Setting

Let’s turn now to soft costing skills that leverage System 1 behavioral thinking to address price-setting. They are intuitive, tacit, behavioral, and improvisational skills to help your pricing orientation become more nimble, productive, and satisfying to both price-setters and customers. These are the fast processes of Kahneman’s Thinking Fast and Slow. The first foundational soft costing skill is to debias the cost biases covered in detail in the last section. Always ask, what biases are present in your current costing? The usual suspects—guilty, often as not—are the four well-documented biases discussed earlier and shown in figure 5.3, left: Standardized costing bias, Sunk-cost bias, Average costing bias, and, Average customer costing bias. Other soft costing skills are intuitive and relate more to cost-sensing and cost-probing skills (see figure 5.3, middle); they include exploratory cost discovery, true cost to serve indicators and attributes, and margin leverage based on true contribution margins.

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Exploratory Cost Discovery

Exploratory cost discovery uses soft exploratory research methods such as personal observation, depth interviews, and personal consultation of operations and customer-facing personnel to discover cost biases and distortions and probe established cost data, systems, and processes to see what is really happening. To illustrate, Gundersen Health System performs more than four hundred knee replacements annually. For a decade, Gundersen raised the price of that surgery about 3 percent annually; by 2016, its average list price was over $50,000. However, large, sophisticated, pricesensitive payers, such as Medicare and private insurance companies, began to question Gundersen’s prices, which prompted managers to research its costs with in-depth exploratory cost discovery. A consulting team personally interviewed and then observed doctors and nurses to “record every minute of activity and note instruments, resources and medicines used. The hospital tallied the time nurses spent wheeling around VCR carts, a mismatch of available postsurgery beds, unnecessarily costly bone cement and delays dispatching physical therapists to get patients moving. The actual cost? $10,550 at most, including the physicians. The list price was five times that amount.”11 Yet this wasn’t deliberate price-gouging, just naive, biased price-setting. Knowing little about its true costs, Gundersen had simply increased prices to meet margin targets based on the reported accounting costs for its orthopedic department—a good example of the biased distortions of cost-driven pricing. Discovering and debiasing those cost distortions led to efficiencies and lower costs, saving 18 percent in knee replacement cost, enhancing bottomline profits, and enabling Gundersen—as a nonprofit hospital system—to invest those profits in new equipment, construction, and acquisitions. As for pricing, now Gundersen could better segment the market to offer more competitive prices to price-sensitive payers, such as corporate buyers in large employer alliances that routinely required lower prices to win their group business. Under Gundersen’s new contract pricing, corporate employees could get discounts of more than 30 percent off the list price— because Gunderson was setting prices based on a healthy 73 percent true margin. Over its eighteen months of exploratory cost discovery, Gundersen had developed a useful soft costing skill that led to more profitable pricesetting and a continuing keen awareness of costing distortions that might bias pricing throughout its operating system.

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True Cost-to-Serve Indicators and Attributes

A second soft skill relates to true cost-to-serve indicators and attributes of customers, to quickly recognize which customer orders are high versus low cost to serve. For example, high cost-to-serve customers order custom products, order in small quantities, seek customized delivery schedules such as urgent delivery requests, make frequent and resourceintensive demands on technical support or field service, or are persistent complainers. Low cost-to-serve customers, by contrast, order in standard product configurations in high quantities with predictable schedules and make fewer and less resource-intensive use of tech, field, or claims support. Robert Kaplan of Harvard Business School summarized the differences in a helpful cost-to-serve customers framework, as shown in figure 5.4, left. McKinsey consultants expanded on these differences in cost to serve, what they termed “hidden costs” that go unnoticed during price-setting or are rarely accounted for in cost accounting systems. They categorized these hidden costs into three buckets that represent another way of recognizing true cost-to-serve indicators and attributes of customers, shown in figure 5.4, right.12 Nonstandard Order Costs. A customer might, for example, order building materials in nonstandard sizes or with higher-quality specifications. Such an order requires extra calibration and processing and leaves offcuts that result in “lost revenues when the excess material produced is sold as non-prime stock at a discount, or additional costs when it has to be reprocessed.”13 Bottleneck Costs. These costs occur whenever demand approaches or exceeds the maximum capacity output that can be produced in the short run with existing plant, equipment, personnel, and capital stock. The firm might have to turn to less efficient and more expensive source inputs (such as coal or nuclear instead of natural gas for electric power generation). It might have to lease or purchase additional short-term capacity (such as purchasing electricity from neighboring wholesale distributors). It might have to turn away customers, even some that would be more profitable to serve than the current customers (opportunity cost). All these circumstances cause the cost to serve to surge.

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Kaplan’s cost-to-serve customers framework High cost-to-serve customers

Low cost-to-serve customers

Order custom products

Order standard products

Small order quantities

High order quantities

Unpredictable order arrivals

Predictable order arrivals

Customized delivery

Standard delivery

Change delivery requirements

No changes in delivery requirements

Manual processing

Electronic processing (EDI)

Large amounts of pre-sales support (marketing, technical, sales resources)

Little to no pre-sales support (standard pricing and ordering)

Large amounts of post-sales support (installation, training, warranty, field service)

No post-sales support

Require company to hold inventory

Replenish inventory as produced

Pay slowly (high accounts receivable)

Pay on time

McKinsey’s hidden costs framework Nonstandard order costs: • Nonstandard sizes • Higher-quality specifications • Extra calibration/processing Bottleneck costs: • Demand ≥ capacity • Less efficient/higher cost inputs • Lease short-term capacity • Opportunity cost of more profitable customers Customer service costs: • Excess tech, call center support • Excess packaging costs • Inventory holding costs • Customer receivables costs • Collections costs • Claims costs

Figure 5.4 

True cost-to-serve indicators and attributes Adapted from: Robert S. Kaplan, Using ABC To Manage Customer Mix and Relationships (Harvard Business School, 9-197-094, April 7, 1997), 1. Johan Ahlberg, William E. Hoover, Jr, Hanne de Mora, and Tomas Nauclér, “Pricing Commodities: What You See Is Not What You Get,” McKinsey Quarterly no. 3 (1995): 66–77.

Customer Service Costs. These include nonproduction personnel costs such as tech support and call centers, as well as packaging costs, inventory holding costs, customer receivables and collection costs, and customer claims on problem orders. McKinsey estimates that these “additional costs can represent as much as 20 percent to 40 percent of revenues, and sometimes vary dramatically between orders that carry identical invoice prices.”14

Another often overlooked cost attribute is replacement cost rather than historical cost. Costing for price-setting is forward-looking, focused on the cost to serve the next customer order. For example, a gas station buys gasoline to replenish its inventory. If the wholesale cost of gasoline

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recently spiked due to a terrorist attack on wells in an important producer nation, then the true cost of serving the gas station’s next customer is the new higher cost of gasoline that is required to replenish inventory today, the replacement cost. The market for gasoline has changed—the wholesale cost is higher—even if some gasoline currently in inventory was purchased months ago at a lower cost. Nagle and Müller explain the consequences of ignoring this change. Even though the sales appear profitable from a historical cost standpoint, the company must add to its working capital (by borrowing money or by retaining a larger portion of earnings) to pay the new, higher cost of [gasoline]. Consequently the real “cash” cost of making a sale rises immediately by an amount equal to the increase in the replacement cost of [gasoline].15

Price-setters should always be alert to customer orders that are higher versus lower cost to serve. Amazon, for example, instituted an easy-toremember mnemonic truism for high cost-to-serve orders they designate as “CRaP,” short for “Can’t Realize a Profit.” These orders are heavy, bulky, and expensive to ship and usually amount to just $15 or less. Amazon went to its major manufacturers and asked them to adjust their packaging to create a new larger, higher-priced, default Amazon online order. For example, “working with Coca-Cola to change how it ships and sells [smartwater], Amazon notified Dash customers [who use Amazon’s Dash instant online ordering button], it was changing that default item to a 24-pack for $37.20” instead of a $6.99 six-pack.16 And it negotiated with Coca Cola to ship those orders directly from its warehouses rather than from Amazon’s warehouses. Amazon made similar arrangements with Unilever, Mars Wrigley, Kellogg, and Campbell Soup.

Margin Leverage Based on True Contribution Margins

Another important soft costing skill is margin leverage based on true contribution margins. Knowing your true contribution margin provides an immediate strategic indicator of how best to leverage incremental sales revenue to grow incremental profit contribution. Like a fulcrum that is placed closer to an object to make it easier to lift, so it is with contribution margins and margin leverage. High-margin businesses leverage incremental

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profitability by driving unit sales volume, referred to as volume-driven margin leverage strategies. Every dollar of product sold yields a sizable and immediate incremental profit contribution. Financial analysts note that businesses with high what they term “operating leverage” usually have a large proportion of fixed costs to variable costs and consequently have high gross margins. For example, many pharmaceutical firms have high estimated contribution margins. Novo Nordisk (which manufactures insulin and other drugs) has 88 percent gross margins (an approximation for contribution margin), Amgen (a biopharmaceutical manufacturer) has 83 percent gross margins, and Celgene (which produces medicines for cancers and inflammatory disorders) has 93 percent gross margins. Every dollar of product sold by these companies yields more than 80 cents in incremental profit contribution to invest in engineering, R&D, corporate overhead, and shareholder profits. This further enables them to make large investments in advertising, promotional pricing, product innovation, and high-volume distribution channels in order to maximize sales volume. Private-label brands, such as Whole Foods’s 365 Everyday Value, have higher retail margins than the national brands their retailers carry, which enables the retailers to do more “demand-based pricing” using price promotions and loyalty programs in their stronger private-label brand categories.17 By contrast, many low-margin firms incentivize their sales force to sell more units, but each unit sold generates a small incremental profit contribution. They therefore should apply the soft skill of margin leverage in a different way: cross-selling bundles to their customer base and always maintaining price and margin integrity. Electronics distributors such as Arrow Electronics have gross margins of about 12 percent. Sysco, the largest wholesale distributor of groceries and food products in North America, has a gross margin of about 19 percent. Warehouse club retailers usually have gross margins of about 6 percent, but they leverage their profitability by charging their customers a membership fee—say, $60 annually for consumers and $120 for businesses—thus driving another highly profitable revenue stream through its large customer base. In addition, they sell large sizes or double or triple packages, increasing the value of the customer’s average shopping cart. Costco, for example, uses these margin leverage strategies to consistently achieve gross margins of 13 percent, double the category average. U-Haul, the most profitable firm in its category, strategically sets low prices and margins on truck and trailer rentals to maintain a large rental

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customer base. Most of its profit contribution, then, comes from add-on products and services offered conveniently—but at premium prices—at the U-Haul retail location: customers always need more boxes, dollies, lifts, and packaging supplies. For truck manufacturers, selling new trucks to large long-haul trucking companies like Ryder merely provides the sales base from which to sell much more profitable bundled options, such as financing, leasing, long-term rentals, service contracts, and replacement parts. Before the COVID-19 pandemic, many brick-and-mortar retailers, such as Best Buy, Target, Walmart, and Home Depot, smartly had turned their stores into efficient customer pickup hubs that reduce the cost to serve for e-commerce orders that are expensive to ship to high cost-todeliver residential locations. Marketing consultants estimate that for an online customer order “it costs retailers about $5.60 in packaging, labor and fuel to deliver goods ordered online. Factoring in other costs, retailers stand to make a 25 percent gross profit on a shipment of $82, the size of an average online order during [the] holiday season. The store pickup option raises that margin to at least 33 percent.”18 However, the emphasis on store pickup creates an opportunity for cross-selling other products and services, a smart low-margin leverage strategy: “more than a third of customers who come to collect their orders end up buying something else.  .  .  . During the holidays, that number increases to 86 percent.”19 At the onset of the pandemic, these retailers used e-commerce shopping and contactless store pickup options to significantly grow revenues and, more important, leverage their incremental profitability. Just as financial leverage, such as debt financing, magnifies the power of a company’s equity investment to grow its business, margin leverage for pricing magnifies the power of the company’s sales to drive profitability— whether through maximizing sales volume with high-margin products and services, or cross-selling product and service bundles and pricing and margin integrity with low-margin ones.

Hard Analytic Costing Skills for Incremental Costing and Price-Setting

Hard costing skills are those that leverage System 2 thinking to address price-setting. They are analytical and systematic, data-driven, procedural, methodical, structural, and deliberate. These are the slow processing of Kahneman’s Thinking Fast and Slow. System 2 costing skills leverage

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the more analytic and deliberate thinking to provide guardrails to guide your pricing orientation, making it more strategic and more consistently profitable. Let’s look at three useful hard analytic costing skills: activity-based costing, price waterfalls analysis, and pricing breakeven sales calculations (see figure 5.3, right).

Activity-Based Costing

Some costs that, by definition, normally might be categorized as fixed costsinstead might be considered in some settings as incremental costs that are relevant for pricing. For example, costs for technical support and call centers are often salaries and benefits that grow in a step fashion, driven by changes in volume, such as hiring incremental support persons due to incremental changes in sales volume. You might need only 7.5 people, but you have to hire 8. These are “semi-variable” costs, but they appear as fixed costs and overheads in cost accounting systems that are incapable of assigning costs to individual customers or orders. Activity-based costing (ABC) is one way to estimate these semivariable costs to serve. The latest and simplest version is time-driven activity-based costing (TDABC). ABC involves, first, quantifying the resource investment in a firm’s service capability, such as a customer order center, customer service department, or technical service department. This investment might include compensation for employees and supervisors as well as costs for supporting resources such as office space, computers, internet communications, and furniture. For example,20 a small firm, Smithsonian Financial Services, serves retail stock brokers and spends $590,000 on its twenty-five-person customer service department in a typical quarter. A customer service employee works an average of twenty-two days per month, seven hours per day (one hour for breaks), for a total of 9,240 minutes per month, or 27,720 minutes per quarter for the department. With twenty-five employees, Smithsonian’s customer service department has a quarterly practical capacity of about 693,000 minutes. Thus, the cost per minute for a customer service employee is $0.85 ($590,000 ÷ 693,000), as shown in table 5.2a. This calculation can be easily updated per quarter or month to ensure that it is based on fresh data and approximates the department’s current incremental cost to serve customers.

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Table 5.2a  Estimating cost to serve customers with TDABC, Smithsonian Financial Services

Customer Orders (Minutes/Order) Customer Inquiries (Minutes/Inquiry) Credit Checks (Minutes/Credit Check) Employee Work Days/Month Employee Work Hours/Day Employee Work Minutes/Month Employee Work Minutes/Qtr Customer Service Employees Practical Department Capacity (Minutes/Qtr) Customer Service Department Expenditure/Qtr Cost Per Customer Service Employee Minute Cost Per Customer Order Cost Per Customer Inquiry Cost Per Credit Check

Capacity Data 8 44 50 22 7 9,240 27,720 25 693,000 $590,000 $0.85 $6.81 $37.46 $42.57

Adapted from Robert S. Kaplan, “Time-Driven Activity-Based Costing,” Harvard Business School 9-106-068, Rev: May 15, 2009.

Next, TDABC estimates the time required to perform an activity, such as an order, inquiry, or credit check. Using direct observation or employee interviews, assume that we have arrived at the following estimates (note that estimates are sufficient): • Orders • Inquiries • Credit checks

8 minutes each 44 minutes each 50 minutes each

As table 5.2a, bottom, shows, each order costs the firm $6.81 ($0.85/min. × 8 min) in customer service department expenditures; each inquiry costs $37.46 ($0.85 × 44), and each credit check costs $42.57 ($0.85 × 50). Smithsonian sells to three customer types. Average customers engage in average activity each quarter, resulting in department capacity utilization of 79 percent (see table 5.2b). High cost-to-serve customers do 40 percent

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Table 5.2b  Estimating cost to serve customers with TDABC, Smithsonian Financial Services

Average customer Capacity utilization Customer orders/qtr Customer inquiries/qtr Credit checks/qtr Department capacity utilization

Average activity

45,000 1,400 2,500 79%

High cost to Low cost to serve customer serve customer 40% More Activity

63,000 1,960 3,500 110%

40% Less Activity

27,000 840 1,500 47%

Adapted from Robert S. Kaplan, “Time-Driven Activity-Based Costing,” Harvard Business School 9-106-068, Rev: May 15, 2009.

more activity than average each quarter (40 percent more orders, inquiries, and credit checks); if the department served only those customers, capacity utilization would be 110 percent, requiring the firm to augment its customer service capacity. Low cost-to-serve customers do 40 percent less activity than average each quarter (40 percent fewer orders, inquiries, and credit checks); if the department served only those customers, capacity utilization would be 47 percent, freeing up customer service representatives for additional profit-generating activities elsewhere. Smithsonian’s quarterly sales revenue is $5 million with a 50 percent gross contribution margin, as shown in table 5.2c. Based on the foregoing data, the total cost to serve customers is, on average, about $465,000 per quarter, resulting in an average net contribution margin of 41 percent. For high cost-to-serve customers—who produce the same revenue but generate 40 percent more orders, inquiries, and credit checks—the total cost to serve on average is about $652,000, resulting in a net contribution margin of 37 percent. And for low cost-to-serve customers—who produce the same revenue but generate 40 percent fewer orders, inquiries, and credit checks—the average total cost to serve is about $279,000, resulting in a net contribution margin of 44 percent. Clearly, it makes sense for Smithsonian to consider ways to use its pricing to encourage customers to behave in ways that reduce the cost to serve and create more incremental profit contribution, perhaps by reducing order frequency, increasing order size, or setting a schedule of fees for inquiries and credit checks.

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Table 5.2c  Estimating cost to serve customers with TDABC, Smithsonian Financial Services

Cost per customer order Cost per customer inquiry Cost per credit check

Average customer $6.81 $37.46 $42.57

Cost customer orders/qtr Cost customer inquiries/qtr Cost credit checks/qtr Total cost to serve customers

$306,494 $52,444 $106,421 $465,359

$429,091 $73,422 $148,990 $651,503

$183,896 $31,467 $63,853 $279,216

$5,000,000 $2,500,000 50% $465,359 $2,034,641 41%

$5,000,000 $2,500,000 50% $651,503 $1,848,497 37%

$5,000,000 $2,500,000 50% $279,216 $2,220,784 44%

Revenue/qtr Gross contribution/qtr Gross CM% Minus total cost to serve customers Net profit contribution Net CM%

High cost to serve Low cost to serve customers customers $6.81 $6.81 $37.46 $37.46 $42.57 $42.57

Adapted from Robert S. Kaplan, “Time-Driven Activity-Based Costing,” Harvard Business School 9-106-068, Rev: May 15, 2009.

With TDABC, the resulting cost-to-serve estimates can be applied in real time to track and possibly adjust the costs to serve individual customers. Robert Kaplan, the originator of ABC, summarized. An ABC system gives a clear and accurate picture of a company’s gross margins by individual product or SKU, and the company’s costs of serving its diverse customer base. This picture provides the basis for companies to take actions on process improvement, pricing, and managing customer relationships that transform unprofitable products, orders, and customers into profitable ones.21

As you can see, time-driven ABC is not difficult, and the payoff can be significant. A useful framework to hypothesize cost to serve is to use an incremental cost-to-serve driver assessment, first identifying cost-to-serve drivers that lead to higher costs to serve, then using customer-facing personnel to subjectively rate customer segments on each driver, as shown in figure 5.5. The resulting cost-to-serve score provides a baseline hypothesis

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Cost-to-serve driver

Segment A

Segment B

Segment C

Special product customization

5

2

3

Small order quantities

4

1

2

Delivery urgency

4

1

3

Technical support required

5

2

4

Installation and training

4

2

3

High cost delivery locations

3

2

3

Pay slowly

5

1

2

Cost-to-serve score

30

11

20

High

Low

Moderate

Cost-to-serve assessment

Subjective cost estimation scores, 5 = high, 1 = low

Figure 5.5 

Incremental cost-to-serve drivers assessment.

of which customers will cost more to serve but also the cost drivers that can be fruitfully incorporated into an ABC analysis. In addition, it leads to conversations among customer-facing managers and price-setters—and with customers themselves—about how to manage cost to serve to make the customer relationship more profitable and mutually satisfactory.

Pocket Price Waterfalls

A popular innovation of McKinsey’s Cleveland office is the pocket price waterfall. The purpose of the pocket price waterfall is to ensure that customers pay for the value they receive. A price waterfall categorizes, or places in buckets, the various ways in which the price customers actually pay gets reduced from list price. Figure 5.6 shows a price waterfall example from Deloitte consultants. The process starts (at the far left) as list price (here called “rack rate” and often “suggested retail price”). From there, the waterfall lists sequentially the price adjustments, or profit leakages or erosion, that commonly occur in a business relationship with a customer or channel

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% 120 15

Process erosion

Market erosion

100 20

Selling erosion Policy erosion

80 10 5 10

60

5

100

5 5

40 65

3

6 50

Performance erosion

3 2

20

15 26

5 6

tu

-p

Se

Fe a

In

Ra

ck ra te ol gm re u icy p en lif ne td t go isc ou Un tiat e n au d t di th sc or ou ize n d t di sc ou Bo nt ok Fr in ee g pr cu i st ce om iza tio Fr n ee fre i g In ht vo ice Pa p ric ym Un e en m tt er er ite Lo m ya d s pa l ym ty re ba en te td isc ou n ts Ou Sh Ot or ts id tp he e a ro co ys ffm in m vo i ice ssio n di sc ou nt Po s ck et pr Co ice st of W sa ar le an s ty cla Po i m ck s et m ar gi n

0

Figure 5.6 

Pocket price waterfall Source: Chuck Davenport, John Norkus, and Michael Simonetto, “Capturing the Value of Pricing Analytics,” in Gerald E. Smith, ed., Visionary Pricing: Reflections and Advances in Honor of Dan Nimer, Advances in Business Marketing & Purchasing Vol. 19 (Bingley, UK: Emerald Group Publishing, 2012), 303.

partner. The example in figure 5.6 involves market erosion (an uplift here due to feature upgrades) and selling erosion, such as segment discounts, negotiated discounts, and unauthorized discounts. It also involves policy erosion, such as free customization, free freight, payment terms, and loyalty rebates; process erosion, such as unmerited payment discounts, short pays, outside commissions, and other off-invoice discounts; and performance erosion, such as warranty claims and cost of sales. Notice, in this example, that invoice price is 50 percent of the initial rack rate, a leakage of 50 percent due to on-invoice drivers. However, most managers fail to notice leakage from offinvoice drivers. The pocket price is 26 percent of rack rate, meaning a leakage of 24 percent due to off-invoice drivers, and 74 percent total leakage due to all drivers.22

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The pocket price waterfall identifies incremental price improvement or profit drivers that, for example, might “result from deliberate or accidental inaccuracies in customer payments, such as short pays tied to unmerited early payment discounts or unpaid payment terms penalties.”23 Here are some examples cited by the Deloitte consultants.24 Leakage from Volume Discounts. For a provider of lawn chemicals, a price waterfall analysis “revealed that many large customers had purchased volumes that qualified them for [large discounts.] Yet, these customers also had significant product returns that net-net reduced them to the status of medium-sized customers. However, the returns never affected the [customers] or the resulting discount.”25 Leakage from Free Freight. Some distributors charge a fixed delivery fee for each order. “However, like free freight, fixed freight in the form of a delivery fee puts the burden on the distributor to be operationally efficient. If delivery costs for a transaction exceed the delivery fee, as they often do, the excess costs become a profit leakage against the invoiced revenue.”26 Leakage from Order Discounts. A specialty chemicals manufacturer offered pricing incentives to customers who ordered full-pallet quantities. They then “found that customers filled pallets with low-profit, low-priced products to get a steep discount on one or two bags of the expensive and (formerly) profitable product. Perhaps even more interesting, some customers returned bags of product, thereby making money on the return and on the discount. . . . [T]he manufacturer plugged the leakage and offered the discount only on orders that merited it. That fix provided over $300,000 in annual cash profit in the United States alone.”27

With a baseline price waterfall in hand, price-setters can reengineer the waterfall to increase profitability. In figure 5.7, an example from McKinsey shows the existing baseline price waterfall for a typical basic goods manufacturer alongside a reengineered price waterfall, with notes and markings (on the right) to show changes to improve price and profitability. In the lower region of the figure, baseline “width losses, high surface finishes, and special material properties” involve special processing typically included as free services, resulting in profit leakages. The reengineered waterfall moves these items from off-invoice to on-invoice as service extras in the price list, adding basis points in price improvement and incremental profit.

Percent of invoice price Price structure changes Opportunities for cost reduction

Customer rebate Volume discount

0 2 0.4 2 (0) 2 100 101.6 3 (0) 2 2 0.5 0.5 2 2 2 2 90.5 95.1 78 78

Freight charge Charge for service extras Invoice price Quarterly bonuses Contract payment terms Late payments Credit notes Freight cost Pocket price Standard product cost Packaging Capital cost for stock Admin and sales cost Width losses High surface finish Special material properties Pocket margin

Link bonuses to order attractiveness

105 105 5.4 5.4

Charge for full freight cost

List price

2 2 0.1 0.1 3 3 1 0.5 2 1.5 1 0.5 3.4 9.5

Establish service extras in price list

Typical waterfall Reengineered waterfall

Figure 5.7 

Reengineering the pocket price waterfall Source: Exhibit from “McKinsey Quarterly 1995 Number 3”, 1995, McKinsey Quarterly, www.mckinsey.com. Copyright (c) 2020 McKinsey & Company. All rights reserved. Reprinted by permission.

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In the baseline waterfall, freight costs are a free off-invoice item; in the reengineered waterfall, a freight charge is instituted, making freight oninvoice to generate price improvement and incremental profitability. All told, the reengineered price waterfall improves the on-invoice price by 1.6 basis points; then, with additional savings in off-invoice transaction and cost-to-serve savings, the firm achieves total improvement in pocket margin of 6.1 basis points. The price waterfall provides price-setters with a useful and accessible hard skill for incremental costing and profit contribution, a template for which questions to ask, which patterns to notice, and which leakages to plug. For example, Deloitte recommends two waterfall principles. First, look for controllable profit levers: “The waterfall represents profit levers that the team can attribute to a specific transaction and can therefore control. Few allocations of fixed cost should be allowed, although some allocations [using activity-based costing] may be needed. The waterfall rarely includes unaltered accounting data.”28 Second, “the team should pursue insights into pricing at the lowest level of granularity; that is, the line-item on the invoice.”29 The waterfall is especially useful in improving profitability by either reducing cost to serve or getting paid for the incremental value delivered. The waterfall spotlights the value of customer relationships, what drives that value, and the mix of profitable and less profitable customers.

Pricing Breakeven Sales Calculations

When considering prices or price changes, economists ask, “What is demand at those prices and the shape, or slope, of the demand curve?” Though few managers know the demand curve with precision, they are not totally ignorant about demand. In chapter 3, we discussed the soft skill of soft probability estimation, which relies on subjective estimates of probability outcomes—such as whether changes in market demand or sales volume will exceed or fall short of a breakeven sales change in unit sales volume— which enables a subjective estimate that the price change will be profitable. To facilitate this, we use the complementary hard skill of pricing breakeven sales change calculations based on Tom Nagle’s early pricing breakeven work and expanded in later editions of his popular text, The Strategy and Tactics of Pricing,30 and related articles.31 Finding the breakeven sales change requires first establishing a baseline profit contribution against which to measure changes in price and consequent changes in profit contribution. The baseline might be the firm’s

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Price decrease

Price increase Baseline profit contribution = B + C

Baseline profit contribution = A + B

P1

P2 Loss

A

Gain

P2

A

P1 C

B

Q1

Breakeven sales change

C

B

Gain

VC

Loss

VC

Q2

Q2

Breakeven sales change

Q1

Figure 5.8 

Breakeven sales change illustrated Adapted from: Thomas T. Nagle and John E. Hogan, The Strategies and Tactics of Pricing: A Guide to Growing More Profitability (Upper Saddle River, NJ: Pearson Prentice Hall, 2006), Instructors Manual, Chapter 9.

current profit contribution, a budgeted or planned profit contribution, or a possible strategy scenario for making contrasting comparisons. Figure 5.8 shows the conceptual dynamics of a price decrease (left) and a price increase (right); the baseline scenario is defined by price P1, volume Q1, and variable cost VC. Thus, baseline total profit contribution consists of the total areas of boxes A + B for a price decrease, or boxes B + C for an increase. For a price decrease, box A represents the resulting loss in profit contribution due to the change in price, and box C represents the gain in profit contribution due to the volume increase expected as a result of the price reduction. Thus, the price change (decrease) will breakeven when the area of box A equals that of box C, and it will be incrementally profitable when box C is larger than box A. For a price increase, box A represents the resulting gain in profit contribution due to the change in price, and box C represents the loss in profit contribution due to the volume decrease expected resulting from the price increase. The price change (increase) will breakeven when the area of box A equals that of box C and will be incrementally profitable when box A is larger than box C.

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Proactive price change

With change in variable costs

–ΔP BE =

– ΔCM BE =

CM + ΔP

Reactive price change

With change in fixed costs

ΔP BE =

CM

ΔP CM ΔCM Vb CMnew

CM + ΔCM

–ΔCM BE =

CM + ΔCM

+

ΔFC CMnewx Vb

= Change in price ($) = Contribution margin ($) = Change in contribution margin ($) = Baseline unit sales volume = New contribution margin

Figure 5.9 

Back-of-the-envelope formulas for breakeven sales change analysis Adapted from: Gerald E. Smith and Thomas T. Nagle, “Financial Analysis for Profit Driven Pricing,” Sloan Management Review 35, no. 3 (1994), 76.

Figure 5.9 shows four simple breakeven sales change formulas that apply to different situations. Proactive Price Change (upper left). This could be a special price promotion, a request from a customer for a price reduction, or a price increase. The breakeven sales change is calculated as BE = −ΔP/(CM + ΔP), where BE is the breakeven sales change, ΔP is the change in price being considered, and CM is the baseline contribution margin used for comparison. Price Change with Change in Variable Costs (upper right). This involves a price change accompanied by a change in variable costs. For example, a packaged goods manufacturer might offer a bonus pack promotion with 33 percent more product for the same price or for a reduced price, requiring a larger and more costly special bonus pack size. The breakeven sales change is BE = −ΔCM/(CM + ΔCM), where ΔCM is calculated as the difference in price (new price minus baseline price) minus

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the difference in variable costs (new VC minus baseline VC), or ΔCM = ΔP − ΔVC. Notice that when there is no change in VC (ΔVC = 0), this calculation reduces to the simple calculation of the proactive price change (upper left). Price Change with Change in Fixed Costs (lower right). This involves a price change accompanied by a change in fixed costs. For example, our packaged goods manufacturer might also run an advertising campaign to support the price promotion. The breakeven sales change is calculated as BE = −ΔCM/(CM + ΔCM) + ΔFC/CMnew xVb, where ΔFC is calculated as the incremental expenditure in fixed cost (e.g., a change in advertising expenditure) and CMnew is the contribution margin that will be realized after the price change. Notice that when there is no change in FC (i.e., ΔFC = 0), this calculation, too, reduces to the calculation for a proactive price change (upper left). Reactive Price Change (lower left). When a competitor changes price, should you match that price or hold your current price? The breakeven sales change that determines the point at which it is more profitable to match rather than hold price is calculated as BE = ΔP/CM. In the case of a competitive price cut, your firm stands to lose sales volume. If management believes that the loss in volume will be greater than the calculated breakeven sales volume, then it will be more profitable to match the competitor’s price cut. For a competitive price increase, your firm stands to gain sales volume; if that gain is expected to be greater than the calculated breakeven sales volume, then it will be more profitable to hold your current price.

Heinz used a popular twenty-four-ounce bottle of ketchup, called the “Red Rocket” by retailers, to counter the growth of retailer private-label brands.32 At various times throughout the year, the Red Rocket was discounted $6.00 per case to retailers ($0.30 per bottle), encouraging retailers to reduce their retail price from $1.69 to 99 cents. In fact, retailers loved the 99-cent price point from such a popular brand and enthusiastically supported Red Rocket promotions with endcap displays, feature ads, and high retail price pass-through (meaning that most of the trade discount Heinz offered to retailers was passed through directly to consumers). Plus, consumers generally buy more than just ketchup, making for profitable total basket store sales. Let’s calculate the breakeven sales change for the Red Rocket trade promotion using the proactive breakeven sales calculation: BE = −ΔP/(CM + ΔP). Though the suggested retail price was $1.69, the standard manufacturer’s

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price of the Red Rocket was $1.27 (the price Heinz actually received from retailers). Heinz’s change in price, ΔP, is −$0.30, or −23.6% (the minus sign denoting price reduction). If Heinz received a 65 percent contribution margin,33 or $0.83, before the promotional price discount, that would be the baseline contribution margin, or CM. The breakeven sales change, therefore, is calculated as BE = −(−23.6%)/(65% + −23.6%) = + 57%. (The calculation can also use currency values rather than percentages.) In other words, if the Red Rocket promotion causes sales volume to grow by more than 57 percent, it will be a profitable price promotion. What if Heinz offered a 10 percent Red Rocket bonus pack in addition to the 30-cent discount? That would increase variable costs for each unit sold by +15%, or about +$0.07 in incremental variable costs of materials and packaging. Let’s calculate the proactive breakeven sales change for the bonus pack consumer promotion, along with the 30-cent discount, using the breakeven sales calculation with a change in variable costs: BE = −ΔCM/(CM + ΔCM), where ΔCM = ΔP − ΔVC (see figure 5.9, upper right). The details are the same as in the foregoing example, except that we must now include the change in variable costs, ΔVC. Therefore, Heinz’s change in price, ΔP, is −$0.30, or −23.6%; its change in variable costs, ΔVC, is +15%, or +$0.07, based on its baseline variable cost; and its baseline contribution margin, or CM, is 65 percent, or $0.83. The breakeven sales change, therefore, is calculated as BE = −(−23.6% − 15%)/(65% + [−23.6% −15%]) = +146%. In other words, if the bonus pack promotion will cause sales volume to grow by more than 146 percent, then it will be a profitable price promotion. If Heinz decided to support the 30-cent promotion—along with the 10 percent bonus pack—with an new advertising campaign of, say, $10 million, then we would need to calculate the breakeven sales change with a change in fixed costs (figure 5.9, lower right). Here we would begin with the proactive breakeven sales change just calculated, +146%, due to the changes in price and variable costs. We then add the change in fixed costs to the calculation: BE = −ΔCM/(CM + ΔCM) + ΔFC/CMnew xVb, where ΔCM = ΔP − ΔVC, ΔFC = $10 million, and CMnew is the new contribution margin after the changes in price and variable cost, or $0.83 + (−$0.30 − $0.07) = $0.46. In addition, the baseline sales volume before the promotional programs is 108 million bottles (5.4 million cases with 20 bottles per case). The breakeven sales change is BE = −(−$0.30 − $0.07)/($0.83 + [−$0.30 − $0.07]) + $10 million/($0.46 × 108 million units) = +146% + 20% = +166%. In other words, if the combined bonus pack and 30-cent discount promotion with a $10 million ad campaign will cause sales volume to grow by more than 166 percent, then it will be profitable.

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Finally, consider the scenario in which Heinz is considering neither a discount price trade promotion nor a bonus pack consumer promotion but, rather, is suddenly confronted with a 20 percent price reduction by its largest competitor, Hunt’s Ketchup. Heinz must choose whether to match that price reduction, and therefore also lower its price by 20 percent, or hold its price at its current baseline level. Let’s calculate the reactive breakeven sales change. In this case, ΔP is −20%, the change in price initiated by Hunt’s, and CM is 65 percent, the baseline contribution margin from our earlier scenarios. The breakeven sales change, therefore, is BE = −20%/65% = −38.5%. In other words, in this competitive price cut scenario, if Heinz managers believe that their sales volume will fall by more than 38.5 percent due to the Hunt’s pricing move, then it will be more profitable to match Hunt’s competitive price reduction. If not, they should hold price firm. However, if Hunt’s had raised its price by 20 percent, then the breakeven sales change would be = +20%/65% = +38.5%. In other words, in this competitive price increase scenario, if Heinz managers believe that their sales volume will increase by more than 38.5 percent, then it will be more profitable to hold their price firm. If not, they should match Hunt’s increase.

The Pricing Breakeven Sales Change and Simulated Scenarios

An accessible extension of the pricing breakeven sales change is the use of a simple worksheet to calculate simulated “what if ” scenarios, as shown in figures 5.10a and 5.10b. After calculating the breakeven sales change, these scenarios ask the question: what if sales were to change by X%, what would be the actual change in profit contribution? For example, we calculated the breakeven sales change for the Heinz Red Rocket 30-cent discount promotion to be +57 percent, or an increase of 61.6 million units compared with the baseline sales volume of 108 million units. But what if sales were to increase only +30 percent, as shown in column B of scenario 3 in table 5.5a? That would be an increase of 32.4 million units (column C), which would result in a $15.5 million loss in profit contribution (columns D and F). And what if sales were to increase by 60 percent (scenario 6 of column B), or 64.8 million units (column C)? This would result in a $1.7 million increase in profit contribution (columns D and F). What if Heinz were to also invest in a $10 million advertising campaign to support the 30-cent discount promotion? Table 5.5b shows the worksheet calculations for the same scenario assumptions. If sales were

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Scenario A 1 2 3 4 5 6 7 8 9 10

Net change in profit Actual change in contribution profit contribution Incremental fixed costs relating to = actual change in = (Actual – BE) × Actual change in Actual change in profit – IFC CMnew price change sales volume (%) sales volume (units) B C D E F –$26.9 Million –$26.9 Million 10% 10.8 Million $0 –$21.2 Million –$21.2 Million 20% 21.6 Million $0 –$15.5 Million –$15.5 Million 30% 32.4 Million $0 –$9.7 Million –$9.7 Million 40% 43.2 Million $0 –$4.0 Million –$4.0 Million 50% 54.0 Million $0 $1.7 Million $1.7 Million 60% 64.8 Million $0 $7.4 Million $7.4 Million 70% 75.6 Million $0 $13.2 Million $13.2 Million 80% 86.4 Million $0 $18.9 Million $18.9 Million 90% 97.2 Million $0 $24.6 Million $24.6 Million $0 100% 108.0 Million

Change in price, ΔP Baseline sales volume Breakeven sales change (%) Breakeven sales change (units) CM baseline CM new, after the price change

–$0.30 (–23.6%) 108.0 Million 57% 61.6 Million $0.83 $0.53

Figure 5.10a

Breakeven simulated “what if ” scenarios Adapted from: Gerald E. Smith and Thomas T. Nagle, “Financial Analysis for Profit Driven Pricing,” Sloan Management Review 35, no. 3 (1994), 76.

After the price change, "What if"

Scenario

Actual change in Net change in profit profit contribution Incremental fixed contribution Actual change in sales Actual change in sales = (Actual – BE) × costs relating to price = actual change in volume (%) volume (units) CMnew change profit – IFC

A 1 2 3 4 5 6 7 8 9 10

B 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Change in price, ΔP Baseline sales volume Breakeven sales change (%) Breakeven sales change (units) CM baseline CM new, after the price change

C 10.8 Million 21.6 Million 32.4 Million 43.2 Million 54.0 Million 64.8 Million 75.6 Million 86.4 Million 97.2 Million 108.0 Million

D –$26.9 Million –$21.2 Million –$15.5 Million –$9.7 Million –$4.0 Million $1.7 Million $7.4 Million $13.2 Million $18.9 Million $24.6 Million

E $10.0 Million $10.0 Million $10.0 Million $10.0 Million $10.0 Million $10.0 Million $10.0 Million $10.0 Million $10.0 Million $10.0 Million

F –$36.9 Million –$31.2 Million –$25.5 Million –$19.7 Million –$14.0 Million –$8.3 Million –$2.6 Million $3.2 Million $8.9 Million $14.6 Million

–$0.30 (–23.6%) 108.0 Million 57% 61.6 Million $0.83 $0.53

Figure 5.10b

Breakeven simulated “what if ” scenarios with incremental fixed costs Adapted from: Gerald E. Smith and Thomas T. Nagle, “Financial Analysis for Profit Driven Pricing,” Sloan Management Review 35, no. 3 (1994), 76.

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to increase only 30 percent (column B of scenario 3), an increase of 32.4 million units (column C), with incremental fixed costs of $10 million (column E), the result would be a net $25.5 million loss in profit contribution (column F). And if sales were to increase by 60 percent (scenario 6 of column B), an increase of 64.8 million units (column C), with incremental fixed costs of $10 million (column E), the result would be an $8.3 million loss in profit contribution (column F). Breakeven sales change calculations are a fast and efficient way to define the market response required to achieve incremental profitability from a given price change. They are especially useful because they leverage System 1 behavioral thinking, which is more holistic, considering subjectively a variety of possible factors that might drive the success or failure of a price change. Going one step further, asking “what if ” questions about possible market responses enables price-setters to use breakeven sales change calculations to estimate pro-forma changes in profit contribution, whether gains or losses. Tom Nagle and I summarized this as follows. Strategic pricing requires that managers focus on long-term profitability. We have suggested a profit-driven approach to pricing, which focuses on changes in profit contribution and defines the market response necessary to achieve incremental profitability. In effect, this frames the managerial pricing decision in terms of market response and profitability, and invites managerial evaluation at a strategic level, in an integrated forum, that combines the expertise of all key disciplines relating to the profitability of a pricing decision.34

Conclusion

This chapter has shown how to use costs as a vital contributor to a full and balanced pricing orientation that leads to sustained profitable strategic pricing results—but not all costs, only those incremental costs that are truly relevant to pricing. However, price-setters need to be more careful than they often are to ensure that the cost data they use are not distorted with the costing biases we have discussed in this chapter: standardized costing bias, sunkcost bias, average costing bias, and average customer costing bias. Price-setters must make sure that they are using cost data in a forward-looking way—to maximize incremental profit contribution. Enterprise accounting systems will not always be on your side. But soft intuitive costing skills can help you overcome these systemic accounting limitations, such as exploratory

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cost discovery, true cost-to-serve indicators and attributes, and margin leverage strategies based on true contribution margins. And hard analytic costing skills are more accessible than most would think, including activity-based costing (TDABC), price waterfalls analysis, and breakeven sales calculations. The point of these true costing-for-pricing principles is not cost recovery, as most accountants think of costing, but maximizing incremental profit contribution for pricing by understanding and managing the true cost to serve each new customer order. As we saw with the soft skill of margin leverage, truck manufacturers don’t necessarily recover their costs by selling trucks; instead, as low-margin sellers, they leverage profitability by cross-selling other high-profitability services, accessories, and options that yield more profitable customer transactions and, over the long term, more profitable customer relationships. See the following templates to help with soft and hard costing skills for pricing: • Template 5.1: Incremental cost-to-serve drivers template • Template 5.2: Pricing breakeven sales change template • Template 5.3: Pricing breakeven simulated “what if ” scenarios template

Templates

Cost-to-serve drivers

Customer Segment A

Customer Segment C

Customer-facing team members rate each customer segment on each cost-to-serve driver, 5 = high, 1 = low

List cost-to-serve drivers

Cost-to-serve score (sum)

Customer Segment B

Sum cost-to-serve scores assess each customer segment as high, moderate, or low cost-to-serve

∑

Cost-to-serve assessment (high, moderate, low) Subjective cost estimation scores, 5 = high, 1 = low Template 5.1 

Incremental cost-to-serve drivers template.

∑

∑

Baseline

New

Change (Δ)

Price

Incremental variable cost Baseline CM

Contribution margin (CM)

Enter data, calculate the breakeven sales change, %BE = –ΔCM/(CM + ΔCM)

Incremental fixed costs %Breakeven sales change Unit breakeven sales change

Template 5.2 

Pricing breakeven sales change template.

After the price change, "What if"

Scenario A

Net change in profit contribution Actual change in profit contribution Incremental fixed costs = Actual change in profit Simulated actual change Actual change in sales – incremental FC = (Actual – BE) × CMnew relating to price change in sales volume (%) volume (units) B C D E F

1 2 3 4 5 6 7

For scenarios in column A, enter simulated actual changes in sales volume (%) that you wish to simulate (column B). Using column B, calculate actual changes in sales volume (column C). Then calculate actual change in profit contribution (column D). Subtract incremental fixed costs relating to the price change (column E), to then calculate net change in profit contribution (column F)

8 9 10 Change in price, ΔP Baseline sales volume Breakeven sales change (BE%) Breakeven sales change (BE units) CM baseline

Enter values in these cells as inputs for the calculations in columns A through F above

CM new, after the price change

Template 5.3 

Pricing breakeven simulated “what if ” scenarios template. Adapted from Gerald E. Smith and Thomas T. Nagle, “Financial Analysis for Profit Driven Pricing,” Sloan Management Review 35, no. 3 (1994): 76.

6 Customer Value-Driven Pricing Orientation Biases and Skills

Customer value-driven pricing is broadly regarded by both pricing scholars and practitioners as a gold standard for price setting. Many successful firms have embraced it: Allstate, ARDEX, GE, Johnson & Johnson,1 SKF, SAP, HP, Grainger, Metso, Applied Industrial, Maersk, and many others.2 During his tenure as SKF’s CEO, Tom Johnstone said, “One of the most important tasks we have today throughout the SKF Group is to create, deliver, and document the value that our products and solutions bring to our customers.”3 In this chapter, we will learn the soft and hard skills and biases of a customer value-driven pricing orientation and how it can improve price-setting, pricing confidence, and profitability.

True Customer Value Principles for Pricing Orientation

Let’s begin with the theoretical maxim for a profitable pricing orientation: set prices and sell units until the marginal revenue derived from selling the next unit is equal to its marginal cost; that is, MR = MC (see figure 6.1). In this chapter we turn to marginal revenue principles for price-setting, focusing first on customer value. A customer value-driven pricing orientation frames price-setting as a customer-centered task in which managers

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Marginal Cost

Marginal Revenue

=

MR

Customer value

Customer willingness to pay

Customer valuedriven pricing orientation

Customer WTPdriven pricing orientation

MC

Competitor prices

Incremental cost to serve

Competitiondriven pricing orientation

Costdriven pricing orientation

Figure 6.1 

Theoretical maxim for a profitable pricing orientation, focus on customer value-driven pricing influences.

estimate, communicate, and manage perceptions of the value that customers receive. Price is framed not as a fee, charge, or tariff to be paid but as a sharing of the value created for the customer. If you create $100 in value for customers, then a price of $50 represents an equal sharing of value between seller and buyer. Customer value is the value that is delivered to customers. More precisely, it is the worth—in economic, monetary, or psychological terms—of the benefits a customer gets from using your product or service over its life.4 Figure 6.1, left, shows two customer-centric pricing orientations: a customer value-driven pricing orientation and a customer willingness-to-pay (WTP)–driven pricing orientation. Both are customer-focused, but they are different. A customer value-driven pricing orientation frames price-setting in terms of the beneficial value customers get; and a customer willingnessto-pay–driven pricing orientation (addressed in detail in chapter 7) frames price-setting in terms of the price customers are willing to pay (WTP), or give—these themes were discussed in chapter 2. Some pricing practitioners mistakenly conflate value and willingness to pay into a common customer orientation for pricing, glossing over the differences between two

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significantly different price-setting impulses. Some customers, for example, perceive that the benefits of a Bentley car are highly valuable (the get), but few can afford, or are willing to pay, $200,000 for it (the give). The smart price-setter manages both constructs separately as part of a balanced pricing orientation, ensuring that prices reflect both perceived value and willingness to pay.

Customer Value

To promote and manage perceptions of the value that customers get, you must first understand and estimate the actual value of the benefits they receive. The distinction between actual and perceived value is important. It is not sufficient to merely ask customers their perceptions of value. They might not know or fully appreciate, or be able to articulate, the actual value they get. It is on you, the price-setter, to figure that out. The end result of this value estimation is a customer value model that becomes a blueprint for price-setting and educating customers on the value you deliver to them. In most B2B and many B2C (business to consumer) applications, the best way to estimate actual customer value is to estimate objective customer value, a hard analytic value skill centered on quantifiable economic, monetary value, discussed next. In purchase settings typified by psychological benefits, however, estimating actual customer value requires estimating subjective customer value, a soft intuitive value skill, discussed in detail later in the chapter. In this section, I lay out three sequential stages, with their component pieces, for estimating customer value using the basic customer value framework of figure 6.2, starting with stage 1, Competitive Reference Value. 1. Competitive Reference Value is the starting benchmark for customer value estimation. It is measured as the price of the next best competitive alternative (NBCA) to your brand offering, also called “reference price.” This means first discovering what customers deem to be their competitive frame of reference. At the onset of the COVID pandemic, for example, many consumers defined “in-home connected fitness” as a suddenly salient frame of reference for Peloton’s stationary bike and the fitness needs it satisfied (priced at $1,895 for the basic model plus $39.95/month for subscription access to Peloton video instructors). In the old “gym membership fitness”

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Negative differential value

Stage 2

Stage 3

Positive differential value

Customer value

Stage 1

Competitive reference value

Figure 6.2 

The customer value model—“the value the customer gets.” Adapted from John L. Forbis and Nitin T. Mehta, “Value-Based Strategies for Industrial Products,” Business Horizons 24, no. 3 (May-June 1981): 32–42; Gerald E. Smith and Thomas T. Nagle, “Financial Analysis for Profit-Driven Pricing,” Sloan Management Review 35, no. 3 (Spring 1994): 80; and Thomas T. Nagle and Georg Müller, The Strategy and Tactics of Pricing, 6th ed. (New York: Routledge, 2018).

frame, Peloton buyers might have considered health clubs as competitive substitutes, ranging from Planet Fitness for $10/month to Equinox clubs for $225/month. In the new COVID-driven “in-home connected fitness” frame, they considered other connected fitness machines like NordicTrack’s Studio Cycle for $2,000, or Echelon’s EX5S bike for $1,640, plus a monthly membership fee. Sometimes there is no competitive substitute; instead, the reference might be the composite expenditures the customer incurs to achieve a benefit outcome. Rev.com, a speech-to-text platform, provides on-demand transcription, closed-captioning, and translation services for $1.25 per audio minute. This might be compared with the alternative frame of reference of finding, qualifying, and contracting with a local professional—a time-intensive and expensive process.

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Employee savings

• • • •

Employee turnover savings Worker productivity savings Misallocated personnel savings Training and retraining savings

Bottleneck savings

• • • •

Overtime wage savings Temporary employee savings Independent contractor savings Opportunity cost savings

Working capital savings

• • • •

Inventory savings Longer replacement cycles Asset turnover savings Better production/process yields

Figure 6.3 

Positive differentiation value—cost savings value drivers.

2. Positive Differential Value is the unique differential value that customers get from using their preferred product or service over and above that of the baseline competitive substitute, the NBCA. Central to this stage is the discovery of differential value drivers; that is, whatever—in the customer’s view—makes your product or service worth choosing over those of competitors, especially the NBCA. There are two types of positive differential value drivers. • Cost Savings Value Drivers enable the customer to save money, time, or effort by using your brand compared with the competitive reference alternative. Figure 6.3 shows a representative sample of cost savings drivers. For example, when a customer OEM firm is operating at capacity, an electronics contract manufacturer like Flextronics International (Singapore) or Sumitronics (Tokyo) can save the customer firm bottleneck costs that otherwise would be expended to meet a surge in demand, leading to overtime wage savings, temporary employee savings, or opportunity cost savings of turning away profitable customer demand. • Revenue and Margin Gain Value Drivers enable the customer to improve revenues or profit margins by using your brand rather

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Time-to-market gains

• • • •

Early market share gains Early premium price gains Scale economies margin gains Platform rollout gains

Product differentiation gains

• • • •

Superior product margins Superior service margins Complementary bundle margins Cost-competitive volume gains

Customer WTP* gains

• • • •

More profitable customer mix Repeat purchase likelihood gains Customer satisfaction gains Customer loyalty gains

*WTP = willingness to pay Figure 6.4 

Positive differentiation value—revenue and margin gain value drivers.

than the competitive reference alternative. Figure 6.4 shows a representative sample. Here, an electronics contract manufacturer can enable the customer OEM firm to launch a new product into the market more quickly than would have been possible otherwise. The customer will realize incremental gains in sales volume, market position, and market penetration, and, consequently, gains in superior pricing and contribution margins. 3. Negative Differential Value is—as seems intuitively obvious but is not always considered—the opposite of positive differential value. It occurs when your product delivers inferior value to customers relative to the competitive reference alternative. Three types of negative differential value drivers typically apply (see figure 6.5). • Inferior Performance Drivers result in less value to the customer who is using your brand compared with a competitive reference alternative. For example, new model Buicks offer a three-year/ 36,000-mile bumper-to-bumper warranty, whereas Volkswagen

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Inferior performance

• • • • •

Product performance deficiencies Warranty deficiencies Service deficiencies Replacement parts deficiencies Speed, response deficiencies

Switching costs

• • • • •

Incompatibility costs Termination costs, fees, penalties Training and learning costs Shopping costs Transaction change costs

Purchase risk

• • • • •

Performance risk Financial risk Social risk Physical risk Psychological risk

Figure 6.5 

Negative differentiation value—negative value drivers.

offers a comprehensive six-year/72,000-mile warranty. By comparison, Buick’s warranty is an inferior performance value driver. • Switching Cost Drivers are incurred when the customer must spend time, money, or effort to switch to your brand from a competitive reference brand. For example, a bank’s bill-pay system is likely free, but the time and effort required to reestablish a new checking account with bill-pay at another bank seems costly to the bank’s loyal customers—bill-pay creates switching costs that deter them from switching banks. Switching costs can include incompatibility costs (like switching to Android from Apple’s iOS), termination costs (such as early termination fees for a mobile phone plan), transaction change costs (such as changing to a new credit card for an online merchant), training and learning costs (such as time and effort to learn a new online platform), and shopping costs (physical and mental time and effort to shop for different brand alternatives). • Purchase Risk Drivers are the risks customers incur by purchasing a new, unfamiliar brand. Behavioral scientists have documented five such risk drivers: performance risk that the brand fails to perform as well as expected; financial risk that the customer might lose money; social risk that the customer might incur undesirable

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social consequences, such as lost or devalued social and business relationships; psychological risk that the customer’s personal and/ or business values might be compromised; and physical risk that the new product might harm the customer physically.

Customer Value Models

Customer value estimation culminates in a customer value model. One sector leading the way in customer value-driven pricing is health care, with insurance companies, medical buying groups, and government procurement agencies seeking “outcome-based contracts” that pay medical providers based on the customer value they deliver to patients and payors. For example, statins long have been considered effective at combatting cholesterol and heart disease. Yet, some high-risk patients—who have already had a heart attack, stroke, or other cardiovascular event—are unable to achieve optimal LDL-C cholesterol levels despite taking a statin, leaving them vulnerable to further cardiovascular events costing $45,000 or more. To address these high-risk patients, Amgen introduced Repatha, one of a new class of PCSK9 inhibitors that work differently from statins and have been found to reduce LDL-C by approximately 60 percent.5 What is the customer value of Repatha to patients (and insurance companies as payors)? Amgen performed a large-scale value-driven pharmaco-economic study that compared the customer value of Repatha with that of standard statin-only drug therapy. The results were statistically compelling, published in a respected medical journal, and offer substantive evidence for a value-based price. Using their data, let’s build a customer value model for Repatha following the three-stage estimation process outlined earlier. Stage 1: Competitive Reference Value. The closest competitive substitute to Repatha is standard statin drug therapy, which researchers discovered costs customers, on average, $837 annually, as depicted in the bottom rectangle of figure 6.6. Stage 2: Positive Differential Value. Amgen’s value researchers identified four primary positive differential value drivers: • Fatal Event Cost Savings. Repatha lowered the risk, or probability, of a fatal cardiovascular event and its related expenses, which,

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Stage 1 competitive reference value

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Quality adjusted life years

Quality of life (QALY) (+)

Lower risk of coronary artery bypass, stent, angioplasty

Revascularization differential cost savings $22,875

Lower risk of ischemic stroke, heart failure, acute coronary syndrome (ACS)

Non-fatal event differential cost savings $19,060

Lower risk of death from cardio vascular disease

Fatal event differential cost savings $4,392

Long term postcardiovascular differential costs $7,748

Post-stroke, post-heart failure, peripheral artery disease

Administered by daily injection (–)

Stage 3 negative differential value

Customer value ~ $39,416

Statins therapy cost $837

Figure 6.6 

Customer value model for Repatha. Data source: Peter P. Toth, Mark Danese, Guillermo Villa, Yi Qian, Anne Beaubrun, Armando Lira, and Jeroen P. Jansen, “Estimated Burden of Cardiovascular Disease and Value-Based Price Range for Evolocumab in a High-Risk, Secondary-Prevention Population in the US Payer Context,” Journal of Medical Economics 20, no. 6 (2017), https:// www.tandfonline.com/doi/full/10.1080/13696998.2017.1284078.

according to the study’s calculations, lowered the expected total costs (reflecting the lower probability of a fatal event with Repatha) from $24,255 with statin-only therapy to $19,863 with Repatha, a savings of $4,392. • Nonfatal Cost Savings. Repatha lowered the risk, or probability, of a repeat nonfatal event such as ischemic stroke, heart failure, or acute coronary syndrome, which, based on the study’s calculations, lowered these expected total costs (reflecting the lower probability with Repatha) from $53,206 with statin-only therapy to $34,145 with Repatha, a savings of $19,060. • Revascularization Cost Savings. Repatha lowered the risk, or probability, of a repeat vascularization event such as coronary artery bypass, insertion of a stent, or angioplasty procedure, which, based

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on the study’s calculations, lowered these expected total costs from $60,797 with statin-only therapy to $37,922 with Repatha, a savings of $22,875. • Quality of Life. By avoiding repeat cardiovascular events, patients enjoyed longer and more satisfactory lives, which is clearly difficult to estimate and is therefore a psychological value driver—it adds positive differential value for patients, but subjectively. Harvard Medical School researchers studied and modeled quality of life and, with many research-based assumptions, conceptualized what they call “quality-adjusted life years,” or QALYs (pronounced QUAL-eez).6 For our purposes, we will denote this psychological value driver by designating a place in our illustrative customer value model with a plus sign, meaning that quality of life clearly adds differential value, but we cannot reliably estimate how much. Stage 3: Negative Differential Value. Value researchers identified one negative differential value driver: long-term post-cardiovascular condition costs. Repatha increased the costs associated with long-term postevent conditions such as poststroke costs, post-heart-failure costs, and peripheral artery disease costs with increased expected total costs of $96,768 with statin-only therapy, compared with $104,516 with Repatha, a negative differential value of $7,748. Also, Repatha must be administered with a daily injection, which is more time-consuming and uncomfortable compared with taking a statin orally, denoted by a minus sign.

Setting the Value-Based Price, or Recommended List Price

The total estimated customer value of Repatha is thus at least $39,416 and likely greater, considering the psychological value of the QALYs we didn’t calculate (see figure 6.6). What value-based price should Amgen set for Repatha? That is, what should be the recommended list price (or, in other settings, the suggested retail price)? Amgen originally set it at $14,100 per year. Is that a fair price? Relative to standard statin therapy, it was very expensive—a 1,585 percent premium—but such a comparison is deceiving. Relative to the actual value that customers receive, its list price seemed more reasonable. This price captures a 36 percent share of the value created for patients and payors; that is, for every dollar of customer value Repatha delivered to customers, Amgen shared 64¢ of that value with patients and payors and kept 36¢ for its operating costs, reinvestment, and profit.

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Even then, under contractual outcome-based pricing arrangements with its large insurer customers, Amgen negotiated final price agreements in which the price could be lowered if Repatha’s health outcomes failed to achieve the value-driven performance targets Amgen promised based on its customer value research. Three years later, insurance company payors and pharmacy benefit managers (PBMs such as Express Scripts and CVS Caremark), aided by independent customer value research such as that of Harvard’s Institute for Clinical and Economic Review, influenced Amgen to further refine Repatha’s customer value estimates. Faced as well with a new competitor, in October 2018, Amgen lowered Repatha’s list price by 60 percent to $5,850 per year.

Value Biases in Price-Setting

Today, a minority of companies use formal value-driven pricing with hard customer value modeling skills; estimates of its adoption are less than 20 percent.7 One obstacle is value illiteracy, a common price-setting bias. But there are other value biases to look for, including proportional value bias and heuristic value estimation bias (see figure 6.7, left). Let’s turn to them now.

Soft intuitive value skills Customer value biases and debiasing Value illiteracy bias Proportional value bias Heuristic value estimation bias

Value discovery, value sensing

Hard analytic value skills Value calculation, value communication

Subjective customer value models

Objective customer value models

Customer value driver discovery (value projection mapping) Customer value data gathering

Value metrics for price-setting Value communication tools and strategies

Probing for value

Figure 6.7 

Soft and hard skills of customer value in a pricing orientation—a checklist inventory.

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Value Illiteracy Bias

Many, or even most, price-setters have little formal training in pricing— a symptom of broader price illiteracy. Value illiteracy—what value is and how to estimate it—is even more pronounced. Estimating customer value is a challenging System 2 analytic task requiring effortful, methodical, and deliberate cognitive calculations. Add to that the fact that most managers have so little knowledge about price-setting; of necessity, they devolve into simple System 1 mental heuristics about value and price based on their own personal experiences in buying and selling. For example, they assume, heuristically, that good customer value must relate to something that is inexpensive or whose price is better than expected, or that the best way to increase customer value is by reducing price. But value has been carefully defined by the greatest minds in economics and business, and some of the best valuedriven pricing companies have learned how to leverage this knowledge for price-setting. In 1776, Adam Smith, known as “the father of capitalism,” originally distinguished between two value constructs that are important to notice. The first, value in use, is the absolute value a buyer gets from using a product or service.8 In 1848, John Stuart Mill further clarified its meaning: “The use of a thing, in political economy, means its capacity to satisfy a desire, or serve a purpose”;9 it is relevant for price-setting when there are no competitive alternatives to compare with (see figure 6.8, left). For example, in 2019, Lucara Diamond Corporation discovered the largest uncut diamond (since 1905) in Botswana, called the “Sewelo diamond”; it later was sold to luxury brand Louis Vuitton’s parent firm LVMH. With virtually no competitive diamonds against which to compare, its absolute value was literally incomparable, and its price, not publicly revealed, depended solely on the imputed value in use of its buyer, LVMH. The second construct, value in exchange, is operative with the presence of competitive alternatives. It is the value a customer gets from using a product or service, as Mill said, in comparison to those “commodities with which we compare it.”10 In other words, value in exchange isolates what we earlier called comparative reference value, the value that “originate[s] in the very commodity [class] under consideration”11—all competitive diamonds in the jewelry store have this commodity value. It then considers separately differential value, reflecting “all causes [or value drivers] . . . which originate in [the brand] itself, affect[ing] its value in relation to all [comparative reference]

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Value in use

Value in exchange Value in use

The absolute value of what customers get, to “satisfy a desire, or serve a purpose”

Differential value The value of what customers get, relative to competitive alternatives Competitive reference value Commoditized value

The absence of competitive alternatives

Price

What customers are willing or able to pay, or give

The presence of competitive alternatives

Figure 6.8 

Essential constructs of customer value and price.

commodities”12—for example, the exceptional clarity, cut, color, and quality of your superior diamond (see figure 6.8, middle). These customer value distinctions are important. As I noted in chapter 2, pharmaceutical companies routinely target new disease categories, called “indications,” for early governmental approval to establish new frames of reference in the minds of customers with little or no competition while the drug is under patent protection. Why? Because their price is set relative to total customer value in use, as there are no competitive referents. Even Amgen’s Repatha, cited earlier, was first to market with a new PCSK9 inhibitor drug, and with no PCSK9 competitors to compete against, Amgen could set its price at $14,100, relative to the significant value in use that Repatha offered to patients.13 Three years later, with the introduction of a major competitor, Praluent by Regeneron and Sanofi, Amgen had to reduce Repatha’s price by 60 percent to $5,850. Now value in exchange became operative:

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compared with the competitor, Amgen had to separate out commoditized reference value (what both drugs offered to patients) from its differential value (what Repatha uniquely offered to patients relative to Praluent). John Stuart Mill made one more important clarification: customer value must “be distinguished from Price.”14 That is, the value you deliver (what customers get) must be estimated and managed separately from the price you set (what customers pay, or give; see figure 6.8, right). This is another reason that in this book, we study separately customer valuedriven pricing orientations (which focus on the “get” with customer value) and customer willingness-to-pay–driven pricing orientations (which focus on the “give” with price. Functionally, then, value illiteracy bias arises when price-setters are unable to do the basic tasks of customer value estimation, or when they confuse value with price. Without these basic value skills, managers fall back on old but mistaken heuristics—price-setting shortcuts such as discounting price because it seemingly is a better value for customers. Consider the financial cost of value illiteracy: unwittingly underpricing customers and leaving money on the table, or naively overpricing customers, who then take their business elsewhere. The impact of value illiteracy is especially visible among the sales force, whose customers regularly push back on price. Andreas Hinterhuber empirically studied value quantification skills among sales managers, writing in summary: The value quantification capability refers to the ability to translate a firm’s competitive advantages into quantified, monetary customer benefits. The value quantification capability requires that the sales manager translate both quantitative customer benefits—revenue/ gross margin increases, cost reductions, risk reductions, and capital expense savings—and qualitative customer benefits—such as ease of doing business, customer relationships, industry experience, brand value, emotional benefits or other process benefits—into one  monetary value equating total customer benefits received.  .  .  . Value quantification capabilities substantially and positively influence firm performance—always.15

Proportional Value Bias

Proportional value bias is the tendency to approach customer value estimation in proportional terms—that is, in ratio terms—rather than in subtractive or

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absolute terms. For example, with proportional value logic, value is estimated as the benefits customers get relative to what they give; that is, the ratio of benefits to price. This kind of value logic is mistaken for several reasons. First, value is conceptualized as one term (benefits/price, or B/P), which managers then use to set price. But benefits are always difficult to estimate; consequently, according to proportional value logic, the simplest way to deliver greater value to customers (to increase that ratio) is to lower the denominator—that is, to cut price. Instead, value should be conceptualized as two separate terms in a subtractive relationship: V = B − P. The value of benefits should be estimated and managed first—independently. It then informs the price, which is set and managed separately. This is transparent price-setting that encourages customers to add up the value of the benefits they receive and then subtract the price they pay. Reflecting on how changes in demand and supply affect value, Mill summarized the proper relationship: “we see that the idea of a ratio . . . is out of place, and has no concern in the matter [of value]: the proper mathematical analogy is that of an equation.”16 In other words, V = B − P, which requires separate calculation of both benefits and price. The second shortcoming of proportional thinking is that price-setters typically apply it to value estimation by anchoring the worth of benefits to price, a heuristic form of anchoring and adjustment bias. For example, it would assume that the value of a snow blower with an eight-horsepower engine is 33 percent greater than the value of one with a six-horsepower engine ([8 − 6 HP]/6 HP = 33 percent). If the six-horsepower snow blower costs $800, according to this logic, it follows that the maximum price for the eight-horsepower blower would be $1,064 ($800 × 1.33). But in fact, the more powerful snow blower might deliver much more differential value to customers than a mere 33 percent, or $264. For instance, say clearing a driveway takes an hour with the six-horsepower machine but only half an hour with the eight-horsepower one. If you live in Chicago and receive an average of sixteen snowfalls per winter, you save half an hour for each, or eight hours per winter. For a busy on-call tradesperson like a plumber, electrician, or interior designer with a billable hourly rate of, say, $100 (to keep the math simple), the more powerful snow blower would be worth at least $800 more annually and, over a five-year product life, would be worth $4,000 more (or $3,335, discounting for the time value of money at a 10 percent discount rate). Anchoring value estimation to price is a costly heuristic that understates the true differential value. A third shortcoming of proportional value estimation is that it assumes that differences are linear—that 33 percent more horsepower must be

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sa Val dv ue an ta ge

Changing market shares

Perceived price

di

B

Share loser E

C

A Share gainer

ad Va va lue nt ag e

VE

L

D

Customer-perceived benefits

Figure 6.9

McKinsey value map. Exhibits from “McKinsey Quarterly 1997 Number, 1” 1997, McKinsey Quarterly, www.mckinsey.com. Copyright (c) 2020 McKinsey & Company. All rights reserved. Reprinted by permission.

equivalent to 33 percent greater benefit to the customer. Management consultants have even codified this linear bias into their strategic pricing models, as shown in figure 6.9. In this model, developed by McKinsey consultants, brands aligned along the diagonal Value Equivalence Line, VEL, are “economically stable”; their perceived benefits equal their perceived price (brands B, C, and D). If their perceived benefits fall by, say, 15 percent, then their price must also fall by 15 percent or they will become disadvantaged and lose share (brand E). The mistaken heuristic here is simple proportional value bias. Researchers writing in Harvard Business Review summarized this bias. Decades of research in cognitive psychology show that the human mind struggles to understand nonlinear relationships. Our brain wants to make simple straight lines. In many situations, that kind of thinking serves us well: If you can store 50 books on a shelf, you can store 100 books if you add another shelf, and 150 books if you add yet another. Similarly, if the price of coffee is $2, you can buy five coffees with $10, 10 coffees with $20, and 15 coffees with $30.17

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Yet, in many product categories and settings, the differential benefits between competitive alternatives form a nonlinear relationship. For example, what is the value of plush toilet paper? As COVID-19 infections began to escalate in North America, on Tuesday, March 10, 2020, the price for Quilted Northern Ultra Plush Toilet Paper (24 Supreme Rolls, 24 = 99 Regular Rolls) soared to $129.99 at resellers on Amazon; its price normally was $27.99. For toilet paper at that moment, its customer value changed dramatically because the likelihood of getting access to its benefits changed due to changes in demand and supply. Imagine if Amgen had applied proportional value processing to set the price for Repatha. The differential risk of heart attack or ischemic stroke might be 30 percent better for Repatha patients than, say, for those using standard statin therapy (costing $837 annually). According to proportional logic, Repatha’s price therefore should have been just 30 percent higher than standard statin therapy, or $1,088 per year, less than a tenth the launch price that Amgen originally set in 2015 and a fifth its final market-adjusted price three years later, in 2018. Proportional value bias commoditizes, or undervalues, the most highly differentiated positive value drivers of your product and overvalues its less differentiated, more commoditized dimensions. It virtually assumes that all value drivers are weighted equally because it applies the same proportional differences to all benefits (or value drivers) as it does for price. Be alert to avoid proportional value bias in your own company’s price-setting.

Heuristic Value Estimation Bias

After I had worked with an enthusiastic corporate sales group on valuedriven pricing, the corporate head of sales gave a hearty endorsement, which I’ll paraphrase: “Value pricing is easy. Just take the back of an envelope and do some calculations to help the customer see the value of what you’re selling. Anyone can do it.” That was well intended, but the back-of-theenvelope approach was a heuristic—a mental short cut that undermined a more effective and strategic pricing orientation centered in customer value. This was heuristic value estimation bias. A customer value-driven pricing orientation is only as compelling as the enterprise’s commitment to customer value, the authority of those who drive it, and the quality of the data they deliver to customers.

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When a thousand field salespeople produce a thousand hand-scratched variants of value estimation, customers see fleeting value impulses, superficial evidence, and insignificant documentation (the back of an envelope); those who deliver the evidence offer contradictory or confusing interpretations. It’s not that field salespeople shouldn’t have one-on-one value conversations with customers. It’s their job to use value-selling skills and tools to help customers customize, tailor, and maximize the value they receive in a process that is not one-way sales-driven but two-way collaboratively driven to maximize customer value. Still, the drive, the direction, for customer value should originate from the top and then permeate the organization and the sales team. A value-driven pricing orientation requires high-quality value research that produces a storehouse of compelling value data, with customer value models, frameworks, and estimates in which customers have confidence. It requires formal customer value models that form the backbone for your list price—the formal price that reflects a customer’s full value realization. And it requires tools, skills, and training—in value, price menus, customer value templates, models, and value communications—to arm customerfacing personnel to do their jobs. Nagle and Müeller said, “Pricing decision-makers require quality information. . . . To make informed pricing decisions, marketing managers need data on customer value and competitive pricing. Sales managers need data to support their value claims and defend price premiums.”18

Soft Value Skills for Price-Setting

Soft value skills are especially useful to customer value estimation when value is driven more by psychology than economics. These are skills that facilitate soft value discovery and engage customers in a collaborative coprocess of value discovery. The first foundational soft value skill is always value debiasing. Always ask, what biases can be found in your current approaches to value? Start by being alert for the three well-documented sources of value bias discussed earlier and shown in figure 6.7: value illiteracy bias, proportional value bias, and heuristic value estimation bias. Then let’s turn to additional soft value skills related to other behavioral dimensions of value discovery, shown in the middle of figure 6.7, including subjective customer value models, customer value driver discovery, customer value data gathering, and probing for value.

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Subjective Customer Value Models

In 1979, Karl Weick, an organizational theorist at the University of Michigan, developed the behavioral theory of sensemaking, based on the social construction of reality theory.19 With sensemaking, firms view their decision-making as being part of a socially enacted environment that is equivocal, emergent, and ambiguous; they repeatedly act and make sense of feedback—of what seems to be going on, retrospectively. This kind of sensemaking makes sense in the price-setting of many purchases that offer intangible or experiential benefits to buyers—such as vacations, perfumes, cosmetics, and fitness equipment—whereby value estimation must be adapted to address more psychological and intangible benefits. I call this subjective customer value  estimation, in which we can socially construct subjective customer value models (see table 6.1, left). With this approach we

Table 6.1  Subjective vs objective customer value models Subjective customer value models

Objective customer value models

For • Pricing for mostly intangible, psychological benefits (like B2C)

For • Pricing for mostly tangible, economic benefits (like B2B)

Ask • What is the magnitude of the differential value that customers get? • Given a price, what is the likelihood that total customer value is greater than price?

Ask • What is a quantified estimate of the differential value that customers get? • Given value, what price should be set relative to total customer value?

Analytical approach • A reference frame Competitive reference values • Differential value drivers Psychological, intangible Experiential outcomes • Possible price vs total value

Analytical approach • The NBCA Competitive reference value • Differential value drivers Cost savings Revenue and margin gains Set price • Total value

Subjective value estimation Subjective sensing, sensemaking

Objective value estimation Deliberate, objective hard data

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ask, what is the magnitude, or degree, of differential value that customers get?—because subjective value is difficult to estimate precisely. What is the likelihood that total value is greater than a possible proposed price? Our analytical approach involves focusing on a reference frame with its approximate reference values (prices of competitive alternatives in the frame), sensing the magnitude of differential value drivers that might be psychological, intangible, or experiential, and acting—testing—potential price vis-à-vis subjective value and making sense of the customer feedback that is observed. This process contrasts with objective customer value estimation, presented earlier in the chapter, to construct objective customer value models (see table 6.1, right). There we asked, what is a quantified estimate of the actual differential value that customers get? What price should be set relative to actual customer value? Our analytical approach involved identifying a next-best competitive alternative (NBCA) and its reference value (that is, its reference price), quantifying differential value drivers such as cost savings and revenue/margin gains, and then setting price relative to total value. Our customer value estimation methodology was deliberate, using objective hard data. A popular reality series on Home and Garden Television, Flip or Flop, demonstrates subjective customer value estimation and pricing with a (now former) husband-and-wife team, Tarek and Christina, who were buying, fixing, and selling houses in southern California. In one instance, they purchased a neglected three-bedroom, two-bath, 1,200-square-foot house for $425,000, which they projected could, with renovations, be sold at a higher price. They studied the reference frame: a nice neighborhood in Garden Grove, California. Recent competitive home sales prices—known as “comps”—were about $550,000, the approximate reference value. They implicitly asked (see table 6.1, left), “What is the magnitude of the differential value that we could create with this house if we brought it up to comparative neighborhood standards (eliminate its negative differential value) and add our own unique design innovations (new positive differential value)?” Their subjective value estimation could be summarized in before/after subjective customer value models (see figure 6.10). Tarek summarized their value-driven thinking mid-project: “In addition to creating a walk-in master closet, we made more closet space in the other two bedrooms by moving the water heater outside. That, combined with opening up the kitchen and adding space for the laundry, will add a ton of value.” Finally, they must set a price based on their subjective value estimation of the finished property. Listen to their conversation, subjectively

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Before

After

Identify differential value drivers. Allocate 100 percentage points for expected relative value of each

Experiential value, laundry space remodel 15%

Positive differential value drivers

Reference frame, reference value

Experiential value, other bedrooms remodel 20% Experiential value, master bedrooms remodel 25%

Subjective customer value Hypothesized value is >$599,000?

Experiential value, open-concept kitchen 40%

Negative differential value Outdated interior

Reference frame

Neglected house

Comparable Homes in Garden Grove, California

Poor curb appeal

Reference Value Recent prices of comparable home sales, or “Comps”

$550,000

Subjective customer value $425,000

Reference frame Reference frame, reference value

Comparable Homes in Garden Grove, California

Reference Value Recent prices of comparable home sales, or “Comps”

$550,000

Figure 6.10 

Subjective customer value models—flipping a home in Garden Grove, California.

sensing together, testing possible price points, implicitly considering “What is the likelihood that total value will be greater than price?” Tarek: So we have to come up with a list price. Christina: What are the comps? Tarek: They’re not spectacular: $530,000, and $545,000. . . . So here’s what I thought. You’re probably going to think I’m crazy. I’d say we list it at $599,900. . . . The market’s hot. Christina: What? Don’t you think $50,000 above best comp is ridiculously high? . . . Alright. It seems awfully high, but let’s give it a shot.

Their test hypothesis: customer value would be greater than $599,900. Several weeks later they sold the house for a full-price offer of $599,900. Their profit after expenses: $76,000. Of course, this example showcased a profitable high-value “flip,” but you can imagine many other lesser-value “flops” in which they subjectively tested a higher price and successively

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adjusted it to make sense of market feedback and the subjective customer value of their selling property. Subjective customer value modeling taps the cognitive advantages of System 1 processing—fast, automatic thinking that relies on holistic framing and comparisons based on memory associations—but still invokes the structured thinking of customer value models, with reference value and differential value. We see similar patterns in the art world. Olav Velthuis studied pricing decisions by gallery owners in New York and Amsterdam, finding that dealers of contemporary art make decisions based on what he terms “pricing scripts,” which serve to simplify pricing decision[s] by applying a set of [decision] rules. When working with a new artist the typical script is to set prices low and to [anchor on reference] work by comparable artists. For more established artists the script involves setting the current price level based on past prices. Gallery owners tend to increase prices when demand is high, such as when the artist’s last show sold out; periodically, such as every year; or based on reputations, such as when the artist receives favorable reviews by critics. [He] finds that the pricing scripts employed by gallery owners are supplemented with reference values which provide numerical values for certain kinds of decisions, such as conventional minimum prices for certain kinds of media and certain sizes of works.20

In 2017, my son was infected by a potentially fatal complication of the E. coli virus called “hemolytic-uremic syndrome” (HUS). After he had been in the intensive care unit (ICU) for several weeks with little improvement, the medical team recommended he receive a new medication, Soliris, by Alexion Pharmaceuticals, which is used to treat rare blood disorders. It cost $29,500 per dose; a full course would cost about $450,000. We agreed to proceed. Within two weeks, he was much improved and ready to come home, and he made a full recovery. What was the customer value of Soliris? Under the circumstances, it was certainly subjective—but incalculable. It had the potential to save the life of a son, a husband, the father of four young children, a promising university professor. Given this rare value, Alexion had to set a price. How would you go about building a subjective customer value model for such a medication? Soliris is one of a new class of so-called orphan drugs that target small patient populations with unmet lethal or debilitating diseases, usually in

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oncology and immunology. The value of these life-saving drugs is highly compelling, leading major payors, such as insurance companies and government health agencies, to reimburse them even at very high prices. Here is Alexion’s subjective customer value estimation rationale: In a short statement, [Alexion] said [its] drug pricing depends on a “unique decision-making framework” that takes into account “the rarity and severity of the disease, the absence of effective alternative treatments, indirect medical and social costs, and clinical data that demonstrate the impact of the drug on patients who desperately need it.”21

In other words, Soliris’s subjective customer value model included (a) very high psychological value drivers—the disease’s rarity (very low probability of incidence, important to insurers) and severity (very high value outcome, critically important to patients); (b) difficult-to-calculate economic value drivers, including “indirect medical costs and social costs”; and (c) “clinical data that demonstrate the impact of the drug on patients who desperately need it”—namely, morbidity and mortality data. What was the likelihood that total customer value was greater than a possible price of $29,500 per dose? Through probing and sensing, subjectively, the hypothesized test price became clear to price-setters. Further, since Soliris was a new product, there was no competitive reference product and thus no reference value. With no competition, customer “value in use,” discussed earlier, was the standard for price-setting. Soliris was able to create its own frame of reference and strategically set its own reference price in a new market space; strategists call this a “blue ocean strategy.”22

Customer Value Driver Discovery: Value Projection Mapping

What are the most important value drivers to customers? Discovering customer value drivers is a soft skill that taps the native talents and experience of customer-facing personnel such as salespeople, order desk personnel, customer service personnel, and field engineers or technicians. To discover customer value drivers, pricing thought leaders recommend a team ideation process that sequentially explores how features, benefits, and value drivers are related, an exploratory process—value projection mapping (see figure 6.11). Most managers are accustomed to thinking of products and services in terms of their distinguishing features (a two-carat diamond ring, a 5G

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Differentiating features

How is the product or service differentiated, or how does it deliver superior performance vis-à-vis competitors?

Differential customer value drivers

Differentiating benefits

What are the differentiating benefits customers get from differentiating features?

What are the differential value drivers that differentiating benefits deliver to customers (savings, gains, or experiential customer value outcomes)?

Figure 6.11 

Customer value driver discovery— value projection mapping.

smartphone). But they often fail to extend their thinking beyond; that is, to how those differentiating features lead to differentiating benefits for customers, which then lead to differential value drivers that create customer savings or gains or high-worth experiential value (such as taking a neverto-be-forgotten vacation or being saved from a serious illness). With differential value drivers, we go beyond how customers benefit from using the brand to the consequential monetary or experiential outcomes that features and benefits deliver to customers. For example, owners of vehicles with high-performance gasoline engines such as powerboats, off-road motorcycles, and vintage sports cars appreciate the superior differential customer value of Sunoco’s very-high-octane ethanolfree gasoline (100–112 octane rating), compared with regular gasoline (87–89 octane). “Very-high-octane” and “ethanol-free” are differentiating features, which lead to several differentiating benefits for customers, such as the delight of faster or smoother acceleration and the assurance that your engine is in top running condition. In turn, these differentiating benefits deliver several high-worth differential customer value drivers or, with psychologically oriented products or services, experiential customer value outcomes; for example, a higher likelihood of winning a race, a lower likelihood of avoiding a catastrophe at sea (like an engine failure in a boat during a storm), or preserving the life of an engine worth tens of thousands of dollars. Compared with regular gasoline at $3 per gallon, Sunoco’s high-octane ethanol-free fuel is $10 per gallon at the pump or $23 per gallon in 110-ounce tin cans sold in hardware and home center stores.

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Begin customer value driver discovery by meeting with teams of customer-facing personnel and take them through the following three-stage ideation process, value projection mapping, as follows. 1. Differentiating Features. What are the differentiating features of your product or service? In what ways is its performance superior compared with that of competitors? 2. Differentiating Benefits. How do these differentiating features lead to consequential differentiating benefits that flow from them? Think in terms of how customers feel while using it and why its differentiating features make them feel that way. For example, an intuitive design (such as Samsung Galaxy’s foldable smartphone design) might make your smartphone more enjoyable or easier to use. 3. Differential Customer Value Drivers. How do these differentiating benefits lead to consequential differential customer value drivers? What monetary savings, gains, or high-worth experiential customer value outcomes flow from the differentiated benefits (which flow from the differentiated features)? Think in terms of measurable monetary customer value driver dimensions (cost savings or gains in income, margins, or revenue) and high-worth experiential customer value outcomes—intangible and psychologically driven— that customers seek to achieve, preserve, or ensure, such as preserving a health outcome, winning an important event, achieving a milestone goal, or avoiding a disaster or negative outcome.

After ideating this list of differential value drivers, ask your customer-facing professionals to estimate the relative importance to customers of each value driver by using a simple estimation method, called a constant sum scale. Allocate 100 points among all the differential customer value drivers, to the extent that each value driver is hypothesized to be of greater or lesser importance to customers. Value drivers that are given more points should then be given higher priority for exploration and validation during field interviews and data collection with customers, which I will discuss next. I recommend designating these value drivers as hypothesized differential customer value drivers. Your next step will be to test and validate your hypotheses, in the same way you would do field research to validate hypotheses of a scientific theory. Figure 6.12 shows a sample customer value driver discovery template, a generic version of examples from my field value research and client work.

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Differentiating features

Differentiating benefits

Differential customer value drivers

Cost saving value drivers

Higher precision, accuracy as Higher production yields for Raw material savings from less percent of standard OEM customers waste

Smaller installed footprint, less space

OEM customers can design smaller products, cost Bill of material (BOM) savings savings

Revenue/margin gain value drivers

Unique manufactured design Safety, quality of workplace

Superior field engineering

Lower accident incidence, lower accident insurance premiums

Hypothesized importance

20%

10%

5%

Time-to-market, category share gains

40%

Time-to-market, premium price gains

25%

Faster design-in into OEM end-product designs

100%

Σ = 100 Figure 6.12 

Customer value driver discovery, value projection mapping illustration

Customer Value Data Gathering

An essential soft customer value skill is knowing how to engage with customers to gather insightful value data. This is more complicated than might be expected. First, customers can resist talking about proprietary data on customer use, value in use, or cost in use; they might be suspicious of the motives of your research. They also might find it difficult to articulate the actual value they get from your product or service. Those barriers call for skillful guidance by your value research team throughout the data-gathering process. Second, your value research team members likely do not know how to talk about value; they often bias data collection by asking the wrong questions or, more likely, fail to probe effectively with customers about how they truly realize the value that you deliver. Salespersons or others on the value interview team can bias data gathering by becoming defensive when customers discuss your product’s or service’s shortcomings, frustrations, or

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disadvantages, believing that customers don’t adequately understand it. The best advice to these well-intended team members is to remain quiet, listen to what the customer says, and take good notes. Of the various major research methodologies, such as survey research, experimental research, and exploratory research, the most useful for gathering customer value data is exploratory research. Specifically, I recommend depth interviews with customers: two value interviewers (one to ask questions, the other to take notes) with one, two, or perhaps a few customer interviewees. As the interview begins, be sure to allay concerns about the purpose of your research: it is to understand how your product or service creates value for customers, which customer value drivers are most valuable to them, and how much that customer value is worth to them. I recommend a five-phase approach to customer value data gathering. Phase 1: Organize customer value research teams. Each team should include customer-facing people with experience in product marketing, field engineering, and field sales. Train team members in customer value driver discovery, value estimation, and value model building and then organize them into two-person interview task teams to conduct depth customer value interviews. James Anderson and James Narus suggested, Having salespeople involved at the start is particularly important. They know the customer and how the offering is used; they also know which customers might be willing to cooperate in value research. Salespeople who are part of a value assessment initiative from the outset are also more likely to understand and appreciate it. They will, therefore, support the approach and can then persuasively relate their experiences to others in the sales force.23 Phase 2: Select a sample of customers. As an exploratory method, depth interviewing involves smaller sample sizes, perhaps up to a dozen customers or firms. These are much smaller samples than large-scale market surveys, which involve thousands of respondents. Your sample should have representative customers from important customer segments. Plan to do several pilot interviews to refine your process before moving forward with your broader customer value research effort. [It’s] a good idea to start with a segment in which [you have] particularly close, collaborative relationships with customers, [solid]

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knowledge of how customers use the offering in question, or relatively simple offerings.24 Phase 3: Construct a customer value interview guide. Large-scale surveys use longer questionnaires with many closed-ended questions. By contrast, depth interviews use shorter interview guides—just one to three pages— to guide the interview team. The questions are more open-ended, such as, “In what ways would the [product or service] be valuable to you? Tell me more about why [for each way].” “In what ways does the [product or service] save you money? Tell me more about [each way].” More directed questions then can be used to probe specific hypothesized customer value drivers, discussed in the next step. Figure 6.13 shows the basic structure of a customer value interview guide. Phase 4: The customer value interview—probing for value. Depth interviews are best, but focus groups can work as well for gathering customer value data. An important soft skill is learning how to “probe for value.” Have available a list of hypothesized customer value drivers (from your customer value driver discovery team’s work) to explore and validate possible drivers; it might take interviews with several customers to probe all the drivers on the list. Now, use the interview guide (from phase 3), proceeding sequentially through sections 1 through 8 (see figure 6.13). However, at sections 5 and 6 (on customer value drivers), be ready to pause, then probe possible customer value drivers more deeply, one by one, to get at the data needed for customer value estimation. Probing for value is a soft value skill (see figure 6.14) that involves asking customers sequentially about needs—the most pressing problems and issues with the product/service category being explored; solutions—how they address these needs now, including how they temporarily solve or manage them and how they improvise; and value—how those improvised or temporary solutions result in costs (money, time, or opportunity) and the estimated magnitude of those cost savings. If the customer had a product or service solution like yours that could address those needs, how might they save time or money or realize monetary or psychological gains? Phase 5: Analyzing; building the customer value model. Following the interviews, sit down with the interview team and ask, what were the most important takeaways? What were the most important customer value drivers? How much value do we—or could we—create for the

Data inquiries

Section 1 Introduction

The interview team, their roles Purpose of this value research

2 Customer business model

Customer’s most important customer segments How the customer drives profitability

3 Product/service description

Define/describe your product/service Concept statement, ad concept, video clip

4 Competitive reference value

Next best competitive alternatives (NBCA) Competitive reference price

5 Customer value drivers (open ended)

Possible customer cost savings Possible customer revenue gains, margin gains Possible experiential customer value outcomes

6 Customer value drivers (hypothesized)

Hypothesized customer cost savings Hypothesized customer revenue gains, margin gains Hypothesized experiential customer value outcomes

7 Willingness to pay

What might someone pay for this product/service? What might you tradeoff in your budget for this? What do you typically pay for that?

8 Buying center

Who is involved in this type of purchase? What are their roles and interests? Who is influential?

Figure 6.13 

Customer value research, interview guide template

Ask

Using questions like these

1 Customer’s needs

What are your most pressing issues, problems, challenges with regard to [______________]?

2 Customer’s solutions

What do you do now to address them? How do you solve them, and manage them?

3 Customer value

In what ways do they cost you? Time, money, other? How much do they cost? How much might you save/gain?

Figure 6.14 

Customer value research, probing for value

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customers? After the interviews have been transcribed and analyzed, the data are available to build customer value models. Anderson and Narus suggest other ways to supplement customer value data gathering: During the [value discovery] session, [team] participants were asked what kinds of information they thought should be used in a value model and then where in the organization to look for that information. The [value] consultants discovered sources of data in places that neither they nor the customer’s management had previously identified. The value research team also needs to be creative in finding other sources of information. Independent industry consultants or knowledgeable personnel within the supplier company can be good sources of initial estimates.25

Hard Value Skills for Price-Setting

Hard value skills are useful for customer value estimation and value communication. We will look at three hard value skills: objective customer value models, value metrics for price-setting, and value communication tools and strategies (see figure 6.7, right).

Objective Customer Value Models

Earlier we were introduced to the soft value skill of subjective customer value models for estimating value with intangible, psychological benefits (as in many B2C settings). Here we focus on objective customer value models for value estimation with mostly tangible, economic benefits (as in many B2B settings). Here is a simple illustration. John Deere sells a product, labeled the 750J bulldozer, to earth-moving contractors. According to the product’s website, All dozers move the earth. But if you want one that does more with a lot less effort, you’ll choose a John Deere J-Series. State-of-the-art electronic controls put you in complete command of a whole arsenal of hydrostatic advantages, including power turns, counterrotation, and infinitely variable travel speeds. What’s more, Total Machine Control lets an operator customize decelerator mode and response,

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forward/reverse ground-speed ranges, steering modulation, and forward/reverse speed ratios. Nothing else even comes close.

Field research on original J series models found that because of its innovative automatic dual-path hydrostatic drive transmission (like an automatic transmission for bulldozers), its contractor customers could achieve 10 to 15 percent greater productivity than they could with competitive bulldozers. The field research further found that over a bulldozer’s product life of about 10,000 hours, contractors spent about 90 percent as much on parts and service and about 35 percent as much on fuel as they had spent on the initial purchase.26 Caterpillar is the dominant brand in the industry; its D6N model is the competitive substitute of choice for many contractors and sells for $350,000. Based on this customer field data, let’s build an objective customer value model to estimate the value of the John Deere J-Series bulldozer. Begin with the customer value model’s foundation, the $350,000 competitive reference value, shown at the bottom of the chart in figure 6.15. This is the price of the next-best competitive alternative (NBCA), the Caterpillar D6N. Because the 750J delivers 10 to 15 percent greater productivity than competitive machines, customer value discovery suggests four intuitive differential customer value drivers: • Labor Savings, because the 750J requires 10–15 percent fewer hours to do the same work as the Caterpillar D6N (based on 10–15 percent productivity savings); • Fuel Savings, because the 750J requires 10–15 percent less fuel for the same work output; • Replacement Parts and Service Savings, because the 750J requires 10–15 percent less expenditure on replacement parts and service over its product life; and • Machine Replacement Savings, because the 750J must be replaced 10–15 percent less often.

The value estimation of these customer value drivers is now straightforward, as shown in figure 6.15. Assuming the lower end (10 percent) of the range of productivity savings, • Labor savings is $30,000 (10 percent time saved × 10,000 hours product life × $30 hourly wage for heavy equipment operators).27 • Fuel savings is $12,250 (10 percent fuel saved × $350,000 reference price × 35 percent spent on fuel over the product’s life).

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Employee turnover savings (+) Customer mix gains (+)

Sunk costs in replacement parts (–)

Performance risk (–)

Negative differential value drivers

Machine replacement Cost savings

Positive differential value drivers

Competitive reference value

$350,000 × 10% = $35,000 Replacement parts savings $350,000 × 90% × 10% = $31,500 Fuel savings $350,000 × 35% × 10% = $12,250

Total customer value

Labor savings 10,000 hours × 10% × $30 = $30,000

~$458,750

$350,000 Caterpillar D6N

Figure 6.15 

Objective customer value model, John Deere 750J Bulldozer Data Sources: Publicly available sources, Deere and Company: Industrial Equipment Operations, Harvard Business School Case #9-577-112.

• Replacement parts and service savings is $31,500 (10 percent parts savings × $350,000 reference price × 90 percent spent on replacement parts and service). • Machine replacement cost savings is $35,000 (10 percent longer product life × $350,000 reference price, or replacement cost).

In addition to these concrete quantitative monetary savings, there are differential value drivers that require specific localized customer data to estimate, such as customer mix gains—the contractor can target higher-margin jobs in tight-space urban settings thanks to the 750J’s hydrostatic automatic transmission—and employee turnover savings due to the machine’s being easier to operate. But there are negative differential value drivers as well: Caterpillar’s loyal contractor customers have sunk costs in replacement parts that

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would immediately become obsolete on switching to Deere—a switching cost. And for a new customer, there is a risk that the product will not perform as expected, a key concern for contractors working with tight deadlines— performance risk. These would be estimated at the customer level. You can see that the quantified estimate of the total customer value of the 750J is about $458,750. Our estimation is built on concrete data and credible assumptions. Still, this is a hypothesized objective customer value model. The model must always be subjected to field validation—refining, and customizing the value model with real customers, a valuable customer conversation to have.

Value Metrics for Price-Setting

Our next hard analytic value skill is value metrics for price-setting. Students and clients are quick to focus on the mechanics of customer value estimation and look for a value-driven “formula” for setting price. But value-driven pricing is more strategic and customer-centric, focusing first on the amount of value you create for customers (value estimation) and then on how value should be strategically shared—that is, how much value you keep as price in order to reinvest (value capture) and how much to leave on the table as customer incentive (value shared). You might choose to capture a high proportion of value and share less value with the customer; this is a skim pricing strategy, whereby your high-value capture, and higher price, limits the number of potential buyers. Or you might choose to capture a low share of value; this is a penetration pricing strategy whereby your low-value capture, and lower price, expands the number of potential buyers and penetrates a larger share of potential customers. Or, you might capture a mid-range share of value that focuses on neither highlighting low price nor justifying high price but focuses instead on marketing and communicating your unique differential value, a neutral pricing strategy. Four metrics are helpful with setting the value-driven price, using a system of ABCs, shown in figure 6.16. For illustration, assume that John Deere’s proposes a list price for the 750J of $375,000. • Net Differential Value is the new net value that the 750J offers customers beyond the reference value offered by competitors, shown as about $108,750, or distance A in figure 6.16, and calculated as positive differential value minus negative differential value. This measure represents the rational price-setting region for a value-driven price. Setting price lower than reference price (here

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Employee turnover savings (+) Customer mix gains (+) Machine replacement Cost savings $350,000 × 10% = $35,000

Positive differential value drivers

Performance risk (–)

Labor savings 10,000 hours × 10% × $30 = $30,000

C

$350,000 Caterpillar D6N

Negative differential value drivers

Total customer value

Replacement parts savings $350,000 × 90% × 10% = $31,500 Fuel savings $350,000 × 35% × 10% = $12,250

Competitive reference value

Sunk costs in replacement parts (–)

A

Net differential value ~$108,750

B

~$458,750

Recommended list price Value capture $375,000 $25,000, or 23% Value-driven price-setting metrics Net Differential Value, ~ $108,750 Value Capture, 23% Relative Price Premium, 7.1% Relative Differential Value, 31%

A B ÷ A B ÷ C A ÷ C

Figure 6.16 

Value metrics for price-setting, John Deere 750J Bulldozer. Data Sources: Publicly available sources, Deere and Company: Industrial Equipment Operations, Harvard Business School Case #9-577-112.

$350,000) encourages price competition because competitors have an incentive to retaliate against your discounted price (made even more urgent by the 750J’s significant net differential value offered) by likewise reducing their prices. • Value Capture is the share of net differential value that you capture as price—distance B—while sharing the remaining new value created with the buyer. At a list price of $375,000, Deere captures $25,000, or 23 percent of net differential value created (calculated as B ÷ A). • Relative Price Premium is the percentage by which the new 750J price, $375,000, exceeds the reference price of the Caterpillar D6N, $350,000. It is 7.1 percent (B ÷ C). • Relative Differential Value is the percentage by which the new net differential value offered to Deere’s 750J customers exceeds the

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Caterpillar’s reference value. It is 31 percent (calculated as $108,750 ÷ $350,000, or A ÷ C).

These value price-setting metrics, summarized for the 750J in the lower right of figure 6.16, provide useful guidance. For example, the 750J’s net differential value (figure 6.16, distance A) is about $108,750, and its relative differential value is 31 percent (A ÷ C), a significant differential in net new value offered compared with the reference product, the Caterpillar D6N. At a list price of $375,000, the 750J model’s relative price premium would be 7.1 percent (B ÷ C), which is significant in a very competitive product category. However, its value capture is 23 percent (B ÷ A), meaning that 77 percent of net new value created is shared with customers. At this price, John Deere would be pursuing a neutral pricing strategy. Let’s go back to Repatha in light of these value price-setting metrics. At its original price of $14,100, its net differential value (distance A) was $38,579 and its relative differential value compared with the reference value of statin therapy ($837) was 4,509 percent, pointing Repatha’s price-setters in the direction of a blue ocean strategy, establishing a new strategic frame of reference with no competitors, discussed earlier in the chapter. At $14,100, its value capture was 36 percent (B ÷ A)—significant but not extraordinary given its strategic context as a really new pharmaceutical product. Competitive intensity was low, as Repatha was a pioneering entrant, and because customers had little knowledge of this new biotechnology, Amgen would have to invest considerably in educating the market. Several years later, new competition with the entry of Praluent by Sanofi and Regeneron raised competitive intensity in the new PCSK9 inhibitor market, putting downward pressure on Repatha’s value capture and its value-based price, which was eventually reduced to $5,850.

Value Communication Tools and Strategies

Objective customer value models and value metrics for setting price are classic System 2 analytic hard skills of behavioral economic theory. They require slow, procedural, methodical, and deliberate cognitive effort. The value communication tools and strategies that naturally flow from such a process require that on their part, customers be capable of similarly demanding System 2 analytic thinking. The customers who fit this requirement are what behavioral science researchers call “high-knowledge customers;” they prefer to evaluate a product or service using concrete, factual, statistical

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Value estimation

Value engagement

Basic information strategies

Value statistics and data

Value calculators

Value messaging

Value worksheets

Value estimation, models

Value websites/apps

Advanced application strategies

Value field studies

Sampling programs

Value papers/articles

Customer simulations

Value marketing materials

Customer testing/trials

Figure 6.17 

Customer value communication System 2 analytic tools and strategies

data about intrinsic performance and already have well-developed frames of reference in memory. High-knowledge customers are often high-intensity users; for example, engineers who work daily with high-performance statistical software, such as MedCalc (in biomedicine) and RapidMiner (for machine learning). The best tools for appealing to high-knowledge customers are System 2 analytic customer value communication tools and strategies, which include two types (summarized in figure 6.17). Value Estimation focuses on basic information strategies such as value statistics and concrete, hard data relating to value. These data are used for objective customer value estimations and to build basic customer value models (see figure 6.17, left). These basic models are hypothesized and built based on basic value research; they are then validated and customized in the field as customer-facing personnel interact with customers to tailor value models to customers’ situations. These basic information strategies become building blocks for more advanced applications strategies—for example, larger value field studies such as pharmacoeconomic clinical trial studies. The results are published as value white papers or journal or industry articles and in related media publicity and are used as marketing support materials for field sales and selling support team members.

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Value Engagement is designed to engage customers in actually calculating, estimating, and refining value estimates and to support active conversations and thinking about value. They, too, include basic information strategies such as value calculators, value worksheet templates, and value website or mobile apps (see figure 6.17, right), which enable customers to manipulate value data to tailor or customize customer value models to their own applications. These basic information strategies become building blocks for advanced application strategies—such as customer sampling programs, customer simulations, and customer testing and trial programs—that enable customers to experience firsthand the value they receive (or will receive). There are many examples of available value communication tools around us. Figure 6.18 shows a Cohesity total cost of ownership (TCO) calculator, which includes a System 2 value calculator, and a System 2 value simulator—to facilitate both value estimation and value engagement. Let’s turn now to System 1 behavioral tools and strategies. In many situations, customers might be unable or unmotivated to cognitively engage in deliberate System 2 analytic thinking. They will rely instead

Figure 6.18 

Cohesity hard analytic value communication strategy Source: Cohesity, https://www.cohesity.com/solution/backup-and-recovery/tco/

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on quicker and easier System 1 memory-driven thinking, using associations or comparisons from memory to infer value rather than calculate it. These customers are what behavioral scientists call “low-knowledge customers”; they prefer holistic, abstract information that is familiar and easily processed about a product’s or service’s extrinsic dimensions—such as a familiar brand name or an endorsement by a trusted spokesperson. They seek frames of reference (from memory, or suggested to them) to help them make useful associations, comparisons, and inferences about value. Low-knowledge customers are usually light users or are people such as managers or buyers who don’t use the product themselves but might influence its purchase. For example, senior executives who must sign off on a purchase but have neither the time nor cognitive capacity to delve into the details of value estimation might be low-knowledge customers. System 1 behavioral customer value communication tools and strategies especially appeal to low-knowledge customers. Two such tools are summarized in figure 6.19. Value Content focuses on basic information strategies that behavioral scientists have found are easier to process for low-knowledge customers, such as narrative, story, case study, or picture or video content (see figure 6.19, left). These become building blocks for advanced amplification

Value content

Value framing

Basic information strategies

Narrative content Story content Case study content Picture/video content

End-benefit frames

Advanced application strategies

Opinion leaders

Partner alliances

Testimonials

Membership frames

Endorsements

Paradigm shifts

Outcome frames Positive-negative framing

Figure 6.19 

Customer value communication System 1 behavioral tools and strategies

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strategies such as securing endorsements, associations with opinion leaders, and testimonials from everyday or influential users. Value Framing is designed to suggest frames of reference to customers to help them in framing value. These include basic information strategies such as end-benefit frames (framing product use relative to a valuable endbenefit, such as framing a premium champagne as the right choice for a wedding), outcome frames (such as insurance coverage that saves a family’s finances after a house fire), and positive-negative framing (a family loses money after a house fire because they didn’t have good insurance coverage)—see figure 6.19, right. These frames of reference can be amplified with advanced amplification strategies such as (a) partner alliances that associate the brand with well-known value partners (Nike and Michael Jordan partner with Air Jordan sneakers; or Apple and fashion brand Hermés Paris partner with Apple Watch Hermés, (b) membership frames (for example, California State University, San Bernardino, highlights its top 10 status among large universities that best serve military and veteran students), and (c)  paradigm shifts (VerbalAdvantage says that its vocabulary system delivers the equivalent of “a Harvard graduate’s vocabulary in only 15 minutes a day”), suggesting that VerbalAdvantage is categorically similar to Harvard training in vocabulary.

Conclusion

The first priority of a customer value-driven pricing orientation is debiasing the hidden biases that pervade your pricing process relating to customer value, such as value illiteracy bias, proportional value bias, and heuristic value estimation bias (see figure 6.7). Then turn to soft skills relating to value discovery and value sensing: subjective customer value models, value projection mapping for customer value driver discovery, customer value data gathering, and probing for value. Finally, lean on hard skills relating to value calculation and value communication: objective customer value models, value metrics for price-setting, and value communication tools and strategies to engage customers in conversations about customer value. Salespeople are frequently sent into the field poorly equipped to negotiate with customers because the marketing and pricing teams have failed to arm them with the value communication tools and strategies they need to be successful with customers. The customer value soft and hard skills

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discussed in this chapter instill in all members of your price-setting team the confidence that the prices they set are backed by compelling data, models, estimates, and strategies that communicate the unique differential value customers get from your product or service. See the following templates to help with soft and hard customer value skills for pricing: • Template 6.1: Customer value driver discovery template • Template 6.2: Objective customer value model template • Template 6.3: Subjective customer value model template

Templates

Differentiating features

Differentiating benefits

List unique product or service features that differentiate, or deliver superior performance vis-à-vis competitors

Translate each unique product or service feature into a differentiating benefit that customers get

Differentiating customer value drivers

Hypothesized driver importance

Translate each unique differentiating benefit into customer value drivers— savings, gains, experiential customer value outcomes

Allocate 100 points among value drivers to prioritize future data gathering, estimation, and testing

1 2 3 4 5 6 7

∑ = 100 Template 6.1 

Customer value driver discovery template.

Value Driver Estimate $______

2. Estimate differential value customers get from positive, then negative value drivers

Value Driver Estimate $______

Value Driver Estimate $______

Negative differential value drivers

Value Driver Estimate $______

Value Driver Estimate $______

Positive differential value drivers

Total customer value $_______

Value Driver Estimate $______

A Value Driver Estimate $______ Value Driver Estimate $______

Net differential value $______

B

Value capture $______, or___%

3. Calculate total customer value (TCV), then strategically recommend list price using value ABC calculations as guidance

Recommended list price $_______

Value-driven price-setting metrics

Competitive reference C value

Net Differential Value, $_______ Value Capture, ___% Relative Price Premium, ___% Relative Differential Value, ___%

1. Competitive reference price of the NBCA

A B ÷ A B ÷ C A ÷ C

Template 6.2 

Objective customer value model template.

Positive differential value drivers

2. Identify differential value drivers. Allocate 100 percentage points among positive, then negative value drivers

Psychological, intangible, experiential outcomes

Driver #1 ___%

Driver #1 ___%

Driver #2 ___% Driver #3 ___%

Driver #4 ___%

Driver #2 ___%

3. Subjectively assess magnitude of differential value

Driver #5 ___% Driver #6 ___%

Competitive reference value

1. Reference frame, competitive reference prices

Template 6.3 

Subjective customer value model template.

Subjective magnitude of differential value

Negative differential value drivers

Total customer value

4. Subjectively assess total customer value (TCV), then strategically ask: Given a possible price, what is the likelihood that TCV > price? Sensing, sensemaking

Possible list price $_______

7 Customer Willingness-to-Pay–Driven Pricing Orientation Biases and Skills

A value-based price is useful in setting recommended list price, the valuejustified price standard for customers. Not all customers pay full list price; some pay less based on their individual perceptions of the value they need, their price sensitivity, and their ability or willingness to pay. In behavioral economic terms, the value-based list price establishes a cognitive value anchor, evoking the behavioral power of System 1 anchoring and adjustment, which customers then use to intuitively form and inform their personal frame of reference, defining the beneficial customer value they get and the price they expect to pay in exchange. In this chapter we focus on customer willingness to pay (WTP) as a pricing orientation, its biases and soft and hard skills, and how they should be managed for price-setting and profitability.

True Willingness-to-Pay Principles for Pricing Orientation

In chapter 6 we turned attention to true marginal revenue principles for price-setting, starting with the value customers get from using the product or service. The other side of the customer ledger reflects price that customers are able to pay, are willing to pay, or give in exchange for customer value

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Marginal Revenue

Marginal Cost

=

MR

Customer value

Customer willingness to pay

Customer valuedriven pricing orientation

Customer WTPdriven pricing orientation

MC

Competitor prices

Incremental cost to serve

Competitiondriven pricing orientation

Costdriven pricing orientation

Figure 7.1 

Theoretical maxim for a profitable pricing orientation, focus on customer WTP-driven pricing influences.

(see figure 7.1). The theoretical maxim for a profitable pricing orientation is to set prices and sell units until the marginal revenue derived from selling the next unit is equal to its marginal cost; that is, MR = MC. Inherent in this rule is the proposition that a product or service be sold to different customers at different prices to maximize incremental profit contribution—the founding principle of price discrimination theory in economics and segmented pricing in strategic pricing.

Segmented Pricing

Segmented pricing is customer-driven pricing in that different prices are set for a product or service that vary by customer or customer segment, and each customer segment’s price reflects (a) the value customers perceive they get, (b) customers’ price sensitivity, and (c) their willingness or ability to pay. Figure 7.2 shows that given a typical downward-sloping demand curve, a firm could charge a uniform price to its customers, P1, and sell Q1 units, appealing to customers in segments 1 and 2, and receiving total sales

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Price P2

B P1 Demand

A

P3

C

Segment 2 Q2

Segment 1

Quantity

Segment 3 Q1

Q3

Figure 7.2 

Price discrimination and segmented pricing.

revenues of area A. However, a uniform pricing strategy like this foregoes incremental sales revenue to customers in segment 2 who are willing to pay a higher price, P2 (area B in figure 7.2), or those in segment 3 who are willing to purchase only at a lower price, P3 (area C). For example, NBCUniversal launched its Peacock streaming video service in early 2020 with access to its popular content library that included shows such as Parks and Recreation, Downton Abbey, and 30 Rock. Peacock could have set a single price of $5 per subscriber per month for ad-supported viewing; ads generate, say, $3/month per subscriber, yielding $8/ month in average revenue per user (ARPU).1 At that price it might sign up 5 million subscribers, realizing $40 million in monthly revenues.2 At that single price, however, it would forego incremental revenues from binge-watching enthusiasts who perceive extraordinary value in watching the entire content library ad-free (estimated at 30 percent of total paying subscribers). They are willing to pay a top price of $10/month, yielding an incremental 7.5 percent in revenues ([ΔP $5 − $3 lost ad revenue] × 30% × 5 million subscribers), or $3 million in incremental monthly revenues (area B, figure 7.2).

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However, it would also overlook the zero-cost behavioral heuristic in which free goods become irresistible, mentioned in chapter 2’s box. It would forgo sales to a much larger price-sensitive customer base, estimated at 15 million subscribers, who would sign up for free but limited content access, with an advertising-based subscription, yielding an incremental 112.5 percent in revenues, or $45 million monthly (area C, figure 7.2, $3/month subscriber ad revenue x 15 million). By segmenting its prices, Peacock realizes an incremental 120 percent yield in revenue: $48 million monthly, or $576 million annually. By early 2021, Peacock had in fact signed up 33 million subscribers in less than a year using roughly this segmented pricing strategy.

Price Offerings, Not Product Offerings

Why are some customers willing to pay a price that is different from the price other customers pay for the same product or service? The answer is because the core product or service is only part of the larger set of augmented features and auxiliary services that surround the core product offering, creating many ways to distinguish among price offerings that customers might pay for, shown in figure 7.3. For price-setting, you can distinguish between price offerings for the core product or service; then price offerings for augmented features such as good/better/best levels of quality, precision, performance, style, capacity, or sub-brand name; and then price offerings for auxiliary services such as warranties, delivery, availability, installation, customization, service, technical support, financing, or other customer care. For example, automobile manufacturers offer the ability to “build your own” car online. For customers interested in BMW’s popular 3-Series line, the website allows them to choose from among two core product model price offerings (inner ring, figure 7.3)—a sedan (base price $40,750) or an all-wheel drive xDrive model for $2,000 more. Next, the customer chooses from among augmented features price offerings (middle ring, figure 7.3), including style—Sport Line (base price), Luxury +$1,950, or M Sport +$5,200. Also, they can choose a color—Alpine White or Jet Black (base price), or a selection of metallic colors +$550. The next choice is made from a selection of wheels, interior trim options, and electronic Driving Assistance Packages (Basic, Professional, Parking Assistance, and Track Handling), all presenting different price offerings. Finally, at the local dealer, the customer chooses from among auxiliary service price offerings (outer ring,

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Auxiliary services Warranties

Augmented features

Customer care

Quality Precision

Core product, service

Delivery

Financing

Tech support

Style Performance Availability Brand

Service Installation

Customization

Figure 7.3 

Setting different prices for different price offerings. Adapted from Anita Elberse, “Principles of Product Policy,” Harvard Business School 9-506-018, rev. October 12, 2005, https://www.hbs.edu/faculty/Pages/item. aspx?num=32682; and Theodore Levitt, “Marketing Success Through Differentiation— of Anything,” Harvard Business Review (January-February 1980).

figure 3), including extended warranty packages, financing options, and a special college graduate program. In total, BMW online offers customers 36 discrete price offering dimensional choices, resulting in 50,688 possible combinations with price offerings ranging from $40,750 to $75,759, summarized in figure 7.4. BMW’s digital customer site has enabled segmented pricing at almost the individual customer level, approaching what economists call “first-degree price discrimination”—setting individual prices that reflect what each customer would be willing to pay.

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Price offerings

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Core product

2

Core model

3 2 2 2 3 4

Style Color Wheels Upholstery Featured packages Driving assistance packages

Option #1 Sedan $40,750 (Base)

Augmented features

Option #1 Sport line (Base)

Option #2 xDrive Sedan +$2,000

Option #2 Luxury +$1,950

Alpine white/Jet black (Base)

Option #3

Option #4

M sport +$5,200

Metallic colors +$550

18" Non-run flat (Base)

19" all season run flat +$600

Sensatec (Base)

Vernasca leather +$1,450

Convenience +$2,350

Premium +$4,700

Driving assistance package +$500 Professional package +$1,700

Executive +$5,900 Parking assistance package +$700 Track handling package +$2,450

M performance analyzer +$242 M performance floor mats +$250 Comfort coat hanger +$119 Folding table +$165 Rubber floor liner Sets +$246

11

Accessories

Luggage compartment mats +$140 Luggge rack +$394 Roof box +$916 Roof rack +$321 Racing cycle holder +$137 Roof box +$1,160

Augmented Services 2 4 1

Financing Extended vehicle coverage College graduate program

Discrete price offering dimensions 36 50,688 Total price offering combinations

Option #1

Option #2

Lease for 36 months

Finance for 3.29% per month

Pre-paid maintenance

Keycare coverage

Option #3 Tire & wheel coverage

Option #4 Extended vehicle protection

$1,000 off with diploma

Range of price offerings: Core product model $40,750 Premium all options model $75,759

Figure 7.4 

BMW segmented pricing “build your own” price offerings. Data sourced from bmw.com. BMW 3 Series image from Wikimedia Commons.

Customer WTP-Driven Biases in Price-Setting

Customer WTP-driven pricing biases arise when price-setters use System 1 behavioral heuristics and mental shortcuts to set price based on what customers are willing to pay. Their habits are easy to detect: asking customers about price acceptability, or asking a price they might be willing to pay, frequent discounting to ensure that price is below an acceptable WTP threshold, and uniform pricing bias, discussed next. Let’s focus on three specific WTP biases (see figure 7.5).

Uniform Pricing Bias

One of the most common behavioral biases encountered among pricesetters is uniform pricing bias, the expectation that prices should be the

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Soft intuitive customer WTP skills Customer WTP-driven biases for debiasing Uniform pricing bias

Price sensitivity, price segmentation

Hard analytic customer WTP skills H Price modeling, Price analytics

Segmenting customers for price sensitivity

Direct willing-to-pay questioning bias

Customer pricing analytics

Price framing

Conjoint analysis

Persistent pricediscounting bias

Price fencing and price menus

Price elasticity and price adjustment analytics

Managing customers for value and price sensitivity

Figure 7.5 

Soft and hard skills of customer WTP in a pricing orientation—a checklist inventory.

same for different customers of the same product or service.3 A young corporate pricing manager once asked me, “Are you suggesting that we should charge different prices to different customers for the same product?” Yes. He replied, “That would be unethical.” I explained with a frequently rehearsed rule of strategic pricing: “You never set prices for products or services; instead, you set prices for customers or customer segments who buy price offerings of various configurations of products or services.” The trouble with uniform pricing bias—charging a single price to all customers—is that your price is too low for some customers who receive high customer value, thus leaving money on the table (segment 2 in figure  7.2), profit contribution that could be used to fund investment in innovation or the cost to serve premium customers. Uniform pricing bias is especially troubling with high cost-to-serve customers who should pay additional fees and prices. For example, Home Depot charged an express delivery fee of $79 to deliver heavy, bulky garden supplies during the coronavirus stay-at-home advisory in early 2020, which enabled timely planting for summer garden crops. Home Depot charged an extra fee for these high-value and high cost-to-serve customer orders to avoid the threat to ongoing profit contribution and operating profits, despite the sudden emergence of the pandemic. At the same time, a uniform price might be too high for customers who are price sensitive or unable to pay, leaving

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them on the sidelines to seek solutions from other, lower-priced competitors. This is profit contribution that is blindly foregone because these customers are never considered. Pricing consultants Hermann Simon, Frank Bilstein, and Frank Luby related a conversation with a senior manager at a Fortune 500 company whose products differed little from those of its competitors. Some of its customers were getting high-value services and special treatment but not paying for it, a symptom of uniform pricing bias. “Do you charge for next-day delivery?” “No.” “Do you charge for odd lots?” “No.” “Do you charge for nonstandard sizes or lengths?” “No. That’s all included. We just charge by the square foot.” Another company charged its customers based strictly on the pounds of product purchased. The pricing consultants concluded, If all these services are included in the overall price, then these companies sell service by the square foot or service by the pound. In such situations, we feel it is legitimate to ask how much a pound of service should cost. And how does it convert into a square foot of service? . . . Ask their sales or marketing managers how much their companies charge for service, and you will receive an abrupt answer along the lines of “You can’t charge for service in our industry” or “It’s included because our customers expect it.”4

Recall the successful structure of Peacock’s three-tier segmented pricing, or BMW’s 36 discrete price offering dimensions for its 3-Series line of cars. Consider your own pricing. Are price offerings strategically designed for customers to choose from—and to pay for? A strategic menu of segmented price offerings is especially important for sales representatives to assist customers in choosing the value they can and are willing to pay, discussed later in the chapter.

Direct Willing-to-Pay Questioning Bias

Many firms send out internet surveys directly asking customers the price they would be willing to pay, and many sales representatives ask exactly

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that. “Of course, you can’t ask customers directly how much they are willing to pay—they’ll likely shade the truth (by giving a lower price) to their benefit,” said Rafi Mohammed.5 Moreover, customers might have insufficient knowledge to make informed willingness-to-pay judgments. Or they might not truly understand the value they get from your product or service. Still firms routinely orient their price-setting toward asking what the customer is willing to pay. At a focus group during my field research, managers involved in price-setting at a large service firm were asked who typically makes pricing decisions. manager: The Business Relations [i.e., sales] guys who own the account [will make the pricing decision]. And also the Product Manager.  .  .  . [The Business Relations guys] come and confirm [the price] with the product guys, but at the end of the day they [the sales guys] probably already have talked with the client [customer]. . . . I mean, the client gives the company a set price and then [we] have [discussions] or arguments depending on if it’s a big enough deal. And the finance people run models on what the sales people have told them . . . [and] the finance guys show you if it’s not a good deal or it’s a good deal. moderator: [Are there] any kinds of truisms . . . [or] rules of thumb that you would typically hear at a meeting which talks about these kinds of [pricing decisions]? manager: Get the deal and keep the client.

Here, Sales Nation (called “Business Relations” managers in this company) drives price-setting. The entire process is undermined by tactical bias relating to the availability heuristic from behavioral economics: using the customer’s recently stated price—what the customer says it is willing to pay—as the starting point for price-setting. This stated price, biased to the downside because customers expect to negotiate on price, is easily evoked from memory and then repetitively used by finance, product management, and sales. It thereby becomes a firm reference anchor from which to begin price-setting deliberations—an example of anchoring and adjustment bias that pulls price downward. All of this behavioral bias is reinforced with the truism that drives price-setting in this firm: “get the deal and keep the client.” Direct price-questioning bias leads to more systemic issues that are based on irrational and erratic price-setting driven purely by what customers

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say—or what you think—they are willing to pay. This colorful example from my field research with the CEO of a service company illustrates. We don’t have a formalized pricing.  .  .  . [T]he first contract [with AlphaCo] was at [$60] per unit. And that squeezed us really, because we have to pay [our contract workers] on the other side, and boy, we were sailing I would say close to the wind on that one.  .  .  . But she [Lisa, a field manager] did all the work [with AlphaCo], but she would sort of throw out “I’m going to go for [$75] [Rich]—we’re in it to get [$75], why the hell shouldn’t we get [$75]. And they’re not paying us overtime.” She would bounce it off me, and I would say: “[expletive], go for it [Lisa], but be careful—we don’t want to lose the [revenue] there if we get too pricey.” So [now] we’re a big item [and] getting [$8 million] from [AlphaCo]. And they’re into reducing the cost of their [engineering] department and [Judith Madsen], who is the VP, mentioned in passing that “Look, we’ve got to reduce [our budget] by $4 million a year.” The issue is that now we’re up to [$60] . . . [and] now they’re trying to cut back a bit. And we might have to surrender the boat.

An interview with the firm’s former chief operating officer offered further insight: “A lot of [customers] would say that if you go over the [$70] mark, you are out of my ballpark. He [the CEO] would ask them, ‘What is your ballpark?’ That would be it for the pricing process. We really don’t go through any elaborate thing with them. . . . But tell us what you would like us to do it for.” In this firm, direct willing-to-pay questioning resulted in a chaotic patchwork of prices across geographies and customers with little rationale and subject to the passions and impulses of the buyer and seller. Setting prices based on directly asking willing to pay is costly financially but also behaviorally and psychologically, as price-setters engage in an exhausting cacophony of fluid and inconsistent price-setting, similar to the biblical proverb, “ever learning and never able to come to a knowledge of the truth.”6 Research methods that rely on asking customers their willingness to pay (Gabor and Granger’s Direct Response Method, or Van Westendorp’s Price Sensitivity Meter) are susceptible to direct willingness-to-pay questioning bias. Simon and his colleagues said, Some of the standard methods for direct questioning on prices (“Would you buy product X at $19.95? At $14.95?”) have developed

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systematic errors over the last few decades. As a result, these research methods generally underestimate a customer’s true willingness to pay. Consumers have developed a fine-tuned conditioning and sensitivity toward price that they lacked at the time these methods first gained usage in the 1950s. This is particularly true of industrial companies with dedicated professional purchasing departments. The nature of the direct questions only reinforces this systematic error, because it overly sensitizes the respondent to the price.7

Persistent Price-Discounting Bias

Pricing is especially prone to persistent price-discounting bias in which pricesetters, given a choice among prices, consistently choose the lower price option, seen in various manifestations. During my field research with a focus group at a university executive pricing program, respondents were asked about the rationale for their firms’ price-setting. Two managers from the same firm provided this insightful exchange about their price-setting goals: Manager 1: It all comes down to 20-20-20 . . . 20 percent revenue growth, 20 percent profit margin, and 20 percent [earnings] growth. Manager 2: [However, we] are prepared to change it in the marketplace, because we are afraid to let go of [a customer]. Manager 1: We wouldn’t want to lose our biggest client. . . . Even if they were taking us to the cleaners [with low prices], we wouldn’t want the next trade pub[lication] to come out with [news about losing them].

Here, their smart rule of thumb, 20-20-20, is easy to remember with repetition and alliteration. However, it is clear that in practice, the “20 percent margin” goal quickly gets pushed aside to ensure that the “20 percent revenue growth” goal is achieved. The firm’s real pricing goal—in practice—is 20 percent revenue growth, “even if they were taking us to the cleaners” with price discounting. Their behavioral persistent price-discounting bias was then further amplified by loss aversion bias: “We wouldn’t want to lose our biggest client . . . we wouldn’t want the next trade pub to come out with [news about losing them],” said the pricing managers. Remember the biased propensity toward price discounting discovered by Ohio State and Notre Dame researchers when testing prospect theory and price-setting (discussed in chapters 2 and 3). When given the choice of cutting price by 7.5 percent to get a chance at a 40 percent gain in sales or

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keeping price constant with no change in sales, 87 percent of respondent managers chose the risky, or risk-prone price cut, contradicting prospect theory. In firms with a sales volume goal framing orientation, pricing managers were overwhelmingly risk-seeking, 97 percent choosing to repeatedly cut price. These findings place price-cutting in league with impulsive risk-seeking behaviors like skydiving, rock climbing, gambling, using recreational drugs, or engaging in promiscuous sexual activity. Why do pricing managers consistently engage in such surprisingly risky price-cutting behaviors? Because they misframe the goal of price-setting in terms not of profits or value but of customers and sales to customers. When price discounting is used strategically as part of thoughtful valuedriven segmented pricing (discussed next), it can effectively grow profitability, increase customer price satisfaction, and increase employee morale with more stable and rational pricing. But misframing pricing’s purpose to justify persistent price discounting trains customers, salespersons, and price-setters to behaviorally expect lower prices as the norm—to lower the product’s or service’s reference price in the market. Summarizing, Reed Holden said, Price discounting has become the crack cocaine of business. A brief high is followed by devastating effects on both revenue and profits. It starts when managers begin [to] use price discounts to hit revenue targets. They assume it is only temporary, that price discounting is a quick fix to a small problem. Soon that quick fix becomes a major element of their business. Customers learn that if they hold orders for the end of the quarter, even the strongest managers will panic and offer a price discount. The impact of this habit is that margins and profits fall. Discounting causes managers to lose sight of what’s really at stake: how customers value a firm’s products and services.8

Soft Customer WTP-Driven Skills for Price-Setting

Turning now to soft skills of customer WTP-driven price-setting, the first foundational soft customer-driven pricing skill is to self-diagnose how you approach customer willingness to pay and debias your customer pricesetting. Always ask, what biases are present with your current approaches to customers and their willingness to pay? Look for uniform pricing bias, direct willing-to-pay questioning bias, and persistent price-discounting bias (see figure 7.5, left). Next, there are four essential customer-driven soft pricesetting skills that enable you to design different prices and price offerings

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for different customer segments in the marketplace—to strategically enable segmented pricing (see figure 7.5, middle): assessing customer price sensitivity, price framing, price fences and price menus, and strategies to manage price sensitivity and value.

Segmenting Customers for Price Sensitivity

A useful soft skill is to anticipate—or sense—customer willingness to pay for segmenting based on price sensitivity. Figure 7.6 shows a hypothesized customer segment and price sensitivity map template that is useful for hypothesis generation about what might drive customer price sensitivity by segment. Across the horizontal dimension are nine drivers, originally identified by Thomas Nagle in 1987. The rows represent different frequently observed customer segments ranging from low-knowledge versus highknowledge buyers to out-of-pocket versus shared-cost buyers. You can adapt these general categories to customers in your own price-setting.

Price sensitivity drivers Customer segment Low knowledge vs. High knowledge buyers

Competitive Extremeness reference aversion

Switching costs

X

Risk averse vs. Risk

Difficult comparison

Brand loyalists vs.

X X

Brand switchers

Special occasion vs. Everyday buyers

X

X

X

X

Prestige, exclusivity vs.

X

Everyday buyers

Heavy users vs.

X X

Light users

Out-of-pocket vs.

X

Shared-cost buyers

Inexperienced/newto-market buyers

X

Perceived fairness

X X

prone buyers

Experienced vs.

Total End-benefit Price-quality expenditure Shared cost

X X

X

Figure 7.6 

Customer segment and price sensitivity map. Price sensitivity drivers from Thomas T. Nagle and Georg Müller, The Strategy and Tactics of Pricing, 6th ed. (New York: Routledge, 2018).

X

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To illustrate, low-knowledge buyers are less price sensitive than highknowledge buyers (figure 7.6, row 1); they are driven by low knowledge of competitive reference alternatives and the perceived risk of difficult-tocompare competitive alternatives. For example, HVAC contractors set higher prices for low-knowledge residential home owners than for highknowledge commercial landlords, even considering project size and scope, because residential home owners have little ability to compare differences among competitive HVAC companies. Thus, for low-knowledge buyers, use benefit framing strategies that leverage broad framing (see chapter 2), such as offering high-value, easy-to-process product solutions with worryfree maintenance or service plans; these also make your brand offering difficult to compare with that of competitors. Risk-averse buyers are less price sensitive than risk-prone buyers (figure 7.6, row 2). They are driven by “extremeness aversion”; that is, aversion to extreme price points (too high is too expensive, too low signals uncertain quality), and they use price as a signal for quality—higher price is perceived as higher quality. For example, the cloud content management company Box offers three pricing tiers for small and medium businesses: Starter for $7, Business for $20, and Business Plus for $33 per user per month. Its most popular seller is the middle-priced Business offering—acceptable quality, acceptable price. Further, the high-priced offering in the product line, $33, acts as an upper price anchor, leveraging anchoring and adjustment bias to anchor perceptions of higher quality and value. Therefore, add a premium higherpriced offering to a create a middle-tier acceptable price for risk-averse buyers, such as “good-better-best” product line pricing, discussed shortly. Prestige/exclusivity-driven buyers are less price sensitive than everyday buyers (figure 7.6, row 5); they are driven by a desired end benefit and price as a signal for quality. In 2019, the EMC Club at Boston’s famous Fenway Park offered corporate customers an exclusive-quality game experience with dining reservations and climate-controlled seating for $31,833 per season ticket (compared with $5,748 for an everyday infield grandstand season ticket). The EMC Club impresses customers’ clients and friends, and its price excludes price-sensitive rowdy fans who might otherwise disturb the prestigious setting for an important guest. Therefore, consider the customer’s end benefit that drives purchase, and use high prices to signal high quality but also to manage exclusive access to the benefits of high-priced offerings. Experienced buyers are more price sensitive than inexperienced/ new-to-market buyers (figure 7.6, row 8); they are driven by knowledge of competitive reference alternatives and extremeness aversion to extreme price

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points. Here, pricing rationale is based on price fairness, in which price must be perceived as “reasonable, acceptable, or just.”9 For example, Netflix encountered a fairness “backlash in 2011 when it unbundled video streaming from its older DVD-by-mail service, resulting in a 60 percent price increase for subscribers who wanted to keep both plans [i.e., experienced customers]. Netflix lost 600,000 [of these] subscribers.”10 Experienced buyers judge price fairness in three ways: (a) comparing current price with past price, (b) comparing current price with prices of similar competitive offerings, and (c) comparing current price with seller’s costs. Inexperienced or new-to-market buyers infer costs based on extrinsic indicators such as prominent advertising and marketing, impressive packaging, or expensive assets such as desirable purchase locations. For price fairness, do smaller, more frequent price increases, reminding customers how price is a fair sharing of differential value created.

Price Framing

For price sensitivity, price framing is a soft skill that strategically anticipates— or senses—how customers might prefer to pay in exchange for the value they receive based on principles of consumer behavioral theory. Effective price framing can achieve long-term profit gains and competitive advantage when it differentiates from other competitive price frames, as seen with Adobe’s subscription price framing in chapter 1 and T-Mobile’s product price framing in chapter 2. Tom Nagle and Georg Müeller provide an extended discussion on how to strategically design price frames in their text, The Strategy and Tactics of Pricing. Figure 7.7 shows a selection of possible price frames, and figure 7.8 shows a hypothesized customer segment and price-framing map template that is useful for hypothesis generation about possible price frames that might align with customer segments based on their varying price sensitivity. Across the horizontal dimension are price frames; vertical rows represent customer segments, similar to those of figure 7.6. Of course, you can adapt these general categories to customers in your own price-setting. Low-knowledge buyers prefer simple, easy-to-process, holistic pricing transactions (figure 7.8, row 1), such as product price framing in which customers pay one price in exchange for simple lifetime product ownership and use. Parents purchasing a laptop computer for their newly enrolled college student might buy a reliable model that lasts the duration of the

Cost to serve

+

+ Price paid for product ownership

+

Core + price

+

Product price framing

Value adds

Price paid per membership period

+

Subscription price framing

Core + Fees Price framing

Price paid per oz, per ml, per GB, per . . .

Price paid per bundle Price paid per usage, or per user

Usage price framing

Bundled price framing

Price paid for unlimited use

Price paid for limited trial

Initial price + price per use Two-part price framing

Flat-rate price framing

Metric price framing

Freemium price framing

Figure 7.7 

Price framing. How customers pay: the structure of price and metrics they pay.

Price frames Customer segment Low knowledge vs. High knowledge buyers

Product price Core + fees Subscription User/usage Bundled framing price framing price framing price framing price framing

X

Flat rate Two-part Freemium price framing price framing price framing

X

Risk averse vs. Risk prone buyers

X

Brand loyalists vs. Brand switchers Special occasion vs. Everyday buyers

Metric price framing

X

X X

X

X

X

X

Prestige, exclusivity Driven vs. Everyday buyers

X

X

Heavy users vs. Light users

X

X

X

Out-of-pocket vs. Shared-cost buyers

X

X

X

Experienced vs. Inexperienced/newto-market buyers

X

X

X

Figure 7.8 

Customer segment and price framing map. Source: Price sensitivity drivers, from Thomas T. Nagle and Georg Müller, The Strategy and Tactics of Pricing, 6th ed. (New York: Routledge, 2018).

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student’s college education. Microsoft offers a Surface Laptop 3 for $999 with a 13.5-inch touchscreen, industry-leading processors, and fast-charging, all-day-lasting battery. Another preference of low-knowledge buyers is bundled price framing, which is structured as price paid per bundle of products or services. For example, Comcast Xfinity offers the Standard Triple Play Bundle with internet, TV, and voice telephone for a price of $89.99 per month, rather than offering each service separately (internet $49.99/month, TV $59.99, and telephone $34.95, total $144.93/month). With mobile phones, buyers have less need for a landline voice telephone; yet many low-knowledge cable buyers continue to keep monthly landline service as part of their Comcast bundled price, yielding incremental profits for the firm and raising perceived switching costs for these low knowledge buyers. Heavy users are price sensitive buyers and prefer low unit price transactions, seeking high transaction value—meaning getting a “good deal” relative to list price (figure 7.8, row 6). Core + fees price framing appeals to such buyers; in this case, price is structured as price paid for a core product or service, plus optional fees for value-added features and/or higher cost to serve. For example, airlines offer attractive low-priced core fares in reservation systems that broadly draw many price-sensitive buyers armed with price fare knowledge from the internet. They also offer upgrades and charge fees to those less price-sensitive buyers who cost more to serve (e.g., excess bags) or seek additional value (e.g., extra legroom). Nearly half of some airlines’ profit comes from so-called ancillary fees. Heavy users are also prone to flat-rate pricing bias; they prefer a higher fixed fee and low (or zero) incremental charges, such as unlimited mobile call or data plans with fixed usage “buckets.” These users tend to pick larger fixed-fee buckets than they really need. Flat-rate price framing is structured as price paid per unlimited use. Thus, public transit systems set fixed prices for heavy users of public transportation; for example, Boston’s MBTA mass transit monthly pass with unlimited use is $90 per month. Flat-rate price framing is popular in the internet economy with heavy users—for instance, with streaming video services such as Netflix, Disney+, or ESPN+, which offer all-you-can-stream video content for a flat monthly price. Usage price framing, whereby price is structured as price paid per user or usage, so that total price paid reflects the total value the customer receives, appeals to heavy-use buyers who pay only for actual usage. But it also appeals to light-use buyers who can get access to the product or service at a scaled-down entry price—we saw this with Amazon Web Services E2C pricing in chapter 3. Another example: Salesforce significantly

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expanded the customer relationship management (CRM) market with its more affordable cloud-based usage price framing, offering a complete CRM solution for $75 per user per month—compared with CRM software companies charging large upfront enterprise license fees for a CRM software platform installation—product price framing. Related to usage price framing is metric price framing, which is structured as price paid per ounce, per liter, per gigabyte, or other purchase metric. For example, the cost for injectable aesthetic treatments (like Botox) traditionally is about $10–$15 “per unit” of material used; a frown line might require eighteen to twenty-four units, and a forehead up to twenty units. However, Sisu, a new, Ireland-based startup, plans instead to price “per treatment area” (of which there are sixteen on the face and neck—like jowls, marionette lines, dimpled chin, crow’s feet), that differentiates from competitors. See chapter 3 for a detailed discussion of metric price framing. Brand loyalists prefer bundled price framing—bundled transactions, membership, relationships, and transaction pricing structures that reflect the customer’s relationship with the seller (figure 7.8, row 3). Subscription price framing is appealing, structured as price paid per membership period, granting relationship access to a product or service. For example, MasterClass, an e-learning subscription service, offers access to video lessons from some of the world’s best actors, chefs, athletes, and other experts for $15 per month. Big names include chef Thomas Keller, movie producer Ron Howard, and professional athlete Steph Curry. For segmented pricing, this price frame offers buyers inexpensive access to high-value membership; and for sellers, it efficiently delivers incremental profit contribution streams from larger mass markets via digital machine-based customization. Brand loyalists, as well as prestige/exclusivity driven buyers (figure 7.8, row 5), also prefer two-part price framing, structured as price paid for initial access plus price paid per use. This gives customers exclusive access (excluding access to others) and thereby ensuring greater value for those fewer customers with access. For example, the Palos Verdes Golf Club near Los Angeles charges an initial fee to join of $49,000, plus annual membership dues of about $9,000. Golfers then pay $275 per round to play on a well-maintained course with less course wear and tear due to the exclusion of other price-sensitive golfers. To obtain a low usage fee of $11/hour, Zipcar customers pay an annual membership fee of $70, which gives them exclusive access to nearby cars, a book-and-drive app, plus savings on insurance, parking, and maintenance compared to owning a car. Risk-averse buyers prefer limited total customer commitment and inexpensive trial pricing transactions (figure 7.8, row 2). Freemium price

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framing appeals to these buyers; price is structured as price paid for limited trial, a free entry price for limited benefits, and premium prices for higher benefit levels. Key to the success of freemium price framing is the monetization of a large customer base. For example, Mint.com sustains a large base of customers who use free online personal financial management software, which it monetizes using advertising (for highly price-sensitive buyers), upgraded premium accounts that offer credit-report monitoring (for value-driven buyers), plus referrals to financial services firms and selling user data (for Mint.com users who have specialized needs).11 Innovations in price framing are increasingly creative, especially in the digital economy. Hermann Simon and Martin Fassnacht offer an extended discussion of some of these new price-framing models, including interactive models such as “Name Your Own Price” (e.g., Priceline), “Pay What You Want” (e.g., tipping), Two-Sided pricing models (e.g., newspapers and magazines that set prices for both subscribers and advertisers), Negative Pricing models (e.g., solar companies that pay customers for surplus electricity generation), Sharing Price models (e.g., rental rates for private unoccupied real estate using Airbnb), Prepaid pricing models, and Bonus Systems, which use bonus points from loyalty or frequent-purchase plans.

Price Fencing and Price Menus

Price fences separate customers into price segments by qualifying their willingness to pay based on different price policies, rules or requirements for compliance. With price fencing—in behavioral economic terms—what is being purchased and consumed remains essentially the same, and therefore buyers’ preferences should be unaffected, according to economic theory. But when and how consumption actually takes place varies, as well as the cost and effort surrounding purchase and consumption. As a result, the task of price-setters is to construct price fences by creatively designing value and price trade-offs that enable customers to make purchase and consumption choices that accurately reflect the true value they seek and the price—and cognitive effort—they are willing to pay. Nagle originally conceptualized price fencing in a classic article in 198412 and offers an expanded discussion in his current best-selling text with Müller.13 My summary here builds on these treatments from a behavioral economics perspective. Customers reveal their true value preferences and true price sensitivity by being willing to make two types of trade-offs in exchange for price, illustrated in figure 7.9. They can trade off when, how, and how much value is

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Trade-off value

Trade-off transaction costs Price versus quantity

Price versus quality

G-B-B product line price fences

Price versus time convenience

Price versus location convenience

Price versus privacy Price versus preferred usage

Peak-load price fences

Price versus priority access

Location-ofpurchase price fences

Buyer identification price fences

Price versus cognitive effort

Priority access price fences

Time-ofpurchase price fences

Quantity discount price fences

Price versus behavioral convenience

Willing-toearn price fences

Behavioral change price fences

Figure 7.9 

Price fencing. What customers pay: the trade-offs of price and willingness to pay.

received—value-driven trade-offs—in exchange for price (see figure 7.9, left). Or they can trade off the monetary, physical, and cognitive costs surrounding actual purchase and consumption—transaction cost–driven trade-offs (figure 7.9, middle and right). Let’s examine how these trade-offs inform the design of price fences. VA L U E - D R I V E N T R A D E - O F F S F O R D E S I G N I N G P R I C E F E N C E S

Three price-fencing strategies require customers to make value-driven trade-offs in exchange for better price (see figure 7.9, left). Good-better-best (G-B-B) product line price fences require trade-offs involving seemingly imperceptible differences in quality that buyers might not fully appreciate prior to purchase. Simple G-B-B price fencing separates different types of buyers: risk averse versus risk prone, or experienced versus inexperienced buyers. For example, Lennox International offers premiumquality “best performance” variable-capacity central air conditioners that are

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the quietest, most efficient, and best at removing humidity and achieving balanced temperature. They also offer “better-performance” two-stage models, which are quiet and cool evenly but reportedly not as effectively as variablecapacity models. Or they offer “basic-performance” single-stage models, which are not as quiet and have temperature fluctuations and leave cold spots in the living space. Some buyers might believe that they need a best-performance model, and others that they can get by with a basic-performance model. But chances are, most buyers will still end up purchasing the middle offering in the product line, the better-performance model, because of the behavioral economic principle of extremeness aversion (discussed earlier), whereby buyers suspect uncertain quality at low prices and expensive quality at high prices and therefore default to the middle price position. Peak-load price fences require trading off price against time of preferred usage and consumption. It is used in electric utilities, toll roads, and congestion pricing in cities such as New York City. Customers reveal higher willingness to pay by consuming the product or service at preferred and more valuable times for higher price, or less preferred and less valuable times for lower price; this separates risk-averse versus risk prone buyers. Recall how ride-sharing servicers like Uber and Lyft use surge pricing to charge customers higher prices during peak demand periods. Utpal Dholakia summarized: Surge pricing achieves two important goals for Uber and its customers. One is that it increases supply of drivers.  .  .  . Uber, researchers found that surge pricing doubled the number of drivers during a busy period after a sold-out concert in New York City. Second, surge pricing is an effective way to control customer demand and allocate available rides to those people who value them more. Some Uber customers may simply find the high surge price to be unacceptable and find other means of transportation. Others, who value the ride more, will be willing to pay the surge price.14

Priority access price fences also separate risk-averse versus risk prone buyers and similarly require customers to pay a higher price to get the product or service they value most. For example, prior to the COVID-19 pandemic, to avoid waiting in line for rides at Universal Orlando Resort, risk-averse buyers could purchase an Express Pass for $69.99 to $89.99 per person to skip the regular lines, in addition to the park’s daily entry fee of $119 per person—a 59 to 76 percent price premium for priority access. In behavioral economic terms, customers framed an Express Pass purchase

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not as a price surcharge—a loss—but as an insurance policy for ensured access—an expected gain. A Tripadvisor reviewer wrote: “Strongly urge the express pass. It is well worth the investment!”15 T R A N S AC T I O N C O S T – D R I V E N T R A D E - O F F S TO DESIGN PRICE FENCES

The second form of price fencing requires customer trade-offs in transaction costs incurred in advance of or surrounding purchase and consumption, such as extra time, inconvenience, increased effort, or cognitive exertion (see figure 7.9, middle and right). Here are some examples. Quantity discount price fences separate heavy versus light users and require trading off price against quantity purchased. Customers reveal low willingness to pay by being willing to increase purchase quantity. For example, Staples sells fine Hammermill Copy Paper with the following quantity discount schedule, which offers an attractive “certain gain” to high-volume buyers: Package price

$9.06

$26.99

$39.99

Reams/package

1

5

10

Price/ream

$9.06

$5.40

$4.00

Staples also offers quantity step discounts to encourage customers to commit to a longer-term purchase quantities: “25 percent back in rewards when you purchase $75 of ink or $200 of toner.” And with a cumulative volume discount, Staples Rewards program members get 2 percent in rewards toward future purchases based on annual spending up to $999 and 5 percent above $1,000. These reward programs separate brand loyalists versus brand switchers, by offering valuable though “uncertain gains” that have only a probability of being redeemed as rewards points versus the immediate cash discount on Hammermill paper, which appeals to heavy users or brand switchers. Willing-to-earn price fences require trading off price against cognitive effort. They separate heavy versus light users, out-of-pocket versus shared cost buyers, and experienced versus inexperienced buyers—customers reveal low willingness to pay by incurring time and effort to qualify for promotional fulfillment requirements. For example, the Stop and Shop grocery chain offers digital coupons to price-sensitive customers: “Save $1.00 on any ONE (1) Kleenex Bundle Pack (expires in 5 days), Save 50¢ when you buy FIVE CUPS any variety of Yoplait (expires in 14 days), Save $3.00 on ONE Crest Gum Detoxify, Gum and Enamel Repair (expires in 25 days)”, and so on. Even with a helpful mobile app, the cognitive effort

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required to sift through disorganized random deals, reward points, and expiration dates is significant. In behavioral economic terms, only pricesensitive buyers are willing to invest in System 2 analytic processing effort to avoid “losing” a discount, motivated by loss aversion. Time-of-purchase price fences require trading off price against the convenience of when to purchase. Customers reveal low willingness to pay by purchasing at inconvenient times, which are strategically set by the seller. For example, retailer Black Friday promotional sales the day after the American Thanksgiving holiday offer highly promoted price discounts for customers who are willing to forego their holiday weekend plans, competing against aggressive bargain hunters before on-sale inventory disappears. Only the fittest and most price-sensitive experienced buyers will win their low-priced prize. Automobile dealers similarly schedule sales events around President’s Day, Memorial Day, and Independence Day, all primed to separate price-sensitive experienced buyers who are willing to purchase at a less convenient time for a lower price from inexperienced buyers who are not. Location-of-purchase price fences require trading off price for the convenience of where to purchase, or location convenience. Customers reveal low willingness to pay by purchasing at inconvenient locations and incurring high transaction costs; this appeals to heavy versus light users and experienced versus inexperienced buyers. For example, Costco customers are required to drive longer distances to get the very low prices that this warehouse retailer offers. In Massachusetts, five Costco stores service about four hundred square miles of the larger Boston metropolitan geography. By comparison, Shaw’s grocery chain has twenty stores to service the same geographical area and are much more conveniently located but with higher prices than Costco. Buyer identification price fences require trading off price for privacy. Customers reveal low willingness to pay by revealing their identity; this separates low-knowledge versus high-knowledge buyers. For example, hotels offer 55-plus prices to seniors in exchange for revealing their approximate age. Home Depot accesses the user’s internet IP address to set price based on user location: a “250-foot spool of electrical wiring [was priced at] $70.80 in Ashtabula, Ohio; $72.45 in Erie, Pa.; $75.98 in Olean, N.Y. and $77.87 in Monticello, N.Y. . . . Staples.com showed higher prices most often— 86 percent of the time—when the ZIP Code actually had a brick-andmortar Staples store in it, but was also far from a competitor’s store.”16 Behavioral change price fences require trading off price against behavioral convenience that might result in higher cost to serve. Customers reveal low willingness to pay by changing their purchase behaviors to enable the seller to serve them more efficiently and less expensively. For example,

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Fedex charges an address correction fee of $17 per correction to encourage customers to more carefully enter their address information. It charges for “dangerous goods (dry ice)” ($5.85 per package), “delivery reattempts” (7.3¢ per pound), and “collect on delivery COD” ($15.50 per package), and so on. The U.S. Postal Service charges extra for non-standard-sized packages that store less efficiently in its trucks and warehouse locations. PRICE MENUS

Economists refer to price menus and “menu costs” (the term originates from restaurant menus), the costs of designing and changing prices that include, for example, charges by pricing consultants to design price points (using price framing and price fencing), and costs to install and replicate the price menu in computer systems, displays, websites, and print materials. Price menus offer choices among different value and price configurations, called “price offerings” (discussed earlier in the chapter), that enable customers to select the value they prefer to get for the price they are willing to pay, or give. Price menus are a visible manifestation of the firm’s pricing strategy; they are the culmination of the company’s value estimation to set list price strategically, segment the market by price sensitivity, and strategically frame price and construct price fences that structure trade-offs required to obtain lower price. Firms that fail to spend time on menu design often end up with poorly designed, difficult-to-navigate, or nonexistent price menus, which places a burden on sales representatives who are tasked with selling customers based on value and willingness to pay. This also makes the purchase decision confusing for customers and causes them to search elsewhere to easier behavioral decision menus and lower decision costs. Figure 7.10 shows a basic price menu from Fedex for a ten-pound package shipment from Boston to Miami on October 13, 2020. Customers can pay more, $161.07 for earliest-arrival overnight delivery, or 87 percent less, $21.55, for a slower, three-day delivery. This is but a small portion of the Fedex price menu; its larger menu includes numerous web pages designed to appeal to different customer segments, with price framing and price fencing over many shipping priority options, delivery zones, shipment types, weights, and various surcharges. M A N AG I N G CU STO M E R S FO R VA LU E A N D P R I C E S E N S I T I V I T Y  A final

willing-to-pay soft skill is strategically managing customers and customer relationships with respect to value, price sensitivity, and how they choose to negotiate with you. Reed Holden’s pricing work has been at the forefront of

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Amounts are shown in USD

Arrives on Wed, Oct 14

Delivered by 8:00 AM

$161.07

®

FedEx First Overnight

Base rate Fuel surcharge Estimated total

Next day service

$156.00 $5.07 $161.07

Delivered by 10:30 AM

$129.06

®

FedEx Priority Overnight

Delivered by 4:30 PM

$129.01

FedEx Standard Overnight

2nd day service

Arrives on Thurs, Oct 15

Delivered by 10:30 PM FedEx 2Day

® $92.05

® AM

Delivered by 4:30 PM

$80.28

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FedEx 2Day

Arrives on Fri, Oct 16

3rd day service

Delivered by 4:30 PM

$53.12

®

FedEx Express Saver

Delivered by End of Day FedEx Ground

$21.55

®

Figure 7.10 

FedEx basic price menu: shipping a ten-pound package from Boston to Miami on October 13. Source: FedEx, https://www.fedex.com/en-us/online/rating.html.

this area, and I quote or paraphrase from his book, Negotiating with Backbone, in this section.17 There are four types of buyers with different value and pricing motivations, and they should be managed differently. A thumbnail sketch of each is found in figures 7.11a and 7.11b and described below. Price Buyers buy solely on price; they are high-knowledge, sophisticated, and highly price-sensitive buyers trained in price negotiation. For example, government agencies like the U.S. General Services Administration (GSA) are price buyers. The products they purchase must be certified to meet precise specifications; your bid must be lower than those of other suppliers. The buying decision is made exclusively on price. How should you manage price buyers? Opportunistically. Know the cost to serve these customers and establish a walkaway price up front; ensure that you make a positive profit contribution from each order. Offer these buyers a basic version of your product or service, without extra features or service. Require

Who they are Price buyers

How to manage How to manage price buyers:

Driven by best price

Opportunistically Price buyers buy solely on price. To ensure that their operation will not be disrupted by product shortages caused by the low-price vendor, price buyers usually have high levels of product or service expertise. They have internal resources to do the due diligence on the vendors they select to ensure they can deliver. The procurement department generally handles price buying. If the company requires technical expertise, it tends not to rely on the vendors but has the expertise in house. Price buyers resist forging relationships with their vendors, and they recoil at paying extra for services. They do not require or want ROI calculations to prove value and differentiation. Instead, they rely on specifications that the production or technical department sets. With price buyers, there’s little or no game playing. The focus on price is undisguised.

Relationship Buyers

• • • • • • • •

Price buyers

Evaluate orders opportunistically Know cost to serve customer Establish a “walk-away price” Positive contribution for each order Basic product, no service Low-cost transaction methods No long-term contracts Aggressive negotiation strategy

How to manage relationship buyers: With care

Driven by best solution

Relationship buyers often look more like partners than mere customers. They rely on their suppliers to provide the necessary products and services in a timely manner and to be available to answer questions, respond to problems, and help the buyer out of certain jams. Often they expect a team dedicated to their needs. They trust their suppliers to invest in Relationship understanding their business, products, or services. To that end, they tend buyers to be unusually transparent in their business practices, as well as honest about their difficulties. They tend to give suppliers an open look at the purchase process and essentially unfettered access to the key decision makers . . . Relationship buyers tend to have only one supplier for a particular purchase . . . [or sometimes] a relationship with a second supplier as a safety measure or a way to manage risk.

• • • • • • •

Stress System 1 value communication strategies Emphasize intangible benefits Use bundled solutions and packages Complete product/service menu Offer account loyalty incentives Give responsive, priority service Stress warranties, contracts, security

Figure 7.11a 

Managing your customer portfolio for value and price sensitivity. Adapted from Reed K. Holden, Negotiating with Backbone: Eight Sales Strategies to Defend Your Price and Value, 2nd ed. (Old Tappan, NJ: Pearson, 2016).

Who they are Value buyers

How to manage How to manage value buyers: Intelligently

Driven by best value and best price

Value buyers want proof of financial value or return on their investment (ROI) and want to know how your solutions will make a difference for them. They want to understand the financial implications of your offering. They might be loyal to a supplier that has done this work in the past, but they won’t let vendors rest on their laurels. . . . The individual handling the procurement for a value buyer is often a department manager . . . they are looking for ways to reduce costs, work more efficiently, and earn more profits. These firms do extensive internal value calculations to present to their customers. But value on its own doesn’t give customers the full picture; only when you compare your solution to a competitor’s and show the financial return on your differences do you allow them to make good decisions. Poker player buyers

Value buyers

Act like they’re driven by price

Poker players are buyers who want both value and the differential benefits that come with having a durable relationship with their suppliers, but they have learned that a certain amount of gamesmanship allows them to get the value they want at discount prices. Poker players have learned to obscure their real requirement and bluff about their true intentions in an Poker player attempt to manipulate their suppliers into offering price discounts or other buyers concessions. They pretend to be price buyers when they are really not. In other words, poker players have learned that if they focus on price, they can get vendors to offer discounts without compromising high-value features and services. This is why give-gets are real gems in negotiations. They stand up to and expose the poker player’s bluffs.

• • • • • • • •

System 2 value engagement strategies Establish total economic value Use economic value models Engage in value consulting Trade up, trade down Use tailored configuration Manage to a price/value menu Use innovator/lead buyer programs Use alpha/beta test relationships Use unbundled pricing

How to manage poker player buyers: With confidence

• • • • • • •

System 1 value communication strategies Know your value to the customer Price with confidence Use give-gets to expose true WTP Look for sudden changes in behavior Formerly loyal customer behavior Suddenly price-driven behavior More assertive procurement Manage the buying center Have a good relationship with the decision maker

Figure 7.11b 

Managing your customer portfolio for value and price sensitivity. Adapted from Reed K. Holden, Negotiating with Backbone: Eight Sales Strategies to Defend Your Price and Value, 2nd ed. (Old Tappan, NJ: Pearson, 2016).

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low-cost purchase transaction methods, such as online buying options with no human interaction. Avoid long-term contracts with price buyers that tie up your delivery capacity with low-margin business; the opportunity cost of turning away other, more profitable buyers makes long-term contracts costly. Prepare your team with aggressive price-negotiating skills. Relationship Buyers mostly purchase based on best solution; they are driven by the value of the relationship that they have with their trusted suppliers, which is called “relationship value.” The opposite of price buyers, they are usually less price-sensitive, more risk-averse, more brand-loyal, and worry about switching costs. For example, McDonald’s still views its suppliers as strategic partners, part of a three-legged stool of strategy that includes customers and franchise operators. The company works with its key suppliers without contracts and with a simple handshake. As you would expect, trust is a key component of that relationship. Everyone works for the betterment of the customer, the franchise, and the company.18

Relationship buyers are often small to midsized companies that rely on their trusted vendors to set fair prices and provide solutions. How should you manage relationship buyers? With care. Emphasize the intangible benefits of the relationship. Offer bundled solutions and packages complete with full service and support. Use bundled price-framing strategies that frame the purchase in terms of a full-solution relationship rather than transactional purchases. Offer account loyalty incentives and responsive and high-priority service, including rescuing customers in times of need. Offer generous warranties, contracts, and relationship security. Value Buyers are driven by best value for best price; they are looking for “tangible financial value” (economists refer to this as “economic value”). Like price buyers, these are high-knowledge, risk-taking, highprice-sensitive buyers who negotiate on price and perceive that there are low costs to switch between competitive brands. The key difference is that they are willing to pay for proven value. They like early, even exclusive, access to cutting-edge innovation that enables them to offer rare, unique differential value to their customers. Two examples of value buyers are Toyota and Walmart. Yes, these organizations can be brutal in their determination to extract price concessions, but they also have a long history of working with and supporting vendors  .  .  . [and they] have the resources to help their

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vendors get better. However, these firms will switch if they discover a vendor that is better at providing value.19

How should you manage value buyers? Intelligently. Establish total customer value—especially net differential value—using objective economic value models and value engagement and communication strategies that help them estimate customer value. Teach salespersons how to engage with these buyers in “value consulting.” Use a well-designed value-pricing menu with various price offerings to help customers trade up or down to find the best value for the best price. Invite value buyers into alpha- or beta-test relationships, or as part of innovator or lead-buyer programs. And reframe price using unbundled price-framing strategies. Poker Player Buyers, like relationship buyers, purchase mostly based on the value of the relationship; or, like value buyers, based on tangible financial value. However, they act instead as if they were price buyers, driven by best price. Like relationship buyers, they are often less price-sensitive, more risk-averse, and more brand-loyal; or, like value buyers, they’re always looking for leading-edge differentiation value. But their posture appears priceaggressive; they act as if they were risk-takers who are willing to switch if they don’t get the price they want. For example, Elon Musk caught wind of a customer’s threats on Reddit to press for lower price when learning his Tesla delivery would be delayed, despite already having been offered a lower price. Here is the customer’s post: The sales rep “insinuated that if I don’t agree to . . . finalize the sale on Friday . . . then the discounted pricing we already agreed on wouldn’t be honored. . . . I think if I really pressed them on that they would fold but not entirely sure. . . . I am probably going to try and get something out of this on their side.”20

Musk instantly dispatched an email to Tesla employees that leaked to Twitter: There can never—and I mean never—be a discount on a new car coming out of the factory in pristine condition. . . . I always pay full price when I buy a car and the same applies to my family, friends, celebrities, no matter how famous or influential.21

The poker player customer instantly folded: “Wow so this kind of blew up in an unexpected way for sure . . . just want to say that the whole process with Tesla has been incredible [sic] smooth, no pressure.”22

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How should you manage poker player buyers? With confidence. Know the value you deliver to your customer, then price with confidence. If customers demand lower price, subtract value from the deal and test their posturing— this is called “give/gets.” Look for sudden changes in customer behavior, such as from loyal to suddenly price-driven behavior. And manage relationships with all persons involved in the purchase—called “the buying center”—not just the procurement manager; especially manage your relationship with the key decision maker.

Hard Customer WTP Skills for Price-Setting

Robust analytical hard skills are embraced by many companies with sophisticated pricing who use big data to model what customers actually pay, or revealed willingness-to-pay. These sellers include airlines, hotels, and ridesharing and auto rental companies, among many others. They employ hard pricing skills and assets that are the standard of the revenue management pricing world. Here is a selection of these hard skills for assessing willingness to pay that are accessible to most price-setters.

Customer Pricing Analytics

With pervasive big data and cloud computing power, customer pricing analytics are increasingly accessible to most firms, including small and midsized firms. Pricing analytics require good-quality transaction data across stock-keeping units (SKUs) and usually require data scrubbing for analysis, which eliminates data entry errors, duplicates, and formatting errors. With good-quality data, various pricing analytics tools guide and provide structure for price-setting. One useful tool originally developed by McKinsey, “price band analytics,” is a way to identify pricing bias in your customer transactions that otherwise goes unnoticed. Figure 7.12 shows a chart of transaction prices and volumes for all customer orders for a given product/service SKU, with prices on the vertical axis and volume per customer order on the horizontal axis. This figure illustrates that most transaction prices are within the company’s strategy guidelines for their price band (the range bars around each transaction). However, some transactions (in the shaded areas) are outliers, well outside the price band. These warrant focused inquiry to discover the source of the bias—for example, an

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Price $3.90 $3.70 Outliers $3.50 Strategic price band

$3.30

Trans price Key Dlr price Buy Grp price Cost

$3.10 $2.90 $2.70 $2.50 0

200

400

600

800

1000

1200

1400 1600 Sales volume

Figure 7.12 

Price band analytics: SKU #65419, order transactions. Source: Disguised client transactions data.

aggressive customer negotiating on price, a rarely used special discount, or salespersons in need of better pricing discipline. Related to price band analytics are “price distribution analytics,” which show a frequency distribution of transaction prices for all customer orders for a given product/service SKU, as shown in figure 7.13. Price ranges are arrayed on the horizontal axis, with their corresponding share of SKU volume and share of SKU gross profit dollars on the vertical axis. You can see that price points above $3.20 are proportionately greater profit contributors relative to volume. Here, if customers paying less than $3.10 (the shaded area) increased the price they pay to at least $3.10, the incremental gross profit contribution would be $31,035. Vendavo pricing consultants recommend “price optimization analytics” as a useful analytics skill, based not on transaction data but on “deal data”—the deals that you won and those you lost. Add to this the “pocket margin” data associated with each deal (margins adjusted for all discounts and allowances, discussed in chapter 5). These two data streams enable a straightforward price optimization projection.

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60%

50%

48% 43%

40% Outliers % Tot vol % of Tot GP

30%

20% 18%

20%

If minimum P=$3.10, ΔGP$ =$31,035

13% 11%

10%

8% 2%

4%

4%

5%

10% 6% 6% 2%

1%

0% P=3.79

P=3.59

P=3.49

P=3.39–3.30 P=3.29–3.20 P=3.19–3.10 P=3.09–3.00

P=2.81

Figure 7.13 

Price distribution analytics: SKU #43521, order transactions. Source: Disguised client transactions data.

For example, suppose that historical transaction data for a certain segment shows pocket margins averaging 20 percent, but going as high as 50 percent [see figure 7.14, section a]. Based on this data it is tempting to tell the salesforce to aim for 50 percent margins, since that level of profitability has been achieved in the past. Now suppose the deal data shows that we have in fact previously priced many deals at a 50 percent margin, but lost 98 percent of them [see Figure 7.14, section b]. Suddenly 50 percent margins no longer look like such a good idea. Look again at Figure [7.14, b]. What is the optimal profit margin? If we multiply the margin by the win rate, we get an expected profit curve [see Figure 7.14, c]. This clearly shows the profit-maximizing margin, which is the margin that strikes the best balance between winning deals and earning profit on each deal [about 25 percent in this example].23

Pricing Solutions consultants in Toronto use a pricing tool called “Microsegmentation Analytics,” which drills down into a firm’s customer

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80%

10%

60% 40%

5%

20%

0%

0% 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50% 55%

15%

Pocket margin Margins as high as 50% have been achieved...

Pocket margin ...but with 50% margin, win rate is only 20%

18% 16% 14% 12% 10% 8% 6% 4% 2% 0% 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50% 55%

20%

20%

Expected profit (%)

100%

Win rate (%)

25%

0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50% 55%

% of units sold

Use of deal data for price optimization (a) Transaction history (b) Win rate vs. pocket margin (c) Pocket margin optimization

Pocket margin Optimal margin is around 25%

Figure 7.14 

Vendavo price optimization analytics. Source: Allan Gray, Michael Lucaccioni, Jamie Rapperport, and Elliott Yama, “Pricing Software: Ten Predictions for the Future,” in Visionary Pricing: Reflections and Advances in Honor of Dan Nimer, Advances in Business Marketing & Purchasing 19, ed. G. E. Smith (Bingley, UK: Emerald, 2012), 278.

base and product portfolio to create a two-dimensional segmentation matrix, shown in figure 7.15. Their process follows four simple steps: • Segment customers based on value into Bronze, Silver, Gold, and Platinum customers; • Segment products based on value differentiation as perceived by customers, into Differentiated, Slightly Differentiated, or Undifferentiated; • Establish floor, target, and expert price levels for all customers/ product segment combinations in the microsegmentation matrix based on gross margin contribution goals; and • Identify specific pricing opportunities by comparing current pricing levels with new pricing targets.

The microsegmentation tool color codes (shown in shades of gray) product-customer segments based on their existing versus potential gross margin contribution (high margin contribution, below threshold margin

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FSD - Matrix by revenue

FSD - Upper quartile PSL customer group

PSL product group

Bronze

Silver

Gold

PSL customer group Platinum

$178,475

$1,031,541

$238,690

$8,596,359

Differentiated

PSL product group

Bronze

Silver

Gold

Platinum

39%

16%

31%

12%

14%

10%

13%

12%

10%

9%

Differentiated $180,150

$4,633,491

Slightly differentiated

Slightly differentiated $3,400,847

Undifferentiated

$10,971,030

$26,392,948

$22,186,058

FSD - Median

Undifferentiated

FSD - Lower quartile PSL customer group

PSL product group

Bronze

Silver

Gold

PSL customer group Platinum

38%

16%

29%

9%

13%

10%

11%

8%

7%

5%

Differentiated

PSL product group

Bronze

Gold

Platinum

–9%

10%

12%

8%

11%

8%

7%

4%

5%

2%

Differentiated

Slightly differentiated

Silver

Slightly differentiated

Undifferentiated

Undifferentiated

Figure 7.15 

Pricing solutions microsegmentation analytics. Source: Improving Bottom Line Results with a Transformative Pricing Strategy, Case Study: Foodservice Pricing Strategies, PricingSolutions.com, accessed February 25, 2021, https://www.pricingsolutions.com/improving-bottom-line-results-transformative -pricing-strategy/.

contribution). In one case study, they report 2–3 percent in annual, short term bottom line improvement through achievable pricing opportunities, greater confidence in pricing methods and decisions through a companywide pricing culture change, and consistent and sustainable pricing processes which were implemented and supported by the Pricing Tool and the Pricing Solutions team.24 Bain and Company consultants summarized the importance of datadriven tools and software analytics: Most companies can raise their game by adopting pricing software tools. Based on the performance of historical deals, software solutions—whether in-house or from a provider such as Vendavo or Price f(x)—can provide frontline reps with real-time pricing feedback based on the characteristics of a deal under way. Using dedicated pricing software is associated with much stronger pricing decision making, our survey analysis shows. Yet despite the proven value of pricing software, only 26 percent of survey companies use it.25

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Conjoint (Trade-Off) Analysis

Conjoint analysis is an effective hard pricing skill to discover and test willingness to pay by simulating the trade-offs that customers make when considering a purchase. Rather than ask customers the price they are willing to pay, which leads to predictable direct willing-to-pay questioning bias, discussed earlier, conjoint analysis presents customers with descriptive/visual test concepts of the product or service (including different test prices) and, in a simulated purchase, asks them to choose from among the product concepts shown or to rate or rank them in terms of their appeal. Conjoint analysis is a scientifically based method based on experimental design that virtually decomposes the customer’s purchase decision process to diagnose which product attributes are most important, configure a best design based on the test simulation, and estimate price sensitivity. There are various sophisticated conjoint methods and models. Let’s first look at a “full-profile” example that illustrates the usefulness of conjoint analysis to price-setters. Southern Resorts, a sample hotel property developer, has targeted Jupiter Inlet on Florida’s Atlantic coast as an attractive locale to build a new hotel, to be called “Southern Resort at Jupiter.” Southern wants to test four product dimensions (called “attributes” in conjoint), each with three variations. The first dimension is property location, with three possibilities (called “attribute levels”): (a) an oceanfront location on the beach, (b) a location four blocks from the oceanfront on Florida’s Intracoastal Waterway, or (c) a location inland ten blocks from the oceanfront. The other three test dimensions and their variations are room size: large (500 square feet), midsized (430 square feet), and small (370 square feet); price per night stay: $189, $299, and $459; and brand: Southern Resort at Jupiter and two possible competitors, the Jupiter Resort Hotel and Wyndham Grand Jupiter hotel. Figure 7.16 shows these test dimensions in a simple 3 × 3 matrix formed by price and property location; the remaining two dimensions, room size and brand, are then placed in the interior cells in such a way that the design is “balanced” (the occurrence of each attribute level is evenly matched with the occurrence of all other attribute levels). Thus, this simple 3 × 3 matrix has become a 4 × 3 conjoint design (called a “Graeco-Latin square,” one of many possible fractional factorial experimental designs). We use this design to test customer preferences for the nine product concepts shown in the test matrix of figure 7.16; because the design is balanced, these nine

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Property Location

Price per night stay

$189

$299

Oceanfront

Intracoastal Inland 4 blocks from Oceanfront 10 blocks from Oceanfront

Jupiter Resort Hotel

Wyndham Grand Jupiter Southern Resort at Jupiter

Small room (370 sf)

1

Midsized room (430 sf)

3

Southern Resort at Jupiter

Jupiter Resort Hotel

Wyndham Grand Jupiter

Midsized room (430 sf)

Large room (500 sf)

Small room (370 sf)

4

5

Wyndham Grand Jupiter Southern Resort at Jupiter

$459

Large room (500 sf)

2

Large room (500 sf)

7

Small room (370 sf)

8

6

Jupiter Resort Hotel Midsized room (430 sf)

9

Figure 7.16 

Southern Resort at Jupiter—conjoint analysis experimental design.

concepts actually represent unbiased consumer judgments on 34, or 81, possible configurations. To gather data, Southern Resorts management administered the conjoint experimental test online to a sample of three hundred respondents in their demographic target market, asking them about their preferences when staying at a hotel at Jupiter Inlet. Respondents were sequentially presented with a picture and description (a “concept”) of each of the nine hotel configurations, corresponding to its respective cell in figure 7.16, and answered questions about each configuration. For example, concept 3 (upper right) shows the Southern Resort at Jupiter hotel, with a large room (500 square feet), located inland 10 blocks from the oceanfront, with a price of $189 per nightly stay. Using a questionnaire, respondents indicate their preference for each concept by rating them one at a time (or ranking or choosing among preferred concepts in other variations of conjoint administration). Figure 7.17 shows the results of the online test. For price per night stay (columns 2–3), the average customer ratings (called “part-worths” in conjoint analysis, meaning part of the full worth of the product) on a 9-point

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Test attributes

Price per night stay

Average rating (PWs)

Property location

Average rating (PWs)

Room size

Average rating (PWs)

Brand name

Average rating (PWs)

Attribute Level 1

$189

6.40

Oceanfront

7.57

Small Room 370 SF

3.25

Jupiter Resort Hotel

5.95

Attribute Level 2

$299

4.03

Intracoastal 4 blocks from oceanfront

4.50

Midsized Room 430 SF

3.95

Wyndham Grand Jupiter

Attribute Level 3

$449

2.68

Max-Min Range

$260

3.72

B

Relative importance (%)

29.7%

C

Willingness to pay ($)

$69.89 per PW

D

A

Inland 10 blocks from oceanfront 10 blocks

A

A

5.10

A

2.33

Large Room 500 SF

5.62

Southern Resort at Jupiter

5.24

130 SF

2.37

1.20

41.8%

18.9%

9.6%

$36.62 per Block

$1.27 per SF

4.75

Jupiter Resort

+ $83.87

Wyndham Grand

+ $24.46

Figure 7.17 

Southern Resort at Jupiter—conjoint willingness to pay, relative importances.

scale (1 = low, 9 = high) were 6.40 for price level $189, 4.03 for $299, and 2.68 for $449 (see the left area A of figure 7.17). Average customer ratings (part-worths) for the remaining three dimensions, each with their three respective attribute levels, are shown to the right in corresponding area A. Looking at the range of customers’ ratings (comparing maximum to minimum) in area B, it is apparent that property location had the broadest range of customer responses; expressing this range in percentage terms (area C), property location accounted for 41.8 percent of customers’ decision thinking when considering a hotel at Jupiter Inlet, price accounted for 29.7 percent, room size 18.9 percent, and brand name, 9.6 percent. Now let’s turn to willingness to pay. The results show that as price increased from $189 to $449 (a difference of $260), customers’ average rating (part-worth) decreased from 6.40 to 2.68 (a difference of 3.72), suggesting that each differential part-worth was worth $69.89 ($260 ÷ 2.68), shown in area D; now use this to estimate willingness to pay. With respect to property location, as the distance increased from the oceanfront (a total difference of ten blocks for the inland location), customers’ average rating (part-worth) decreased from 7.57 to 2.33 (a difference of 5.24), or 1.91 blocks per part-worth (10 ÷ 5.24). Given that each part-worth was worth $69.89, customers are willing to pay $36.62 per block in distance from the oceanfront ($69.89 ÷ 1.91).

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With respect to room size, the same logic shows that customers are willing to pay $1.27 per additional square foot. With respect to brand name, customers’ average rating (part-worth) for the Wyndham Grand was 0.35 greater than the new Southern Resort at Jupiter (5.10–4.75), and for the Jupiter Resort Hotel, it was 1.20 greater. Again, because each part-worth was worth $69.89, compared with the new Southern Resort at Jupiter, customers are willing to pay $24.46 more to stay at the Wyndham Grand (0.35 × $69.89) and $83.87 more to stay at the Jupiter Resort Hotel. Figure 7.18 shows that these willingness-to-pay calculations can be represented graphically to show customers’ price sensitivity, a revealed price sensitivity based on the conjoint simulation. High price-sensitive customers will evoke low willingness to pay per part-worth, and vice versa for low price-sensitive customers, which then drives willingness to pay for each component dimension of your product or service being tested using conjoint analysis.

Comparative price sensitivity 10.00

High price sensitivity WTP $35.09 per PW

9.00

Moderate price sensitivity 7.00 WTP $69.89 per PW

Average rating (PWs)

8.00

6.00 5.00 4.00 3.00

Low price sensitivity WTP $144.44 per PW

2.00 1.00 0.00 $189

$299 Price per night stay

Figure 7.18 

Southern Resort at Jupiter—price sensitivity and willingness to pay.

$449

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One advantage of conjoint analysis is its ability to easily segment customers, because part-worths, relative importance, and willingness to pay are all estimated at the individual customer level—one conjoint model for each respondent customer. Individual conjoint models thus can be combined in virtually any type of segmentation—price-sensitive versus non-pricesensitive buyers, oceanfront-driven customers, brand-driven buyers, and various combinations of demographic variables: geography, age, income, and so on. The foregoing example used the full-profile conjoint method. Other methods include choice-based conjoint (CBC, whereby respondents choose their most preferred full-profile concept), adaptive conjoint analysis (ACA, which varies the choice sets that are presented to respondents based on their preference), and menu-based conjoint analysis (MBCA, in which respondents choose what they want in their ideal product). “Conjoint analysis methodology has withstood intense scrutiny from both academics and professional researchers for more than 30 years. It is widely used in consumer products, durable goods, pharmaceutical, transportation, and service industries.”26 P R I C E E L A S T I C I T Y A N D P R I C E A DJ U S T M E N T A N A LY T I C S

Economists advocate estimating customer willingness to pay by calculating price elasticity of demand. Price elasticity is a revealed preference method based on customers’ actual purchase behaviors. Start with a product’s current price and sales volume as a baseline and ask, how does volume change in response to a price change, measured as %ΔQ/%ΔP?27 Some products experience low price elasticity, or inelastic demand; volume purchases change little in response to a change in price—price elasticity is less than 1.0. This might apply in purchases of gasoline or heating oil, for instance. Other products experience high price elasticity, or elastic demand; their volume purchased changes much more in responses to a change in price— price elasticity is much greater than 2.0. Still other products exhibit moderate price elasticity of 1.0 to approximately 2.0. To illustrate price elasticity and price adjustment analytics, Allerton Auto Parts, an illustrative independent auto parts retailer, carries many SKUs and tracks how price changes affect changes in volume for each (see figure 7.19). For example, the last price change for replacement batteries was a promotional price reduction of 5 percent; replacement battery sales increased 15.4 percent in response. Price elasticity of demand for replacement batteries during this period was therefore 3.08 (calculated as 15.4% ÷ −5.0%,

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SKU

Baseline CM %

Last change Actual Price in price sales change elasticity %ΔP %ΔQ %ΔQ/%ΔP

Breakeven Assessment Future price sales change of the BE* price change adjustments

Replacement battery

29.4%

–5.0%

15.4%

3.08

20.5%

Unprofitable

Raise

Alternator

24.0%

–2.5%

13.2%

5.39

11.4%

Profitable

Lower

Starter motor

22.7%

7.1%

–6.8%

0.96

–23.9%

Profitable

Raise

Voltage regulator

25.2%

–3.2%

4.3%

1.34

14.6%

Unprofitable

Raise

Brake rotor

17.2%

–2.4%

4.5%

1.88

16.2%

Unprofitable

Raise

Brake calipers

15.5%

4.3%

–5.0%

1.16

–21.7%

Profitable

Raise

Brake pads

13.1%

–1.7%

15.0%

8.82

14.9%

Profitable

Maintain

Brake master cylinder

19.0%

2.5%

–1.9%

0.76

–11.6%

Profitable

Raise

Headlight assembly

37.3%

3.3%

–11.0%

3.33

–8.1%

Unprofitable

Raise

Remote keyless entry

42.6%

4.3%

–2.3%

0.53

–9.2%

Profitable

Raise

Wiper motor

37.6%

–2.5%

8.5%

3.40

7.1%

Profitable

Raise

Sunroof motor

42.1%

–4.0%

12.3%

3.08

10.5%

Profitable

Lower

*Breakeven sales change, BE = –%ΔP/(%CM + %ΔP) The data for this example is not factual, for illustration only.

Figure 7.19 

Allerton auto parts price elasticity and price adjustment analytics

expressed in absolute value terms); sales of replacement batteries were sensitive to changes in price. Furthermore, we can broaden our analysis by using the breakeven sales change (discussed in chapter 5) to see whether or not this promotional price discount was profitable. Figure 7.19 shows that for a 5 percent price cut, the firm would have to sell 20.5 percent more units to breakeven. Therefore, because the actual change in unit sales was only 15.4 percent, the price change was in fact unprofitable. The firm should consider raising price in the future. By contrast, for a remote keyless entry, farther down in figure 7.19, a recent price increase of 4.3 percent led to a 2.3 percent decline in sales volume. Its price elasticity of demand was 0.53 (absolute value of −2.3% ÷ 4.3%), exhibiting inelastic demand, which is not sensitive to changes in price. Its breakeven sales change suggests that with a 4.3 percent price increase, the firm could have absorbed a 9.2 percent decline in sales volume and still breakeven. As it lost only 2.3 percent in sales, the price change was profitable, and the firm should consider raising price again in the future.

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On brake pads, the firm lowered price only 1.7 percent, and sales volume increased 15.0 percent, yielding a high price elasticity of 8.82. But the breakeven sales change was 14.9 percent, so the price reduction was profitable, but only marginally. The firm should monitor sales volume activity and consider maintaining price for the present. The price elasticity and price adjustment tool shown in figure 7.19 is a concise way to use two useful hard pricing skills—price elasticity and breakeven sales analysis—to fine-tune price and maximize incremental profit contribution. The worksheet format can be adapted to a few or many SKUs and quickly target which prices should be adjusted up or down. It can even be automated using machine learning to fine-tune price changes to find the most profitable combination of price and volume, given the product’s contribution margin.

Conclusion

Undisciplined price-setters easily fall prey to System 1 behavioral biases with a customer willingness-to-pay–driven pricing orientation, such as uniform pricing bias, direct willing-to-pay questioning bias, and persistent price-discounting bias. The first foundational soft skill of customer-driven pricing is always to debias these biases. Then apply the soft skills of price sensitivity and price segmentation that form the behavioral backbone for segmented price-setting strategy, such as segmenting customers for price sensitivity, price framing, price fencing, and price menus and managing customers for value and price sensitivity. Add to these soft skills hard customer pricing analytics—price band analytics, price distribution analytics, price optimization analytics, and microsegmentation analytics—and finally conjoint analysis, and price elasticity and price adjustment analytics to form a solid foundation for building and delivering customer-driven pricing tools and strategies. See the following templates to help with soft and hard customer willing to pay skills for pricing: • Template 7.1: Price elasticity and price adjustment analytics template • Template 7.2: Conjoint analysis experimental design template • Template 7.3: Conjoint willingness to pay, relative importances template

Templates Baseline CM %

SKU

Breakeven Assessment Future price sales change of the BE* price change adjustments

Last change Actual Price in price sales change elasticity %ΔP %ΔQ %ΔQ/%ΔP

For various SKUs, track price elasticity, then pricing profitability using the Breakeven Sales Change formula

*Breakeven Sales Change, BE = –%ΔP/(%CM + %ΔP)

Template 7.1 

Price elasticity and price adjustment analytics template.

Test Attribute 1 ______________

Test Attribute 2 ___________

Level 1 ______ Level 1 ______

Level 2 ______

Level 3 ______

Level 2 ______

Test Attribute 3 _____ Level 1 ______

Level 3 ______

Test Attribute 3 _____ Level 2 ______

1

Test Attribute 3 _____ Level 3 ______

2

3

Test Attribute 4 _____ Level 1 ______

Test Attribute 4 _____ Level 2 ______

Test Attribute 4 _____ Level 3 ______

Test Attribute 3 _____ Level 3 ______

Test Attribute 3 ______ Level 1 ______

Test Attribute 3 _____ Level 2 ______

4

5

6

Test Attribute 4 _____ Level 2 ______

Test Attribute 4 _____ Level 3 ______

Test Attribute 4 _____ Level 1 ______

Test Attribute 3 _____ Level 2 ______

Test Attribute 3 _____ Level 3 ______

Test Attribute 3 _____ Level 1 ______

7 Test Attribute 4 _____ Level 3 ______

8 Test Attribute 4_____ Level 1 ______

Template 7.2 

Conjoint analysis experimental design template.

9 Test Attribute 4 _____ Level 2 ______

Test Attributes

Test Average Rating Attribute 1 (PWs)

Test Average Rating Attribute 2 (PWs)

Test Average Rating Attribute 3 (PWs)

Test Average Rating Attribute 4 (PWs)

A

A

A

Level 1

Level 2

A Level 3

Max-min range

B

Relative importance (%)

%

C

%

%

%

Willingness to pay ($)

$

D

$

$

$

Template 7.3 

Conjoint willingness to pay, relative importances template.

8 Competition-Driven Pricing Orientation Biases and Skills

Competition-driven pricing is among the most accessible of pricing orientations to succumb to. Salespersons in B2B selling situations cave on price when customers mention a competitor’s lower price. Manufacturers offer their retail partners a special price discount category, a “meeting comp” price discount, to ensure that they don’t lose the sale to a competitor. In the digital economy, these impulses get enabled and amplified by competitive platforms like Amazon, Shopify, and eBay. Even the smallest sellers of craft products have access to sophisticated price optimization algorithms that leverage artificial intelligence to deliver cutting-edge competition-driven pricing. In this chapter we explore competition-driven pricing orientations, their biases and soft and hard skills, and how to avoid getting dragged into a downward spiral of declining prices, margins, and anemic profitability.

True Competitive Principles for Price-Setting

A competition-driven pricing orientation focuses on the marginal revenue side of profitable price-setting (see figure 8.1). However, firms that follow a competition-driven pricing orientation embrace the tenet that their prices must be the same as, or lower than, competitor’s prices to win in competition.

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Marginal Revenue

Marginal Cost

=

MR

MC

Customer value

Customer willingness to pay

Competitor prices

Incremental cost to serve

Customer valuedriven pricing orientation

Customer WTPdriven pricing orientation

Competitiondriven pricing orientation

Costdriven pricing orientation

Figure 8.1 

Theoretical maxim for a profitable pricing orientation, focus on competition-driven pricing influences.

The economic theory of perfect competition assumes that competitive offerings are virtually identical to your own and that you are a price taker— price is determined by competition. Irrelevant theory? For many managers it seems close to reality. Hermann Simon and his colleagues reported on a conversation with a marketing vice president at a manufacturing company. Our products have few advantages anymore. You could probably call them commodities. Competition is clobbering us, customers have put us under enormous pressure, and we’ve done all we can on the cost side. What can I do about this? What options do I have to get higher profits?1

This hopeless view of the competitive price-setter’s world leads to a cascading series of competition-driven pricing biases that need to be intelligently debiased. Bruce Henderson, founder of Boston Consulting Group and early thinker in business strategy, wrote in the Harvard Business Review in 1989, Consider this lesson in strategy. In 1934, Professor G.  F. Gause of Moscow University, known as “the father of mathematical biology,”

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published the results of a set of experiments in which he put two very small animals (protozoans) of the same genus in a bottle with an adequate supply of food. If the animals were of different species, they could survive and persist together. If they were of the same species, they could not. This observation led to Gause’s Principle of Competitive Exclusion: No two species can coexist that make their living in the identical way. Competitors that make their living in the same way cannot coexist—no more in business than in nature. Each must be different enough to have a unique advantage.2

Henderson’s argument is that differential business strategy is essential for breaking away from the behavioral laws of human evolution. Competitors must make their living in different ways in order to coexist—for price-setting, this means not only creating but protecting your differential value from harm in competition. Still, managers (price-setters) are innately susceptible to the primal biases that drive human competition; they compete behaviorally to ensure that they survive; therefore, how can you compete more intelligently to protect from harm in competition? “Competition existed long before strategy. It began with life itself. . . . Darwin is probably a better guide to business competition than economists are,”3 said Henderson. Let’s turn to some of the biases of a competition-driven pricing orientation.

Competition-Driven Biases in Price-Setting

Competition-driven pricing biases arise when price-setters use System 1 behavioral heuristics to set price based on competitors’ prices. Their actions are driven by three behavioral impulses centered in price competition: competitive loss aversion bias, market share bias, and competition-oriented pricing bias (see figure 8.2, left).

Competitive Loss Aversion Bias

Behind by only four points to the New England Patriots in Super Bowl XLIX, the biggest game of the year, the Seattle Seahawks, led by Coach Pete Carroll, were at the one-yard line with just twenty seconds left. They had built their team that year on the best running back in football, Marshawn Lynch. But rather than hand off to Lynch, a safe bet, they attempted a deceptive pass play. Malcolm Butler of the Patriots cleverly read the play

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Soft intuitive competition skills Competition-driven biases and debiasing Competitive loss aversion bias Market share bias Competition-oriented pricing bias

Hard analytic competition skills H

Value protection, competitive moves Deciphering competitive pricing moves Cooperative competitive moves

Competitive modeling, competitive analytics Algorithmic competitive pricing Customer value models in competitive settings Profit pools

Retaliatory competitive moves Opportunistic competitive moves

Figure 8.2 

Soft and hard skills of a competition-driven pricing orientation.

and intercepted the pass. Game over. The Seahawks lost. The headlines captured the aftermath: “Seahawks players were reportedly just as mad as fans that Marshawn Lynch didn’t get one more chance to potentially win the Super Bowl.”4 One sports writer later wrote of the anger among the Seattle players in the locker room aftermath. Everybody got to add their two cents on what the decision to throw instead of run cost them personally. Everybody got to hear explanation after explanation for why it happened. Everybody got to pull out any other grudge they’d been holding prior to (again, ironically) Malcolm Butler’s interception and slap that on the table, too. The sniping and bitterness never went away.5

A seminal finding of Kahneman and Tversky’s prospect theory is that “losing something (an amount of money, an item, etc.) feels worse than gaining the same thing. It is a simple, but powerful bias that is encapsulated in the expression ‘losses loom larger than gains.’ ”6 However, competition amplifies the effect of loss aversion on decision-making and performance. Competitive loss aversion motivates competitors to play with greater

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intensity, work harder, expend more effort and resources, and take more risk. Researchers at the University of Chicago and the Wharton School studied 18,000 NBA basketball games and found that being behind at halftime increased a team’s chance of winning by 5.8 to 8.0 percentage points more often than expected.7 They further found that “merely telling people they were slightly behind an opponent led them to exert more effort . . . more than being tied, slightly ahead, or receiving no competitive feedback at all.”8 In similar ways, competitive loss aversion drives competitors to price harder and more intensely, sometimes with toxic effects. This excerpt from my field research from a vice president of marketing and sales illustrates the intense pricing dynamics at one competition-driven software firm. We try to find out how our competition is pricing and we very much let the [competitive] market price drive [our price].  .  .  . [We track] what the latest deals are going down. . . . I get that from our customers, either the ones we lose or the ones we win. . . . You have to live within the market to get the [business]. . . . I just bring [our employees] back to the market. My objective is . . . to win the bid. Its competitive . . . [and] to convince [other employees] I  .  .  . directly quote what our competitors are pricing at, and size of those competitors, and how major of a player they are in the market that we have to compete with to give them a sense of what is going on in the market, who our competition is, and how we are to compete against that.

The fear of losing in competition is palpable, stressing competitors’ size and sophistication. The outcomes of this narrow pricing orientation were disruptive and destructive. Multiple interviews across functional departments within the company revealed considerable instability, low employee morale, and high turnover among software engineers, who felt powerless in a pricing process driven by the narrow heuristic bias of marketing and sales. engineer 1: If the revenue isn’t sufficient on the project then with the limited number of resources that we can dedicate to that project, [it makes] me as a person responsible for making a portion of the deliverable, or maybe the whole deliverable, [I have] to put in the efforts of 120–150 percent . . . to make up for the deficiency in resources, and that deficiency is typically based on the pricing, on underpricing. engineer 2: It just turns us into essentially a meat-grinder. We’re people with extreme hours and below market value on a lot of occasions.

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There is a high burnout rate even for senior people who are exceptional, [they] get to a point where they can’t do it for more than 3-4 years, and the more junior people, it’s even less than that.

Competition can motivate you to perform at your best, but pricing in competition is more complicated. Especially when negatively framed as potential losses, failure, retaliation, or retribution, pricing in competition evokes competitive loss aversion, a powerful bias that, as with this firm, can undermine and overwhelm the behavioral foundations of the enterprise.

Market Share Bias

General Electric’s Power Division is GE’s oldest and, for many years, was also its largest. Beginning in 1981, legendary CEO Jack Welch had stressed the importance of competitive rank and market share: the business must be number one or two in its category. In 2001, Welch’s successor, Jeff Immelt, doubled down on this heuristic, pushing for market share at all costs. In 2015, Steve Bolze, head of GE’s power business with aspirations to succeed Immelt, chased an ambitious sales growth goal of 5 percent, even though the Power Division hadn’t achieved that kind of growth in years. To boost sales, the division targeted service contracts that had been sold to power generator firms to help them maintain the large turbines they had purchased from GE. But then GE fell from grace. GE teams started offering discounted turbine upgrades to customers in exchange for extending the length of contracts to as far out as 2050. [They found] ways to change underlying assumptions, such as the frequency of overhauls, to boost their profitability. [And they] gave customers discounts on their service contracts, lowering their overall value, in exchange for renegotiations that let the company bill the customers sooner.9

In April 2017, the Power Division’s problems became public, especially the shortfall in cash flow from service contracts, which should have been profitable. Instead, its revenue quality was hollow and tipping the entire company into crisis. By late 2018, GE’s third CEO in two years, Larry Culp, took on the daunting task of dismantling and reconfiguring the company while federal criminal and civil investigators scrutinized GE’s modified service

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contracts that had been used to drive short-term reported profits. Meanwhile, the division slogged through a $92 billion backlog of low-margin business that would take years to work off. The epilogue: GE’s stock was delisted from the prestigious Dow Jones Industrial Average, a position it had held since 1907. Market share perverts how managers measure and interpret market demand. I give my students a Harvard Business School case study in which one exhibit shows three separate price points and the sales volume (market demand) associated with each, along with their corresponding market shares. When asked to calculate price elasticity with respect to changes in demand, many students instead approach the task in terms of changes in market share. Students and managers are so trained to cognitively work with 100-point percentage measures that they naturally default to percentage market share thinking. Richard Thaler shared this humorous example from his teaching: Early in my teaching career . . . a midterm exam [I gave] caused . . . an uproar. [The students’] principal complaint was that the average score was only 72 points out of 100. . . . We employed a curve in which the average grade was a B+, and only a tiny number of students received grades below a C. I told the class this, but  .  .  . they still hated my exam. . . . On the next exam, I raised the points available for a perfect score to 137. This exam turned out to be harder than the first. Students got only 70 percent of the answers right but the average numerical score was 96 points. The students were delighted! . . . Rationally, no one should be happier about a score of 96 out of 137 (70 percent) than 72 out of 100, but my students were.10

Market share thinking leads price-setters to mistakenly reframe market competition, as Thomas Nagle pointed out, not as a positive-sum game in which all competitors win by creating differentiation value for customers with unlimited market potential, but as a zero-sum game in which competitors win by stealing share from one another.11 Such action inevitably raises the risk of a price war, a negative-sum game in which competitors destroy value and profitability in the marketplace. One price war in financial services began in 2017. Charles Schwab cut its transaction fees on stock and ETF trades from $8.95 to $6.95, a 22 percent reduction. Within weeks, Fidelity undercut Schwab’s prices, reducing online trading commissions on stocks and ETFs from $7.95 to

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$4.95, a 38 percent price cut. Within hours, Schwab retaliated by matching Fidelity’s price. Then TD Ameritrade announced a change in trading fees from $9.99 to $6.95, a 30 percent price cut. Then, in early 2018, Vanguard Group announced the elimination of transaction fees on all ETFs. Schwab announced that starting March 1, it would sell 503 ETFs with no transaction fee. Fidelity likewise announced that it would offer more than 500 ETFs with no transaction fee.12 Irrationally, competitors chased increasingly smaller customers with smaller assets to manage and diminishing profitability per new customer acquired. COMPETITION-ORIENTED PRICING BIAS

Market share bias is not necessarily the same as competition-oriented pricing bias—a relentless focus on setting prices to meet or beat competitors’ prices. Small firms in big ponds might not focus on market share but still be driven by competitive prices. In 2000, Amazon opened the door for small and midsized firms to sell through its online platform and reach its large customer base. The rules for seller partners, according to CEO Jeff Bezos, were that “the company [have] ‘very objective customer-centered algorithms’ that automatically award the ‘buy box’ to the lowest price seller, provided ‘they actually have it in stock and can deliver it.’ ”13 In other words, sellers are incentivized to compete on price within an ocean of unknown competitors worldwide. Figure 8.3 shows the Amazon “Buy Box,” also known as “Featured Offer,” for a routine purchase; note the best-positioned seller to the right, disguised as the ABC clothing company. Many successful Amazon sellers use pricing algorithms like Feedvisor, shown in figure 8.4, to dynamically discover the lowest competitive prices and adjust their prices to get into the Buy Box. Amazon has since adjusted its Buy Box algorithm, giving priority access first to its “Fulfilled by Amazon” (FBA) retail partners, those who warehouse and ship their products with Amazon. FBA fees include selling plan fees ($0.99/item sold, or $39.99/month flat fee), referral fees (from 6 to 25 percent of sales per category), and warehouse fulfillment fees (about 10 to 20 percent of sales). Despite Amazon’s advantages, for FBA partners, pricing and profitability are still highly competitive. After achieving record sales through Amazon, Beauty Bridge, a small New Jersey cosmetics firm, saw its sales decline as Amazon itself entered the beauty business and consistently won the Buy Box. Beauty Bridge decided not to join the FBA program and saw its sales decline by 30 percent. With little choice, it finally joined FBA and saw its sales but not profits recover,

[ABC]

Featured offer

[ABC]

Seller

Figure 8.3 

Amazon’s coveted “Buy Box,” or “Featured Offer.” Source: Amazon, https://www.amazon.com/Paul-Fredrick-Pinpoint-Collar-French/dp /B07HX1JV9W/ref=sr_1_58?dchild=1&m=A3DM9ZTSZGUSMW&qid=1602268944&ref inements=p_6%3AA3DM9ZTSZGUSMW&s=apparel&sr=1-58&th=1&psc=1.

Figure 8.4 

Intelligent pricing algorithms to help sellers dynamically discover lowest price on Amazon. Source: Feedvisor, https://feedvisor.com/amazon-repricer.

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due to the fees paid to Amazon.14 Another firm, BareBones WorkWear, a Sacramento clothing retailer that had been selling on Amazon since 2004, later removed most of its SKUs from Amazon, closing a warehouse and call center previously dedicated to Amazon sales. “Competition between us and Amazon is just insurmountable,” BareBones chief operating officer Mason Moore said. The profit margins for most clothing items were too low, he said, to allow for the company to sell through the Fulfilled by Amazon, or FBA, program. But, he said, “FBA is really the only avenue that we see as any feasible way to do business with Amazon.” [Now] BareBones has just five items listed on Amazon—all of them fulfilled by Amazon.”15

Competition-oriented pricing is laden with toxic pricing bias and anemic profit performance, but many price-setters fail to see it. Researchers at the Wharton School and Monash University, Australia, found that in surveys of marketing faculty and managers, “52 percent said that profits for firms with competitor-oriented goals would be higher or much higher than other firms, while only 26 percent thought they would be lower or much lower.”16 But the researchers note how misguided this view really is; that in general, “competitor-oriented objectives harm firm performance.” Over a forty-four-year period, the correlation between a competitor orientation and return on investment (ROI) ranged from −.54 to −.37. Indeed, even “GE’s ROI was lower in the decade after it espoused a competition-oriented market share goal than it was in the preceding decade.”17 The first priority with respect to competitive soft pricing skills is to diagnose how you approach price competition and address your competitive biases. Look for competitive loss aversion bias, market share bias, or competition-oriented pricing bias (see figure 8.2, left). The remaining soft skills relating to competitive pricing rely on a well-known theory of the primal behavioral biases that drive human competition—game theory— which we turn to next.

Game Theory and Price-Setting

Behavioral motivations underlying price competition can be found in the origins of biology itself. At an elemental level, success in pricing, business, life—indeed, in evolutionary biology—is based on the simple interplay

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among rivals who contend in a game that features two instinctive behaviors. They were defined in business settings by Marian Chapman Moore, then at Duke University: competition and cooperation,18 referred to as “cooperate or punish” behaviors in economics. Those who survive in the long run do so by sometimes competing intensely and other times cooperating peaceably; the outcome depends on your and your competitors’ choices. The best-known and one of the most insightful behavioral economic models of competition is the prisoner’s dilemma, which is used to study rational behavior in competitive situations where the outcome of any player in the game is affected by what the other players do. Oxford evolutionary biologist Richard Dawkins said, [The prisoner’s dilemma] is so simple that I have known clever men misunderstand it completely, thinking that there must be more to it! But its simplicity is deceptive. Whole shelves in libraries are devoted to the ramifications of this beguiling game. Many influential people think it holds the key to strategic defence planning, and that we should study it to prevent a third world war. . . . Many wild animals and plants are engaged in ceaseless games of Prisoner’s Dilemma, played out in evolutionary time.19

In this section I build on the early insights of Richard Harmer and Thomas Nagle in pricing and Richard Dawkins in biology. Imagine that you run a small law firm that does legal claims work for large insurance companies. You and another small competitor law firm will compete today to win a desirable project from an insurance company client; you are the only two law firms invited to bid. You can choose to compete on price to ensure that you get the contract, or you can cooperate on price, stressing the quality and value of your legal services. Your competitor has the same choices, leaving four possible outcomes, depending on your mutual choices, shown in figure 8.5: Mutual Cooperation (upper left): Both choose to cooperate on price and stress the quality and value of their legal services; both share the contract, and each receives compensation of $10,000. Mutual Defection (lower right): Both choose to compete on price to ensure that they get the contract; both share the contract, but at a low price; each will lose $5,000. Temptation to Defect (lower left): You choose to compete on price, and your competitor chooses to cooperate on price. You win the contract and

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Cooperate

Cooperate on price

Defect

Compete on price

Your competitor’s decision

Your decision

Cooperate

Defect

Cooperate on price

Compete on price

+10, +10

–15, +25

Mutual cooperation

Sucker’s payoff

+25, –15

–5, –5

Temptation to defect

Mutual defection

Figure 8.5 

Game theory: a prisoner’s dilemma payoff matrix. Adapted from Richard Dawkins, The Selfish Gene: 40th Anniversary Edition (Oxford: Oxford University Press, 2016), 264.

make $25,000; your competitor loses $15,000 because they fail to cover their costs for the period. Sucker’s Payoff (upper right): You choose to cooperate on price, and your competitor chooses to compete on price. They win the contract and make $25,000; you lose $15,000 because you fail to cover your costs for the period.

Looking closely at figure 8.5, you can see the primal competitive impulse that tempts both competitors. You are always rationally better off competing on price, according to the game, regardless of whether your competitor cooperates or competes on price. The same is true for your competitor. (Compare the left payoffs vertically in figure 8.5, +25 > +10, or −5 > −15. Similarly, compare the right payoffs horizontally.) Indeed, the game predicts that if both players are rational, they will compete on price, share the contract, and suffer a loss of $5,000. Yet, if they had cooperated on price, both could have shared the contract, each with a $10,000 profit.

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This is a simultaneous game—decisions by competitors are made simultaneously in the moment, a basic assumption of game theory. The behavioral logic behind this simple one-round game is “rational self-interest.” However, in a variation, a “repeated” prisoner’s dilemma, this simple game is repeated an indefinite number of times between the same players. The insights that emerge from this extended game are both rich and robust as the behavioral logic gets transformed into “strategic mutual interest.” Dawkins said, The successive rounds of the game give us the opportunity to build up trust or mistrust, to reciprocate or placate, forgive or avenge. In an indefinitely long game, the important point is that we can both win . . . rather than [compete] at the expense of one another.20

Competitive Moves

In a repeated game, because you can now track your competitor’s prior behavior—the competitor’s moves—the iterative game enables you to (a) interpret competitive moves; (b) discern competitive motivations; (c) build reputation, trust, and respect; and (d) strategically defend to avoid harm in competition.21 Importantly, this transforms the game into a reactive game. Because you can observe your competitor’s last move (t − 1), you therefore now choose how to respond in this current move (t = 0), shown in figure 8.6, with one of four possible competitive moves: Cooperative: Your competitor last chose to cooperate on price, stressing the quality and value of their legal services. In response, you now choose to also cooperate on price (figure 8.6, upper left). Cooperative moves are indicators of stable competition and steady, reliable long-term payouts. Opportunistic: Again, your competitor last chose to cooperate on price, stressing the quality and value of their legal services. In response, you now choose to compete on price to ensure that you get the contract. Opportunistic moves are exploitative and can be indicators of unstable competition. Hopeful: Now, consider a more aggressive competitor. Your competitor last chose to compete on price to ensure that they got the contract. In response, you now choose to cooperate on price, stressing the quality and value of your legal services, hoping for more stable competition. In the game, hopeful moves are indicators of unstable competition and uncertain, risky long-term payouts.

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Defect Compete on price

Cooperate

Cooperate on price

Cooperative

Hopeful

Defect

Your decision this round (t = 0)

Cooperate Cooperate on price

Compete on price

Your competitor’s move last round (t – 1)

Opportunistic

Retaliatory

Figure 8.6 

Repeated prisoner’s dilemma and reactive competitive moves. Note, according to the Federal Trade Commission: “A plain agreement among competitors to fix prices is almost always illegal, whether prices are fixed at a minimum, maximum, or within some range. Illegal price fixing occurs whenever two or more competitors agree to take actions that have the effect of raising, lowering or stabilizing the price of any product or service without any legitimate justification . . . Not all price similarities, or price changes that occur at the same time, are the result of price fixing. On the contrary, they often result from normal market conditions . . . Price fixing relates not only to prices, but also to other terms that affect prices to consumers, such as shipping fees, warranties, discount programs, or financing rates.” Source: Federal Trade Commission, accessed November 3, 2022, https://www.ftc.gov/tips-advice/ competition-guidance/guide-antitrust-laws/dealings-competitors/price-fixing.

Retaliatory: Again, your competitor last chose to compete on price to ensure that they got the contract. In response, you now choose to also compete on price to ensure that you get the contract, attempting to defend in competition. Retaliatory moves are indicators of unstable price competition within the industry and poor payouts.

Over time, your decisions reveal competitive patterns and profiles that define your reputation as a competitor. For example, figure 8.7 shows the moves from two rival corporate pricing teams from my field research, who

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C = Cooperative O = Opportunistic H = Hopeful R = Retaliatory

Alpha move profile C

H 29%

O

29% R

0%

43%

Orange industry Alpha

Beta

C

Cooperate on price

Compete on price

O

R

Compete on price

Compete on price

O

H

Cooperate on price

Compete on price

R

R

Compete on price

Compete on price

O

R

Compete on price

Compete on price

R

H

Cooperate on price

Cooperate on price

H

C

Cooperate on price

Cooperate on price

C

Beta move profile C H 14% O

14% R

43%

29%

Figure 8.7 

Diagnosing competitive moves in reactive price competition. Note: See the Federal Trade Commission statement on price fixing, cited in Figure 8.6.

were competing in a repeated prisoner’s dilemma game. You can see that team Beta is an aggressive competitor, usually competing on price to ensure that they win (figure 8.7, right); 43 percent of their moves are opportunistic and 29 percent are retaliatory. By contrast, team Alpha is a conservative competitor, usually cooperating on price and stressing quality and value (figure 8.7, left); 29 percent of their moves are cooperative and another 29 percent are hopeful. In general, which strategies (meaning sequences of moves) yield the best long-term payouts? University of Michigan researchers invited game theory experts to design strategies and then, using a computer simulation, tested which ones led to the best payouts. The winning strategy was the simplest, Tit for Tat, which “begins by cooperating on the first move and thereafter simply copies the previous move of the other player.” They further found that the top-scoring strategies were consistently “nice” (cooperative) and the bottom performers were “nasty” (opportunistic). The findings suggest that in competition, you are better off being nice rather than nasty. However, those were computer simulations, and real competitors might not act as predictably or rationally. What happens with real decisionmakers? Over many years, I have simulated the repeated prisoner’s dilemma

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game with teams consisting of MBA students and managers. From 154 team results, among the top 25 scoring teams, 18 were nice competitors (100 percent cooperative moves, 10 teams) or mostly nice competitors (67 percent or more cooperative moves, 8 teams), but 7 were mostly nasty (50 percent or more opportunistic moves). None of the teams was perfectly nasty (100 percent opportunistic moves). Therefore, here is one takeaway with “real price-setters” in simulated games: being a nice competitor appears to lead to better performance over time than being a nasty one, as seen with 72 percent of the best performers. However, nasty competitors still exhibit a behavioral tendency to compete opportunistically to exploit, and 28 percent of the time, they are among the very best performers. As with price-setting and prospect theory, in game theory we also find the compelling lure of opportunism and price competition—and it can pay off. The flip side of this finding is that when nasty competitors succeed, they often do so at the expense of another docile, mostly nice, competitor, as seen with teams Alpha and Beta in figure 8.7. So, it might pay to be a nice rival who attempts to cooperates on price, but you must keep a vigilant lookout for nasty competitors that compete on price and protect yourself from the harmful fallout of opportunistic or retaliatory price competition. What is new in theory here, as economic researchers at the University of Michigan and Harvard Business School pointed out in related recent research, is that “firms can [pursue] pricing strategies that react to price changes by competitors”22—a reactive pricing game paradigm. Traditionally, economic researchers have “almost exclusively assumed that firms play a simultaneous pricing game,” including “antitrust authorities [who] have almost universally assumed that firms play a simultaneous [pricing] game”—making pricing decisions focused in this moment. However, based on reactive theory, economic modelers are starting to show that competition-driven price-setters using “pricing algorithms can increase prices relative to the standard simultaneous price-setting” game-theoretic paradigm,23 discussed later in the chapter.

Competition-Driven Soft Skills for Price-Setting

With this theoretical foundation, let’s turn to soft skills that leverage game theory to protect from harm in competition (see figure 8.2, middle): deciphering competitive pricing moves and three types of competitive moves— cooperative, retaliatory, and opportunistic. Each will be explored separately.

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Deciphering Competitive Pricing Moves

Noticing and deciphering the moves that typify competitive pricing games— opportunistic, retaliatory, hopeful, and cooperative—is a useful soft skill that helps you avoid reactively cutting price in competition and, instead, manage price in competition for healthier long-term returns. For example, a large client company had typically competed with premium prices against a handful of competitors in a technology products industry. The firm, however, had been mired in retaliatory and sometimes opportunistic price competition with another large, aggressive peer competitor. According to industry analysts, a year earlier, the competitor had announced a price increase, and there was hope among many that all competitors would take notice. The client firm noticed the price change but failed to decipher the competitor’s price increase as a possible hopeful move (figure 8.6, upper right)—the competitor was hoping to avert harm from the industry’s retaliatory price competition. The harmful status quo continued. Sometime later, the client firm decided to initiate its own price increase on a major product line, a risky but hopeful move similar to the competitor’s earlier move. Shortly thereafter, the competitor announced its own price increase (a cooperative move; figure  8.6, upper left). It also expanded its price increase to an accessory product line. The client firm wisely waited for a next move but nonetheless had finally deciphered how competitive moves had affected price competition. Dow Corning had a 40 percent share in silicones but increasingly contended with smaller low-cost competitors that were competing on price. It sensed—and deciphered—from persistent competitive pricing moves that these aggressive competitors would refuse to abandon their retaliatory pricing behaviors. So, rather than instinctively cut price as well, Dow Corning set up a new sub-brand, Xiameter by Dow, as a low-cost flanking price brand—an intelligent retaliatory move to defend—with prices 20 percent lower than Dow Corning’s primary brand. Compared with Dow Corning’s 7,000 products, technical service, and customer support, Xiameter sells only 350 products, doesn’t offer technical service, and requires online customer orders, with additional fees for other order methods, rush orders, or changes. The move helped Dow Corning protect its primary brand while segmenting off customers and competitors who insisted on competing on price. Four years after Xiameter’s launch, Dow Corning’s profits had turned from a $28 million loss to a $500 million profit.24

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Cooperative Compete on quality

Hopeful Compete on quality

Cooperative • • • • •

Cooperative public statements Framing/Reframing away from price competition Leverage competitive advantage Publicly pre-announce price moves Category management

Opportunistic • Preemptive moves • Opaque price strategies • Competitive bluffing

Compete on price

Opportunistic

Retaliatory • Flanking price brand • Surgical price retaliation • Protective customer promotions • Price match guarantees Compete on price

Retaliatory

Figure 8.8 

Competitive moves and strategies. Note: See the Federal Trade Commission statement on price fixing, cited in Figure 8.6.

Cooperative Competitive Moves

Figure 8.8 shows a summary of various competitive moves and strategies to avoid harm in competition and protect your differential value. Let’s begin with cooperative competitive moves, in the upper section of figure 8.8. Cooperative public statements: Instead of reactively cutting price in competition, a more rational response is often to communicate your pricing intent to customers and competitors through public press announcements and trade journals—which typically are viewed as credible sources for information, according to researchers.25 In 2019, in the midst of rising price competition, Old Dominion Freight Line (ODFL) communicated its cooperative pricing intent publicly to the business media. Quoted in the Wall Street Journal, ODFL’s CEO said, “We’re not losing business accounts, but

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we’re maybe losing certain lanes because we were outpriced. . . . It’s just been opportunistic. Competitors are taking the [price] decrease that we weren’t willing to take.”26 The firm’s CFO continued, “The price-for-volume game has not played out over the long run, and it’s certainly not something that we want to play.” Then ODFL provided supporting data to other trade media outlets: ODFL’s trucking volume had grown 58 percent since 2011, double that of competitors; its on-time delivery rate increased from 94 percent in 2002 to 99 percent in 2019; its cargo claim ratio declined from 1.5 percent to 0.02 percent over the same period; and revenue per shipment had increased 4.3 percent annually since 2009.27 An industry analyst summarized: “[we] don’t anticipate a change in ODFL’s strategy. . . . We believe that as the largest LTL operator, ODFL can and should continue to be disciplined on pricing.”28 This was an intelligent use of cooperative public statements. Framing Away from Price Competition: When competition commoditizes perceptions of competitive brands, rather than remaining heuristically fixated on price as the only way to compete—status quo bias—instead consider reframing to diffuse price competition—using price framing, reference framing, or benefit framing presented in chapter 2. An inspiring case study from my field research comes from the education space. For many years, Newton Country Day School (NCDS) in Newton, Massachusetts, had been a Catholic parochial school for girls, offering a private education focused on Catholic values. It competed against many Catholic diocesan and parish schools in eastern Massachusetts, whose annual tuitions in the late 1980s were about $4,000. Since 1960, enrollments in Catholic schools had declined by 50 percent in just thirty years; one-third of Catholic schools had shut their doors, and the remaining survivor schools were under constant financial pressure. Tuition increases were untenable, given Catholicism’s deeply held values of educational access and opportunity for the disadvantaged. To break out of the declining status quo, in the mid-1990s, NCDS undertook a dramatic reference-framing strategy, reframing as an elite college preparatory school for girls and offering a highly selective college-prep education anchored in Catholic values. That shift required substantially raising tuitions and limiting enrollments to outstanding students. Instead of competing against other Catholic parochial schools, in the new frame it now would compete with elite competitive prep schools like the Winsor School of Boston and Dana Hall School of Wellesley, among the best college prep private high schools. Today, NCDS has a tuition of $46,450 and enrollment of 406, and it is ranked third among the Best All-Girls High Schools in Massachusetts, competing successfully with Winsor and Dana Hall. By comparison, in NCDS’s old frame of reference, Catholic diocesan

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and parish high schools, current tuitions are about $10,000 on average. Today, NCDS is vibrant and healthy due to its reference reframing strategy. Another example of reframing away from price competition: The printer ink market is mature and highly competitive, with aggressive brands such as Epson and Kyocera taking share from Canon and HP. To break out of the price-competitive status quo, HP reframed benefits with the introduction of a new customer ink service, HP Instant Ink. New HP printers were equipped with wireless printer ink sensors that could automatically trigger ink reorders before customers ran out. The innovation was revolutionary for its IoT (Internet of Things) technology. Rather than periodically having to travel to a retail store to purchase costly replacement ink cartridges, with HP Instant Ink, customers took advantage of an automated printing solution in which cartridges were shipped to their home when ink ran low. And HP reframed its price, with subscription price framing of $3–$10 per month rather than product price framing per cartridge. HP’s reframing strategy changed the competitive landscape, redirecting away from the price competition of cartridges, bypassing retailers, and creating new deeper digital relationships with customers. Leveraging Competitive Advantage: In price competition, rather than heuristically retaliate on price, a more rational response might be to lean into, or leverage, your competitive advantage. Warren Buffett coined the term competitive “moats” to visualize competitive advantage that leads to superior long-term profits. Your moat, or competitive advantage, might be driven by speed to market (for example, Amazon), network effects (like Apple’s popular App Store with software developers), product innovation (like 3M), or intellectual property (like Google’s secret search algorithm, or Coca Cola’s secret formula).29 In 2011, The Lego Group (TLG) launched Lego Friends, a new Lego brick-building product line targeted at girls; boys were 90 percent of Lego’s users at the time. TLG spent four years doing global research with 4,500 girls and mothers, discovering that “girls, from a very young age, construct starkly divergent worlds of play” compared with boys.30 Not “only did they want ‘friendlier’ colors and lots of detail, they also took frequent breaks during building to begin storytelling and rearranging. Boys, on the other hand, tended to be more linear and build quickly to get to the end result.”31 TLG’s core competitive advantage—learning innovation—commercialized new play ideas with impact (for example, its “Lego System of Play,” patented in 1958). A leading competitor, Mega Brands, with its Mega Bloks line, produced interlocking bricks and parts that were identical to Lego parts but cheaper in quality and price—half the price of Lego’s—and gained a 30

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percent market share. Responding to Lego Friends for girls, Mega Brands announced a new licensing agreement with Mattel to develop construction toys based on Barbie, one of Mattel’s oldest girls’ toys franchises, giving Mega Brands two advantages: a popular girls’ franchise in Barbie and lower prices. In response, TLG could have instinctively reduced Lego Friends’ prices to match. Instead, it leaned into its core competitive advantage of learning innovation commercialization: “We had made so much research and so much testing on girls that we were never in doubt,”32 said a design manager, as they churned out compelling high-quality imaginative design themes for girls that won the war in the marketplace. According to NPD Group, “the market for girls’ construction toys in the U.S. and the main European countries tripled to $900 million in 2014 from $300 million in 2011, largely on the back of the Lego Friends sets.” The share of girls among Lego players increased sharply,33 and among its top-selling products is Lego Friends for girls. Meanwhile, Mega Brands was acquired by Mattel in 2014, but its financial performance subsequently declined, captured in these headlines: “floundering toymaker still has a huge problem. Fewer kids want to play with the company’s Barbie and American Girl dolls, Fisher-Price toys and Mega Bloks.”34 Rather than compete on price, TLG leveraged its competitive advantage to steadily achieve dominance in the new girls’ construction toy market. Publicly Pre-Announce Price Moves: Rather than behaviorally make surprising unanticipated price changes, instead announce price moves in advance that allow customers and competitors to thoughtfully consider the rationale for a price change. In 2018, the New York Times reported that United Airlines “announced last month that it would introduce a fee for economy seats closer to the front of the plane,”35 an announcement made weeks in advance of the fee change. This was done in the context of other choreographed moves that sometimes included a public price announcement: “Just before Labor Day, JetBlue and United Airlines raised the bag fee for most travelers to $30 from $25. Delta Air Lines followed suit on Wednesday, and American Airlines said on Thursday that it, too, was raising its bag charges.”36 Industry adviser Samuel Engel further commented: “The baggage fee is an easy target, and the nice thing about the baggage fee is it’s a way to isolate that increase onto your least loyal customer,” Mr. Engel said. “Airlines have carved out exceptions to baggage fees for their elite frequent fliers, so the pool of people who represent the airlines’ most valuable revenue will not see this increase.”37

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Category Management: In consumer packaged goods categories, researchers have documented “a move towards category management [as] retailers increasingly consider the demand, costs, and prices of competing brands in a joint decision-making process when setting prices for a focal brand. Retailers set prices for different brands to maximize total category profits, and prefer to promote only one brand at a time in a given category.”38 Consequently, you see Coke being promoted for one promotional period, followed by Pepsi being promoted in another period.

Retaliatory Competitive Moves

Sometimes, competing on price thoughtfully is necessary to avoid harm and protect valuable customers in competition, with retaliatory competitive moves (see figure 8.8, lower right). Flanking Price Brand: Earlier, we saw the example of Dow Corning’s Xiameter by Dow brand as a deliberate but rational compete-on-price move to protect its primary brand. Many firms maintain flanking price brands to counter aggressive compete-on-price competitors, satisfy price-sensitive buyers, and contain price-competitive market segments. For example, “General Mills markets both Gold Medal and Robin Hood brand flours. Gold Medal serves as a premium product and commands a premium price from consumers who value quality. However, Robin Hood offers a lower-priced product with a slightly lower level of quality for those who are more heavily influenced by the price.”39 Robin Hood is designed to compete directly with the private-label (store-brand) competitors that compete continually on price. Surgical Price Retaliation: When competitors compete on price, rather than heuristically cut price in response, an alternative move may be to reduce price surgically elsewhere, on a competitor’s valuable customer or market segment. Again, aviation provides good examples. For instance, “an airline [say American] would signal its intention to reduce a fare on a route that was lucrative for its competitor [Delta], which would then retaliate by signaling its intention to reduce a fare on a route profitable for the first airline [American]. [Consequently,] both fare reductions were then abandoned.”40 These overt fare signals were banned from reservation systems for being illegal, but surgical retaliatory moves continue as airlines protect their most valuable routes. In one skirmish amid price competition, American adjusted its business class fare, the equivalent of a modest price increase; only one competitor followed. So American punished the noncooperating airlines by putting $99 one-way fares in ten of United’s nonstop

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routes and ten of Delta Air Lines’ nonstop routes. “American is trying to slap the hands of people who wouldn’t go along with its increase.”41 Protective Customer Promotions: In consumer packaged goods markets, bonus packs offer, say, 50 percent additional product for the same price (Vaseline bonus packs range from 60 to 80 percent). An advantage to bonus packs is that, behaviorally, they deflect attention away from price. Instead, they focus on the greater amount customers get, framing the promotional opportunity as a gain to buyers. Researchers found that bonus packs used the words or symbols “gain,” “bonus,” “more,” “larger,” and/or “+” most frequently, 87 percent of the time.42 Bonus packs usually target a brand’s loyal customers with the intent of ensuring their loyalty. Thus, when harmful competitive events threaten loyal customers (a significant competitor price promotion or new competitor product launch), a bonus pack is useful for taking these customers out of the market until the threat subsides—a protective customer promotion. Price Match Guarantees: Companies threaten their determination to retaliate on price as a competitive deterrent, a practice that is legal.43 For example, Best Buy’s price match policy says, “We won’t be beat on price. We’ll match the product prices of key online and local competitors for immediately available products . . . (including their online prices) and we price match products shipped from and sold by these major online retailers: Amazon.com, Crutchfield.com, Dell.com, HP.com and TigerDirect .com.”44 At one point Best Buy had a “double the difference” price match guarantee, creating a highly visible deterrent as competitors see there is a high likelihood that Best Buy will retaliate on price to protect the sale. The theoretical logic of price-matching guarantees is that “if a firm (credibly) commits to match the price of its rival, then the rival has a reduced incentive to lower its price.”45 Let’s turn next to opportunistic moves. Recall from our earlier discussion on game theory that opportunistic moves can be risky and lead to broader price instability among competitors. However, competitors know that opportunistic moves can also be profitable if undetected in competition. It is important, therefore, to understand various opportunistic moves among competitors.

Opportunistic Competitive Moves

Opaque Price Strategies: Sometimes firms can set opaque prices or make obscure opportunistic pricing moves that are hidden to competitors,

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evoking little competitive backlash.46 For example, Aer Lingus tells its economy-class customers to “Upgrade Yourself:” Fancy flying business class?  .  .  . Make us an offer to upgrade your transatlantic economy booking, and you could be travelling in style in our Business Cabin. You tell us how much you’re willing to pay, and if your bid is successful, you can savour the Aer Lingus Business Class experience on your flight.47

The final fare approved with the customer is known only to Aer Lingus and its customer, enabling the airline to engage in occasional, more profitable opportunistic pricing that is hidden from competitors. Another possibility is using so-called monopoly money, such as gift cards, loyalty points, or airline miles for pricing. For example, airlines can sell airline “miles” at various obscure conversion rates to credit card companies like VISA, MasterCard, or American Express for contract prices that are never revealed in airline reservation systems or to competitors. “There is substantial variation in the range of offerings, and pricing used within a program and across programs, suggesting that this is far from conventional wisdom amongst loyalty managers,” said researchers at New York University and Harvard Business School.48 Competitive Bluffing: Firms sometimes use competitive bluffing to achieve opportunistic ends, such as manipulating list price as a decoy to competitors. Researchers at London Business School, the Wharton School, and CarnegieMellon University found that 3.8 percent of managers said that their last price increase signal was a bluff. Respondents suggested that “this was sometimes done by raising ‘list’ prices ‘nationally’ but then publishing regional prices at the old levels. Alternatively, firms would formally raise prices but informally sell at the previous price level.” A rate of 3.8 percent seems very low; however, for firms with high product category strength, the rate of this type of competitive “bluffing goes as high as 22.8 percent.”49 Beware of competitive bluffing, which competitors view as profitable until discovered. Preemptive Moves: In the middle of the COVID pandemic while airlines like American and Delta retrenched to main hubs and suspended flying to smaller markets, Southwest Airlines moved in, adding four new cities to its network in 2020 and plans for another six in 2021—including moving into Chicago’s O’Hare airport. Due to COVID and a dramatic reduction in activity, O’Hare’s international terminal had extra capacity. “If we don’t move now,” said CEO Gary Kelly, “we risk never getting in there.”

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The head of Southwest’s pilots union “described the expansion strategy as ‘predatory and opportunistic—which we like.’”50 Southwest secures a position in these new markets at low incremental cost, then adds new revenue and profit contribution to the airline’s bottom line: The play . . . is to spread Southwest’s planes into new places rather than continuing to offer so many multiple daily flights in cities where it knows customers aren’t flying as much. Southwest keeps a list of airports where its Boeing 737s can reach from its existing cities. From among those, its planners had been combing for places with untapped demand that it could quickly hook into its network. “Even if that market is still depressed,” [said its chief commercial officer], “it’s still brand new revenue.”

Hard Competitive Skills for Price-Setting

Hard competitive skills leverage System 2 analytic skills for price-setting. These range from advanced technological tools to routine hard skills that are accessible to all price-setters. Let’s look at several of them.

Algorithmic Competitive Pricing

Recall from the reactive game theory paradigm presented earlier that sometimes price-setters can achieve greater profitability by pricing opportunistically—undercutting competitors’ prices, so long as such moves go undetected. Algorithmic and dynamic pricing, a hard technology-based pricing skill set, enables some firms to automate pricing decisions and update prices with imperceptibly high frequency. Researchers at the University of Michigan and Harvard Business School studied actual price competition among five e-commerce retailers that changed prices with varying frequencies using what they termed “asymmetric pricing technologies.” High-frequency algorithmic price-setters were capable of changing price hourly and had lower average prices (Firms A and B, figure 8.9), compared with low-frequency price-setters, who changed prices weekly on Sundays (Firms D and E). For example, by comparison, Firm A changed prices on 37 percent of its products in a given day, with an average 1.9 price changes per product; Firm C changed prices on 0.8 percent of its products each day, making just a single change.51

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Price differences across retailers Pricing frequency by online retailers

E

Price index

130

D

120

Frequency

Period

A B C D E

Hourly Hourly Daily Weekly on Sunday Weekly on Sunday

Any time Any time 3:00 AM to 6:00 AM EDT 1:00 AM to 6:00 AM EDT 12:00 AM to 2:00 AM EDT

Notes: Table summarizes the pricing technology of the five retailers in our data.

C

110

Retailer

B A

100

168

24 2 1 Pricing frequency: Median hours between updates (log scale)

Figure 8.9 

Algorithmic pricing and competition. Adapted from Zach Y. Brown and Alexander MacKay, “Competition in Pricing Algorithms”(paper presented at the Thirteenth Annual Federal Trade Commission Microeconomics Conference, November 6, 2020), 30, 32.

Using a personal care category data set, the researchers modeled changes in margin markup, market share among the five competitors, and firm profits. They compared each firm’s performance using algorithmic competition (in reactive competition) versus a “simultaneous Bertrand” outcome (a game theory equilibrium solution similar to the mutual defection outcome of the prisoner’s dilemma—whereby all competitive firms set low prices; lower right, figure 8.5). Results are shown in figure 8.10. Firm A’s high-frequency algorithmic pricing results in a 4.6 percent margin increase and 11.5 percent share increase, for a total 22.0 percent profit increase. Firm B’s different high-frequency algorithmic pricing results in a 10.1 percent margin increase but a 12.4 percent share loss, for a 6.3 percent profit increase. Firms D and E have low-frequency algorithmic pricing capabilities, resulting in modest increments in margin, share, and profit. Significantly, note that across the category, “algorithmic competition increases average prices by 5.2 percent across the five firms” and increases profits by 9.6 percent. “The effect on markups and profits is especially large for firms with superior pricing technology, i.e., those with the ability to

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Counterfactual effects on markups and profits Simultaneous Bertrand Firm

Algorithmic Competition

Percent Change

Markup

Share

Profit

Markup

Share

Profit

Markup

Share

A Hourly B Hourly C Daily D Weekly E Weekly

1.77 1.82 1.93 2.34 2.42

0.281 0.315 0.136 0.121 0.147

6.4 7.6 3.7 4.8 6.1

1.85 2.01 2.02 2.38 2.46

0.313 0.276 0.138 0.124 0.150

7.8 8.1 4.1 5.0 6.4

4.6 10.1 5.1 1.9 1.6

11.5 –12.4 1.3 2.0 1.8

Profit

22.0 6.3 11.1 4.4 3.7

Aggregate

1.97

1

28.6

2.07

1

31.3

5.2

0

9.6

Notes: Table displays the implied markups, shares, and profits from the calibrated model. The first three columns report the counterfactual estimates with simultaneous Bertrand price-setting behavior. The middle three columns report the predicted values from the model of algorithmic competition that is fitted to the data. The final three columns report the percent changes of moving from simultaneous Bertrand to algorithmic competition. Profits are arbitrarily scaled so that 1 unit corresponds to $100 million of e-commerce in the Personal Care Category.

Figure 8.10 

Competitive impact of algorithmic pricing. Adapted from: Zach Y. Brown and Alexander MacKay, “Competition in Pricing Algorithms”(paper presented at the Thirteenth Annual Federal Trade Commission Microeconomics Conference, November 6, 2020), 30, 32.

quickly adjust prices.”52 But shouldn’t more frequent price competition lead to lower prices and profits? Actually, no, because those firms with highfrequency competitive pricing have an asymmetric pricing technology advantage that is a tacit threat to less technology-advantaged firms. Intuitively, our results are supported by the following logic: A superior-technology firm commits to “beat” (best respond to) whatever price is offered by its rivals, and its investments in [price] frequency or automation makes this commitment credible. The [low-technology] rivals take this into account [when setting their prices], softening price competition.  .  .  . Thus, by unilaterally changing one’s pricing technology, a firm can increase its prices and profits above the usual competitive benchmark . . . without resorting to collusion.53

Algorithmic dynamic pricing is becoming increasingly accessible to small and midsized firms as well. This example from gasoline retailing in the Netherlands illustrates.

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On the outskirts of Rotterdam, Koen van der Knaap began running [a new dynamic pricing] system on his family-owned Shell station in recent months. Down the road, a station owned by Tamoil, a gasoline retailer owned by Libya’s Oilinvest Group, uses it too. During a lateMarch week . . . [prices] for unleaded gas at the two stations—which vary in opening hours and services—bounced around independently much of the time.  .  .  . During some periods, however, the stations’ price changes paralleled each other, going up or down by more than 2 U.S. cents per gallon within a few hours of each other. Often, prices dropped early in the morning and increased toward the end of the day, implying that the A.I. software may have been identifying common market-demand signals [in their local area].54

Whereas many gasoline retailers are mired in frequent price wars, these competitor stations largely avoided this harmful cycle because they were using algorithmic pricing software with machine learning to assist in pricesetting, reportedly “among thousands of companies that use artificialintelligence software to set prices.”55 Airlines use dynamic pricing to trade off among three analytic constructs: inventory, demand, and willingness to pay. For an airline flight, for example, demand influences price, but so does inventory—the number of seats that remain unsold at any given point in time. Because seat inventory is perishable, airlines lower and raise price frequently to ensure that they sell their seat inventory to maximize incremental profit contribution for each flight. The same principles apply in setting prices for hotel rooms (for instance, Marriott and Hyatt), rental cars (Hertz, National), ride sharing (Uber, Lyft), e-commerce (Amazon, eBay), theme parks (Disney, Universal Studios), Broadway shows (Hamilton, Lion King), and professional baseball games (Boston Red Sox, New York Yankees). Figure 8.11 shows a simplified “booking curve” that tracks advance airline ticket purchases, an example of how dynamic pricing works for an airline flight. The chart shows the percentage of seats booked, or sold, 75 days before departure, up to the flight’s planned departure, day 0. For this flight, at 45 days before, just under 20 percent of total seats had been booked (Total Bookings curve); at 30 days before, 40 percent had been booked; and at 5 days before, 80 percent had been booked. Maximum bookings, at 93 percent, were achieved at day 2; the flight departed with 92 percent of seats booked (with a few last-minute cancellations). Notice the two major discount fare classes (Economy Classes EC1 and EC2—there were seven others, but for illustration, we focus on these two).

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Booking by class - Days before departure 100 90

Total bookings

Percent of flight booked

80 70 60 50

EC1, economy class 1

40 30 20 10

EC2, economy class 2 0 75

70

65

60

55

50

RBk

45 40 35 30 Days before departure MBK

25

20

15

10

5

0

Total booked

Figure 8.11 

Algorithmic dynamic pricing for an airline flight. Adapted from ICF data.

The airline planned to book EC1 seats as shown by the dashed line and actually booked seats shown by the nearby solid line. And it planned to book EC2 seats as shown by the dot-dash line and actually booked seats shown by the nearby solid line. Key to dynamic pricing are the forecast demand curves for the two fare classes: EC1 (dashed line) and EC2 (dotdash line). These demand curves are estimated using regression modeling with various customer demand indicator variables such as day of the week, time of day, seasonality, economic conditions, and competitive prices. To see algorithmic pricing at work, note that for this flight, 35 days before departure, actual EC1 bookings (solid line) were higher than forecast bookings; seat inventory was selling at forecast level. However, at 28 days before departure, EC1 bookings fell below forecast; during this period, until day 22, EC1 prices would have been automatically discounted and promoted to accelerate sales of EC1 seat inventory. The same dynamic

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pricing works for EC2 bookings, with promotional sales of EC2 seat inventory occurring from day 23 to day 12. According to McKinsey, dynamic pricing in retail can lead to sales growth of 2 to 5 percent and increases of 5 to 10 percent in margins, along with higher levels of customer satisfaction.56 For retailers, McKinsey recommends five analytic modules for complete dynamic pricing solutions, summarized in figure 8.12.57

Own price

KVI1 module

Long-tail module

Elasticity module

helps set the introductory price through intelligent product matching

calculates how a product’s price affects demand

estimates how much each product affects consumer price perception

Competitor price

Competitiveresponse module recommends price adjustments based on competitor prices updated in real time

Price limits

Omnichannel module coordinates prices among the retailer’s offline and online channels

$

$

Introductory price based on comparable articles

More demand plus lower elasticity triggers price increase

Year 1

Classification as KVI lowers price under that of competitors

Year 2

Price remains lower than that of competitors

Year 3

Automatic coordination with in-store prices

Year 4

1

Key value item. Source: McKinsey analysis

Figure 8.12 

McKinsey’s dynamic pricing solutions modules. Source: Exhibit from “How Retailers Can Drive Profitable Growth Through Dynamic Pricing,” March 2017, McKinsey & Company, www.mckinsey.com. Copyright (c) 2020 McKinsey & Company. All rights reserved. Reprinted by permission.

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Stage 3 Negative differential value Stage 2

Positive differential value Economic value

Stage 1

Competitive reference value

Figure 8.13 

The customer value model. Source: John L. Forbis and Nitin T. Mehta, “Value-Based Strategies for Industrial Products,” Business Horizons 24, no. 3 (May-June 1981); Gerald E. Smith and Thomas T. Nagle, “Financial Analysis for Profit-Driven Pricing,” Sloan Management Review 35, no. 3 (Spring 1994): 80; and Thomas T. Nagle and Georg Müller, The Strategy and Tactics of Pricing, 6th ed. (New York: Routledge, 2018).

Customer Value Models in Competitive Settings

Despite the many seemingly sophisticated competitive pricing models available, the truest and most important way to protect your brand in competition is having confidence in your customer value using reliable customer value models, shown in figure 8.13. Using field data from customers and competitors, these models identify specific competitors with their associated competitive reference prices (reference value) and then make differential value estimations. We saw this skill applied in chapter 6 in the pharmaceutical space and with industrial equipment. However, it is useful and adaptable in many ways. Figures 8.14a and 8.14b show a residential real estate example from a smart real estate firm representing a client who was selling a home in a Boston suburb. An important first step for a home seller is strategically setting the “list price.” Setting price too high excludes buyers who set online

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Comparable No.1

Comparable No.2

Comparable No.3

315 Gardner Street Single Family - Sold Hingham, MA: South Hingham 02043 List Price: $645,000 Sale Price: $645,000 List Date: 9/4/2019 Off Market Date: 9/23/2019 Sale Date: 12/12/2019 Days on Market: 20

211 South St. Single Family - Sold Hingham, MA 02043 List Price: $629,000 Sale Price: $615,000 List Date: 4/4/2019 Off Market Date: 8/23/2019 Sale Date: 10/9/2019 Days on Market: 154

67 Canterbury St. Single Family - Sold Hingham, MA: Weir River 02043 List Price: $659,000 Sale Price: $629,000 List Date: 5/16/2019 Off Market Date: 8/28/2019 Sale Date: 11/12/2019 Days on Market: 104

Adjustments Item Acres Assessed value

Description 0.15 492400

Basement features

Crawl

Cooling

Central air

Exterior features

Patio, storage shed

Fireplaces Flooring Garage spaces Garage description

0 Wall to wall Carpet, Hardwood 0

Heating

Forced air, gas

Laundry level Living area Neighborhood/sub-division Number of bedrooms Number of full baths Number of half baths Price per SqFt Sewer utilities Year built Condition Net adjusted (total) Adjusted price

Second floor 1425 Area 7/on tracks 3 2 0 431.58 City/town sewer 1845 Renovated

+(–) $ Adjustment 40,000

28,000

Description 0.37 572000

+(–) $ Adjustment 38,000

Full, unfinished basement Central air

–5,000

Porch, Patio, Fenced Yard

2 7,500

–10,000 25,000 184,650 $799,650

–17,000

Tile, Hardwood 1 Attached

–850 100,000

–8,000

Description +(–) $ Adjustment 0.74 20,000 572300 Full, interior Access, sump Pump Central air, none Deck, Patio, Professional Landscaping, Sprinkler System, –12,000 Decorative Lighting, Fenced Yard, other (See remarks) 0 Tile, Hardwood

–25,000

Forced air, oil

–7,500

Basement 1700 Area 7 3 1 1 379.41 Private sewerage 1953 Renovated

–5,000 14,600 80,000 18,000 –7,500 –5,000 25,000 96,400 $741,400

1 Detached forced air, hot water baseboard, gas First floor 1600 Area 7 3 2 0 393.13 City/town sewer 1900 Renovated

–20,000

3,500 –9,600 80,000

–10,000 35,000 86,900 $715,900

Figure 8.14a 

Objective customer value models—selling a home in a Boston suburb. Source: Gail Petersen Bell, The Gail Bell Group, Compass, Used with Permission.

search filters beneath your price, but setting price too low results in losses to the seller of thousands of dollars. In this case, the selling firm identified eleven competitive reference homes, or comps, that had recently sold in the area. The firm then went

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Suggested sales price Days on market 154

No. 1 - 211 South St., Hingham, MA

Price $799,650

20

$741,400

104

$715,900

No. 4 - 8 Bradley Woods Drive, Hingham, MA

35

$661,700

No. 5 - 14 Sycamore Lane, Hingham, MA

50

$666,200

1

$500,300

No. 7 - 27 Longmeadow Rd, Hingham, MA No. 8 - 23 Longmeadow, Hingham, MA

116

$486,100

86

$520,650

No. 9 - 7 Hersey St., Hingham, MA

130

$646,000

No. 10 - 28 Pioneer Rd, Hingham, MA

20

$638,100

No. 11 - 39 Scotland St., Hingham, MA

96

$666,450

Indicated value by sale comparison approach

73.82

$640,223

No. 2 - 315 Gardner Street, Hingham, MA No. 3 - 67 Canterbury St., Hingham, MA

No. 6 - 25 Longmeadow Road, Hingham, MA

Figure 8.14b 

Objective customer value models—selling a home in a Boston suburb. Source: Gail Petersen Bell, The Gail Bell Group, Compass, Used with Permission.

through each, using their expert judgment, and estimated the answer to this question: Given this comp’s actual sale price, how much more/less would its sale price have been if it had had the same attributes as the client’s home to be sold? For example, Comparable No. 1 had sold for a sale price of $615,000, but the client’s home had more land, 1.28 acres versus 0.15 acres for the comp (worth +$40,000); a full basement, compared with a crawl space for the comp (worth +$28,000); and was in a nicer neighborhood location (worth +$100,000). In total, if Comparable No. 1 had the same attributes as the client’s home to be sold, its sale price would have been adjusted by $184,650, and it would have sold for an estimated $799,650. Figure 8.14b shows a summary of these customer value modeling estimates, which resulted in a recommended list price of $640,223, a compelling and useful analysis for the home seller as price-setter. Customer value modeling can be useful to guide pricing dynamics in many complex competitive settings.

Profit Pools

Profit pools are a useful tool to better focus price-setting attention on the potential profit of opportunities in the marketplace. Most managers and

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students are trained in business school or on the job to focus mostly on revenues, or revenue growth, assuming that profit is obscure or difficult to calculate. In fact, it is a small step to also think in terms of profit. But rather than estimate bottom-line profit from an accounting system (like net income or operating income), for price-setting and forward-looking market opportunities, focus instead on profit contribution ($CM and %CM), defined as revenues minus incremental variable costs, sometimes approximated as gross profit ($GP or %GP).58 Therefore, if you can estimate a contribution margin rate (%CM) or gross margin rate (%GM), you can easily apply that figure to revenues to estimate total profit contribution—for competitive markets, market segments, customer groups, and so on. For example, in the smartphone category, even though Apple’s category revenue share was 29.5 percent worldwide in 2020, its portion of the category’s profit pool was 66.9 percent, and was as high as 80 percent in the past three years. Rather than irrationally chasing market share, thinking instead in terms of “profit pools” has caused Apple’s competitors to similarly focus on profit share. Samsung was quick to announce that its 2020 profit share was 32.6 percent, up from 18.8 percent in 2019.59 Remember the innate behavioral bias in which price-setters consistently choose to set lower prices to seek new customers and grow customer revenues (discussed in chapter 3), as well as market share bias (discussed earlier in this chapter). Instead, it is much healthier to compete for profits instead of merely customers. Figure 8.15 shows a profit pool example for a disguised fitness equipment firm that quickly and visually drives home a problem that FitEquipCo had in its marketing and pricing. “Although the company was shipping almost 40 percent of all units in the marketplace, it had only about 20 percent of the profits.”60 FitEquipCo has high revenue shares in department stores, discount warehouse outlets, and sporting goods retailers— channels that account for 65 percent of category revenues, and it ranked first in all three. These are indicators of market share bias toward these large revenue channels. But larger profit pools are located elsewhere. For example, specialty fitness channels represent the largest profit pool in the category, and the largest competitor is “Other.” Building on its well-established position in the category, FitEquipCo could leverage its pricing and marketing by pivoting to target this relatively fragmented and disorganized channel. “Through such measures, the company projected, it could increase earnings by $86 million over a three-year period, more than doubling operating profits.”61

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FitEquipCo has about a 40% share of units sold...

...but only about a 20% share of profits

Other

Other Other

C1

Other Other

100% Operating profit

Other Other

100% Units sold

Other Other

C2

Other

80

C1

80 C1

C8 C4

C1

C 10

C5

C8 C2

C 10 C8

C1

60

C9 C8

C8 C7

C7

C9 C 10

C2

60

C2 C1

C6

C1

40 C2

40 C1

C7

C2

C8

C1 20

20 C6

Total units sold: 10M (disguised) Total operating profit: $200M (disguised)

FitEquipCo Competitor = C

0

De pa rtm Di en sco ts to un t/w 12 re are % ho us e 6% Sp ec ial ty/ fitn e 36 ss % Sp or ti n gg oo 16 ds % Ma il/T V/ Int ern 29 et % Ot he r1 %

De pa rtm en ts Di to sco 2 re un t/w 0% are ho u 26 se % Sp ec ial ty/ fit ne 12 ss Sp % or tin gg oo 19 ds Ma % il/T V/ Int ern 1 et O t 8% he r5 %

0

The column widths reflect the proportion of units sold (left) and operating profits earned (right) in each channel.

Figure 8.15 

Estimated profit pool, FitEquipCo. The column widths reflect the proportion of units sold (left) and operating profits earned (right) in each channel. Source: (A Map of the Profit Pool) From “The New Leader’s Guide to Diagnosing the Business” by Mark Gottfredson, Steve Schaubert, and Hernan Saenz, February 2008. Copyright ©2020 by Harvard Business Publishing; all rights reserved. Reprinted by permission of Harvard Business Review.

Conclusion

Competition is the engine that drives economic growth. Not surprisingly, many price-setters adopt a competition-driven pricing orientation. However, when poorly managed, competition can undermine and threaten the good differential value that you deliver to customers. This is especially true when your pricing orientation is undermined by the behavioral biases that distort price-setting in competition, such as competitive loss aversion bias, market share bias, and competition-oriented pricing bias.

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Game theory is foundational to understanding the soft strategy skills of competitive pricing to protect against harm in competition. Recent thinking, in this chapter and among competitive pricing researchers, centers on the importance of a reactive game theory paradigm for soft skills, including deciphering competitive pricing moves, or making strategic competitive moves— cooperative, retaliatory, or opportunistic. Useful hard analytical skills also follow in the reactive paradigm, including algorithmic dynamic pricing, plus valuable skills—customer value models in competitive settings and profit pools. See the following templates to help with soft and hard skills of pricing in competition: • Template 8.1: Reactive Competitive Moves Over Time Template • Template 8.2: Diagnosing Reactive Competitive Moves Template Templates

Cooperate Defect

Compete on price

Your decision this round (t = 0)

Cooperate on price

Your competitor’s move last round (t – 1) Cooperate

Defect

Cooperate on price

Compete on price

Cooperative moves

Hopeful moves

Consecutively place bubbles in respective competitive move quadrants over time

1 Track competitive moves over time to diagnose competitive behaviors and the potential for harm in competition

Opportunistic moves

2

3

4

5

6

7

8

9

Retaliatory moves

Template 8.1 

Reactive competitive moves over time template. See figure 8.6. Note, according to the Federal Trade Commission: “A plain agreement among competitors to fix prices is almost always illegal, whether prices are fixed at a minimum, maximum, or within some range. Illegal price fixing occurs whenever two or more competitors agree to take actions that have the effect of raising, lowering or stabilizing the price of any product or service without any legitimate justification . . . Not all price similarities, or price changes that occur at the same time, are the result of price fixing. On the contrary, they often result from normal market conditions . . . Price fixing relates not only to prices, but also to other terms that affect prices to consumers, such as shipping fees, warranties, discount programs, or financing rates.” Source: Federal Trade Commission, accessed November 3, 2022, https://www.ftc.gov/tips-advice /competition-guidance/guide-antitrust-laws/dealings-competitors/price-fixing.

C = Cooperative O = Opportunistic H = Hopeful R = Retaliatory

Industry _______

For competitive dyads, enter Compete on Price, or Compete on Quality/Value, for each competitor, each round or encounter

_______

Competitor 1 Move profile

C

H __%

O

C __%

R __ %

Competitor 2 Move profile

H __%

O __%

Assess competitor’s move (grey boxes with borders) per round based on the competitor’s choice to Compete on Price or Compete on Quality/Value in response to its competitor’s previous move: Compete on Price or Compete on Quality/Value

__% R

__ %

__%

Tally the incidence of competitive moves for each competitor— Cooperation, hope, opportunism, Retaliation—to develop a Move Profile and diagnose the potential for harm in competition

Template 8.2 

Diagnosing reactive competitive moves template. Note: See the Federal Trade Commission statement on price fixing, cited in Template 8.1.

9 Balanced Pricing Orientations, Profitable Pricing Strategy

The theory-driven approach of this book opens our thinking to new ways of considering pricing. Behavioral theorist Scott Huettel noted the importance of having “tools for improving the process of decision making, the information you acquire and the strategies you adopt. While it’s difficult or maybe impossible to eliminate your biases, you can use those tools to put yourself in situations where you’ll make better decisions, where your biases aren’t weaknesses, but strengths.”1 Pricing orientation provides a new set of behavioral tools to diagnose, redesign, and get better at pricing. Pricing orientation is related to pricing strategy, but its approach is different. Pricing strategy is concerned with what your pricing should be, the structure and design of your prices to achieve long-term returns and sustain competitive advantage. Pricing orientation is concerned with how pricing gets done around here (see figure 9.1); it is more behavioral, tactical, and tacit. If you already have a pricing strategy in place, you are ahead of the curve, yet you still must continuously assess how well your actual pricing—observable in your pricing orientation—aligns with your strategy. Is pricing orientation consistent with pricing strategy? This kind of thinking represents a top-down, or deliberate, approach to pricing strategy and involves designing strategy first and then measuring implementation, execution, and orientation relative to strategy.

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How pricing gets done

Pricing orientation

Pricing strategy What pricing should be

Figure 9.1 

Twin pillars of everyday pricing.

However, if you do not have a formal pricing strategy, you can still diagnose your pricing orientation by noticing how pricing gets done around here—by seeing and sensing the signs and cues that define what your pricing orientation is. Next, how can you adapt and shape your pricing orientation—based on your pricing successes and failures and your understanding of behavioral economic theory presented in this book— to achieve better pricing outcomes? A more successful pricing orientation can then help with the evolution of your broader pricing strategy. I have called this type of price strategy-making emergent pricing strategy.2 It is a bottom-up approach. Innovative pricing practices that seem to be effective get repeated, expanded, shared, and refined into successful pricing patterns that evolve over time and across situations into emerging pricing strategy. This is another paradigm for managing price-setting: how it gets done, how it is biased by those who do it, and how to revise and shape it into profitable pricing strategy. This fresh way of viewing pricing enables us to systematically diagnose pricing practice to discover insights on how to adjust our pricing processes to get not only better outcomes with respect to revenues and profits but also better behavioral outcomes such as employee productivity, employee satisfaction, and customer satisfaction, dimensions that are usually missing from the traditional strategic pricing perspective.

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Diagnosing Pricing Orientation, the “Pricing Orientation Audit”

The pricing patterns and behaviors that get repeated over and over as you make pricing decisions are important, for contained within them is the DNA of your price-setting: how you uniquely make pricing decisions. Understanding these patterns will enable you to determine and then diagnose your pricing orientation, whether you are a sole proprietor setting price or lead a pricing team within a company or business unit. What is your pricing orientation? How do you routinely go about pricing? To do this, it is useful to do a pricing orientation audit. This audit poses a series of inquiries that align with two key silos relating to price-setting patterns, processes, tools, and skills: soft behavioral skills and hard analytic skills. The results of these inquiries show influence from the four cardinal pricing orientations: cost-driven, customer value-driven, customer WTP-driven, and competition-driven in some combination that defines your current pricing orientation (see figure 9.2). Let’s look first at soft behavioral skills.

Soft behavioral skills

Framing, frames of reference Biases and skills Psychological pricing orientation

Hard analytic skills Cardinal pricing orientations

Biases and skills Customer valuedriven pricing orientation

Customer WTPdriven pricing orientation

Biases and skills

Biases and skills

Biases and skills

Customer value Biases and skills Customer WTP

Biases and skills

Social pricing orientation

Costing

Cost-driven pricing orientation

Competitiondriven pricing orientation

Biases and skills

Biases and skills

Figure 9.2 

Diagnosing your pricing orientation—the pricing orientation audit.

Biases and skills Competition Biases and skills

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Soft Behavioral Skills

Inquiries regarding framing and frames of reference biases and skills, explored in chapter 2, include, how does your brand get framed in the marketplace by customers? What is its frame of reference, or reference framing—for example, the brand’s meaning and purpose to customers; what it is, what it does, how it is used, and the real needs it addresses? With which competitive brands do customers say it competes? Then, how does price get framed for price-setting—for example, based on cost, on competitive prices, on customer value, or what customers are willing to pay? These framing audit questions become a baseline for strategic ideation for reframing for the future using price framing, benefit framing, or reference framing. Questions about psychological pricing orientation biases and skills, explored in chapter 3, ask how does pricing get done around here— psychologically? What are the pricing goals that get cited and the goal framing that guides thinking; the nudging that gets used to encourage price-setting; the pricing rules of thumb and truisms that get repeated in memory by price-setters; the formulas, templates, and algorithms that get canonized as true and valid; the price metrics that are automatically adopted as measures to calculate and set price; and the subjective forecasting (or soft probability estimation) that gets done, and by whom? Within the responses to these psychological audit questions are clues to how your psychological pricing orientation aligning toward one or more combinations of the four cardinal pricing orientations, as shown in figure 9.3. Inquiries about social pricing orientation biases and skills, explored in chapter 4, ask “how does pricing get done around here—socially among those formally or informally considered members of the price-setting team or group? How does price get decided, and how are pricing decisions made? Who is involved? What roles do they play? Who is influential and why? What cultural nations for pricing do they come from, and what biases therefore seem to be influential on price-setting within the organization? One useful approach is to ask members involved in pricing-setting to allocate 100 points among those persons who are influential in pricesetting; this is called a constant sum scale. Then design the allocation task slightly differently: ask team members to allocate 100 points among the cultural nations involved in price-setting: Finance, Accounting, Sales, Marketing, Production, and Pricing. The responses to these social audit questions

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Your pricing DNA: Patterns, processes, orientations Psychological pricing orientation Goal framing and price-setting Goal framing & nudging Pricing rules of thumb, truisms Canonized formulas, templates, algorithms Price metric framing Forecasting, soft probability estimation Chapters 2–4

Cardinal pricing orientations

Customer valuedriven pricing orientation

Customer WTPdriven pricing orientation

Biases and skills

Biases and skills

Cost-driven pricing orientation

Competitiondriven pricing orientation

Biases and skills

Biases and skills

Chapters 5–8

Figure 9.3 

Diagnosing psychological pricing orientation—the pricing orientation audit.

and tasks are clues about your social pricing orientation and will begin to map onto some variation of the predominant cardinal pricing orientations shown in figure 9.4: cost-driven, customer value-driven, customer WTPdriven, and competition-driven. In the example illustrated in figure 9.4, finance and accounting are responsible for over half of price-setting’s decision influence, and we would expect to see other social clues in our audit findings suggesting that pricesetting shows signs of cost-driven biases in the firm’s pricing orientation. In addition, there are soft skills that are domain-specific to each of the four predominant pricing orientations. These include identifiying and debiasing specific biases such as standardized costing bias from the cost-driven pricing orientation, value illiteracy bias from the customer value-driven pricing orientation, and market share bias from the competition-driven pricing orientation. Figure 9.5 shows a complete inventory of the pricing orientation biases we’ve presented in this book.

Your pricing DNA: Patterns, processes, orientations

Cardinal pricing orientations

Social pricing orientation Relative influence on price-setting Constant sum scale, Σ = 100

Customer valuedriven pricing orientation

Customer WTPdriven pricing orientation

Biases and skills

Biases and skills

Cost-driven pricing orientation

Competitiondriven pricing orientation

Biases and skills

Biases and skills

35

25

15 10

10 5

Finance Accounting

Sales

Marketing Production Pricing

Chapters 2-4

Chapters 5-8

Figure 9.4 

Diagnosing social pricing orientation—the pricing orientation audit.

Oriented towards System 1 behavioral processes Cost-driven Standardized costing bias Sunk-cost bias Average costing bias Average customer costing bias

Customer value-driven

Customer WTP-driven

Value illiteracy bias

Uniform pricing bias

Proportional value bias

Direct willing-to-pay questioning bias

Heuristic value estimation bias

Persistent pricediscounting bias

Competition-driven Competitive loss aversion bias Market share bias Competitionoriented pricing bias

Figure 9.5 

Biases identified, and debiasing your pricing orientation—the pricing orientation audit.

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Oriented towards System 1 behavioral processes Cost-driven

Customer value-driven

Customer WTP-driven

Competition-driven

Cost discovery, Cost sensing

Value discovery, Value sensing

Price sensitivity, sensing, price segmentation

Value protection, competitive moves

Exploratory cost discovery

Subjective customer value models

True cost-to-serve indicators and attributes

Customer value driver discovery (projection mapping) Customer value data gathering

Margin leverage based on true contribution margins

Probing for value

Segmenting customers for price sensitivity

Deciphering competitive pricing signals

Price framing

Cooperative competitive moves

Price fencing and price menus Managing customers for value and price sensitivity

Retaliatory competitive moves Opportunistic competitive moves

Figure 9.6 

Soft behavioral skills for pricing orientation—the pricing orientation audit.

Make an inventory of soft behavioral skills your price-setting team currently utilizes for pricing—or could add to your price-setting repertoire, such as margin leverage and true contribution margins from costdriven pricing orientations, customer value driver discovery (value projection mapping) from customer value-driven pricing orientations, and cooperative competitive moves from competition-driven pricing orientations (see figure 9.6).

Hard Analytic Skills, the Pricing Orientation Audit

Hard analytic pricing skills are systematic, the opposite of soft pricing skills but complementary when used in combination; they are data-driven, procedural, methodical, and structured and involve slow deliberate thinking. Hard pricing skills can be challenging to learn and might be inaccessible and consequently ignored by many price-setters. One purpose of this book is to make these skills more accessible to everyday price-setters, even those with little background in business and with small and midsized firms. With training, insight, and accessibility, these hard pricing skills can add structure and guidance to pricing orientation—like a fitness regimen adds

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Oriented towards System 2 analytic processes Cost-driven

Customer value-driven

Customer WTP-driven

Competition-driven

Incremental costing, analytics, modeling

Value calculation, value communication

Price modeling, price analytics

Competitive modeling, competitive analytics

Activity-based costing (TDABC)

Objective customer value models

Customer pricing analytics

Algorithmic competitive pricing

Price waterfalls analysis

Value metrics for price-setting

Conjoint analysis

Pricing breakeven sales calculations

Value communication tools and strategies

Customer value models in competitive settings

Price elasticity and price adjustment analytics

Profit pools

Figure 9.7 

Hard analytic skills for pricing orientation—the pricing orientation audit.

structure to everyday exercise—leading to better longer-term economic outcomes, such as revenue and profitability. Figure 9.7 shows a standard checklist inquiry of accessible hard skills that we’ve presented in this book, such as activity-based costing (TDABC) or pricing breakeven sales calculations from cost-driven pricing orientations and various types of customer pricing analytics from customer WTPdriven pricing orientations, presented in chapter 7.

Balanced Pricing Orientations, the Role of Diversity

What we have seen up close in this book are the biases and skills—both soft and hard skills—associated with different pricing orientations. For example, framing and frames of reference biases and soft skills are so subtle that few notice them until the ground has shifted beneath them. Recall fourthlargest mobile company T-Mobile’s reframing, from bundled price framing (two-year contracts favored by industry leaders Verizon and AT&T) to simple product price framing (purchase your mobile phone outright or with interest-free monthly payments). Those actions set in place a different pricing strategy that led to significant long-term stock market gains that

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eventually set up T-Mobile’s acquisition of the third-largest mobile company, Sprint, in 2020. On the other hand, the German electric utility RWE regretted missing the shift to the green renewable transformation of the German energy system because it continued framing opportunities using costing and profitability measures from its legacy accounting system—anchored in the company’s existing internally focused corporate cost accounting paradigm— which favored investments in existing technologies with internal cost biases. Instead, RWE should have viewed costs more strategically, through an externally focused market costing paradigm with a different view of market-based costs that pointed toward the possibility of emerging profit opportunities (noted in chapter 4). The firm ascribed its errors to status quo bias, confirmation bias, and sunflower bias, which meant that managers always looked to senior leaders first for their opinions, which biased inputs from other diverse leadership perspectives. These biases go unnoticed or seem inconsequential at the time, but over time, they grow to become entrenched and undermine the success of the enterprise—like an aircraft that deviates from its charted course by just one degree early in flight, only to end up hundreds of miles off course from its destination hours later. These biases subtly undermine not only pricing’s profitability but also pricing’s effectiveness and its impact on the morale of employees, team members, managers, and proprietors. Recall in chapter 8 the negative impact of the software development firm’s reliance on a competition-driven pricing orientation, driven by competitive loss aversion bias, always underpricing its software to be price-competitive, and consequently pushing engineers to work ever harder on slimmer margins to produce less profitable work. As one programmer admitted, “It just turns us into essentially a meat-grinder. We’re people with extreme hours and below market value on a lot of occasions. There is a high burn out rate even for senior people who are exceptional.” The keys to effective pricing orientation are balance and diversity— data diversity and decision diversity—in the views and perspectives that are brought to the price-setting table. “There’s lots of talk about diversity these days. We tend to think about that in terms of things like racial diversity and gender diversity and ethnic diversity. Those things are all important. But it’s also important to have diversity in how people think,” said Nobel Prize economist Richard Thaler. “Alfred P. Sloan, the founder of GM, [ended] some meeting, saying something like, ‘We seem to be all in agreement here, so I suggest we adjourn and reconvene in a week, when people have had time to think about other ideas and what might be wrong with this.’ ”3

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The same is especially true of price-setting. Pricing is so contentious and mission-critical that everyone wants to be a part of it, seemingly opening the door to diversity. But political control and power centers lead to imbalance, and then biases of that political nation that wields narrow pricing power. Instead of narrowing our focus, we need to diversify our thinking about price-setting, to debias pricing by inviting new insights and fresh views of the same pricing problem with conflicting and contradictory perspectives. Jeff Bezos stressed this kind of openly innovative culture: One of Amazon’s engineers—not a price-setter—came up with the idea of charging people once a year for unlimited 2-day shipping in exchange for a membership relationship with Amazon, called Amazon Prime—a smart application of subscription price framing that radically set Amazon on a new highly profitable pricing and business strategy. Therefore, the final phase of the pricing orientation audit examines the relative influence of the four cardinal pricing orientations (see figure 9.8, middle). This assessment once again can be done by asking pricing team respondents to allocate 100 points among the perceived influence of each pricing orientation on price-setting using a constant sum scale that sums to 100. The goal should be relative balance and diversity among the four

Soft behavioral skills Hard analytic skills

Soft behavioral skills

Balanced pricing orientation

Framing, frames of reference Biases and skills

Costing Biases and skills

Relative influence on price-setting Constant sum scale, ∑ = 100 Customer value

Psychological pricing orientation

24

26

26

24

Biases and skills

Biases and skills

Customer WTP Biases and skills

Social pricing orientation

Cost driven

Biases and skills

Figure 9.8 

A balanced pricing orientation.

Customer value Customer WTP driven driven

Competition driven

Competition Biases and skills

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cardinal pricing orientations. In the figure, you can see that the diverse perspectives of the four pricing orientations are judged to be roughly equally influential in price-setting. How does the organization achieve pricing orientation balance and diversity? Through leadership, coordination, and formal mechanisms that reinforce diversity. Recall from my field research cited in chapter 4 that a large software firm used a formal pricing committee with six C-level members, each representing one of the key nations of pricing. One of the pricing leaders said, A major improvement was having six voting members only [on the pricing committee] and everyone else was shut up. If you were not presenting, you weren’t allowed to ask questions or contribute. That was a great improvement, but  .  .  . a decision would be made, and then, it wasn’t getting communicated.  .  .  . So what we have done is establish a [cloud] database where all presentations are now stored. The minutes and agendas of all pricing committee meetings are stored so people are notified in advance what topics are coming up. . . . [We also] circulate the agendas and presentations internationally, so people who have a concern can look at this and ask questions in advance. We really want people to shop their proposals around, especially their strategic presentations need to be shopped around in advance. Meetings should be more, “I’ve reviewed it, I’ve got the issues, I’ve gotten an initial response, let’s discuss it at this level.”

Diversity must be cultivated and supported. Providing price leadership and a central C-level pricing decision committee provided direction and detailed follow-through for this organization.

Balanced Pricing Orientation in Practice

How does a balanced pricing orientation lead to better firm performance? Let’s see it in action with one well-known example, with clearly visible clues about its pricing orientation. Apple has consistently achieved enviable profit performance and market growth; in the midst of the COVID-19 crisis, its stock market capitalization has grown to more than $2 trillion, as of the writing of this book. One reason, of course, is Apple’s extremely loyal customers; they love Apple’s design innovation and streams of successful new

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product launches that have enabled it to maintain consistently premium prices. But drill down into Apple’s pricing, and especially the evolution of its pricing orientation. When Steve Jobs introduced the original iPhone in 2007, he publicized the rationale for setting its price, explaining it at the product’s launch at MacWorld 2007 this way: So what should we price [iPhone] at? Well, what do these things normally cost? An iPod, the most popular iPod, $199 for 4 gig nano. What’s a smart phone cost? . . . they generally average about $299 with a two-year contract. . . . And so people spend $499 on this combination. What should we charge for iPhone? Cause iPhone has got a lot more than this stuff, right. It’s got video. Real video. It’s got wha . . . this beautiful gorgeous wide screen. It’s got multi-touch user interface. It’s got wi-fi. It’s got a real browser. It’s got html e-mail. It’s got coverflow and on and on and on. And this stuff would normally cost hundreds of dollars. So how much more than $499 should we price iPhone? Well, we thought long and hard about it, because iPhone just does so much stuff. . . . Well, for a 4 gigabyte model, we’re gonna price it at that same $499. No premium whatsoever. $499. And we’re gonna have an 8 gigabyte model for just $599.4

Note how Jobs framed the price of the iPhone relative to the reference prices of two broadly popular products (easily accessed from memory using System 1 framing theory): the iPod ($199) and other smartphones ($299). This was classic framing strategy—benefit framing, discussed in chapter 2—establishing the iPhone with radically new benefit bundle framing in an otherwise crowded competitive field. And it adopted a customer value-driven pricing orientation, setting high price relative to the customer’s perceived value of the iPod and other smartphones, consistent with our discussion in chapter 6. The superiority of the fully bundled iPhone, Jobs believed, would surely justify a higher price relative to the prices of competitive smartphones, based on its clearly superior differentiation value— with premium prices 67 to 100 percent higher than average smartphone category prices. But there was another unseen psychological impulse at work with iPhone’s price-setting, from the cost-driven pricing nation—finance and accounting, whereby prices were psychologically influenced by cost and margin goals, as discussed in chapter 5.The gross margin on this first

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iPhone was, as an Apple insider might have expected, 56 percent—virtually the same as Apple’s corporate price-setting rule of thumb (discussed in chapter 3) that had been canonized within the company since the 1980s: “55 or die,” meaning minimum margins of 55 percent. After setting the initial suggested list price ($599 for the 8GB model), however, there emerged further tension in iPhone’s price-setting with impulses from a third pricing orientation: customer willingness-to-pay–driven pricing, whereby price-setting was driven by considerations regarding customer price sensitivity, willingness to pay, and concerns with sales volume (discussed in chapter 7). At $599, the original price reflected a market-skimming pricing strategy. Yet, only nine weeks after product release, September 5, Apple cut price dramatically—by 33 percent, to $399—provoking a backlash from early purchasers. Two days later, September 7, Jobs penned a personal letter to early buyers with an apology and an explanation, and a $100 merchandise rebate for iPhone’s earliest customers. Here is what Jobs wrote: To all iPhone customers: I have received hundreds of emails from iPhone customers who are upset about Apple dropping the price of iPhone by $200 two months after it went on sale. After reading every one of these emails, I have some observations and conclusions. First, I am sure that we are making the correct decision to lower the price of the 8GB iPhone from $599 to $399, and that now is the right time to do it. iPhone is a breakthrough product, and we have the chance to “go for it” this holiday season. iPhone is so far ahead of the competition, and now it will be affordable by even more customers. It benefits both Apple and every iPhone user to get as many new customers as possible in the iPhone “tent”. We strongly believe the $399 price will help us do just that this holiday season.5

What was behind these extraordinarily conflicting price-setting actions? During iPhone’s first weekend on the market, of June 29, 2007, it sold 270,000 units averaging 90,000 per day. The Apple team used a classic System 1 soft pricing skill, price sensing (see figure 9.6, middle right): it watched and waited, scanning and sensing iPhone’s early market feedback. By summer’s end, iPhone sales slowed to fewer than 9,000 units per day, lower than forecast. Meanwhile, three major competitors had announced iPhone lookalike products; rumors were circulating that Google was organizing an 84-company Open Handset Alliance, called Android. Smartphone

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category sales, only 10 percent of total mobile phone sales, were doubling year over year, and competitors were readying for the 2007 end-of-year holiday selling season, when 30 percent of annual smartphone sales would occur. Despite June’s bold $599 introductory price, with new competitive lookalike entrants there were signs that a larger smartphone market was rapidly emerging, with more price-sensitive buyers. Hence, the pivot to customer willing to pay driven pricing. You might think that Apple behaved impulsively or irrationally, but in fact it was quite rational, simply reflecting the influence of diverse pricing orientations to discover the most profitable pricing strategy for that moment in a rapidly evolving new market. Recall the profit-maximizing theorem from economic theory: firms maximize profitability by setting prices and selling units until the marginal revenue derived from selling the next unit is equal to its marginal cost, MR = MC (see figure 9.9). Apple incorporated all of these pricing orientation perspectives to sense how to set prices for the new iPhone in each economic moment as the market rapidly evolved. Large corporations expend considerable resources to pursue this maxim. They hire PhD-trained statisticians to model price sensitivity and high-priced MBAs to model costs—all using hard pricing skills

Marginal Revenue

Marginal Cost

=

MR

MC

Customer value

Customer willingness to pay

Competitor prices

Incremental cost to serve

Customer valuedriven pricing orientation

Customer WTPdriven pricing orientation

Competitiondriven pricing orientation

Costdriven pricing orientation

Figure 9.9 

Theoretical maxim for a profitable pricing orientation.

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with sophisticated methods and computing power. However, the pricing orientation approach of this book pursues the same goal, but in a more accessible way. You can similarly pursue pricing opportunities by making profit-maximizing choices—trading off marginal revenue and marginal cost possibilities—by tapping the diverse tacit skills that reside within your current price-setting team. These different price-setters bring both soft and hard skills that are native to their business training and experience—the cultural pricing nations from which they come, discussed in chapter 4—to estimate, sense, and articulate what, in their view, the profit-maximizing choice should be. The key, then, is to ensure that this price-setting choice is fully informed, which requires the balance and diversity of perspectives emanating from the four cardinal directions of price-setting: costing, customer value, customer willing to pay, and competition. Back to Apple. The competing impulses of these different pricing orientations enabled profit-maximizing choices that made iPhone, some argue, the most successful franchise in modern business history. Had it remained fixated on its original premium price ($599), advocated by the customer value-driven pricing view, the iPhone might have become a niche product. Had it remained fixated merely on its corporate rule of thumb “55 or die,” advocated by the cost-driven pricing view, its prices would have ignored market demand, with prices anchored at 55 percent margins on costs. Instead, Apple chose to embrace a price reduction to drive volume and market penetration, advocated by the customer willingness-to-pay view, in the nascent months of its early history. Moreover, contrary to the belief that iPhone has always been marketed at premium prices, Apple in fact maintained a neutral pricing strategy— neither high premium prices nor low penetration prices—for nearly a decade, until the higher prices of the iPhone 7 in 2016 (see figure 9.10). With this strategy it relied on the strength of iPhone’s continuing innovations and sophisticated marketing to power market demand. Yet, throughout its history, the iPhone has retained the retail price integrity of a customer value-driven orientation: its retail prices are rarely discounted, even at resellers, because of the way Apple uses incentives to ensure that retailers sell its products at the minimum advertised price (MAP). Of the pricing nations involved in Apple’s price-setting (Finance, Accounting, Marketing, Sales, and Production), which was most influential? For the iPhone, CEO Steve Jobs was Apple’s pricing team leader, which is evident from his out-front role in iPhone’s emerging pricing strategy. From which pricing nation did he come? Marketing. In his work with Steve

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How the iPhone’s price developed Initial U.S. sales price of iPhone models (in U.S. dollars)* iPhone 4 Jun 24, '10

199-299

iPhone 5 Sep 21, '12

199-399

iPhone 6 Sep 19, '14

199-399

iPhone 7 Sep 16, '16

649-849

iPhone 8 Sep 15, '17

649-849

iPhone X Sep 15, '17 iPhone XR Oct 26, '18

999-1,149 749-899

* Up to and including iPhone 6 Plus, prices were only available including a cellphone contract @StatistaCharts Sources: Apple, Statista research

Figure 9.10 

Apple iPhone’s pricing over time. Source: Niall McCarthy, “How the iPhone’s Price Developed,” Statista, September 5, 2019, https://www.statista.com/chart/11067/how-the-iphones-price-developed/

Wozniak at Atari, and then in cofounding Apple Computer in 1976, it was clear that Wozniak was a talented product designer and Jobs a talented product marketer. Over time, Apple introduced in waves new revolutionary iPhone products and related services—the App store, touch recognition technology, facial recognition technology, and so on. Each new product leveraged psychological benefit framing, framing new product benefits as new-generation, paradigm-changing customer solutions (like iPhone X’s high-security facial recognition), which were highly differentiated relative to earlier generation models and those of competitors (discussed in chapter 2). Warren Buffett said, in a now infamous quote, The single most important decision in evaluating a business is pricing power. If you’ve got the power to raise prices without losing business

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to a competitor, you’ve got a very good business. And if you have to have a prayer session before raising the price by 10 percent, then you’ve got a terrible business.6

Though Buffett long avoided technology stocks, by 2018, he made Apple one of the top three holdings in his investment firm, Berkshire Hathaway.

Leveraging a Balanced Pricing Orientation for Profitability

A client firm, Excelon (a disguised name), is the largest North American wholesale distribution firm in its category in the construction industry, achieved through decades of strategic mergers and acquisitions. Over time, the firm has developed a well-balanced pricing orientation, with shared influence from three pricing nations within the firm: Finance, Sales and Marketing. The construction industry typically involves B2B sales and selling, and pricing gets negotiated with customer accounts in relational selling in person, by phone, or online. Early in its history, pricing had been led by the sales organization. However, the firm was surprised to find that selling prices varied considerably from one customer account to another, which made little sense. Because field sales personnel (from Sales Nation, see chapter 4) had responsibility for negotiating final prices with customers, pricing seemed to be persistently influenced by price-sensitive customer accounts—exhibiting symptoms of direct willing-to-pay questioning bias and persistent price discounting bias that is endemic to customer WTP pricing orientations. In response, the firm deliberately reoriented to a more balanced pricing orientation through the shared influence of three pricing nations, with Finance, led by the CFO, then taking the lead in price-setting. The CFO stressed the importance of the firm’s profitability, net profit margins, and gross margins for price-setting. The Sales organization continued to have a voice, stressing the importance of customer willingness to pay, as customerfacing sales and order desk personnel sensed through relational selling the actual prices that customers were willing to pay. And Marketing stressed the importance of customer value in price-setting, estimating the differential value of the brand and product line vis-à-vis competitors in the market. “How did pricing get done around here?” Pricing decisions involved formal and informal cross-functional meetings with constructive dialogue, discussion, and tension among these three pricing nations. But Finance,

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with its stewardship of enterprise profit, was charged with leading pricesetting and vested with broad price approval. One reason for Finance and the CFO’s price leadership in this firm was margin leverage based on true contribution margins, a cost-driven soft skill that is vital for driving the firm’s financial performance (see figure 9.6, left). Wholesale distributors, in general, operate with low contribution margins. Therefore, price stability, margin integrity, and cross-selling of different products and services to the same customers are key to leveraging profitability (see chapter 5’s discussion of margin leverage). With persistent effort, Excelon had developed a balanced and effective pricing orientation. Several years later, Excelon acquired a competitor distributor, The Paramount Group (also disguised), whose market footprint included New York and New Jersey, highly price-competitive markets. At the time of the acquisition, Paramount was a neglected company with undisciplined pricesetting and poor profit performance. It had a penchant for competing on price, cutting deals with customers, and pricing goals oriented toward driving sales volume. Following the acquisition, the Excelon executive team worked closely with Paramount to reorient its price-setting to reflect a more balanced pricing orientation. Excelon reframed price-setting goals for the division, to focus not only on sales volume (with the Sales organization) but also gross margins and profit contribution (via the Finance team), as well as customer value based on the strength of the brand and product line (via the Marketing team). Implicitly relying on System 1 behavioral economic soft skills, the company restructured selling commissions to align individual personal goals (gain goal frames, see chapter 3) with corporate goals (normative goal frames). It instituted new price-setting nudges with sales order desk personnel, giving them a small cash incentive to book orders with better prices. Then, to build System 2 analytic hard skills, Excelon contracted with a pricing analytics firm to do price banding analytics and price waterfall analytics, which provided valuable strategic guidance for price-setting. Then, Excelon had the newly acquired division report directly to the former CFO, who was newly promoted to president. He became responsible for oversight of all price-setting for firms in the company’s portfolio, providing at once the diversity, balance, and leadership needed in Paramount’s transformative pricing orientation. Within eighteen months, Paramount’s price-setting culture changed, gross margins grew 500 basis points (40 percent over its premerger margins), and profitability returned as the company became a steady contributor to the larger firm’s success.

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Conclusion

Pricing is a powerful lever with the potential to sink or sabotage an enterprise and must be used wisely. One of the key findings of behavioral researchers is that the complexity of the complete pricing task is so daunting that price-setters and managers inevitably simplify pricing in ways that invite consistent behavioral biases. Rather than view pricing broadly and comprehensively through a strategic lens, they narrow their view to focus on the most familiar and comfortable information available while heuristically setting aside other essential but less familiar information. For example, a chief financial officer inevitably focuses on internal corporate financial information that is readily available from financial accounting systems; costs (and margins) are innately assumed to drive pricing profitability, but less emphasis is given to external information about customers and markets because to the CFO, these information domains are less familiar. A building contractor with little training in costing or financial management focuses on external competitive information as driving pricing and business profitability because of a constant exposure to competitive bidding to win contracting jobs. This is what behavioral economists call availability bias; it is information that is most easily called up from memory. Nonetheless, effective pricing requires a balanced and complete view of the forces that will determine the success or failure of a pricing decision. The reason for this needed balance is foundational to pricing theory. As we have stated various times throughout the book, the theoretical maxim underlying pricing orientation is that to maximize profits, the firm should sell its output until its marginal cost is equal to its marginal revenue, or MC = MR (see figure 9.9). Built into this formulation is the presumption of a balanced and complete pricing orientation toward the internal and external forces that determine when profit maximization will be realized—captured comprehensively in the combined influences of the four cardinal pricing orientations. Marginal cost represents internal pricing performance drivers: costs and related measures such as margins, return on investment, asset efficiency, and similar financial drivers. Marginal revenue represents external pricing performance drivers relating to the market: customer value, willingness to pay, and competitive influences. These market drivers are more difficult to measure, but all drivers—internal and external—are nonetheless vital to a balanced and complete view of pricing.

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We’ve seen the importance of cultivating soft behavioral skills for pricesetting, as well as hard analytic skills. Underlying these skill sets are the foundational research findings of behavioral economics, which today defines the cutting edge of pricing practice. For example, “Uber is so fond of economists that it employs more than a dozen PhDs from top [universities] at its San Francisco headquarters. The group acts as an in-house think tank for Uber, gathering facts from quants and data scientists and synthesizing them. . . . Officially, this team is known as ‘Research and Economics.’ Internally it’s also been called Ubernomics.”7 You might not have a behavioral economics department in your firm, but you should be hypersensitive to how behavioral economics can undermine your price-setting effectiveness, or how it can be leveraged to make it more intelligently—more holistically—effective. The point of this book is to leverage behavioral economics and its findings, to make these findings accessible to the everyday price-setter, those with involvement or leadership in price-setting—to architects, lawyers, engineers, plumbers, and artists, and to pricing leaders in small and midsized businesses, as well as large corporations. “Top-performing firms invest in building the capabilities of the pricing team through training and forums to share best practices. This runs counter to the norm at many B2B sales organizations, which give little or no formal training on price realization,” said Ron Kermisch and David Burns of Bain Consulting.8 Citing one specialty chemical producer, “Product and sales staff could not explain their pricing decisions, and often resorted to a rule of thumb summed up by one product manager as, ‘I estimate I can raise the price by four cents per pound.’ Not surprisingly, she had raised prices by four cents per pound for four straight years, leaving money on the table.”9 They concluded with a testimonial of the promise of managing price-setting more thoughtfully: By analyzing the various products and their markets, the chemical producer found pricing opportunities that enabled it to increase earnings before interest and taxes by 35 percent within two years. Just as important, the company set out to raise its game on pricing capabilities. It created forums for sharing best practices, trained product managers in doing fundamental pricing analysis, and trained salespeople on how to have better pricing discussions with their customers. New dashboards monitored progress toward pricing goals and flagged places where sales reps might be getting too aggressive, or weren’t getting aggressive enough. Finally, the CEO reinforced these measures by demanding that the product and sales teams report on pricing actions taken, as well as

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results, so that effective pricing remained a high priority. The company established itself as a pricing leader in its markets and continued to optimize margins, both by raising prices and, in selective cases, by lowering prices to drive the right balance of price versus volume gains.10

This firm had developed pricing strategy by paying close attention to “how pricing gets done around here,” leading to an evolving and emerging pricing strategy. This thoughtful approach is the key to getting price right, by leveraging the intuitive principles of behavioral economics to achieve more profitable, more effective, and more satisfying pricing. See the following templates to help evolve a complete and balanced pricing orientation: • • • •

Template 9.1: Framing/Reframing Innovation Template Template 9.2: Psychological Pricing Orientation Template Template 9.3: Social Pricing Orientation Template Template 9.4: The Pricing Orientation Audit

Templates

Baseline framing

Framing bias

Framing opportunity

Framing innovation

Recognize signs of framing bias.

Recognize signs of framing opportunity.

Imagine reframing price and value.

Look for:

Look for:

Ideate:

Status quo bias

Evidence of commoditization

Price framing, how customers pay, or give

Broad versus narrow framing, hidden, obscure, overlooked frames

Reference reframing, to neighboring or new frames of reference

Current value proposition. How do customers frame your product/service? What do they say it is, what it does, how it is used?

Referent competitors. Who do customers say are competitive substitutes in the frame?

Loss aversion Sunk-cost fallacy Ownership endowment

Approximate reference price. What do customers expect to pay for competitive substitutes in the frame?

Differential value drivers. What do customers say are the unique differential value drivers of your product/service?

. . . among your price-setting team, among competitors

Evolving customer expectations— technology, trends Opportunity gaps— old frames versus customer expectations

Benefit framing to bundled benefit frames or customer solution frames See Template 2.2 Reference Framing Assessment Template and Template 2.3 Benefit Framing Assessment Template

Template 9.1 

The pricing orientation audit: framing/reframing innovation template (see template 2.1).

How pricing gets done around here

Evident pricing biases

Pricing goals Frequently stressed informal pricing goals—volume, margins, market share

Nudging, goal framing How do you set/manage hedonic goals, gain goals, normative goals?

Diagnose your psychological pricing orientation • How does pricing get done around here—psychologically? • List examples of pricing practice. • List potential pricing biases that seem evident for each.

Pricing rules of thumb, truisms

Canonized pricing formulas, templates, algorithms Pricing metric framing What are your pricing metrics, what customers pay for what they get?

Forecasting, soft probability estimation How do you tap the expertise of customer-facing personnel for pricing?

Template 9.2 

The pricing orientation audit: psychological pricing orientation template (see template 3.1).

Pricing nations

Personnel involved in pricing

Price leadership

Influence on pricing (Allocate 100 points)

Evident pricing biases

Finance personnel Accounting personnel • Marketing personnel

• •

Sales personnel Production personnel

•

•

Diagnose your social pricing orientation How does pricing get done around here—socially? How many personnel are involved from each pricing nation? Where does pricing leadership reside? Where does pricing influence reside? (Allocate 100 points among the pricing nations) List evident pricing biases

Pricing personnel

Template 9.3 

The pricing orientation audit: social pricing orientation template (see template 4.1).

Influence on pricing Allocate 100 points

Current biases

Current soft skills

Current hard skills

Figure 9.8

Figure 9.5

Figure 9.6

Figure 9.7

Cost-driven pricing

Pricing orientations

Chapter 5

Customer value-driven pricing Chapter 6

Customer WTP-driven pricing Chapter 7

Competitiondriven pricing

Assess the relative influence of each pricing orientation on your price-setting—allocate 100 points. List in summary form in the table, across pricing orientations: Current biases, current soft skills, current hard skills. List target opportunities to improve to achieve balance in your broader pricing orientation.

Chapter 8

Template 9.4 

The pricing orientation audit: summary diagnosis.

Opportunities to improve, achieve balance

n ote s

1. Pricing Orientation | Pricing Strategy 1. Mark J. Zbaracki, Mark Ritson, Daniel Levy, Shantanu Dutta, and Mark Bergen, “Managerial and Customer Costs of Price Adjustment: Direct Evidence from Industrial Markets,” Review of Economics and Statistics 86, no. 2 (May 2004): 528. 2. “Common Mistakes that Hurt Profits,” B2B CFO, February 14, 2020, accessed May 26, 2020, https://www.georgiab2bcfo.com/common-mistakes-that-hurt-profits/. See Ron Kermisch and David Burns, “Pricing: A Survey of 1,700 Companies Reveals Common B2B Pricing Mistakes,” Harvard Business Review (June 7, 2018): 5. 3. Neil Irwin, “Why Surge Prices Make Us So Mad: What Springsteen, Home Depot and a Nobel Winner Know,” New York Times, October 14, 2017, https://nyti.ms/2kP5DVs. 4. Scott Huettel, Behavioral Economics: When Psychology and Economics Collide (Chantilly, VA: Great Courses, 2014), 1. 5. Daniel Kahneman, Thinking, Fast and Slow (New York: Farrar, Straus and Giroux), 91. Kindle Edition. 6. Gerald E. Smith, “Managerial Pricing Orientation: The Process of Making Pricing Decisions,” Pricing Strategy & Practice 3, no. 3 (1995): 28–39. 7. Kahneman, Thinking, Fast and Slow, 11. 8. “Game-Changing Analytics—A Disney Perspective,” Center for Pricing and Revenue Management, Columbia Business School, October 7, 2015, https://www8.gsb .columbia.edu/cprm/disney. 9. “Game-Changing Analytics.”

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10. “Hotel Revenue Management: Strategies to Boost Topline Revenue at Your Property,” SiteMinder, accessed February 4, 2021, https://www.siteminder.com/r/hotel -revenue-management-strategies/. 11. Stephan M. Liozu, “Penetration of the Pricing Function Among Global Fortune 500 Firms,” Journal of Revenue & Pricing Management (October 2019): table 6. 12. Liozu, “Penetration of the Pricing Function Among Global Fortune 500 Firms,” table 6. 13. Zbaracki et al., “Managerial and Customer Costs of Price Adjustment.” 14. Huettel, Behavioral Economics, 327. 15. Nick Statt, “Why MoviePass Really Failed,” The Verge, September 19, 2019, https://www.theverge.com/2019/9/19/20872984/moviepass-shutdown-subscription -movies-helios-matheson-ted-farnsworth-explainer. 16. Kara Sprague, Reborn in the Cloud, McKinsey & Company, July 1, 2015, https:// www.mckinsey.com/business-functions/mckinsey-digital/our-insights/reborn-in-the-cloud. 17. Sprague, Reborn in the Cloud. 18. Sprague, Reborn in the Cloud. 19. Sprague, Reborn in the Cloud. 20. Christine Moorman, “Adobe: How to Dominate the Subscription Economy,” Forbes, August 23, 2018, https://www.forbes.com/sites/christinemoorman/2018/08/23 /adobe-how-to-dominate-the-subscription-economy/#2df7032752e8. 21. Yahoo Finance comparison, April 1, 2012, to October 8, 2020, ADBE versus GSPC. See also Nico Grant, “Adobe Gains as Revenue Tops Estimates on Expanded Portfolio,” Bloomberg, June 18, 2019, https://finance.yahoo.com/news/adobe-reports -revenue-tops-estimates-204201579.html. 22. Julie Meehan, “The Price of Pricing Effectiveness: Is the View Worth the Climb?,” Journal of Professional Pricing (third quarter 2019): 20. 23. Gerald E. Smith, “Emergent Pricing Strategy,” in Visionary Pricing: Reflections and Advances in Honor of Dan Nimer, Advances in Business Marketing & Purchasing, vol. 19, ed. G. E. Smith (Bingley, UK: Emerald), 99–122. 24. Henry Mintzberg, “Crafting Strategy,” McKinsey Quarterly, 3 (Summer): 71–90.

2. Framing and Strategic Frames of Reference 1. See Thomas T. Nagle and Georg Müller, The Strategy and Tactics of Pricing, 6th ed. (New York: Routledge, 2018); Gerald E. Smith, ed., Visionary Pricing: Reflections and Advances in Honor of Dan Nimer, Advances in Business Marketing & Purchasing 19 (Bingley, UK: Emerald, 2012); and John L. Forbis and Nitin T. Mehta, “Value-Based Strategies for Industrial Products,” Business Horizons 24, no. 3 (MayJune 1981): 32–42. 2. See Scott Huettel, Behavioral Economics: When Psychology and Economics Collide (Chantilly, VA: Great Courses, 2014), 427.

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3. Irwin P. Levin and Gary J. Gaeth, “How Consumers Are Affected by the Framing of Attribute Information Before and After Consuming the Product,” Journal of Consumer Research 15 (December 1988): 374–78. 4. The findings that follow are reported from Joel E. Urbany and Peter R. Dickson, “Prospect Theory and Pricing Decisions,” Journal of Behavioral Economics 19, no. 1 (Spring 1990): 69–81. 5. Joel E. Urbany and Peter R. Dickson, “Evidence on the Risk-Taking of PriceSetters,” Journal of Economic Psychology 15 (1994): 127–48. 6. Urbany and Dickson, “Prospect Theory and Pricing Decisions,” table 3. 7. Urbany and Dickson, “Evidence on the Risk-Taking of Price-Setters,” 133. 8. Joel E. Urbany, “Are Your Prices Too Low?,” Harvard Business Review (October 2001): 26. 9. “Studies of Decision-Making Lead to Prize in Economics” (press release), Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel 1978, October 16, 1978, https://www.nobelprize.org/prizes/economic-sciences/1978/press-release/. 10. Daniel Kahneman, Jack L. Knetsch, and Richard H. Thaler, “The Endowment Effect, Loss Aversion, and Status Quo Bias,” Journal of Economic Perspectives 5, no. 1 (Winter 1991): 194. 11. Kahneman et al., 197–98. 12. Kahneman et al., 194. 13. Hal R. Arkes and Catherine Blumer, “The Psychology of Sunk Cost,” Organizational Behavior and Human Decision Processes 35, no. 1 (February 1985): 124–40. 14. Daniel Kahneman, Thinking, Fast and Slow (New York: Farrar, Straus and Giroux), 336–37. Kindle Edition. 15. S. Reyburn, “Why Christie’s Put a Rare Leonardo da Vinci in a Contemporary Auction,” New York Times, November 3, 2017, https://www.nytimes.com/2017/11/03/arts /christies-leonardo-da-vinci-auction.html. 16. A. Cheng, “P&G’s Gillette Woes Have Translated to This Good News for Consumers, Forbes, January 24, 2018, https://www.forbes.com/sites/andriacheng/2018/01/24 /pgs-gillette-woes-have-translated-to-this-good-news-for-consumers/#5adb877e7d54. 17. S. Ghosh, “Analysts Say iPhone Sales Could Shrink by 17 Percent if Apple Doesn’t Make a Drastic Change to Its Business,” Business Insider, December 6, 2017, https://www.ctpost.com/technology/businessinsider/article/Analysts-say-iPhone-sales -could-shrink-by-17-if-12409322.php. 18. Ghosh, “Analysts Say iPhone Sales Could Shrink.” 19. J. Tenebruso, “Better Buy: Verizon Communications Inc. vs. T-Mobile,” Motley Fool, April 2, 2018, https://www.fool.com/investing/2018/04/02/better-buy-verizon -communications-inc-vs-t-mobile.aspx. 20. J. Del Ray, “The Making of Amazon Prime, the Internet’s Most Successful and Devastating Membership Program,” Vox, May 3, 2019, https://www.vox.com/recode/2019 /5/3/18511544/amazon-prime-oral-history-jeff-bezos-one-day-shipping. 21. L. Columbus, “10 Charts That Will Change Your Perspective of Amazon Prime’s Growth,” Forbes, March 4, 2018, https://www.forbes.com/sites/louiscolumbus

306

2 . F R A M I N G A N D S T R AT E G I C F R A M E S O F R E F E R E N C E

/2018/03/04/10-charts-that-will-change-your-perspective-of-amazon-primes-growth /#2da562823fee. 22. Columbus, “10 Charts,” 86. 23. S. Kitchens, “One Bookstore Finds the Secret to Succeeding in the Amazon Age,” Wall Street Journal, April 28, 2019, https://www.wsj.com/articles/how-one-independent -bookstore-succeeds-in-the-amazon-age-11556503740. 24. E. Penttinen and J. Palmer, “Improving Firm Positioning Through Enhanced Offerings and Buyer–Seller Relationships,” Industrial Marketing Management 36: 553. 25. Penttinen and Palmer, “Improving Firm Positioning,” 556. 26. “SKF Integrated Maintenance Solutions Improve Asset Performance,” RELIABLEPLANT, accessed February 14, 2021, https://www.reliableplant.com/Read/23400 /SKF-integrated-maintenance-solutions. 27. P. Morel, G. Stalk, Jr., P. Stanger, and P. Wetenhall, Perspectives: Pricing Myopia (Boston: Boston Consulting Group, 2003), 9. 28. Victor Hristov, “Two Years Ago, Apple Watch Was Born: Here’s What’s Happened Since,” phoneArena.com, April 26, 2017, https://www.phonearena.com/news/Two -years-ago-Apple-Watch-was-born-heres-whats-happened-since_id93426. 29. Dan Lovallo and Lenny Mendonca, “Strategy’s Strategist: An Interview with Richard Rumelt,” McKinsey Quarterly (November 2007), https://www.mckinsey.com /business-functions/strategy-and-corporate-finance/our-insights/strategys-strategist -an-interview-with-richard-rumelt. 30. Lovallo and Mendonca, “Strategy’s Strategist.” 31. Lovallo and Mendonca, “Strategy’s Strategist.”

3. Psychological Pricing Orientation: Psychological Price-Setting Bias and Skills 1. Daniel Kahneman, Thinking Fast and Slow (New York: Farrar, Straus and Giroux, 2011), 33. 2. Kahneman, Thinking, Fast and Slow, 35. 3. Zhenling Jiang, “An Empirical Bargaining Model with Digit Bias—A Study on Auto Loan Monthly Payments,” SSRN (July 28, 2020), https://ssrn.com/abstract=3445171. 4. Jim Henry, “The Surprising Ways Car Dealers Make the Most Money Off You,” Forbes (February 29, 2012), https://www.forbes.com/sites/jimhenry/2012/02/29/the -surprising-ways-car-dealers-make-the-most-money-off-of-you/#2f204cfa1e6f. 5. Kahneman, Thinking, Fast and Slow, 64. 6. Kahneman, Thinking Fast and Slow, 31. 7. Kahneman, Thinking, Fast and Slow, 33. 8. Tobias Baer, Sven Heiligtag, and Hamid Samandari, The Business Logic in Debiasing, McKinsey & Company (May 23, 2017), 1, https://www.mckinsey.com /business-functions/risk/our-insights/the-business-logic-in-debiasing.

3 . P S Y C H O L O G I C A L P R I C I N G O R I E N T A T I O N  307

9. Katie Couric, “Malcolm Gladwell on Why We Can’t Tell When Someone’s Lying,” Medium, September 9, 2019, https://medium.com/wake-up-call/malcolm-gladwell-on -why-we-cant-tell-when-someone-s-lying-32ab4df948bc (emphasis mine). 10. Couric, “Malcolm Gladwell on Why We Can’t Tell When Someone’s Lying.” 11. John D. Stoll, “ ‘Feel the Force’: Gut Instinct, Not Data, Is the Thing,” Wall Street Journal, October 18, 2019, https://www.wsj.com/articles/the-secret-behind-starbucks -amazon-and-the-patriots-gut-instinct-11571417153. 12. Stoll, “ ‘Feel the Force.’ ” 13. Siegwart Lindenberg and Nicolai J. Foss, “Managing Joint Production Motivation: The Role of Goal Framing and Governance Mechanisms,” Academy of Management Review 36, no. 3 (July 2011), 504. 14. Joel E. Urbany and Peter R. Dickson, “Evidence on the Risk-Taking of PriceSetters,” Journal of Economic Psychology 15 (1994): 134, 138. 15. Joel E. Urbany and Peter R. Dickson, “Prospect Theory and Pricing Decisions,” Journal of Behavioral Economics 19, no. 1 (Spring 1990): 69. 16. Urbany and Dickson, “Prospect Theory and Pricing Decisions.” 17. Noam Scheiber, “How Uber Drivers Decide How Long to Work,” New York Times, September 4, 2016, https://www.nytimes.com/2016/09/05/business/economy/how-uber -drivers-decide-how-long-to-work.html?_r=0. 18. Siegwart Lindenberg and Nicolai J. Foss, “Managing Joint Production Motivation: The Role of Goal Framing and Governance Mechanisms,” Academy of Management Review 36, no. 3 (July 2011). 19. Lindenberg and Foss, “Managing Joint Production Motivation.” 20. Noam Scheiber, “How Uber Uses Psychological Tricks to Push Its Drivers’ Buttons,” New York Times, April 2, 2017, https://www.nytimes.com/interactive/2017/04/02 /technology/uber-drivers-psychological-tricks.html (emphasis mine). 21. Scheiber, “How Uber Uses Psychological Tricks.” 22. For more on nudging, see Richard H. Thaler and Cass R. Sunstein, Nudge: Improving Decisions About Health, Wealth, and Happiness (London: Penguin, 2009). 23. Thaler and Sunstein, Nudge. 24. Thaler and Sunstein, Nudge. 25. Thaler and Sunstein, Nudge. 26. Daniel Kahneman and Amos Tversky, “Prospect Theory: An Analysis of Decision Under Risk,” Econometrica 47 (1979): 263–91. 27. See Tridib Mazumdar, S. P. Raj, and Indrajit Sinha, “Reference Price Research: Review and Propositions,” Journal of Marketing 69 (October 2005): 84–102. 28. “Range-frequency Theory, Wikipedia, accessed February 17, 2021, https://en .wikipedia.org/wiki/Range%E2%80%93frequency_theory. 29. “Range-frequency theory.” 30. Scott Huettel, Behavioral Economics: When Psychology and Economics Collide (Chantilly, VA: Great Courses, 2014), 32. 31. Huettel, Behavioral Economics.

308

3 . P S YC H O L O G I C A L P R I C I N G O R I E N TAT I O N

32. Gustav Theodor Fechner, D. H. Howes, and E. G. Boring, eds., Elements of Psychophysics [Elemente der psychophysik], Vol. 1, trans. H. E. Adler (1860; repr., New York: Holt, Rinehart & Winston, 1966). 33. Thanks to my Boston College MBA student John Colligan for his thoughtful insights on this section. Bitcoin evaluation source: Yahoo Finance, accessed May 3, 2021. Bitcoin millionaire data source: Stephen Gandel, “There may now be as many as 100,000 bitcoin millionaires,” MoneyWatch, February 23, 2021, accessed April 1, 2021, https://www.cbsnews.com/news/bitcoin-millionaires-100k/. 34. Hal R. Arkes, Cynthia A. Joyner, Mark Pezzo, Jane Gradwohl Nash, Karen Siegel-Jacobs, and Eric Stone, “The Psychology of Windfall Gains,” Organizational Behavior and Human Decision Processes 59 (1994): 331–47. 35. Richard P. Larrick and Jack B. Soll, “The MPG Illusion,” Science 320 (June 20, 2008): 1594. 36. Larrick and Soll, “The MPG Illusion” (supporting online material), 3. 37. Nicolaj Siggelkow and Christian Terwiesch, Connected Strategy: Building Continuous Customer Relationships for Competitive Advantage (Boston: Harvard Business School Press, 2019), 190. 38. Patrick Campbell, “The Value Metric: Optimize Your Pricing Strategy for High Growth” ProfitWell (blog), updated July 23, 2020, https://www.priceintelligently .com/blog/bid/195287/the-value-metric-optimize-your-pricing-strategy-for-high -growth. 39. Campbell, “The Value Metric: Optimize Your Pricing Strategy for High Growth.” 40. Jason Zweig, “You Pay for Netflix and Spotify Monthly. What About Financial Planning?,” Wall Street Journal, April 5, 2019, https://www.wsj.com/articles/you-pay -for-netflix-and-spotify-monthly-what-about-financial-planning-11554478211. 41. See Huettel, Behavioral Economics, 2014, for a discussion of Simon’s contribution. 42. Austin V. Shapard and Hans F. Olsen, “2019 Q3 Market Outlook: The Divergent Market, Fiduciary Trust,” accessed August 3, 2019, from https://www.fiduciary-trust .com/insights/market-outlook/. 43. Huettel, Behavioral Economics, 202. 44. Philip Meissner, Olivier Sibony, and Torsten Wulf, “Are You Ready to Decide?,” McKinsey Quarterly (April 1, 2015), https://www.mckinsey.com/business-functions /strategy-and-corporate-finance/our-insights/are-you-ready-to-decide. 45. Alicia Adamczyk, “These Are the Odds You’ll Win Tonight’s $350 Million Powerball Jackpot,” CNBC, June 1, 2019, https://www.cnbc.com/2019/05/31/these-are -the-odds-youll-win-the-350-million-powerball-jackpot.html#:~:text=The%20 odds%20of%20winning%20the,prize%20are%201%20in%2024.87. 46. Jeffrey Broobin, “A Harvard Law School Takes a Look at Prenuptial Agreements,” Streetview, accessed February 17, 2021, https://www.streetdirectory.com/travel_guide/13993 /legal_matters/a_harvard_law_school_takes_a_look_at_prenuptial_agreements .html#:~:text=A%20recent%20release%20of%20a,end%20up%20in%20a%20divorce. 47. Huettel, Behavioral Economics.

4 . S O C I A L P R I C I N G O R I E N T A T I O N  309

4. Social Pricing Orientation: Cultural Price-Setting Bias and Skills 1. Dan Simons, “How Experts Recall Chess Positions,” dansimons.com (blog), February 2014, accessed February 18, 2021, http://blog.dansimons.com/2014/02/how -experts-recall-chess-positions.html. 2. Colin Woodard, American Nations (New York: Penguin, 2011), 3. 3. Woodward, American Nations, 3. 4. Woodward, American Nations, 11. 5. Bryan Taylor, “The Rise and Fall of the Largest Corporation in History,” Business Insider, November 6, 2013, https://www.businessinsider.com/rise-and-fall-of-united-east -india-2013-11. 6. Richard Lancioni, Hope Jensen Schau, and Michael F. Smith, “Intraorganizational Influences on Business-to-Business Pricing Strategies: A Political Economy Perspective,” Industrial Marketing Management 34 (2005): 126. 7. Deloitte identified this second bias as expert bias. 8. Charles Alsdorf, Timothy Murphy, and Val Srinivas, Capital Allocation: How to Recognize Bias in Your Decision-Making, Deloitte CFO Insights, February 2018, https:// www2.deloitte.com/us/en/pages/finance/articles/capital-allocation-recognize-bias .html, 2. 9. Alsdorf et al., Capital Allocation, 2. 10. Alsdorf et al., Capital Allocation, 2. 11. Alsdorf et al., Capital Allocation, 4. 12. M. Shotter, “The Origin and Development of Management Accounting,” Meditari Accounting Research 7 (1999): 213, 217–18. 13. Shotter, “The Origin and Development of Management Accounting.” 14. Rebecca Fay and Norma R. Montague, “I’m Not Biased, Am I? Avoid 5 Common Judgment Biases That Can Affect Accounting and Auditing Decisions,” Journal of Accountancy (February 2015): 28. 15. Lancioni et al., “Intraorganizational Influences on Business-to-Business Pricing Strategies,” 127. 16. Fay and Montague, “I’m Not Biased, Am I?,” 28. 17. Fay and Montague, “I’m Not Biased, Am I?,” 28. 18. Bernhard Günther, “A Case Study in Combating Bias” (interview), McKinsey Quarterly, May 11, 2017, https://www.mckinsey.com/business-functions/organization /our-insights/a-case-study-in-combating-bias, emphasis mine. 19. Thomas J. Watson, Wikipedia, accessed February 18, 2021, https://en.wikipedia .org/wiki/Thomas_J._Watson. 20. Matt Smith, “To Understand Where You Are Going You Need to Understand Where You Have Been,” Predictable Revenue (blog), July 2, 2015, https://predictablerevenue .com/blog/history-professional-sales-training. 21. Smith, “To Understand Where You Are Going.”

310

4 . S O C I A L P R I C I N G O R I E N TAT I O N

22. Ellen Bolman Pullins and Prabakar Kothandaraman, “Mastering Sales Leadership: The Case for Graduate Education,” Sales Education Annual 2018, issue 12, https:// salesfoundation.org/wp-content/uploads/2018/04/SEF1801-2018-Annual-Magazine _FINAL_DigitalDownload.pdf. 23. “Better Sales Management: The Current State of Sales Manager Training: 4 Lessons from New Research,” VantagePoint, https://www.vantagepointperformance.com /the-current-state-of-sales-manager-training-4-lessons-from-new-research/. 24. Mark Thacker, “Five Topics to Cover for Effective Sales Leadership Training,” Forbes, December 26, 2018, https://www.forbes.com/sites/forbescoachescouncil/2018 /12/26/five-topics-to-cover-for-effective-sales-leadership-training/#7ca438d850f9. 25. Dan Lovallo and Olivier Sibony, “The Case for Behavioral Strategy,” McKinsey Quarterly (March 2010): 15–16. 26. Marc De Swaan Arons, “How Brands Were Born: A Brief History of Modern Marketing,” Atlantic, October 3, 2011, https://www.theatlantic.com/business/archive /2011/10/how-brands-were-born-a-brief-history-of-modern-marketing/246012/. 27. Lancioni et al., “Intraorganizational Influences on Business-to-Business Pricing Strategies,” 128. 28. Ambiguity aversion, Wikipedia, accessed February 18, 2021, https://en.wikipedia .org/wiki/Ambiguity_aversion. 29. Lancioni et al., “Intraorganizational Influences on Business-to-Business Pricing Strategies,” 130. 30. Gerald E. Smith, ed., Visionary Pricing: Reflections and Advances in Honor of Dan Nimer, Advances in Business Marketing & Purchasing 19 (Bingley, UK: Emerald, 2012), 14. 31. Smith, Visionary Pricing, 15. 32. Richard A. Lancioni, “A Strategic Approach to Industrial Product Pricing: The Pricing Plan,” Industrial Marketing Management 34 (2005): 180. 33. Lancioni et al., “Intraorganizational Influences on Business-to-Business Pricing Strategies,” 125. 34. Dieter Kiewell and Eric V. Roegner, “The CFO Guide to Better Pricing,” McKinsey on Finance 5, no. 12 (Autumn 2002): 17. 35. Daniel Kahneman, Jack L. Knetsch, and Richard H. Thaler, “Anomalies: The Endowment Effect, Loss Aversion, and Status Quo Bias,” Journal of Economic Perspectives 5, no. 1 (Winter 1991): 194. 36. Kahneman et al, “Anomalies,” 194. 37. Dan Ariely, Predictably Irrational, Revised and Expanded Edition: The Hidden Forces That Shape Our Decisions (New York: HarperCollins, 2010). 38. Steven D. Levitt and Stephen J. Dubner, Freakonomics: A Rogue Economist Explores the Hidden Side of Everything (New York: HarperCollins, 2006), 6. 39. Levitt and Dubner, Freakonomics,” 65. 40. Kelly J. Andrews, “Sharing the Wealth: How ESOPS Turn Employees Into Owners and Companies into Industry Leaders,” Edward Lowe Foundation, accessed February 18, 2021, https://edwardlowe.org/sharing-the-wealth-how-esops-turn-employees -into-owners-and-companies-into-industry-leaders/.

5 . C O S T - D R I V E N P R I C I N G O R I E N T A T I O N B I A S E S A N D S K I L L S  311

41. Hermann Simon and Martin Fassnacht, Price Management: Strategy, Analysis, Decision, Implementation (Cham, Switzerland: Springer, 2019), 354. 42. John Hagel III, John Seeley Brown, and Lang Davison, “The Best Way to Measure Company Performance,” Harvard Business Review, March 4, 2010, https://hbr.org /2010/03/the-best-way-to-measure-compan.html. 43. Simon et al., “Price Management: Strategy, Analysis, Decision, Implementation.” 44. Ron Kermisch and David Burns, “Is Pricing Killing Your Profits?,” Bain & Company Brief, June 13, 2018, https://www.bain.com/insights/is-pricing-killing-your-profits/. 45. Kermisch and Burns, “Is Pricing Killing Your Profits?”

5. Cost-Driven Pricing Orientation Biases and Skills 1. Nuway Software, Richard Ivey School of Business, University of Western Ontario (Version (A) 2009-04-15, 2009), 8. 2. Paul Hunt, “What the San Francisco Giants Can Teach You About Optimizing Your Pricing Strategy,” Financial Post, September 19, 2013, https://financialpost.com /executive/c-suite/what-the-san-francisco-giants-can-teach-you-about-optimizing -your-pricing-strategy. 3. National Research Council, Measuring Human Capabilities: An Agenda for Basic Research on the Assessment of Individual and Group Performance Potential for Military Accession (consensus report) (Washington, DC: National Academies Press, 2015), 58, https: //doi.org/10.17226/19017. 4. Architect Discussion Forum, accessed February 19, 2021, http://archinect.com /forum/thread/12142/salary-billing-rate-equation. 5. Alain Samson, ed., “Sunk Cost Fallacy,” in The Behavioral Economics Guide 2018 (with an Introduction by Robert Cialdini), Behavioraleconomics.com, accessed February 19, 2021, https://www.behavioraleconomics.com/resources/mini-encyclopedia -of-be/sunk-cost-fallacy/. 6. Benjamin Potter, Acquisition Cost Allocation at Progressive Insurance (Charlottesville, VA: Darden Business Publishing, University of Virginia, 2011), 2. 7. Jason Woleben, “Ad Spending at State Farm, Progressive Tops $1B in 2019; GEICO Nearly Hits $2B,” S&P Global, March 13, 2020, https://www.spglobal.com /marketintelligence/en/news-insights/latest-news-headlines/ad-spending-at-state -farm-progressive-tops-1b-in-2019-geico-nearly-hits-2b-57549297. 8. Post by jtownagent on Thursday, July 28, 2011, 10:42 a.m., in “Feedback on Being an Independent Agent for Progressive” (discussion board), Insurance Journal, accessed February 19, 2021, https://www.insurancejournal.com/forums/viewtopic.php?f=2&t=3 763&sid=e063d016fe9297c846d08c0188079eb9. 9. “Kellogg Gross Profit Margin,” YCharts.com, February 19, 2021, https://ycharts .com/companies/K/gross_profit_margin (emphasis mine). 10. Paul Ziobro, “UPS Tries a New Twist on Surge Pricing,” Wall Street Journal, May 1, 2017, https://www.wsj.com/articles/ups-tries-a-new-twist-on-surge-pricing-1493631000.

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11. Melanie Evans, “What Does Knee Surgery Cost? Few Know, and That’s a Problem,” Wall Street Journal, August 21, 2018, https://www.wsj.com/articles/what-does -knee-surgery-cost-few-know-and-thats-a-problem-1534865358?mod=trending_now_5. 12. Johan Ahlberg, William E. Hoover, Jr., Hanne de Mora, and Tomas Nauclér, “Pricing Commodities: What You See Is Not What You Get,” McKinsey Quarterly no. 3 (1995): 66–77. 13. Ahlberg et al., “Pricing Commodities.” 14. Ahlberg et al., “Pricing Commodities.” 15. Thomas T. Nagle and Georg Müller, The Strategy and Tactics of Pricing: A Guide to Growing More Profitability, 6th ed. (New York: Routledge, 2018). 16. Laura Stevens, Sharon Terlep, and Annie Gasparro, “Amazon Targets Unprofitable Items, with a Sharper Focus on the Bottom Line,” Wall Street Journal, December 16, 2018, https://www.wsj.com/articles/amazon-targets-unprofitable-items-with-a-sharper -focus-on-the-bottom-line-11544965201?cx_testId=0&cx_testVariant=cx_1&cx_artPo. 17. Shuba Srinivasan, Koen Pauwels, and Vincent Nijs, “Demand-Based Pricing Versus Past-Price Dependence: A Cost-Benefit Analysis,” Journal of Marketing 72, no. 1 (March): 15–27. 18. Nivedita Balu and Siddharth Cavale, “How U.S. Retailers Turn Their Bane Into Boon with ‘Click and Collect,’ ” Reuters, February 25, 2019, https://www.reuters .com/article/us-usa-retail-pickup-analysis/how-us-retailers-turn-their-bane-into-boon -with-click-and-collect-idUSKCN1QE1VE. 19. Balu and Cavale, “How U.S. Retailers Turn Their Bane Into Boon.” 20. This example is adapted from Robert S. Kaplan and Stephen R. Anderson, “Time-Driven Activity-Based Costing,” Harvard Business School 9-106-068, rev. May 15, 2009, https://hbswk.hbs.edu/item/time-driven-activity-based-costing. 21. Kaplan and Anderson, “Time-Driven Activity-Based Costing,” 2. 22. Michael V. Marn and Robert L. Rosiello, “Managing Price, Gaining Profit,” McKinsey Quarterly no. 4 (1992): 19. 23. Chuck Davenport, John Norkus, and Michael Simonetto, “Capturing the Value of Pricing Analytics,” in Visionary Pricing: Reflections and Advances in Honor of Dan Nimer, Advances in Business Marketing & Purchasing 19, ed. G. E. Smith (Bingley, UK: Emerald), 307. 24. These examples are excerpted from Davenport et al., “Capturing the Value of Pricing Analytics,” 295–329. 25. Davenport et al., “Capturing the Value of Pricing Analytics,” 307. 26. Davenport et al., “Capturing the Value of Pricing Analytics,” 306. 27. Davenport et al., “Capturing the Value of Pricing Analytics,” 304. 28. Davenport et al., “Capturing the Value of Pricing Analytics,” 303. 29. Davenport et al., “Capturing the Value of Pricing Analytics,” 303. 30. Nagle and Müller, The Strategy and Tactics of Pricing. 31. See Gerald E. Smith and Thomas T. Nagle, “Financial Analysis for Profit-Driven Pricing,” Sloan Management Review 35, no. 3 (Spring 1994): 80. 32. See Rebecca Goldberg and Ronald Wilcox, “How Heinz Got Retailers and Consumers to Accept a Larger Ketchup Bottle,” Washington Post, April 3, 2015 https://www

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.washingtonpost.com/business/case-in-point-how-heinz-shook-up-the-ketchup-bottle -for-greater-profitability/2015/04/02/716b2d32-d71f-11e4-ba28-f2a685dc7f89_story .html. Also see Rebecca Goldberg, Heinz Ketchup: Pricing the Product Line (Charlottesville, VA: Darden Business Publishing, University of Virginia 2011). 33. My assumption. 34. Smith and Nagle, “Financial Analysis for Profit Driven Pricing,” 84.

6. Customer Value-Driven Pricing Orientation Biases and Skills 1. Stephan M. Liozu and Andreas Hinterhuber, “Pricing Orientation, Pricing Capabilities, and Firm Performance,” Management Decision 51, no. 3 (2013): 594. 2. Andreas Hinterhuber, “Value Quantification Capabilities in Industrial Markets,” Journal of Business Research 76 (2017): 163. 3. Hinterhuber, “Value Quantification Capabilities in Industrial Markets.” 4. Some pricing scholars use the term “economic value” to describe the worth of the economic benefits a customer gets from using the product or service. (See, for example, Thomas T. Nagle and Georg Müller, The Strategy and Tactics of Pricing, 6th ed., New York: Routledge, 2018.) Others use the term “financial value” for the same description with respect to the monetary worth of benefits (see Reed K. Holden, Negotiating with Backbone: Eight Sales Strategies to Defend Your Price and Value, 2nd ed. [Old Tappan, NJ: Pearson, 2016]). Similarly, “psychological value” refers to the worth of psychological benefits the customer gets. L. L. Thurstone said, “An object has high psychological value for the man whose positive affect or appetition is strong for it, whether it be a strong desire to own a car, enthusiasm for a presidential candidate, or the idea of spending six months in Europe” (L. L. Thurstone, “The Measurement of Psychological Value,” Essays in Philosophy by Seventeen Doctors of Philosophy of the University of Chicago, ed. in T. V. Smith and W. K. Wright, Chicago: Open Court, 1929, 157–74, accessed February 22, 2021, https://brocku.ca/MeadProject/Thurstone /Thurstone_1929a.html). I use the more general description that encompasses all three variations: “customer value.” 5. The data for this example come from Peter P. Toth, Mark Danese, Guillermo Villa, Yi Qian, Anne Beaubrun, Armando Lira, and Jeroen P. Jansen, “Estimated Burden of Cardiovascular Disease and Value-Based Price Range for Evolocumab in a HighRisk, Secondary-Prevention Population in the US Payer Context,” Journal of Medical Economics 20, no. 6: 555–64, https://doi.org/10.1080/13696998.2017.1284078. 6. This is how QALY works, based on Harvard’s Institute for Clinical and Economic Review (ICER): One year spent in perfect health equals one QALY. A year with some kind of health problem that affects quality of life would be worth less than one QALY. How much less depends on the severity of the problem. Consider a 55-year-old, whose life expectancy might be another 24 years. If that person is in perfect health, those 24 years would mean 24 QALYs. For

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someone suffering from untreated rheumatoid arthritis, though, those 24 years could be marked by extreme pain and loss of mobility and translate to just 10 QALYs. If a certain drug reduces the pain and improves mobility, it might add back another five QALYs, for a total of 15. ICER works out the QALY benefit by reviewing the available data on the drug and translating the outcomes into QALYs. ICER has affixed a maximum value, $150,000, for each QALY a drug can add. That is based on various health-economics studies into how much Americans are willing to pay for health care and how health-care expenditure compares to per-capita income around the world. Because the arthritis drug adds five QALYs, the maximum cost should come out to five times $150,000, or $750,000. That cost is then spread over the 24 years the patient is expected to use it. That comes out to $31,250 per calendar year.

(Denise Roland, “Obscure Model Puts a Price on Good Health—and Drives Down Drug Costs,” Wall Street Journal, November 4, 2019, https://www.wsj.com/articles/obscure -model-puts-a-price-on-good-healthand-drives-down-drug-costs-11572885123.) 7. Andreas Hinterhuber, “Customer Value-based Pricing Strategies: Why Companies Resist,” Journal of Business Strategy 29, no. 4 (2008): 43. 8. See Adam Smith, “Of the Real and Nominal Price of Commodities, or of Their Price in Labour, and Their Price in Money,” in An Inquiry Into the Nature and Causes of the Wealth of Nations (1776), vol. I, ed. R. H. Campbell and A. S. Skinner, vol. II of the Glasgow Edition of the Works and Correspondence of Adam Smith (Indianapolis: Liberty Fund, 1981). Original edition, 1776. 9. John Stuart Mill, The Collected Works of John Stuart Mill, vol. III, The Principles of Political Economy with Some of Their Applications to Social Philosophy (Books III–V and Appendices), ed. John M. Robson, introduction by V. W. Bladen (Toronto: University of Toronto Press, 1965), chap. I, “Of Value,” 456 (emphasis mine). 10. Mill, Principles of Political Economy, 456. 11. Mill, Principles of Political Economy, 456. 12. Mill, Principles of Political Economy. 13. I assume here that because of Repatha’s new PCSK9 technology, it virtually competes in a new product class of its own, without competitors, excluding the oldtechnology statin therapy. 14. Mill, Principles of Political Economy, 456. 15. Hinterhuber, “Value Quantification Capabilities in Industrial Markets,” 164, 172. 16. Mill, Principles of Political Economy, chap. II, “Of Demand and Supply, in Their Relation to Value,” 467–68. 17. Bart De Langhe, Stefano Puntoni, and Richard Larrick, “Linear Thinking in a Nonlinear World,” Harvard Business Review (May-June 2017): 132–33. 18. Nagle and Müeller, The Strategy and Tactics of Pricing, 22. 19. Karl E. Weick, The Social Psychology of Organizing, 2nd ed. (Reading, MA: Addison-Wesley, 1979). 20. Kathleen M. O’Neil, “Bring Art to Market: The Diversity of Pricing Styles in a Local Art Market,” Poetics 36 (2008): 96.

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21. Kelly Crowe, “How Pharmaceutical Company Alexion Set the Price of the World’s Most Expensive Drug,” CBC Radio Canada, June 25, 2015, https://www.cbc.ca /news/health/how-pharmaceutical-company-alexion-set-the-price-of-the-world-s -most-expensive-drug-1.3125251. 22. Scholars Thomas Nagle and Georg Müller, and some pricing practitioners, apply conjoint analysis to estimate subjective customer value. An advantage to conjoint is its ability to estimate incremental willingness to pay for different test attributes of a product or service, including subjective and psychological attributes such as design, color, image, brand name, and many others. However, conjoint typically has been considered a tool to measure price sensitivity rather than customer value estimation. Its results are constrained by the range of prices input into the conjoint test design that are set by marketing researchers rather than being evoked by customers in open-ended value discovery. The heritage of the conjoint methodology is found in statistics, particularly experimental design. The theory underlying subjective customer value estimation, presented here, comes from social psychology. I present conjoint analysis in depth in chapter 7 as a hard analytic skill associated with willingness to pay and a customer willingness-to-pay–driven pricing orientation. 23. James C. Anderson and James A. Narus, “Business Marketing: Understand What Customers Value,” Harvard Business Review (November-December 1998): 7. 24. Anderson and Narus, “Business Marketing,” 7. 25. Anderson and Narus, “Business Marketing,” 8. 26. See Benson D. Shapiro, “Deere & Company: Industrial Equipment Operations,” Harvard Business School Case #9-577-112, https://hbsp.harvard.edu/product/577112 -PDF-ENG. 27. The median salary for a heavy equipment operator in the United States was $65,179 as of October 20, 2020, divided by 2,000 hours per year for full-time employment equals $32.59. For ease of calculation, I use $30 per hour. Salary data source: salary .com, accessed February 22, 2021, https://www.salary.com/research/salary/benchmark /heavy-equipment-operator-salary.

7. Customer Willingness-to-Pay–Driven Pricing Orientation Biases and Skills 1. “NBCU Chairman Steve Burke Estimated Peacock Ads Would Generate $5 Per Month Per Subscriber,” considered a lofty goal by industry observers. David Bloom, “Comcast’s Peacock: Everything You Need to Know About the New Streaming Service Created from Traditional TV’s Winning Recipe,” NEXT|TV, September 21, 2020, https://www .nexttv.com/news/comcasts-peacock-streaming-service-created-from-traditional-tvs -winning-recipe#:~:text=The%20company%20expects%20%E2%80%9Chundreds%20 of,%245%20per%20month%20per%20subscriber. 2. Calculations for this example are for illustration only, based on publicly available data.

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3. Barak Y. Orbach and Liran Einav, “Uniform Prices for Differentiated Goods: The Case of the Movie-Theater Industry,” International Review of Law and Economics 27 (2007): 129–53. 4. Hermann Simon, Frank F. Bilstein, and Frank Luby, Manage for Profit, Not for Market Share (Boston: Harvard Business School Press, 2006), 51–52. 5. Rafi Mohammed, “How to Find Out What Customers Will Pay,” Harvard Business Review, September 7, 2012, https://hbr.org/2012/09/how-to-find-out-what-customers -will-pay. 6. 2 Timothy 3:7, King James Bible. 7. Simon et al., Manage for Profit, 91–92. 8. Reed Holden, Kick the Discounting Habit: The First Step for More Effective Pricing (Concord, MA: Holden Advisors, 2018). 9. Lan Xia, Kent B. Monroe, and Jennifer L. Cox, “The Price Is Unfair! A Conceptual Framework of Price Fairness Perceptions,” Journal of Marketing 68 (October 2004): 1. 10. Michael Liedtke, “Netflix Is Rolling Out Its Biggest Price Increase Ever to US Subscribers,” Business Insider, January 15, 2019, https://www.businessinsider.com/ap-netflix -raising-prices-for-58m-us-subscribers-as-costs-rise-2019-1. 11. David Gorton, “How Mint.com Makes Money,” Investopedia, July 30, 2019, https://www.investopedia.com/articles/personal-finance/082216/how-mintcom-makes -money-intu.asp. 12. Thomas T. Nagle, “Economic Foundations for Pricing,” Journal of Business 57, no. 1 (January 1984), pt. 2: 3–26. 13. Thomas T. Nagle and Georg Müeller, The Strategy and Tactics of Pricing (New York: Routledge, 2018). 14. Utpal M. Dholakia, “Uber’s Surge Pricing: 4 Reasons Why Everyone Hates It,” January 27, 2016, https://www.govtech.com/applications/Ubers-Surge-Pricing-4-Reasons -Why-Everyone-Hates-It.html. 15. Jeffcornwall, “Great Birthday Trip,” TripadvisorUniversal Studios Florida, https:// www.tripadvisor.com/Attraction_Review-g34515-d102432-Reviews-or10-Universal _Studios_Florida-Orlando_Florida.html#REVIEWS. 16. Jennifer Valentino-DeVries, Jeremy Singer-Vine, and Ashkan Soltani, “Websites Vary Prices, Deals Based on Users’ Information,” Wall Street Journal, December 24, 2012, https://www.wsj.com/articles/SB10001424127887323777204578189391813881534. 17. Reed K. Holden, Negotiating with Backbone: Eight Sales Strategies to Defend Your Price and Value, 2nd ed. (Old Tappan, NJ: Pearson, 2016). 18. Holden, Negotiating with Backbone, 99. 19. Holden, Negotiating with Backbone, 115. 20. James Ayre, “Tesla CEO Elon Musk Responds to Reddit Controversy About Discounts,” CleanTechnica, September 29, 2016, https://cleantechnica.com/2016/09/29 /tesla-ceo-elon-musk-responds-reddit-controversy-discounts/. 21. Ayre, “Tesla CEO Elon Musk Responds.” 22. Ayre, “Tesla CEO Elon Musk Responds.” 23. Allan Gray, Michael Lucaccioni, Jamie Rapperport, and Elliott Yama, “Pricing Software: Ten Predictions for the Future,” in Visionary Pricing: Reflections and Advances

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in Honor of Dan Nimer, Advances in Business Marketing & Purchasing 19, ed. G. E. Smith(Bingley, UK: Emerald, 2012), 277–78. 24. Improving Bottom Line Results with a Transformative Pricing Strategy, Case Study: Foodservice Pricing Strategies, PricingSolutions.com, accessed February 25, 2021, https:// www.pricingsolutions.com/improving-bottom-line-results-transformative-pricing-strategy/. 25. Ron Kermisch and David Burns, “Pricing: A Survey of 1,700 Companies Reveals Common B2B Pricing Mistakes,” Harvard Business Review, June 7, 2018, https://hbr.org /2018/06/a-survey-of-1700-companies-reveals-common-b2b-pricing-mistakes, 5. 26. “What Is a Conjoint Analysis? Conjoint Types & When to Use Them,” QualtricsXM, accessed February 25, 2021, https://www.qualtrics.com/experience-management /research/types-of-conjoint/. Discussion in this paragraph is taken in part from this article. 27. This is the calculation for a point elasticity. Another variation is an arc elasticity, calculated as (Q2 − Q1)/([P2 + P1)/2).

8. Competition-Driven Pricing Orientation Biases and Skills 1. Hermann Simon, Frank F. Bilstein, and Frank Luby, Manage for Profit, Not for Market Share (Boston: Harvard Business School Press, 2006), 5–6. 2. Bruce D. Henderson, “The Origin of Strategy,” Harvard Business Review (November-December 1989), 139. 3. Henderson, “The Origin of Strategy,” 139, 143. 4. Louis Bein, “Seahawks Players Openly Criticize Pete Carroll’s Final Play Call,” SBNation, February 1, 2015, https://www.sbnation.com/2015/2/1/7961649/seahawks -players-reaction-final-play-call-super-bowl-2015. 5. Tom E. Curran, “Bill Belichick’s Masterstroke with Patriots: Getting Them to Move Past Anger of Malcolm Butler Situation,” SportsBoston, February 7, 2019, https:// www.nbcsports.com/boston/patriots/bill-belichicks-masterstoke-patriots-getting-them -move-past-anger-malcolm-butler-situation. 6. “Why Is the Pain of Losing Felt Twice as Powerfully Compared to Equivalent Gains?,” Decision Lab, accessed February 26, 2021, https://thedecisionlab.com/biases /loss-aversion/?utm_term=&utm_campaign=Biases&utm_medium=ppc&utm_source =adwords&hsa_kw=&hsa_ver=3&hsa_src=g&hsa_mt=b&hsa_cam=1044459117&hsa_tgt =dsa-798957620623&hsa_a. 7. Jonah Berger and Devin Pope, “Can Losing Lead to Winning?,” Management Science 57, no. 5 (May 2011). 8. Berger and Pope, “Can Losing Lead to Winning?,” 824. 9. Thomas Gryta and Ted Mann, “GE Powered the American Century—Then It Burned Out,” Wall Street Journal, December 14, 2018, https://www.wsj.com/articles/ge -powered-the-american-centurythen-it-burned-out-11544796010. 10. Richard H. Thaler, “Unless You Are Spock, Irrelevant Things Matter in Economic Behavior,” The New York Times, May 8, 2015, https://www.nytimes.com/2015/05/10 /upshot/unless-you-are-spock-irrelevant-things-matter-in-economic-behavior.html.

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11. Thomas T. Nagle, “Managing Price Competition,” Marketing Management 2, no. 1: 36–45. 12. Asjylyn Loder, “Charles Schwab, Fidelity Escalate Brokerage Price War,” Wall Street Journal, February 12, 2019, https://www.wsj.com/articles/charles-schwab-fires-latest-salvo -in-the-brokerage-price-wars-11549976400. 13. Julia Angwin and Surya Mattu, “Amazon Says It Puts Customers First. But Its Pricing Algorithm Doesn’t,” ProPublica, September 20, 2016, https://www.propublica .org/article/amazon-says-it-puts-customers-first-but-its-pricing-algorithm-doesnt (emphasis mine). 14. Angwin and Mattu, “Amazon Says It Puts Customers First.” 15. Angwin and Mattu, “Amazon Says It Puts Customers First.” 16. J. Scott Armstrong and Kesten C. Green, “Competitor-Oriented Objectives: The Myth of Market Share,” International Journal of Business 12, no. 1 (2007): 117. 17. Armstrong and Green, “Competitor-Oriented Objectives.” 18. Marian Chapman Moore, “Signals and Choices in a Competitive Interaction: The Role of Moves and Messages,” Management Science 38, no. 4 (April 1992). 19. Richard Dawkins, The Selfish Gene: 40th Anniversary Edition (Oxford: Oxford University Press, 2016), 262. 20. Dawkins, The Selfish Gene, 266–67 (emphasis mine). 21. “A plain agreement among competitors to fix prices is almost always illegal, whether prices are fixed at a minimum, maximum, or within some range. Illegal price fixing occurs whenever two or more competitors agree to take actions that have the effect of raising, lowering or stabilizing the price of any product or service without any legitimate justification. . . . Not all price similarities, or price changes that occur at the same time, are the result of price fixing. On the contrary, they often result from normal market conditions.  .  .  . Price fixing relates not only to prices, but also to other terms that affect prices to consumers, such as shipping fees, warranties, discount programs, or financing rates.” Federal Trade Commission, accessed February 26, 2021, https://www .ftc.gov/tips-advice/competition-guidance/guide-antitrust-laws/dealings-competitors /price-fixing. 22. Zach Y. Brown and Alexander MacKay, “Competition in Pricing Algorithms” (paper presented at the Thirteenth Annual Federal Trade Commission Microeconomics Conference, November 6, 2020), 1. 23. Brown and MacKay, “Competition in Pricing Algorithms.” Brown and MacKay refer here to “the simultaneous price-setting (Bertrand) equilibrium,” or the BertrandNash equilibrium model of game theory, which includes the prisoner’s dilemma. 24. Nirmalya Kuma, “Strategies to Fight Lost Rivals,” Harvard Business Review (December 2006): 8–9. 25. Thomas S. Robertson, Jehoshua Eliashberg, and Talia Rymon, “New Product Announcement Signals and Incumbent Reactions,” Journal of Marketing 59 (July 1995): 7. 26. Aisha Al-Muslim, “Old Dominion Will Continue with Pricing Strategy Despite Competition,” Wall Street Journal, April 27, 2019, https://www.wsj.com/articles/old -dominion-will-continue-with-pricing-strategy-despite-competition-11556366400.

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27. Anne Burdakin, “Pedal to the Metal: Old Dominion Freight Is in the Passing Lane,” Motley Fool, March 27, 2020, https://www.fool.com/investing/2020/03/27/pedal -to-the-metal-old-dominion-freight-is-in-the.aspx. 28. Burdakin, “Pedal to the Metal.” 29. See Ryan Downie, “Buffett’s Moat: Is Apple’s Competitive Advantage Sustainable?” (AAPL), Investopedia, February 16, 2020, https://www.investopedia.com /articles/insights/061916/buffetts-moat-apples-competitive-advantage-sustainable-aapl .asp#economic-moats. 30. Adrienne LaFrance, “How to Play Like a Girl,” Atlantic, May 25, 2016, https:// www.theatlantic.com/entertainment/archive/2016/05/legos/484115/. 31. Michael Mazzeo and Greg Merkley, LEGO® Friends: Leveraging Competitive Advantage, Kellogg School of Management, Northwestern University, KEL736, August 1, 2012, 12–13, https://hbsp.harvard.edu/product/KEL736-PDF-ENG. 32. Jens Hansegard, “Lego Builds Stronger Ties to Girls,” Wall Street Journal, December 29, 2015, https://www.wsj.com/articles/lego-builds-stronger-ties-to-girls-1451420979. 33. Hansegard, “Lego Builds Stronger Ties to Girls.” 34. Paul R. LaMonica, “There Is a Toy Glut and Barbies Aren’t Selling,” CNNBusiness, April 12, 2017, https://money.cnn.com/2017/04/21/investing/mattel-earnings-sales -toys/index.html. 35. Zach Wichter, “Airlines Are Raising the Cost of Checked Bags,” New York Times, September 20, 2018, https://www.nytimes.com/2018/09/20/business/airlines-raising-bag -fees.html. 36. Wichter, “Airlines Are Raising the Cost of Checked Bags.” 37. Wichter, “Airlines Are Raising the Cost of Checked Bags.” 38. Vincent R. Nijs, Shuba Srinivasan, and Koen Pauwels, “Retail-Price Drivers and Retailer Profits,” Marketing Science 26, no. 4 (July-August 2007): 475. 39. Nancy Giddens, “Building Your Brand with Flanker Brands,” Iowa State University, updated June 2010, https://www.extension.iastate.edu/agdm/wholefarm/html /c5-51.html. 40. Martin Tolchin, “Six Airlines Settle Suit by Government on Fares,” New York Times, March 18, 1994, https://nyti.ms/2986mZ8. 41. Scott McCartney, “Airfare Wars Show Why Deals Arrive and Depart,” Wall Street Journal, March 19, 2002, https://www.wsj.com/articles/SB1016494375818041680. 42. Jay Carlson, “A Content Analysis of Bonus Pack Promotions,” Journal of Promotion Management 23, no. 6 (2017). 43. From the Q&A section on the U.S. Federal Trade Commission website: “Q: Our company monitors competitors’ ads, and we sometimes offer to match special discounts or sales incentives for consumers. Is this a problem? A: No. Matching competitors’ pricing may be good business, and occurs often in highly competitive markets. Each company is free to set its own prices, and it may charge the same price as its competitors as long as the decision was not based on any agreement or coordination with a competitor.” Federal Trade Commission, accessed February 26, 2021, https://www .ftc.gov/tips-advice/competition-guidance/guide-antitrust-laws/dealings-competitors /price-fixing.

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44. Best Buy Price Match Guarantee, accessed February 26, 2021, https://www .bestbuy.com/site/help-topics/best-buy-price-match-guarantee/pcmcat297300050000.c?id =pcmcat297300050000. 45. Brown and MacKay, “Competition in Pricing Algorithms,” 22–23. 46. Thanks to Samuel Engel for identifying this insight for me. 47. Aer Lingus, “Upgrade Yourself,” accessed February 26, 2021, https://www.aerlingus .com/plan-and-book/plan/upgrade-yourself/. 48. Priya Raghubir and Shelle Santana, Source, Form, and Form of the Source of Money: A Malleable Monopoly Money Phenomenon, unpublished research paper, 2019. 49. Jehoshua Eliashberg, Thomas S. Robertson, and Talia Rymon, Market Signaling and Competitive Bluffing: An Empirical Study (working paper, report no. 96–102, Cambridge, MA: Marketing Science Institute, March 1996), 27. 50. Alison Sider, “‘Predatory and Opportunistic’: Southwest Airlines Seizes the Moment as Rivals Struggle,” The Wall Street Journal, November 16, 2020, accessed November 18, 2020, https://www.wsj.com/articles/southwest-airlines-covid-expansion -airports-11605296205?mod=hp_lead_pos5. 51. Brown and MacKay, “Competition in Pricing Algorithms,” 27. 52. Brown and MacKay, “Competition in Pricing Algorithms,” 3–4. 53. Brown and MacKay, “Competition in Pricing Algorithms,” 3, 22. 54. Sam Schechner, “Why Do Gas Station Prices Constantly Change? Blame the Algorithm,” Wall Street Journal, May 8, 2017, https://www.wsj.com/articles/why-do -gas-station-prices-constantly-change-blame-the-algorithm-1494262674 (emphasis mine). 55. Schechner, “Why Do Gas Station Prices Constantly Change?” 56. Gadi BenMark, Sebastian Klapdor, Mathias Kullmann, and Ramji Sundararajan, “How Retailers Can Drive Profitable Growth Through Dynamic Pricing,” McKinsey & Company, March 27, 2017, 2, https://www.mckinsey.com/industries/retail/our-insights /how-retailers-can-drive-profitable-growth-through-dynamic-pricing. 57. These recommendations are adapted from BenMark et al., “How Retailers Can Drive Profitable Growth Through Dynamic Pricing.” 58. Businesses often use cost of goods sold (COGS) as a surrogate for variable costs. COGS in fact is a biased representation of variable costs, because included with COGS are sunk fixed-cost overheads, which are irrelevant to forward-looking price-setting decisions. For profit pool estimation, gross margins or gross profit using COGS can be used so long as you recognize the bias that is baked into the measures used. 59. Kim Eun-jin, “Samsung Increases Global Smartphone Industry Profit Share to 32.6%,” BusinessKorea, November 30, 2020, accessed March 24, 2021, http://www .businesskorea.co.kr/news/articleView.html?idxno=55955. 60. Mark Gottfredson, Steve Schaubert, and Hernan Saenz, “The New Leader’s Guide to Diagnosing the Business,” Harvard Business Review (February 2008): 71. 61. Gottfredson et al., “The New Leader’s Guide to Diagnosing the Business,” 72.

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9. Balanced Pricing Orientations, Profitable Pricing Strategy 1. Scott Huettel, Behavioral Economics: When Psychology and Economics Collide (Chantilly, VA: Great Courses, 2014), 22. 2. Gerald E. Smith, “Emergent Pricing Strategy” in Visionary Pricing: Reflections and Advances in Honor of Dan Nimer, Advances in Business Marketing & Purchasing 19 (Bingley, UK: Emerald, 2012), 99–122. 3. Bill Javetski and Tim Koller, “Debiasing the Corporation: An Interview with Nobel Laureate Richard Thaler,” McKinsey Quarterly (May 9, 2018), https://www.mckinsey .com/business-functions/strategy-and-corporate-finance/our-insights/debiasing-the -corporation-an-interview-with-nobel-laureate-richard-thaler. 4. “Transcript—iPhone Keynote 2007,” European Rhetoric, accessed February 27, 2021, http://www.european-rhetoric.com/analyses/ikeynote-analysis-iphone/transcript -2007/. 5. “Steve Jobs’s Letter to iPhone Customers,” Wall Street Journal, September 6, 2007, https://www.wsj.com/articles/SB118910674094519630. 6. Katya Wachtel, “Warren Buffett: There’s Only One Thing That Matters to Me When I’m Investing in a Company,” Business Insider, February 18, 2011, https://www .businessinsider.com/warren-buffett-pricing-power-beats-good-management-berkshire -hathaway-2011-2. 7. Alison Griswold, “Uber’s Secret Weapon Is Its Team of Economists,” Quartz, October 14, 2018, https://qz.com/1367800/ubernomics-is-ubers-semi-secret-internal-economics -department/. 8. Ron Kermisch and David Burns, “A Survey of 1,700 Companies Reveals Common B2B Pricing Mistakes,” Harvard Business Review, June 7, 2018: 5. 9. Kermisch and Burns, “A Survey of 1,700 Companies Reveals Common B2B Pricing Mistakes,” 5. 10. Kermisch and Burns, “A Survey of 1,700 Companies Reveals Common B2B Pricing Mistakes,” 5.

index

30 Rock, 203 55 or die, 78, 292, 294 A Cappella Books, 47 Abney, David, 134 acceptable price range, 71 Accounting Nation, 91, 94, 99, 101, 111–12; influence on price-setting, 97 act like owners, 116 activity based costing (ABC), 109, 142, 145, 150, 158, 287; analysis, 146; time-driven activity-based costing (TDABC), 135, 142–43, 145 activity-based costing skills, 101 add-1, 61 add-3, 62 adjunct responsibility, 3 Adobe, 19, 20, 21, 55, 215 advanced application strategies, 196, 198 advertising, 42 Aer Lingus, 265

affect demand, 126 Air Jordan, 198 Airbnb, 25 Alexion, 181, 182 algorithmic price-setters, 267 algorithmic pricing, 267, 270 allocation-driven method pricing, 128 Allstate, 160 Amazon, 18, 45, 47, 64, 72, 73, 104, 139, 176, 250; cloud services, 72; Buy Box, 250; Fulfilled by Amazon (FBA), 250; Music, 39; Prime, 45, 54; Web Services, 71–72 ambiguity aversion, 8, 106 AMC, 19 American Girl, 263 Amgen, 140, 167, 169–70, 172, 176, 194 analytics, 13 anchoring and adjustment, 8, 99, 209 anchoring value estimation, 174 Anderson, James, 186, 189 Android, 292

324 Apple, 41, 51, 52, 78, 104, 198, 276, 290–95; iCloud, 41; iPads, 41, 53; iPhone, 41, 51, 53, 78, 291, 293–95; iPod, 52–54, 291; iTunes, 53–54; Macintosh, 53; Music, 42; One, 42; Stores, 51; Watch, 51, 53, 198 Applied Industrial, 160 ARDEX, 160 Ariely, Dan, 5, 32, 114 Arons, Marc De Swaan, 104 Arrow Electronics, 140 asset specificity, 98 asymmetric pricing technology advantage, 269 AT&T, 44, 287 Atari, 295 attributes in conjoint, 234 augmented features price offerings, 204 automatic intuitive thinking, 60 auxiliary service price offerings, 204 average costs, 133–34 Bain and Company, 4, 117, 233, 299 balanced view, 14 Bank of England, 93 BareBones WorkWear, 252 basic information strategies, 195–96, 198 Basquiat, 36 Beauty Bridge, 250 behavioral economics, 5, 24, 51, 58, 67, 121, 129, 201, 219, 221, 223 behavioral economic theory, 31, 194, 281 behavioral laws of human evolution, 245 behavioral pricing patterns, 10 behavioral soft skills, 107 behavioral theory of sensemaking, 178 Belafonte, Harry, 47 Belichick, Bill, 64 benefits, 174, 176;framing strategies, 214; psychological and intangible, 178, 189 Bentley, 73, 162 Berkshire Hathaway, 296 Best Buy, 141 Bezos, Jeff, 45, 64, 250

INDEX

bias(es), 31, 44, 54, 63; Accounting Nation behavioral, 99, 100; actionoriented, 8, 102; anchoring and adjustment, 174, 214; availability, 99, 298; costing, 125, 133–35, 157; average customer costing, 134–35, 157; behavioral, 32, 33, 54, 58, 88, 99, 123, 206; commoditized framing, 39; competition driven pricing, 243; competition-oriented pricing, 250, 252, 278; competitive loss aversion, 252, 278, 288; computational, 95; confirmation, 8, 94–95, 99, 100, 288; cost-based logic, 79; cost-driven, 284; cost-driven pricing, 81, 135; cultural, 89, 92; cultural price-setting, 88–89; customer 102; customer willingnessto-pay driven pricing, 206; decisionmaking, 73; digit, 61; willing-to-pay questioning, 208, 212, 234, 296; excessive optimism, 8, 102; Finance Nation behavioral, 95–96; forecasting, 81; frame of reference, 287; framing, 24–25, 33–35, 38, 52–4, 56, 58; goalframing, 8; heuristics, 125; heuristic value estimation, 170, 176–77, 198; linear, 175; loss aversion, 211; market share, 248–50, 252, 276, 278, 284; missing information, 82; narrow framing, 8, 94–95; overconfidence, 8, 82, 94, 99; ownership, 89, 113, 116; persistent price-discounting, 211–12, 296; price-setting, 58, 81, 104; pricing orientation, 35, 284; probability, 85; probability estimation, 85; Production/Operations/ Manufacturing Nation behavioral, 108; proportional value, 170, 173, 175–77, 198; psychological pricesetting, 8, 58, 63–64; recognizing framing, 32; Sales Nation, biased behaviors, 103; sensing framing, 55; simplification, 126; social, 8, 88; social influence, 94; social price-setting,

I N D E X  325

8, 58; soft decision-making, 58; standardized costing, 125–29, 133, 135, 157, 284; standardized costing bias penalized high performing products, 129; standardized costing bias subsidized low performing products, 129; status quo, 34–35, 53–54, 260, 288; subconscious, 63; sunflower, 100, 288; sunk costs, 125, 129–30, 132–33, 135, 157; sunk costs framing, 41; System 1 behavioral, 126, 298;System 1 behavioral processing, 85; System 1 heuristic, 7, 81, 246; System 1 memory-based, 128; total cost, 99; toxic pricing, 252; uniform pricing, 206–8, 212; value biases in price-setting, 170; value illiteracy, 171, 177, 198, 284; willingness to pay, 206 Bilstein, Frank, 208 Bitcoin, 73 Black Friday, 223 BMW, 73, 204–5 Bolze, Steve, 248 booking curve, 270 Bordeaux wines, 113 Boston College, 109 Boston Consulting Group, 243 Boston MBTA, 217 Boston Red Sox, 270 Boston University, 109 Botswana, 171 bottleneck costs, 137 bottom-line profit, 276 bottom-up approach, 23 bounded rationality, 77 Bourgeois, 36 Box, 214 blue ocean strategy, 46, 48–49, 182, 194 brand awareness, 42 brand loyalist, 218, 222 brand managers, 27 brand meaning, 46 brand mix, 104 breakeven pricing, 123

breakeven sales calculations, 11, 135, 142, 150, 153–54, 157–58, 287 breakeven sales change, 83, 86, 150–55, 238; formulas, 152; reactive, 155 Brigham Young University’s Marriott School, 99 broad framing, 38 Buffett, Warren, 262, 295–96 Bugatti, 73 Buicks, 165 burden factor, 126 burden rates, 127 burdened hourly labor cost, 127 Burns, David, 117, 299 Butler, Malcolm, 245 business gross profit, 133 Campbell, Patrick, 75 Campbell Soup, 139 Canon, 262 canonized formulas, templates, algorithms, 8, 65, 81, 87 capacity costs, 17 capacity utilization, 107 cardinal directions of price setting, costing, 294; competition, 294; customer value, 294; customer willing to pay, 294 cardinal influence drivers of pricing orientation, 16, 64 cardinal pricing orientations, 18, 121, 123, 282, 284 Carmon, Ziv, 114 Carnegie Mellon University, 33, 107 Carroll, Pete, 245 category commoditization, 39, 43 category management, 264 Caterpillar, 190, 192–94 C-suite, 13 Celgene, 140 Certified Pricing Professional, 109 changes in demand, 249 changes in market share, 249 Christie’s Auction House, 36, 38

326 Chuck E. Cheese, 32 Coca Cola, 97, 139 Cohesity, 196 Columbia Business School, 93, 104, 109 Comcast Xfinity, 217; Standard Triple Play Bundle, 217 commoditization, 40; category, 41 commoditized, 40, 54, 79, 128; category, 51 competition and cooperation, 253 competition driven pricing, 16, 277 competition influence drivers, 17 competitive advantage, 215 competitive bluffing, 266 competitive moves, 10 competitive price cut, 153, 155 competitive price increase 153, 155 competitive pricing, 6, 273 competitive reference alternatives, 214 competitor prices, 16, 124, 161 conjoint analysis, 11, 234, 237; adaptive conjoint analysis, 238; choice-based conjoint analysis, 238; design, 234; experimental design, 235; experimental test, 235; menu-based conjoint analysis, 238; methods and models, 234; willingness to pay, relative importance, 236, 238 constant sum scale, 283 consulting; fully burdened hourly billable rates, 127 consumer behavioral theory, 215 consumer research, 24 contribution margin, 133, 139, 153–54, 286; average net, 144; estimation, 133; rate, 276 contribution margin-based incentive system, 116 convergence and commoditization, 53 Cook, Tim, 51 cooperative competitive moves, 286 Copenhagen Business School, 65 core product model price offerings, 204 core value proposition, 115

INDEX

Coronavirus, 18 Cornell University, 109, 113 corporate pricing manager, 6 cost accountants, 126 cost accounting data, 129 cost-based prices, 132 cost discovery, 135 cost distortions, 136 cost-driven pricing, 16, 123, 128, 136 cost-driven soft skill, 297 cost drivers, 146 cost influence drivers, 17 cost management; external market, 98; internal corporate, 98 cost + margin worksheet, 81 cost of goods sold (COGS), 133 cost-plus-profit, 99, 123; pricing method, 101 cost-probing skills, 136 cost recovery, 99 cost savings drivers, 164, 179 cost sensing, 135–36 cost to serve, 145, 150, 223 customers, 125, 141, 144; driver, 146; estimates, 145; hidden costs, 137; high cost to serve, 134, 137, 143–45, 207; higher versus lower cost, 139; low, 137, 144; savings, 150; score, 145 Costco, 140, 223 costing, 6, 123; data, 124, 157 Could Outcomes, 86 COVID-19, 141, 162, 176, 221, 290 Creative Cloud Suite, 21 Crest Gum, 222 cross selling bundles, 140 Culp, Larry, 248 cultural nations of pricing, 8, 89, 91–92, 94, 97, 109, 111, 283 cultural price-setting skills, 88–89 Curry, Steph, 218 customer-driven price-setting skills, 212 customer negotiation, 13 customer orientation, 161 customer pricing analytics, 229, 287

I N D E X  327

customer sales gains or losses, 31 customer satisfaction, 10, 23 customer segment and price-framing map, 216; template, 215 customer segment and price sensitivity map, 213 customer service costs, 138 Customer Service Cultural Nations, 94 customer value, 6, 16, 24, 124, 160–62, 167, 169, 171, 176–77, 179, 180, 186, 192, 198; data, 189; data gathering, 177, 185; driven orientation, 294; driver discovery, 177, 182, 186, 286; drivers, 182, 184, 186–87, 189, 190; estimations, 162, 167, 170–71, 173, 177, 179, 189; interview guide, 187; interviews, 186, 187; model, 190, 195, 275; research, 170; research teams, 186 customer value discovery, 190 customer value driven pricing, 16, 160, 167 customer value driver discovery—project mapping generic template, 185 customer value driver discovery team, 187 customer value model for Repatha, 168 customer value modeling, 11, 162–63, 167, 169, 177, 181, 189, 273, 278 customer value research effort, 187 customer willingness-to-pay, 6, 16, 124, 161, 213, 296; driven pricing, 292; influence drivers, 17 CVS Caremark, 170 da Vinci, Leonardo, 36–37, 54 Dana Hall School, 261 Darwin, Charles, 245 Dasani, 47 Dash, 139 data analytics, 64 data diversity, 14, 18, 20, 83, 110, 112, 119, 288 data-driven, 10; tools, 233

Dawkins, Richard, 253 debias, 8–9, 18, 35, 38, 74, 82, 119, 129, 135–36, 198 declining differentiation, 41 decision diversity, 14, 18, 20, 83, 110, 112, 119, 288 decision-making; framework, 182; managerial, 24 decision processing, 59 Decision Science, 12 Deezer, 39 Deloitte, 22, 116, 146, 148, 150; CFO Program, 94 Delta Air Lines, 263 demand-based pricing, 140 demand curve, 16, 150, 202, 271 denominator neglect bias, 126 DePaul University, 102 descriptive, 9 Dholakia, Uptal, 221 differential advantage, 9 differential customer value, 183; drivers, 183–84 differential value, 169, 171, 173–74, 179, 181, 192, 245; drivers, 53, 164, 179, 183; negative, 169, 192; positive, 167 differentiating benefits, 184; for customers, 183 differentiating features, 183–84 differentiation value, 26, 179 difficult-to-calculate economic value drivers, 182 digital economy strategy, 25 digital marketing, 42 digital skills, 64 direct cost pricing, 101 Disney, 104, 217, 270 diversity, 290; role of, 287 Dollar Shave Club, 41, 48 dominant frame, 43 dominant framing strategies, 40, 43 dominant price framing, 44 Dow Corning, 259, 264; Xia by Dow, 259 Downton Abbey, 203

328

INDEX

Dr. Carter’s Easy Shave Butter, 48 Duke University, 5, 73, 104, 114, 253 Dutch East India Company, 93 dynamic pricing, 12, 33, 125, 272; models, 134

exploratory cost discovery, 136, 157 exploratory method, 186 exploratory research, 186 Express Scripts, 170 extremeness aversion, 214, 221

e-commerce, 18, 40, 45, 141 economic decision-making, 31 economics-driven pricing strategy, 11 economic growth, 277 economic theory, 109 economic value, 26, 227 economic value models, 228 economic value theory, 26 Eleven Nations of North America, The, 90 Ellsberg, Daniel, 106 Ellsberg paradox, 106 EMC Club, 214 employee stock ownership plans (ESOPs), 115 end-benefit frames, 198 endow, 113 Engel, Samuel, 263 engineering personnel, 4 enterprise pricing platforms,11, 12 Epson, 262 ESPN+, 217 ESSEC Business School, 109 essential constructs of customer value and price, 172 Estimating Cost to Serve Customers with TDABC, 143–45 event marketer, 47 everyday buyers, 214 everyday price-setters, 13, 23 Evian Facial Spray, 47 Evian Natural Spring Water, 47 evolve, 9, 23, 39–40 experienced buyers, 214–15, 222–23; price-sensitive, 223 experiential customer value outcomes, 183; high-worth, 184 experimental design, 234

Fairlife, 70 Fassnacht, Martin, 116–17, 219 fast processes, 135 fatal event cost savings, 167 features, 183 features, benefits, and value drivers, 182 Fedex, 223–24; basic price menu, 225 Feedvisor algorithm, 250 Fenway Park, 214 Fidelity, 249 field sales personnel, 3 finance and accounting personnel, 3 Finance Nation, 91, 94–95, 111–12; influence on Price Setting, 92 financial analyst, 41 financial payoff for effective pricing, 22 financial risk, 166 firefighters, 11 first-degree price discrimination, 205 Fisher-Price, 263 Fitbit, 52 FitEquipCo, 276 fixed costs, 17, 126–27, 129, 130, 132–33, 142, 150, 153–54; and overheads, 142; incremental, 157; intensive services, 128; standardized, 129; sunk, 130, 133 fixed freight, 148 flanking price brand, 259, 264 Flextronics International (Singapore), 164 Flip or Flop, 179 Forbes, 62; magazine, 102 forecasting, 65, 82 Fortune 500, 13 Fortune 1000, 93, 106 forward focus, 124 forward-looking price-setting, 17, 130 Fox, Craig, 84

I N D E X  329

frame of reference, 18, 27–28, 39, 58–59, 70–71, 172, 182, 194, 197–98, 201, 283; broad, 38;commoditized dominant, 39; competitive, 162; converging, 39; differentiated heterogeneous, 39; dominant, 41, 54, 77; evolution, 39; in the marketplace, 31; narrow, 38, 55;opportunity, 39, 54; salient, 162; single, 35; strategic, 24, 194; subscription, 41 framing, 24, 28, 53–59, 128, 283; away from price competition, 260; baseline, 52–53; benefit(s), 25–27, 40, 42, 49, 51, 53, 55, 260, 283, 291, 295; benefit bundle, 291; broad, 36, 53–54; broad terms, 35; broad versus narrow, 32, 55; bundled benefit, 49, 51; bundled price, 32, 43, 54; choices, 24; core + fees price, 43; customer solution benefit, 49–51, 55; customer valuedriven, 291; differentiated price, 55; dimension, 65; flat-rate price, 43; freemium price, 43; goal, 65, 67, 80, 87–88, 283; holistic, 181; individual product, 49–50; innovation, 52–54, 56; metric price, 43;narrow, 36, 38, 53–54, 97; narrow terms, 35; narrow versus broad, 35; neighboring category reference, 46–47; new category, 46; new category reference, 48; new price, 45; new-to-market buyers, 215; opportunities, 39, 52, 56; opportunity gap, 41, 55–56; original category reference, 46; positive-negative, 198; price metric, 70; product price, 43, 287; reference, 25–27, 42, 45–46, 48, 53, 55, 179, 260, 283; strategic framing for pricesetting, 97; strategies, 54; subscription price, 43, 45; to transform perceptions of value, 42; two-part price, 43; usage price, 43, 217; value, 198;value-based, 25, 27 Franken, Al, 47

Franklin, Benjamin, 77 fuel savings, 190–91 Gabor and Granger’s Direct Response Method, 210 gain goal frame, 67–68, 297 gains; prospective, 29; risky, 31 game theory, 249, 252, 278; cooperative competitive move, 255, 259, 278; cooperative public statements, 260; deciphering competitive pricing moves, 258; hopeful competitive move, 255, 259; mutual cooperation, 253; mutual defection, 253, 267; negative-sum game, 249; opportunistic competitive move, 255, 265, 278; positive-sum game, 249; prisoner’s dilemma, 252, 255; reactive pricing game paradigm, 258; repetitive game, 255; retaliatory competitive move, 255, 259, 264, 278; simultaneous Bertrand outcome, 267; simultaneous game, 255; sucker’s payoff, 253; temptation to defect, 253; zero-sum game, 249 Garmin, 52 Gassée, Jean-Louis, 78 General Electric, 50–51, 160, 248 General Mills, 264 Georgia State University, 61 get price right, 14, 300 Gillette, 40, 41 give side; of the value-price exchange, 42 Gladwell, Malcom, 47, 63 GlaxoSmithKline, 48 GM, 288 goal frame orientations for price-setting, 66 Gold Medal flour, 264 gold standard, the, 9, 14 good-better-best product line price fences, 220 good-better-best product line pricing, 214

330 Google, 74, 292; AdWords, 74–75; Play Music, 39 Gouzer, Loïc, 36 gross margin, 133, 144, 291 Gundersen Health System, 136 Hamilton, 270 Hammermill Copy Paper, 222 Hampton Sun Continuous Mist Hydrating Aloe, 47 hard analytic skills, 286, 299 hard analytic costing skills, 141–42, 158 hard analytic price-setting skills, 10–11, 135, 141, 286 hard analytic value skill, 162 hard costing skills, 141 hard customer value modeling skills, 170 hard customer WTP skills, 229 hard pricing skills, 11, 229, 234, 286, 293 hard skills, 63–64, 101, 123, 150, 160, 198, 201, 229, 287, 294; complementary, 150 hard skills of value economics, 23 hard value skills, 189; for price-setting, 189 Harmer, Richard, 253 Harvard Business Review, 31, 116, 175, 243 Harvard Business School, 48, 109, 137, 249 Harvard Medical School, 169 heat map, 68–69 heavy users, 217, 222–23 hedonic goal frame, 67 Heinz, 153, 154, 155 Henderson, Bruce, 243 Hermés, 198 Hertz, 270 heuristics, 7, 10, 38, 79, 88, 124, 173–76; availability, 8, 66, 102, 209; average, 17; behavioral, 77; mental estimation, 126; thinking, 35; zero-cost, 32, 204 heuristically, 128, 171, 298 Hewlett Packard (HP), 55, 160

INDEX

high-frequency competitive pricing, 267 high-knowledge buyers, 214, 227 high-knowledge customers, 194, 195 high-volume buyers, 222 higher cognitive skills, 64 highly differentiated positive value drivers, 176 Hinterhuber, Andreas, 173 historically focused cost reporting, 17 Holden, Reed, 212, 224 Home and Garden Television, 179 Home Depot, 141, 207, 223 home owners, 214 how customers pay, 42, 70 how pricing gets done; socially, 88 Howard, Ron, 218 HP, 262 Huettel, Scott, 5, 14, 71, 78, 86, 280 human cognition in business, 25 Hunt’s Ketchup, 155 HVAC, 126, 214 Hyatt, 270 hypothesized differential customer value; drivers, 184, 187; models, 192 iHeartRadio, 39 Immelt, Jeff, 248 incompatibility costs, 166 incremental cost, 17, 101, 124–25, 134–35, 142, 150, 157; incremental costing, 101 incremental cost to serve, 16, 161; driver assessment, 145 incremental profit contribution, 17, 83, 116, 124, 129–30, 133–34, 139, 144, 270; maximize, 130, 157–58 incremental profitability, 133, 141, 157 incremental sales, 83 incremental variable costs, 154, 276 Independence Day, 223 Indications for disease categories, 48, 172 inexperienced/new-to-market buyers, 214 inferior performance drivers, 165

I N D E X  331

influence drivers, 16 influence perspectives, 18 innovations, 25 INSEAD, 114 intangible products, 37 Intel, 104, 115 internal costs to produce, 125 International Business Machines (IBM), 101, 104 interviews; depth, 186; pilot, 187 intrinsic performance, 194 IoT, internet of things, 262 irrational, 3, 129, 209 Jhangiani, Nik, 97 Jiang, Zhenling, 61 Jobs, Steve, 52, 54, 78, 291, 295 John Deere, 189, 192–94 Johnson & Johnson, 160 Johnstone, Tom, 160 Kahneman, Daniel, 5, 7, 11, 28, 33, 35, 59–60, 62, 66, 71, 94, 113, 135, 141, 246; prospect theory, 28; prospect theory gamble, 30 Kaplan, Robert, 137, 145; cost to serve customers framework, 138 Keller, Thomas, 218 Kellogg, 139 Kellogg School of Management, 104 Kenmore, 71 Kermisch, Ron, 117, 299 Kleenex, 222 Klein, Gary, 11 Knetsch, Jack, 113 KPMG, 64, 99 Kraft, 104 Kyocera, 262 labor-based fees, 128 labor savings, 190 Lancioni, Richard, 110–11 leakage, pocket price waterfall, 147–48, 150; from free freight, 148; from

order discounts, 148; from volume discounts, 148 Lego, 262 Lennox International, 220 Leonardo’s influential art, 38 leveraging competitive advantage, 262 Levine, Tim, 63 light-use buyers, 217 Lion King, 270 Liozu, Stephen, 13 Lipton, 104 list price, 169 London Business School, 266 Los Angeles Rams, 84 loss aversion, 8, 33, 40, 44, 53–54, 223, 246 Louisiana State University, 102 low-frequency price setters, 267 low-knowledge buyer, 214–15, 217, 223 low-knowledge customer, 197 loyalty program, 13 Luby, Frank, 208 Lucara Diamond Corporation, 171 LVMH, 171 Lyft, 27, 134, 221, 270 machine replacement savings, 190–91 Madoff, Bernie, 63 Maersk, 160 Management Science and Integration, 12 managerial pricing, 10 managers frame price-setting outcomes, 31 margin-based goals, 116 margin integrity, 140 margin leverage, 10, 136, 139, 141, 157, 286; based on true contribution margins, 297; low-margin leverage strategy, 141 marginal cost, 15–17, 124, 160–61, 293–94, 298 marginal demand, 17 marginal revenue, 15–17, 124, 160–61, 201–2, 243, 294, 298; principles, 160

332 market-based assets, 104 market-clearing price, 114 market cost management paradigm, 99 market-driven costs, 125 market-driven forces, 125 market erosion, 147 market response, 157 marketing, 27 Marketing Nation, 91, 94, 104, 106–7, 111–12; influence on price-setting, 104 marketing personnel, 4 marketing strategies, 42 markup pricing, 123 Marriott, 270 Mars Wrigley, 139 MasterClass, 218 Matisse, 36 Maytag, 71 maximize long-term profit contribution, 9 maximize profits, 15 MC = MR, marginal cost equals marginal revenue, 15, 124, 160, 202, 293, 298 McDonald’s, 227 McKinsey, 63, 82, 102, 112, 137–38, 146, 175, 228, 272 McKinsey value map, 175 measuring monetary customer value driver dimensions, 184 MedCalc, 195 Medicare, 136 meeting comp price discount, 243 Memorial Day, 23 memory-based associations, 6 memory-driven, 10 mental shortcuts, 88 Mercedes, 60, 73 metric, 45 Metso, 160 microsegmentation, 232 Microsoft, 104, 115, 217 Microsoft Office 365, 41 Might Outcomes, 86

INDEX

Mill, John Stuart, 171, 174 minimum advertised price (MAP), 294 Mint.com, 219 Mintzberg, Henry, theory on emergent strategy, 23 misframing, 212 MIT’s Sloan School of Management, 93, 107, 109 mobile handset subsidies, 43 Mohammed, Rafi, 209 Monash University, 252 Monet, 36 monetizing, 18 Moore, Marian Chapman, 252 Morgan Stanley, 45 MoviePass, 18–19 MR = MC, 124, 202 MRO services, 50 Müller, Georg, 45, 116, 139, 177, 219 Musk, Elon, 73, 228 Nagle, Thomas, 45, 83, 116, 139, 150, 177, 213, 215, 219, 249 narrow framing opportunity, 36 Narus, James, 186, 189 Nasser, Larry, 63 National, 270 NBCUniversal, 203 negative differential value, 165, 179, 192; drivers, 165–66 negative frames, 28 negative pricing models, 219 Negotiating With Backbone, 224 net differential value, 192–94 net value of a purchase decision, 25 Netflix, 41, 215, 217 neutral pricing strategy, 192, 194, 294 New England Patriots, 64, 84, 245 new models of value-price exchange, 25 New York Times, 5, 67, 101, 263 New York Stock Exchange, 93 New York University, 93, 109 Newton Country Day School, 261

I N D E X  333

next best competitive alternative (NBCA), 162, 164, 179, 190 Nimer, Dan, 109 Nobel Prize, 5, 28, 33, 59, 97 nonstandard order costs, 137 normative, 9; goal frame, 67, 116, 297 Notre Dame, 30, 66, 211 Novo Nordisk, 140 Nudge, 5 nudge, 67, 74, 116, 297 nudging, 65, 67–68, 87, 283 objective, 83; customer value estimation, 179, 195 objective customer value models, 12, 162, 179, 189, 194, 198; John Deere 750J Bulldozer, 191 objective forecasts, 83 off-invoice; drivers, 147–48; item, 150; transaction, 150 Ohio State Universities, 30, 66, 211 Old Dominion Freight Line, 260 On Demand Pricing, 72 on-invoice; drivers, 147; price, 150 opaque pricing strategies, 265 operating costs, 133 opportunity costs, 17, 125, 134, 226 opportunity gap, 39–41, 53, 55; for reframing, 45 opt in, 68 optimal price-setting, 14 origins of Finance Nation, 92 out-of-pocket buyers, 222 outcome-based pricing, 170 outcome frames, 198 overestimating the likelihood of lowprobability events, 84 overhead burden, 129; fixed-cost, 129 overhead burden rate, 126 overhead costs; fully burdened, 99 ownership affects, 115 ownership-endowment, 40 ownership-endowment effect, 33, 53–54

Palmer, Jonathan, 50 Palos Verdes Golf Club, 218 Pandora, 39 paradigm, 12, 23, 281; corporate, 98, 288; hybrid, 98; market, 98; Market costing, 288 paradigm, another for price-setting, 4 Parks And Recreation, 203 Paxil, 48 PCSK9 inhibitor drug, 172, 194 Peacock, 203–4 peak-load price fences, 221; priorityaccess, 221; quality discount, 222; time-of-purchase, 223; willing-toearn, 222 Peloton, 162 penetration pricing strategy, 126, 192 Penttinen, Esko, 50 PeopleSoft, 115 perceived ownership, 113 performance erosion, 147 performance risk, 166 personnel involved in pricing, 11 pharmaceutical, 48 pharmaco-economic, 167 physical risk, 167 Pixar, 52 pocket margin, 150 pocket price, 147 pocket price waterfall, 109, 138, 146–48 poker player; buyers, 228 Poland Spring, 47 policy erosion, 147 Poor Richard’s Almanack, 77 positioning, 24 positive differential value, 169, 192 positive framing, 28 Powerball, 85 Praluent, 172–73, 194 predictive, 7 Predictably Irrational, 5 predicting profitability, 7

334 President’s Day, 223 prestige/exclusivity-driven buyers, 214, 218 price band analytics, 11, 230, 238, 297 price bundle, 20 price buyers, 225, 227–28 price-cutting, 31 price change with change in fixed costs, 153 price change with change in variable costs, 152 price confidence, 160 price discrimination and segmented pricing, 203 price discrimination theory, 202 price distribution analytics, 230, 231 price elasticity, 238–40, 249 price fences, 213, 220, 222; behavioral change, 223; buyer identification, 223; location-of-purchase, 223 price fencing, 219, 224; strategies, 220; what customers pay: the tradeoffs of price and willingness to pay, 220 price framing, 10, 25–27, 41–43, 53, 213, 215, 218, 219, 224, 227, 283; broad bundled, 32; bundled, 217, 218, 287; flat-rate, 217; freemium, 218–19; how customers pay: the structure of price and metrics they pay, 216; metric, 218; models, 219; narrow usage, 32, 246; two-part, 218;subscription, 218; usage, 218 price-framing strategy, 18 price illiteracy, 171 price management expenditure, 13 price menu, 213, 219, 224 price metrics, 8, 20, 43, 70–71, 73, 76–77, 87; cost-per-click, 74; featuredifferentiated, 75; flat-fee-based, 73; for segmented pricing, 71; framing, 65, 71; freemium, 73; functional value, 75; off-peak, 72; outcome value, 75; peak usage, 72; profitability and

INDEX

competitive advantage, 76; value alignment, 73; value-based, 75; value metric, 75 price-negotiating skills, 227 price offerings, 204, 224 price optimization, 13, 230 price reframing, 42, 45 price sensing, 292 price-sensitive; buyers, 25, 39, 219, 223, 225, 227–28, 238, 293; customers, 21, 204, 222, 237, 296 price sensitivity, 13, 25, 202, 213, 215, 219, 224, 237, 292; and willingness to pay, 237 price-setters, 25 price-setting goals, 211 price-setting skills, 7, 18, 89, 113; soft, 133 price-setting shortcuts, 173 price-setting strategy; healthy, 14 price-setting team, 9 price structure, 43 price waterfall, 148, 150; analysis, 135, 142, 148, 158; analytics, 11, 297; baseline, 150; principles, 150; reengineered, 148, 150 Priceline, 219 price matching guarantee, 265 prices to end in 9, 60 pricing analytics firm, 297 pricing committee, 290 pricing consultants, 127 pricing dynamics, 275 pricing goals, 8, 283 pricing illiteracy, 4 pricing innovation, 33 pricing investment, 14 pricing leadership, 13 pricing managers, 31 pricing methodologies, 99 Pricing Nation, 109, 110, 294, 296; costdriven, 29; influence on price-setting, 109 pricing organization, 13

I N D E X  335

pricing orientation, 4, 8, 9, 11, 12, 17, 20, 22–24, 32, 38, 81, 96, 112, 119, 135, 142, 201–2, 280–82, 284, 286–94, 296–98; audit, 282, 286–87; balanced, 14, 18–19, 83, 162, 287, 289–90, 296; behavioral motivations, 250; competition-driven, 58–59, 119, 121, 124, 161, 238, 282, 286, 288; costdriven, 17, 58–59, 119, 121, 123–25, 161, 282, 284, 287; customer-centric, 161; customer-driven, 119, 176, 284; customer value-driven, 58–59, 121, 124, 160–61, 173, 198, 282, 284, 286; customer willing-to-pay-driven, 58, 59, 121, 124, 161, 173, 201, 282, 284, 296; effective, 64; optimal, 14; profitable, 124, 160; psychological, 58–59, 65, 283; social, 58–59, 88–91, 112, 118–19, 283–84; strategic, 176; true costing principles, 124; unbiased social, 113; value-driven, 119, 177; willingness-to-pay-driven, 19, 287 pricing paradigm, 8 pricing patterns, 24 pricing problem, 30 pricing researchers, 30, 103 pricing scripts, 181 pricing skillsets, 14 Pricing Solutions, 231, 233; microsegmentation analytics, 233, 238 pricing strategy, 9, 12, 14, 22–24, 280; behavioral driven, 23; consistent long-term, 14; emergent, 23, 281, 300; everyday, 23; long-term, 22; normative, 23; normative driven, 23; setting price, 63 pricing theory, 25, 298 Pricing Tool, 233 Principle of Competitive Exclusion, 245 private-label brands, 140 Proactive Price Change, 152 probing for value, 177, 187, 198 process erosion, 147 product price framing, 19, 40, 44, 215

Production/Operations/Manufacturing Nation, 91, 94, 107–9, 111–12; influence on price-setting, 107 professional cultural nation, 90–91, 119 Professional Pricing Society, 109 profit contribution, 16, 150–51, 155, 157, 276; baseline, 150; price increase, 151; price reduction, 151 profit drivers, 148 profit goals, 117 profit leakage, 148 profit maximization, 15, 298 profit-maximizing, 33, 124, 130; margin, 231 profit-maximizing rule of economic theory, 14–16, 293 profit pools, 12, 276 profit satisficing, 124 profitability, 6 Progressive Insurance, 131; costing and margin compression, 131 projection mapping; for customer value driver discovery, 198 proportional thinking, 174 proportional value estimation, 174 proportional value logic, 173 proportional value processing, 176 prospect theory, 28–31, 66, 71, 211–12, 246 prospective losses, 29 protective customer promotions, 265 Prozac, 48 psychological, 59 psychological price-setting, 64 psychological risk, 167 psychological value, 169; driver, 169, 181 Psychology of Selling, The (E. K. Strong), 101 publicly pre-announce price move, 263 purchase risk drivers, 166 Purdue University, 107 Qcue Software, 125 quality of life, 169

336 quantity step discounts, 222 Quilted Northern, 176 rack rate, 147 RapidMiner, 195 range effects, 70 ratio of benefits to price, 174 rational pricing, 22 rational System 2 processing, 88 Reactive Price Change, 153 real estate, 115 recognizing framing opportunities in the marketplace, 32, 41 recognizing narrow versus broad framing, 32 recommended list price, 169 Red Rocket, 153–54 reference dependence, 28 reference point, 28–29 reference price(s), 27, 35, 39, 48, 162, 179, 182, 212; competitive, 52; high, 47 reference reframing strategy, 47, 55, 261 reference value, 26, 27, 49, 171, 179, 181–82, 192, 194; commoditized, 173; competitive, 162, 167, 190; neighboring competitive, 46; new competitive, 46; of competitive substitutes, 27; original competitive, 46 referent competitors, 53 reframe, 18, 20–23, 25, 31, 42, 71, 76–77; pricing decisions, 31; reference value, 45–46; strategies, 39 reframed benefits, 50 reframed price, 40, 228 reframing differential value, 49 regression model, 271 relationship buyers, 228 relationship value, 228 relative differential value, 193–94 relative price premium, 193 Renoir, 36 Repatha, 167–69, 170, 172–73, 176, 194

INDEX

replacement cost, 138–39 replacement parts and service savings, 190–91 residential real estate, 34 revascularization cost savings, 168 revealed preference method, 238 revenue and margin gain value drivers, 164 revenue and profit management, 12 revenue-based incentives, 117 revenue-based profit incentive commission, 116 revenue management, 12–13 revenue/margin, 179 right price, 19 risk-averse buyers, 214, 218, 221, 228 risk-averse choice, 66 risk-averse decisions, 29 risk aversion, 6, 30–31; in a price increase context, 31 risk-prone, 30–31 risk-prone buyers, 214 risk-prone decisions, 29 risk-seeking choice, 66 risk-seeking in a price cut context, 31 risk-taking buyers, 227 risky loss, 31 rivalry, 32 Robin Hood Flour, 264 Rolls Royce, 73 Rothko, 36 Royal Swedish Academy of Sciences, The, 33 rule of thumb, 8, 78–80, 88, 103, 129, 209, 283, 292, 294, 299; decision, 87; price-setting, 77, 79;pricing, 65; simple gross margin, 109 Rumelt, Richard, 52, 55 RWE, 99, 287 Ryder, 141 SaaS, 75 Sales Alliance Inc., The, 102 Salesforce, 217

I N D E X  337

Sales Management Association, 102 Sales Nation, 91, 94, 101–2, 103, 111–12, 209, 296; influence on price setting, 101 sales volume goal framing orientation, 212 Salvator Mundi, 36–38, 53 Samsung, 51–52, 276 Samuelson, Paul, 35 Samuelson, William, 33 San Francisco Giants, 125 Sanders, Bernie, 47 Sanofi and Regeneron, 194 SAP, 160 satisficing, 33, 77 Schwab, Charles, 77, 249 Schwab Intelligent Portfolios Premium, 77 Science, 73 Sculley, John, 78 Seattle Seahawks, 245 segmented price offerings, 208 segmented prices, 20, 71, 204 segmented pricing, 202, 205, 213, 218 segmented pricing strategy, 204 segmenting customers for price sensitivity, 213 selling erosion, 147 semi-variable costs, 17, 142 Sensemaking in Organizations, 5 setting price relative to total value, 179 Sewelo diamond, 171 sharing of the value, 161 sharing pricing models, 219 Shaw’s, 223 Shell, 269 shipping, 45 shopping costs, 166 Shotter, M., 97 Should Outcomes, 86 Simon, Herbert, 33, 77, 79, 82, 208 Simon, Hermann, 116–17, 219 Simons, Daniel, 89 SiriusXM, 39

Sisu, 218 Sixty Last Suppers, 36–38 SKF, 50, 160 skim pricing strategy, 126, 192, 292 Slacker Radio, 39 Sloan, Alfred, P., 288 slow pricing calculations and analytics, 89 smart price segmentation, 70 Smartwater, 139 Smith, Adam, 171 Smith, Matt, 101 slow process, 141 social influence, 59 social risk, 166 soft behavioral price-setting skills, 10–11, 59, 83, 282–83, 299 soft costing skills, 129, 135–37, 139, 258 soft customer value skill, 185 soft customer WTP-Driven skills, 212 soft intuitive costing skills, 157 soft probability estimation, 65, 82, 86–87, 150 soft psychological skills of behavioral economics, 23 soft skills, 63–64, 110, 123, 150, 160, 182, 187, 198, 201, 213, 215, 224, 284, 287, 294; of margin leverage, 140, 158 soft value skills, 177, 187 soft value skills for price-setting, 177 Soliris, 181–82 Southwest Airlines, 115 Spotify, 39, 41 Sprint, 287 standardized costing, 17, 126, 128 Stanford University, 84, 107 Staples, 222–23 Starbucks, 115 status quo, 40–41 status quo effect, 33, 45, 53 stock exchange in 1602 in Amsterdam, 92 Stop and Shop, 222

338 strategic pricing, 12, see also pricing strategy; discipline, 88; model, 175; sophisticated, 13 Strategic Pricing Group, 103 strategies to manage price sensitivity and value, 213 Strategy and Tactics of Pricing, The, 150, 215 subjective customer value, 162, 181; estimation, 178–79; models, 177–81, 198 subjective estimates, 83 subjective forecasting, 283 subjective probability, 85 subjective probability estimation, 84, 86 subjective probability forecasting, 86 subjective value, 179 subjective vs objective customer value models, 178 subscription framing, 41 subscription price framing, 41, 77 subscription price model, 18 subscription price reframing, 19, 54 Sumitronics, 164 sunk costs, 35, 40, 130; effect, 54 sunk cost fallacy, 33–34, 53–54, 129 Super Bowl LIII, 84 Super Bowl XLIX, 245 SuperGoop! SPF50 Defense Refresh Setting Mist, 47 Super Saver Shipping, 45 surge pricing, 12, 68–69, 134, 221 surgical price retaliation, 264 Swiss watch industry, 51 switch cost drivers, 166 switching costs, 227 Sysco, 140 System 1, 89, 117, 238, 245; anchoring and adjustment, 214; approach, 60; associative price-setting, 8; associative processing, 6, 59–60; behavioral, 68; behavioral cues, 68; behavioral customer value communication tools, 197; behavioral economic soft

INDEX

skills, 297; behavioral economics, 299; behavioral economists, 298; behavioral goal framing, 66, 103; behavioral heuristics, 206; behavioral methods, 82; behavioral patterns, 89; behavioral price setting, 62; behavioral processing, 74, 78, 81, 84, 86; behavioral processing principles, 77; behavioral skills, 83; behavioral thinking, 83, 135, 157; framing theory, 291; intuitive task, 62; memory-based processing, 60; memory-based thinking, 84, 197; mental heuristics, 171; processing, 88, 181; soft behavioral pricing skills, 89; soft pricing skill, 292 System 2, 87; analytics, 88, 267; analytic calculations, 66; analytic customer value communication tools, 195; analytic processing, 6, 7, 59–60, 84, 87, 223; analytic structure, 81; analytic task, 62, 171; analytic thinking, 66, 194, 196; approach, 60, 62; calculations and analytics, 89; costing skills, 142; data analytics, 124; hard analytics skills, 89, 194, 282, 297; for pricing orientation, 287; thinking, 141; tools, 103; value calculator, 196; value simulator, 196 T-Mobile, 43–44, 54, 215, 287 tangible financial value, 227–28 Target, 141 target return pricing, 123 TD Ameritrade, 249 technological disruption, 24 Temple University, 93, 103–4 termination costs, 166 Tesla, 73, 228 The Last da Vinci, 36 The Last Supper, 36 thematic framing link, 36 theoretical maxim for a profitable pricing orientation, 161, 293

I N D E X  339

theories; bounded rationality, 33, 124; classic theory of economics, 33; range, 71; satisficing, 33; System 1 Versus System 2, 59 Thinking, Fast and Slow, 5, 59–60, 94, 135, 141 Thaler, Richard, 113, 249, 288 Thanksgiving, 223 thinking on the margin, 15 Tidal, 39 Tide, 71, 104 Time magazine, 101 total category profits, 264 Toyota, 227 training and learning costs, 166 transaction change costs, 166 transaction costs, 222 trial or repeat purchase, 42 Tripadvisor, 221 true cost to serve, 125, 136–37, 157–58 true-costing-for-pricing principles, 158 true customer value principles for pricing orientation, 160 true differential value, 174 truisms, 8, 65, 78–79, 88, 209, 283; pricesetting, 77 Tversky, Amos, 28, 33, 66, 71, 84; prospect theory, 28; prospect theory gamble, 30 Twombly, 36 two-dimensional segmentation, 232 two-part price framing, 40 two-sided pricing models, 219 U-Haul, 140 U.S. General Services Administration (GSA), 225 U.S. Postal Service, 224 Uber, 25, 27, 67–69, 134, 221, 270, 299 Ubernomics, 299 UCLA, 52 Ultra-Filtered Milk, 70 unbundled price-framing strategies, 228 Uncarrier, 43

underestimating the likelihood of highprobability, 84 Unilever, 139 United Airlines, 263 Universal Orlando Resort, 221 University of Chicago, 246 University of Chicago’s Booth School, 93 University of Cincinnati, 102 University of Groningen, 65 University of Houston and Ferris State University, 102 University of Illinois at Urbana-Champaign University of Michigan, 99, 104, 107, 178, 257 University of Pretoria, School of Accountancy, 97 University of Rochester’s Simon School, 109 University of Texas at Austin, 99 University of Virginia, 109 Universal Studio, 270 upper price anchor, 214 UPS, 134 value, 15, 25, 27, 162, 174; calculation and communication, 198; capture, 192–93; differentiation, 39, 291; perceived, 39; shared, 192 value alignment, 70 value-based marketing, 25, 37, 42, 55 value-based price, 169, 194, 201 value-based pricing, 42, 109 value-based pricing theory, 25 value buyers, 227–28 value checklist, 82 value communication tools and strategies, 194, 196, 198 value content, 197 value consulting, 228 value data, 196 value debiasing, 177 value discover, 177 value-driven buyers, 219

340 value-driven formula, 192 value-driven performance targets, 170 value-driven price-setting process, 82 value-driven pricing, 176, 192 value-driven thinking, 179 value-driven trade-offs, 219–20 value driver, 176, 179, 184 value engagement, 196; and communication strategies, 228 Value Equivalence Line, VEL, 175 value estimation, 178, 186, 189–90, 192, 195 value exchange, 25 value framing, 198 value illiteracy, 170–71, 173 value in exchange, 171–72 value in use, 171–72, 182 value metrics, 194; for price-setting, 190, 194, 198; John Deere 750J Bulldozer, 193 value model, 192 value model building, 186 value-price exchange, 54 value pricing menu, 228 value projection mapping, 182, 184, 286 value proposition, 27 value quantification capability, 173 value quantification skills, 173 value research, 186, 189, 195 value statistics, 195 van Gogh, 36 Van Westendorp’s Price Sensitivity Meter, 210 Vanguard Group, 249 variable costs, 17, 126, 130, 133, 151–52, 154 Velthuis, Olav, 181 Vendavo pricing, 230, 233; optimization analytics, 232 Verizon, 43–44, 287

INDEX

Vogue, 51 Volkswagen, 165 volume-driven margin leverage strategies, 140 Wall Street Journal, 260 Walmart, 141, 227 Walt Disney Parks and Resort Division, 12 Warehouse club, 140 Warhol, Andy, 36–38 Watson, Thomas, J, 101 Weber’s law, 71 Weber State University, 102 Weick, Karl, 5, 178 Welch, Jack, 248 Wharton School, 74, 93, 99, 104, 109, 246, 266 what customers pay, 42 what if questions, 157 what if scenarios, 155 Whole Foods, 115, 140 Williamson, Oliver, 97–98 willingness to pay, 15–16, 201–2, 204–6, 208–13, 219, 221–24, 234, 236–37, 292, 294; driven pricing, 293; principles for pricing orientation, 201; revealed, 229 Winsor School, 261 Wintel Standard, 52 Wisk, 71 Woodard, Colin, 89 Woodard’s Eleven Nations, 90 Wozniak, Steve, 295 yield management pricing, 109 Yoplait, 222 YouTube, 48 Zeckhauser, Richard, 34 Zipcar, 218