Customer loyalty, retention, and customer relationship management 9781846632396, 9781846632389

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07/11/2006

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ISSN 0736-3761

Volume 23 Number 7 2006

Journal of

Consumer Marketing Customer loyalty, retention, and customer relationship management

www.emeraldinsight.com

Journal of Consumer Marketing Volume 23, Number 7, 2006 ISSN 0736-3761

Customer loyalty, retention, and customer relationship management

Contents 371

Access this journal online

372

Editorial

374

379

406

Using private label credit cards as a loyalty tool Rick Ferguson

Segmenting customer-brand relations: beyond the personal relationship metaphor John Story and Jeff Hess

414

Using customer equity models to improve loyalty and profits Kathy Stevens

Look after me and I will look after you! Sharyn Rundle-Thiele

421

A strategic approach to building online customer loyalty: integrating customer profitability tiers Dennis Pitta, Frank Franzak and Danielle Fowler

430

The royalty of loyalty: CRM, quality and retention Mosad Zineldin

438

Masochistic marketing: Volvo Australia’s not ‘‘so safe’’ strategy Go¨ran Svensson, Greg Wood and Michael Callaghan

445

Customer satisfaction and loyalty in a digital environment: an empirical test Jean Donio’, Paola Massari and Giuseppina Passiante

382

The art of storytelling: how loyalty marketers can build emotional connections to their brands Caroline Papadatos

385

Life is not a shopping cart: three keys to building brands and improving customer loyalty Bryan Pearson

387

The role of loyalty programs in behavioral and affective loyalty Blanca Garcı´a Go´mez, Ana Gutie´rrez Arranz and Jesu´s Gutie´rrez Cilla´n

397

Lasting customer loyalty: a total customer experience approach Oswald A. Mascarenhas, Ram Kesavan and Michael Bernacchi

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Customer loyalty programs: are they fair to consumers? Russell Lacey and Julie Z. Sneath

480

Can a brand outperform competitors on cross-category loyalty? An examination of cross-selling metrics in two financial services markets Kerry Mundt, John Dawes and Byron Sharp

483

Does parent satisfaction with a childcare provider matter for loyalty? Timothy L. Keiningham, Lerzan Aksoy, Tor W. Andreassen and Demitry Estrin

Misplaced marketing Movie theaters’ suicide-by-advertising with income from abusing customers Herbert Jack Rotfeld 2006 Awards for Excellence

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The bottom line is that in order to succeed, a loyal customer will remain so if they perceive that they are receiving exceptional value from the defined relationship with a company. This cannot occur without an excellence of effort from the prospect to the product/service itself. Anything less than this will not reap the long-term customer relationship that is so important. Ferguson examines the effectiveness of retail private-label credit cards in a crowded marketplace. The author also presents some suggestions on how to incorporate the strategies of some major retailers into existing (loyalty) programs. Stevens presents a methodology that allows us to compile a Customer Equity Model in order to calculate the true profitability of customers. Such a model allows one to describe past (customer) behavior, predict a customer’s next-year profitability and explore other variables that factor into marketing decisions. Papadatos presents a case study of how one organization reaches out to its customers to glean information that could be used to re-brand a (loyalty) program. The bottom line is that to be distinctive in an overcrowded market, a loyalty program must provide the consumer user with an emotionally engaging experience in the redemption process. Pearson reports the findings of a recent study to explain the reason for a disconnect between consumers and marketers, and why some brand loyalty programs fall short. The author then presents some ideas for rebuilding a relationship with consumers. Garcia Gomez, Arranz and Cillan analyze the behavioral and affective loyalty of retailer customers in order to establish the role played by loyalty programs in the development of these variables. Companies should focus their efforts on developing a reward plan as adapted as possible to concrete needs of each participant in the (loyalty) program so as to be successful. Mascarenhas, Kesavan and Bernacchi explore the concept that it is necessary to both understand and deliver total customer experience in order to sustain lasting customer loyalty given the pressures of commoditization, globalization and market saturation in developed countries. The authors examine three essential interactive elements: physical elements, emotional involvement moments, and value chain moments. They then propose a typology of customer loyalties as a function of “high” versus “low” levels of these three consecutive elements. Story and Hess propose and test segmentation of multi-dimensional customer-brand relationships as a superior method of defining, understanding, and predicting customer loyalty behaviors. These relationship groups display different levels of commitment to the brand and engage in significantly different levels of loyalty behaviors. Rundle-Thiele compares and contrasts marketer’s views on loyalty with their own consumers’ views. The finding reveal that marketers must consider loyalty as a reciprocal concept and must put into place “a look after me and I will look after you” philosophy to be successful in their efforts. Pitta, Franzak and Fowler integrates concepts in consumer loyalty and ongoing case developments in internet practice that provide information and action approaches to consumer markets that may increase the success of providing the want satisfying market offerings. The authors also outline the costs and benefits of some online customer loyalty building practices. Zineldin examines and develops a better understanding of the triangle relationship between quality, customer

Editorial In this special issue of the Journal of Consumer Marketing, we will be examining the concept of customer loyalty, retention and customer relationship management (CRM). Why do some companies tend to succeed in their attempts at not only attracting customers who tend to repeat their purchase of that company’s specific brand, but also tend to have these customer’s become “brand advocates” in attracting new customers for that company? It is a necessity that a company invests in making that company (or brand) more enjoyable to deal with. By attracting a more loyal customer, a premium price could also be charged for that product/service. It is not an easy task, but we can see that a company such as Starbuck’s has taken, what was once a regional brand, and has turned it in to a global powerhouse. Loyalty may be said to rely on having the customer make a connection that transcends the actual purchase of that product or service. The buying experience and the chance to own/use that product/service must be perceived as positive – in essence, a state of mind, which may even reflect that consumer’s personality. All too often a company believes that by improving customer satisfaction, they may increase their customers’ loyalty. Realistically though, loyalty may breed satisfaction, rather than satisfaction creating a loyal customer. Even though coffee has become a commodity item, tell that to a Starbuck’s customer. S/he could most certainly drink a less expensive more powerful coffee, but they choose not to. In addition, it has become commonplace that whenever customer loyalty is discussed, the idea of customer rewards is introduced. This is a reflection of the fact that many of the more “successful” loyalty programs are simply structured reward systems, designed to provide tiers of compensation for their repeat customers. But competition can easily duplicate these efforts, and any competitive advantage obtained early on may be lost. In the long run, those rewards programs that deliver a unique brand asset may be the most successful. Many of these loyalty programs never become self-funding. Why? Inadequate program planning and a lack of true customer insights. From what we have observed about customer loyalty, retention, and the development of positive customer relationships, what could we as marketers do to succeed in our endeavors? We can begin by clearly defining what we want to accomplish. Then we have to determine how we are going to measure our efforts. And, of course, we have to be clear about whom we will be targeting. Strategies abound in terms of achieving loyalty, being able to retain our customers, and developing a positive relationship with said customers. Consider the following suggestions: . Make every effort to create a positive experience. . Create a strategy that is built around your best customers. . Avoid mass targeting. . Emphasize what differentiates you from your competition. . Create a cadre of brand advocates. Journal of Consumer Marketing 23/7 (2006) 372–373 q Emerald Group Publishing Limited [ISSN 0736-3761]

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Journal of Consumer Marketing Volume 23 · Number 7 · 2006 · 372 –373

authors posit that if customer loyalty programs are equitably administered and thoroughly communicated, they will then be perceived favorably by consumers. Mundt, Dawes and Sharp examine the concept of “cross-category” loyalty which is a most important concept to service organizations as they seek to grow by selling additional different products to their existing customer base. The authors find that investments in CRM and cross selling initiatives seem to have less effect on loyalty metrics than previously thought. Keiningham, Aksoy, Andreassen and Estrin investigate the relationship between parent satisfaction and child retention in terms of the childcare provider industry. Their results of this study indicate that parent satisfaction is most important to child retention when the child is very young (infant to one year of age). As children increase in age, parent satisfaction for childcare becomes increasingly less predictive of children’s continued enrollment at a childcare facility. In this issue you will also find another one of our regular features that you will enjoy reading – Misplaced marketing.

relationship management and customer loyalty. The author also presents a five qualities model to measure quality and customer loyalty. Svensson presents us with a case study detailing a marketing approach used by Volvo in the Australian marketplace. This “masochistic marketing approach” is one that is dependent upon the outcome of a series of cause and effect relationships. This is a rather risky strategy, which seems to border on the humiliation of the corporate image itself and which seems to “violate” the basic fundamental of marketing. Donio, Massari and Passiante explore the links existing between customer loyalty attitude, customer loyalty behaviors (measured by customer purchase behaviors) and profitability. The authors present us with a conceptual framework of this relationship, which then could be applied to set up a customized marketing strategy Lacey and Sneath examine the fairness of loyalty programs to consumers regarding two emerging criticisms of loyalty programs: discriminating value proposition segmentation and potential exploitation of captured personal information. The

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Using private label credit cards as a loyalty tool Rick Ferguson COLLOQUY, Milford, Ohio, USA Abstract Purpose – The purpose of this paper is to examine the effectiveness of retail private-label credit cards in a crowded marketplace. It offers ideas, citing examples from successful retailers, for improving the effectiveness of credit cards as a loyalty tool. Design/methodology/approach – The paper examines the strategy behind Gap Inc.’s private label credit card, which allows customers to earn rewards for shopping at Gap, Banana Republic and Old Navy stores. It also examines other retail card programs, including quotes from program leaders who share their ideas. Findings – Statistics are cited which indicate how crowded the marketplace currently is for private label credit cards. By closely examining successful credit card programs, and by interviewing industry leaders, the importance of competitive differentiation in this marketplace is proved. Practical implications – The reader will walk away with some specific ideas for improving the effectiveness of their private label credit card program. Armed with the knowledge of the scope and size of the private label credit card market, readers should gain insight that will improve their decision making about their own program. Originality/value – The paper takes a look at how private label credit cards are currently used as loyalty marketing tools, and follows with suggestions on how to incorporate the strategies of some major retailers into existing programs. Keywords Marketing strategy, Customer loyalty, Loyalty schemes, Credit cards, Customer relations Paper type General review

Although enjoying a long and successful history as a credit tool, the retail private-label credit card has been losing ground of late in the wallet wars. Used as a tool to understand customers, influence their behavior and communicate with them more effectively, however, your private-label card can be an integral part of your loyalty strategy and an effective differentiation for your brand. In October 2004, the San Francisco-based Gap Inc. group of apparel store brands took a bold new leap into the future of private-label credit. The company announced that it would now offer a private label credit card (PLCC) that allowed customers to earn rewards by using one card to shop the company’s Gap, Banana Republic and Old Navy stores. The new program enhanced the existing, brand-specific loyalty programs by allowing points to be earned and redeemed across all three brands (see Figure 1). A latecomer to the private-label game – the company introduced its first store card in 1998 – Gap nonetheless feels poised to leap over the retail private-label market with a crossbranded rewards credit card program that the company is billing as the first of its kind in the specialty retail apparel industry. But the real question is, in the age of co-branded everything, debit cards, Membership Rewards and AAdvantage miles, will the new GapCard find space in consumer wallets? And will Gap customers find reason to use it? “There’s some validity to the point of [the consumer] having too many cards in the wallet,” says Toby Lenk, president of Gap Inc.’s Direct division. “But we have three

amazingly compelling brands. Allowing someone to have a private-label card like ours, that accrues rewards across three true icon brands, is a real competitive differentiator.” Competitive differentiation will become the mantra for the $132 billion (in charge volume) private-label credit card industry – if it isn’t already. That’s because, despite an increase of 4 percent in charge volume at the end of 2003, all other private-label metrics show an industry in a holding pattern, if not in decline (see Figure 2). According to The Nilson Report, outstandings fell 3 percent to $89.29 billion. Total accounts and active accounts on file dropped as well. And while the overall number of credit cards in circulation in the USA rose 4 percent to 1.28 billion, the total number of proprietary cards in use was 636.3 million – marking the first time in the history of the credit card industry that general-purpose cards outnumbered proprietary cards. Those numbers are prompting issuers and retailers to take a hard look at their proprietary card programs to determine their true value and place in this new competitive landscape. Some retailers are choosing to punt their card programs altogether; others are converting their portfolios to cobranded cards. But many retailers are discovering that not only is the private-label card still a vital part of their business; it’s also an underutilized brand asset. In fact, it may be the key to building measurable customer loyalty. Take Gap Inc., for instance. The company made the decision to expand the PLCC’s power after research revealed untapped potential for PLCC sales across store brands, says Lenk, who led the multi-department team effort to enhance the card program: “We found that our cardholders are our best customers,” says Lenk. “They shop twice as often as other customers and at all three brands.” The way to encourage that brand affinity, therefore, was to design a loyalty component that recognizes and rewards customers across Gap Inc.’s brands. Indeed, private-label credit cards with well-engineered rewards programs are doing

The current issue and full text archive of this journal is available at www.emeraldinsight.com/0736-3761.htm

Journal of Consumer Marketing 23/7 (2006) 374– 378 q Emerald Group Publishing Limited [ISSN 0736-3761] [DOI 10.1108/07363760610712885]

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Using private label credit cards as a loyalty tool

Journal of Consumer Marketing

Rick Ferguson

Volume 23 · Number 7 · 2006 · 374 –378

Figure 1 One card, three brands

Figure 2 The best available tool

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Using private label credit cards as a loyalty tool

Journal of Consumer Marketing

Rick Ferguson

Volume 23 · Number 7 · 2006 · 374 –378

more than just winning customer loyalty. Retailers say rewards-based store cards are helping to shape their companies’ very brand identities. “Our credit card is an extension of our brand, and everything we do with our loyalty program enhances customers’ perceptions of our overall brand,” says Rob Rosenblatt, senior vice president of customer relationship management for upscale department store chain Saks Fifth Avenue. “In a category like ours, the customer is very avid, and shopping is an emotional experience. So our loyalty program must connect with her on several levels.” Saks isn’t alone in their strategy. Successful PLCC operators have learned that in order to ensure ongoing usage, they have to think beyond the most basic perks. Ten percent off on the initial purchase with the opening of a PLCC account will no longer cut it. PLCCs now include an ever-changing mix of special rewards and events offered exclusively to PLCC cardholders, designed to trigger sales year-round, including during non-peak periods. That’s because a growing number of retailers are recognizing that when a PLCC includes a meaningful loyalty program, it can function as much more than a credit tool. In helping to drive year-round sales, generate valuable customer behavior data that shapes marketing plans, and create dialogue with customers, PLCCs may be more important to retailers than ever before. And through the adoption of leading-edge data analysis, strategic planning and creative marketing plans, PLCC programs can reach new levels of success.

Sullivan’s team has identified three main “loyalty drivers” of this incremental behavior. All three drivers can be exploited through traditional loyalty-marketing techniques: 1 The economic driver. Middle-market consumers with limited access to credit typically respond to the increased purchasing power provided by the PLCC. By extending credit to a population that wants to do business with you but can’t, you funnel these consumers to you, capture their category share-of-spend and isolate them to your brand. 2 The dialogue driver. The continuing dialogue stream that occurs through the PLCC enhances the relationship between the consumer and the retailer. Statements, direct marketing, the web, the call center and the point of sale all serve as touch points through which retailers can listen and respond to consumers. “Retailers have only begun to tap the potential of the PLCC as a dialogue marketing tool,” says Sullivan. 3 The affinity driver. Typically, customers sign up for the PLCC because they love the brand. They want to experience a relationship with the brand, and to receive recognition and reward. The PLCC extends and deepens that relationship. Up-market consumers, who don’t need the credit but appreciate the brand, respond most to the affinity effect. “The average PLCC customer is simply a more profitable customer – they just need the credit in order to transact and revolve,” says Sullivan. “Other PLCC customers don’t need the credit, but they crave the recognition. You can deliver value to both types of customers through the tier structure of a typical PLCC program – offer a more vanilla card for those customers who first and foremost need the credit, and offer points and special treatment to the upper tiers.”

The case for the PLCC Credit card saturation is at an all-time high. General purpose credit cards are offering richer rewards than ever. So why do retailers continue to launch successful new PLCC programs? In short, retailers offer them because consumers still demand them. “There will always be a shopper who wants to keep her spending segregated on a separate store card, and likes being part of a rewards programs that sends her coupons and gives her reasons to keep coming into the store,” says Margaret Keane, president of GE Retail Consumer Finance, which manages retail PLCC programs for clients including The Gap, Brooks Brothers, and Men’s Wearhouse. In addition, whether retailers handle their card programs in-house or with the help of a credit partner, PLCC programs continue to drive incremental profits for most retailers. “Private-label cards continue to show amazing resiliency,” says Jim Sullivan, Vice President of Retail Marketing for Alliance Data Systems, which manages PLCC programs for clients including Avenue, The Limited and Pottery Barn. Sullivan predicts that, as PLCC marketing becomes more sophisticated, marketers will stop looking solely at A/R metrics and begin looking at incremental sales generated through the card program. In other words, they’ll start thinking like loyalty marketers. But does the PLCC truly generate incremental business? Or does it simply provide brand recognition while cannibalizing existing sales? “We’ve proven through external and internal methodologies that a PLCC can generate significant incremental revenue – 5 to 10 percent in some cases, as a result of 30 to 50 percent of total sales being processed on the card,” says Sullivan.

The loyalty connection Most operators of successful PLCC programs agree that it’s essential to attach a loyalty program to the proprietary credit card. Twenty years of loyalty marketing have conditioned customers to expect some kind of reward when they sign up for a credit card program. “It’s the cost of doing business,” says Jon Grossman, Vice President of Finance for United Retail Group, which operates the 530-store Avenue women’s apparel chain. “The MasterCards and Visas of the world are giving away benefits that have become very powerful with consumers over the last five to ten years, and they’re typically offering a lower interest rate. Today, the consumer has so many choices for financing that simply providing them with a card that says ‘we acknowledge you’ is not enough – retailers must offer an additional incentive just to make their card competitive.” Like many other PLCC operators, United Retail constantly tests different offers associated with its Avenue Card, to see which are most effective in driving sales and ongoing card usage, and rejecting those that are too costly without generating results. In addition to 30 percent off on the lowest-priced item when opening an account, Avenue cardholders currently receive coupons good toward $10 off a future denim purchase, $5 off a future lingerie purchase plus a pair of panty hose, free of charge. After conducting research, the company recently supplemented its basic Gold card benefits 376

Using private label credit cards as a loyalty tool

Journal of Consumer Marketing

Rick Ferguson

Volume 23 · Number 7 · 2006 · 374 –378

program with a new Platinum tier, awarded to its best customers. The platinum customers receive their basic benefits as well as enrollment in a rewards program. Saks Fifth Avenue operates perhaps the most sophisticated rewards program in the PLCC space. Saks gives customers who open a new account 10 percent off on the first day’s purchase, plus membership in its SaksFirst loyalty program, which allows accrual of one point for every dollar spent. SaksFirst offers five different rewards tiers, based on each customer’s spending per calendar year, ranging from 1,000 points (Classic) up to to 25,000 points (Diamond). Members also receive special coupons and offers linked to its marketing partners, which include Ritz-Carlton Hotels, Cunard Line cruises, and British Airways. Throughout the year, Saks offers its customers opportunities to earn double and triple the usual points on each dollar spent during short-term promotions. At the end of each year, Saks sends each SaksFirst member a coupon good for store merchandise worth as much as 6 percent of total purchases. And in 2004, Saks lowered SaksFirst’s qualifying entry level to 1,000 points from 3,000 points. “Through customer and data research, we recognized there was an emerging group of shoppers who really like our brand, and we wanted to make SaksFirst more accessible to them,” Rosenblatt says. The change resulted in a surge of new SaksFirst members and additional store trips. Not all specialty retailers structure their rewards programs around tiers. Most simply offer an introductory discount on the first purchase with a new account, plus an array of special coupons and offers throughout the year. What’s most important, says GE’s Keane, is that retailers test such loyalty program offers throughout the year to make sure they are driving sales and inspiring customers. “We’re starting to see more bells and whistles in rewards programs, as retailers try different creative approaches to keep customers interested,” she says. “We’re also starting to see people pull back a little on giving a big discount upon opening the account, because the philosophy is moving away from a onetime promotion, and more toward rewarding year-round purchases.”

successful? We think it’s because marketers aren’t doing these deals – the credit issuers are just selling plastic.” Many retailers who offer both a PLCC and a co-branded card do so through a two-tier approach that isolates mid-market consumers on a more vanilla PLCC while positioning the co-branded card to compete with general purpose cards. The message is clear: if you need the credit, you’ll take the PLCC card with its high interest rate, and you’ll like it. If you don’t need the credit, then you get points and perks. Sullivan says that retailers are using co-brands to attempt to stem the tide of defections to general purpose cards – but by doing so, they’re ignoring their core audience. “Over time, certain high-end demographics have defected to general-purpose reward cards – and retailers aren’t talking about them,” says Sullivan. “Among other segments, however, we continue to see significant lift. That lift generally comes from the mid-market deciles and income – the middle four to six deciles. For any mid-market brand that doesn’t issue a private-label reward card to those middle deciles, they’re simply leaving money on the table.” Still, co-branded cards are here to stay. Careful management of both programs can result in peaceful coexistence. “We’ve seen great results from some dual card programs,” says Keane. “Some customers have a strong preference for having a more versatile card while the PLCC allows some retailers to dig a little deeper from a risk perspective than they could with a Visa card. Our view is that, if you have two choices, then you need to provide benefits to both card programs.” Likewise, Sullivan believes there should be “a blended, linked approach between the PLCC and the co-brand, so that one does not replace the other.” Before offering a co-branded card, he says retailers ought to carefully explore the question of whether their customers are asking for one. “The co-branded boom is generally a gold rush for numbers, and nobody wins,” he says. “Our segmenting, however, shows us that some consumers are perfect targets for additional card products. We see avid customer segments rejecting the PLCC, and those are key targets for a co-branded card. But to simply take the top off your PLCC file and convert it to co-brand weakens both products.”

Dueling dual-card programs In spite of the continued loyalty activity in private-label credit, the rise in popularity of co-branded, general-purpose credit cards has led many retailers to adopt a co-branded card alongside their PLCC program. In some cases, the co-branded card replaces the PLCC altogether. But what is the true purpose for retail co-branding? Are retailers cobranding because consumers are demanding it? Or are they simply chasing short-term dollars at the expense of long-term brand loyalty? Alliance’s Sullivan says many co-branded card programs offered alongside a retailer’s PLCC are based on deals that “aren’t thought through.” In many cases, he says, a lack of data analysis and customer segmentation when the co-branded programs are introduced leads to the Visa or MasterCard-linked credit card cannibalizing the PLCC. “There is no real diagnosis of need for many co-branded cards,” says Sullivan. “They’re simply adding another layer of plastic to a retailer’s program at a time when consumers are looking to streamline the cards in their wallets. Outside of Federated, where has a merchant co-brand been wildly

The data revolution Whether your offer is PLCC or co-brand, the real battle for customer loyalty will take place in the database. New technologies are starting to revolutionize PLCC opportunities for retailers, although most have not yet begun to tap the potential of new data analysis and targeting systems to achieve better results. While the technology is available for micro-targeting households with super-customized offers, in most cases these capabilities exceed retailers’ physical ability to execute such fine-tuned marketing efforts. Instead, leading-edge PLCC program operators are gradually adopting new analytic capabilities for analyzing cardholders’ spending patterns and demographic household information on a step-by-step basis. The idea is to shape rewards programs and offers more effectively over time. “I’d say we’re in the early stages of (PLCCs adopting) customer segmentation,” says Sullivan. He believes that retailers ought to structure their programs so the PLCC is the primary 377

Using private label credit cards as a loyalty tool

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Rick Ferguson

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source for collecting customer data, and then adopt new methods of sorting customer information for the greatest utility and flexibility. “Much more strategic segmentation could be done around the PLCC and the intensity of brand usage – segregate your strongest brand customers, split your database between revolving customers and those who respond solely to the affinity aspect of the card,” he says. “Understand what’s going on in terms of attitude versus needs. Are needs based on income, age, credit worthiness, attitude or demographics?” The Gap is also working to become more strategic about analyzing data using sophisticated new internal tools. “We pursue all kinds of segmentation schema to better understand our customers and serve them better,” says Lenk. “To analyze the loyalty element, we use a number of different schema: behavior and recency, among others, and we constantly search for new schema to see if they are depictive of behavior.” The goal is to turn those insights into marketing actions, and to harness the knowledge about customers to make sure the right message is sent to the right customer, says Lenk. Currently, The Gap can measure customer spending quantitatively and qualitatively, through market research, to measure the results of its rewards program. But “it’s not totally individualized,” says Lenk, adding that The Gap has “headroom” in its ability to analyze customer data and expand its rewards-based PLCC program. The GapCard may indeed represent the future of private-label credit: cross-branded, responsive to consumer demand, rewarding good customers and leveraged as a communications and branding tool. But will other retailers follow? Will PLCC usage continue to decline across the retail

industry, or will the product find new life as an essential loyalty-marketing tool? For his part, Sullivan thinks the future looks bright – provided retailers stay ahead of the curve. “There is a definite, continuing need for the economic and affinity effect of PLCC. There will be no radical change in that reality,” he says. “There will be a continuing need for merchants and issuers to create smarter, more meaningful value propositions for cardholders. We feel that those three drivers of PLCC behavior will always be present. Those marketers who understand that the PLCC is a loyalty program will win.”

About the author Rick Ferguson is Editorial Director of COLLOQUY, responsible for all COLLOQUY print and online publishing, educational and research projects. Under Rick’s direction, the COLLOQUY magazine and web site provide a worldwide audience of 25,000+ subscribers with news, expert commentary, program summaries and research on all facets of loyalty marketing around the globe. An acknowledged expert in the theory and practice of loyalty marketing, Rick has published numerous articles and white papers describing the characteristics of marketing programs which seek to change customer behavior. As a key member of the COLLOQUY faculty, he’s delivered educational workshops and virtual seminars on the principles, practices and technologies of loyalty marketing in the USA, UK and Singapore. Rick Ferguson can be contacted at: rick.ferguson@ colloquy.com or Tele: 513-248-5910.

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Using customer equity models to improve loyalty and profits Kathy Stevens Epsilon (formerly Frequency Marketing, Inc.) Milford, Ohio, USA Abstract Purpose – The purpose of the paper is to teach the reader how to compile a customer equity model in order to calculate the true profitability of customers. The reader can use the information to make better business decisions across their organization. Design/methodology/approach – Transaction data (as much as can be collected) on individual customers is needed to build the customer equity model. An analyst is needed to devise an algorithm and discern the best way to allocate costs. The goal is to build the best representation of profitability for the company. Once a process is in place for assigning revenue and costs to customers, tables must be programmed to calculate profitability. The final step is to take what is learned in the customer equity model and test ideas for retaining best customers and converting unprofitable customers into profitable ones. Findings – It was found that customer equity models are important tools for describing past behavior, predicting a customer’s next-year profitability, and exploring other variables that factor into marketing decisions. Practical implications – Identifies the need for companies to do a more thorough job of identifying their “best” customers, that is, their most profitable customers. Provides direction on putting together a customer equity model that will help companies reach the above-mentioned findings Originality/value – This paper is valuable to anyone who could benefit by better understanding past behavior of customers, and by predicting a customer’s next-year profitability. The reader can use the information to make better business decisions across their organization. Keywords Customer retention, Consumer behaviour, Expenses, Profit, Customer loyalty Paper type Research paper

customer-level tracking of revenue and costs can improve the model and how each department can help capture that information systematically, accurately, and precisely. You must encourage collaboration with key constituents – or the model will never be used to drive decisions at your company. What skills do you need to create the model? You need an analyst to devise the algorithm – someone with a critical eye to discern the best way to allocate costs. Once the model is completed, you’ll need the systems resources for each department to access the profitability information. You also need a database resource to create tables to program and support the model. Clearly, C-level support can be a boon – having department managers whose goals include cross-departmental initiatives never hurts. When performing the revenue side of the calculation, perspective is important. The goal is to build the best representation of profitability for your company. For retailers, the revenue calculation is straightforward – how much did the customer spend with you? Revenue is also simple for utilities and other subscription environments. But for other verticals – such as the hospitality industry, in which hotel chains run loyalty programs that allow travelers to earn points at both corporate-owned and franchised hotels – defining customer revenue requires more collaboration. In most cases, the time and energy you’ll spend allocating expenses to individual customers far exceeds the time spent on the revenue side. Your first decision: will costs tie back to your annual report, or will you include only incremental costs? Ideally, you want the former; it’s a philosophical stake in the ground that shows your recognition that all costs are incurred to support and grow your customers. However, there are times when your business situation may justify using incremental costs. For example, utilities often have extremely high fixed costs that may be capitalized over

Introduction Loyalty program operators love to talk about marketing to their best customers. But how many marketers actually define “best” customers as their most profitable ones? When I look at the loyalty cards in my own wallet, I see a lot of companies rewarding customers based on revenue – or stays, segments or miles. But isn’t a best customer one who’s exceptionally profitable? Shouldn’t your knowledge of customer profitability be used not only to recognize your best customers, but also to improve the profitability of key customer segments? Enter customer equity models. A customer equity model allocates customer profits through the analysis of customer revenues offset by true cost to serve. A customer equity model is a descriptive model: it describes past behavior without making assumptions about future behavior. Because the model isn’t dependent on any particular software – all you need is transaction data on individual customers – it’s a great tool to get actionable profitability information that allows you to make better business decisions across your organization. What data do you need to build a customer equity model? Whatever data you can get your hands on. As you discuss how to retrieve each revenue and cost item with the respective departments within your organization, stress how better The current issue and full text archive of this journal is available at www.emeraldinsight.com/0736-3761.htm

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Kathy Stevens

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many years. Within reasonable bounds, the number of customers doesn’t impact this fixed overhead amount – even in the unlikely event that your entire customer base attrites, the cost should arguably remain on your balance sheet. After deciding on your expense goal, you then should review each transaction type and outline all possible expense categories: cost of goods sold, servicing costs, advertising costs and bad debt and fraud losses. Again, your goal is to use all customer transactions to develop an individual cost to service. Can the complexity of these calculations become overwhelming? Certainly, but don’t panic. Just rely on available data and plan to refine your model in future iterations. In retail, the toughest calculation is probably the cost of goods sold. Ideally, you’ll have access to cost information on each item purchased. More likely, you’ll have information on margin percentage by groups of products or even category shopped. While the cost of goods sold is important, don’t forget to include other transaction-level costs such as shipping, returns and web site costs. Some costs may be allocated to transactions by geography or channel, including advertising, store and loss prevention costs. Finally, some costs, including direct marketing, private-label credit card and database costs, belong at the customer level. Other sectors have equally unique challenges. Once you have a process in place to assign revenue and costs to your customers, your next step is a big one: programming the tables required to calculate profitability. The program reviews individual customer transactions and joins tables together to properly assign revenue and costs. Store your interim calculations for further analysis. Understanding customer profitability is only half the battle. Ultimately, you have to take action to retain your best customers and convert your unprofitable customers to profitable ones. Consider a typical multi-channel retailer. How can you best use a customer equity model to design some marketing and customer service tests? We’ll assume that you’ve split this universe into three profitability bands – highly profitable, marginally profitable, and unprofitable. For the high-profit group, you could choose to test soft benefits that might alleviate the majority of complaints from these high-value customers (see Table I).

After six months, evaluate the test by comparing the increased expense to the lift in spend and reduction in attrition risk in this segment due to their special treatment. At the same time, you can also devise strategies for your marginal and unprofitable customers. For these customers, you can test tighter return policies, increased fees and costlimiting behavior. Test, and then evaluate. Did a cost to service reduction occur? Did the savings offset any decrease in spend or increase in attrition seen in the test populations? Testing cost-reductions on customers who are already fundamentally unprofitable keeps you from unleashing them on the entire population – thus risking the wrath of your profitable divas. Once you’ve completed your first foray into customer equity models, you’ll encounter two consistent truisms. Truism one: you’ll always unearth another variable that influences profitability. Let’s say you’ve determined the cost of an average customer service call and calculate customer profitability by the number of calls they make. Next, you recognize that some calls are shorter than others and calculate a cost by call type. Then you realize you have to factor in the length of time per call. Where does it end? Start where you can. Improve where and when you can. Don’t let analysis paralysis stop you from utilizing a customer equity model. Truism two: the world marches on. You need to remain current. Changes in the marketplace, whether new technologies or new competitors, can affect the profitability of your customers. Remember that, while a customer equity model describes past behavior, the past is prologue. Your organization’s next goal might be to predict a customer’s next-year profitability based on her behavior from previous years. You can also use the model as a basis to explore other variables that factor into your marketing decisions: attrition risk, potential spend, price elasticity, response and “up-sell ability” should all be in play. As you treat customers differently, their behavior will change, and your goal will be to understand, measure and foster profitable shifts. Such is the essence of loyalty marketing.

Table I Test, measure, recalibrate Channel

Expense ratio

Cost driver

Action

Profitable customers Catalog Catalog Retail Retail

Low Low Low Low

n/a n/a n/a n/a

Test – priority customer service Control Test – loose returns policy Control

Unprofitable customers Web Web Catalog Catlog Catalog

High High High High High

Returns Returns Customer service Customer service Customer service

Test – tighter returns policy Control Test – catalog insert pushing online service Test – IVR message to highlight online service Control

Note: Once your customer equity model is in place, you can design and test customer service initiatives designed to protect your investment in profitable customers, while driving unprofitable ones to lower cost channels and service touch points. As you treat customers differently, their behavior will change, and your goal will be to understand, measure and foster profitable shifts

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Kathy Stevens

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With a Master’s degree in Statistics and over a decade of experience in customer analytics, Kathy Stevens uses data to drive strategic marketing decisions and increase customer yield for top companies like ExxonMobil, Verizon, La Quinta, and Eddie Bauer. A recognized expert in statistical modeling, analytical CRM and risk management, Kathy has the unique ability to apply complex mathematical concepts to everyday marketing situations. Kathy joined Epsilon (then Frequency Marketing, Inc.) in 2003 from General Electric Consumer

Finance where she was Senior Risk Manager – Strategy Development for their Private Label Credit Card business. She previously served as Acquisition Risk Manager for the Customer Information and Risk Management team in Wells Fargo’s Business Banking Group and worked at Capital One, helping to start-up, launch, and analyze their first international credit card in the UK. Kathy received a Master’s Degree in Statistics from Georgia Tech after majoring in mathematics at Wake Forest University. She can be contacted at: kathy.stevens@ frequencymarketing.com or Tele: 513-248-5004.

About the author

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The art of storytelling: how loyalty marketers can build emotional connections to their brands Caroline Papadatos Air Miles Reward Program, Toronto, Canada Abstract Purpose – The purpose of this paper is to highlight the importance of building emotional connections between brands and consumers. Using Canada’s Air Miles Reward Program as an example, the paper aims to stress the importance of using customer insight to drive branding decisions and ensure a long-term emotional attachment to a loyalty program. Design/methodology/approach – The paper thoroughly explains Air Miles’ method of reaching out to its customers to glean information that could be used to re-brand the program. The method, used during focus groups, asked collectors to re-tell stories that were important in their life. Common themes emerged, which Air Miles incorporated into the re-branding of their program. Findings – Through specially-designed focus groups, Air Miles strategists learned that it isn’t enough to be a well-functioning loyalty program. In order to be distinctive in an overcrowded market, Air Miles must provide collectors with an emotionally engaging experience in the redemption process. Practical implications – If your customers talk about your brand as if it’s a part of who they are, you have made an emotional attachment with them. Thus, your program is on the right track. Originality/value – The paper takes a fresh approach to loyalty markting research as well as analyzing and improving customer loyalty. Keywords Loyalty schemes, Corporate branding, Storytelling, Individual psychology, Consumer behaviour, Canada Paper type Case study

100-plus sponsors supporting the coalition, our program is certainly among the top three loyalty brands in the world. But Air Miles is a mature brand in a mature market, and keeping a long-running brand relevant and top of mind with consumers is a never-ending challenge. Brand managers know that the brand is no longer just the words and pictures; the brand is really about the total delivery of a promise to consumers. From the outset, we recognized the need to get to a deeper insight on what it means to feel rewarded and tie that back to the heart and soul of our brand. How do we bring it alive? What story are we trying to tell our Collectors?

How do you take your loyalty brand from mere words and pictures to the promise of a relevant and rewarding consumer experience across millions of interactions and multiple channels? By providing the means to help consumers write the stories of their lives, Caroline Papadatos teaches us how to become expert storytellers for our brands. Loyalty marketers now operate in a landscape of ubiquity. Most consumers participate in four or five reward programs apiece – and that’s just the programs attached to their credit cards. We don’t even consider the countless coffee club, sandwich card and discount programs that float in and out of consumer consciousness. With the loyalty program now a full-fledged commodity, our mandate as marketers is to be distinctive or fade away. But how do you achieve distinction? In part through the rational motivators of loyalty, but hard benefits are merely the cost of entry into the marketplace. Your program must offer a fair value proposition to the consumer, but the moment you require your members to perform math on your program to determine its true value, you’ve lost them. In a commoditized market, it’s the character of (and emotional attachment to) your brand that creates lasting value for your loyalty program. This past year, emotional connectivity between the brand and our Collectors was the starting point for a re-branding initiative for the Air Miles Reward Program in Canada. With more than two-thirds of all Canadians collecting miles and

Archetypal stories provide a solid foundation Shakespeare understood that there are really only six archetypal stories that humans have told one another through the centuries. The job of the artist is to retell those six stories with different plot details and characters so that they become fresh and alive in the minds of the audience. Similarly, the world’s best and most enduring brands are what we like to call “storytelling” brands. Consider Harley-Davidson – certainly one of the world’s enduring brands. It’s the midlife crisis brand. “What we sell,” a Harley-Davidson marketing executive once famously said, “is the ability of a 43-year-old accountant to dress in black leather, ride through small towns and have people be afraid of him.” A Harley rider’s love for his motorcycle has nothing to do with how fast it goes or how much it costs. It has to do purely and simply with the Harley story: when that accountant rides his Harley, he’s unleashing himself from his family and work commitments and, even if only for three hours a week, becoming somebody he has never been and never will be – except when he gets on his Harley. This quite economical example serves to illustrate that the best brands are story brands. There are many stories at the

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heart of the Air Miles brand: ours, and those of our Collectors. But how do we distill those stories to their essence and convey it as part of our brand positioning? Our method was to gather together groups of 20 or so Air Miles Collectors in a room and ask them to tell us their stories. Rather than focus the spotlight on their experiences with Air Miles specifically, we instead turned it back on their own lives with a simple question that served as our theme: what does it feel like to be rewarded?

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Three consistent reward themes For three-hour stretches, we had the groups of collectors perform storytelling exercises in the form of icebreakers, group activities and solo play. Finally our researcher instructed each member of the group to put pen to paper and write his or her own story, based on personal experience, about feeling rewarded. Now Canada is a big country; if Collectors in Calgary like something, Collectors in Montreal hate it and Collectors in Toronto are indifferent to it. And yet, in our storytelling exercises, we found that Collectors from one end of Canada to the other told strikingly similar stories about the reward experience. As we all remember from our college literature classes, stories are comprised of a theme, which serves as the moral of the story, and the plot, which is the device that conveys the theme. Every story we heard contained a theme comprised of three core elements: 1 Hardship: the journey and work involved in overcoming obstacles is absolutely necessary to the feeling of being rewarded. Perseverance in the face of hardship is critical. If a reward comes out of the blue, then we may enjoy the surprise, but we don’t value it the way we do a reward we have worked hard to earn. 2 Reciprocity: critical to the feeling of being rewarded is an equal exchange of value. We all appreciate the give and take of life. You give to charity and feel satisfaction. You stay up all night with a sick friend, and two weeks later your friend returns the favor. The flip side to this expectation of reciprocity, of course, is the minor twinge of disappointment you feel when you don’t get something back. The fair value exchange is key. 3 Defining moments: in all human experiences, there are moments of truth – the moments that change your life. We don’t tend to remember much else about our lives besides these defining moments. Happiness, after all, isn’t a continual state of being. It’s an intermittent cycle, in which periods of remembered bliss alternate with periods of forgetfulness. Such defining moments were a common theme in our collector tales of feeling rewarded.

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that ever was. A father takes his son to his first day of kindergarten. A young woman moves alone to a new town to take a new job. A young man goes to school to earn his degree. In each case, the story begins with this period of anticipation. Crisis: of course, all stories contain conflict. Something bad always happens, and it’s not a bump in the road, but rather an unexpected, life-altering event. That beautiful baby is born deaf. The kindergartener grows to age 10 and then is paralyzed in a car accident. The woman who left her family behind loses her job. The university student loses his scholarship and has to drop out. In every story, the feeling of anticipation is followed inevitably by a period of real despair. Help along the way: and then in each story, help arrives in the form of miracles both minor and major. A nurse recommends a pediatric surgeon in the USA who might be able to restore the baby’s hearing. A relative convinces the paralyzed boy to enter the Special Olympics. A friendly neighbor gives the young woman a hot job lead. A guidance counselor helps the student apply for a work-study program. The arrival of unexpected help is followed immediately by a period of hard work: the mother travels to the USA; the boy trains for the Olympics; the young woman goes from interview to interview; the young man tries to balance classes with a night job. The goal achieved: after this period of trial and tribulation, the story ends with a goal achieved. The baby’s operation successfully restores a portion of her hearing. The paralyzed boy wins his first wheelchair race. The woman lands a higher-paying job than she had before. The student stands on the podium to receive his degree. Now this may be a peculiarly Canadian trait – we’re known for our reserved demeanors – but the period of celebration that follows is decidedly understated. Rather than enjoying a tickertape parade and a call from the President, our collectors celebrate their accomplishments quietly with family and friends. The feeling is one of warm satisfaction that hard work, perseverance and a helping hand helped our protagonists achieve their worthy goals.

Translating the research findings into action So these stories are touching and great and all that, but what do they have to do with a reward program? We are keenly aware of our role in people’s lives. Air Miles and its Sponsors are ultimately providers of rewards – we’re not actually helping the hearing impaired or promoting world peace. Our key insight from this storytelling exercise – our “ah-ha!” moment, if you will – was the notion of the double reward. In each of these stories, the protagonist enjoyed the extrinsic achievement of the goal. But we also saw a more intrinsic, emotional connection to the reward that came later. In the case of the student who earned his degree, the real reward occurred after he took the midnight bus back to his hometown to sit at the kitchen table, degree in hand, and celebrate with his family. For the mother of the deaf child, the real reward came long after the operation, when she looked up from the kitchen sink to see her daughter playing in the backyard with the neighborhood kids. The lesson here is that the material reward matters little in and of itself – it’s what you do with it, and with whom you share the reward, that counts.

Universal sequences of events What we learned about plot was equally revealing. During our research, we collected about 20 group and 100 individual stories. Collectors wrote about meeting their spouses, about the births of their children, about moving to a new town and starting a new job, about starting their own companies. For every story the details were different, but the plots – the sequence of events from Once upon a time to The End – were identical. Each plot contained these core elements: . Anticipation: every story began with a sense of hope for the future. A baby is born, and she’s the most beautiful baby 383

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Caroline Papadatos

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When we looked in the mirror, we realized that both our advertising and the delivery of our redemption experience were very functional. We deliver with executional excellence, but without feeling. We congratulate Collectors for earning their rewards, but we don’t address the inherent possibilities for quiet celebration. It’s not about the tickets that you get to take a flight; it’s about the time you spend on the beach with your kids. And while we can’t say that the Air Miles Reward Program or any other loyalty program is responsible for the warm feelings brought on by that experience, we can say that we provide that crucial help along the way for whatever quest you’re on. In the stories of our Collectors’ lives, that’s our role.

program. This connection is generally best measured in qualitative rather than quantitative terms. If customers talk about your brand as if it’s a mirror image of who they are and what they live for, then you know you’re on track. Our job is to help our Collectors write their own life stories. In a saturated marketplace, that’s the only loyalty strategy that matters. It’s the difference between being distinctive, and becoming extinct.

About the author As Vice President of Marketing for the Air Miles Reward Program, Caroline Papadatos leads the development of program experience, channel experience, brand and marketing communications for Canada’s most popular reward program. Since joining Air Miles in 1998, she has played key roles in segment management, business development and sponsor management. She led the company’s CRM taskforce, and in 2003 became Vice President of CRM, responsible for customer experience management and the creation of new channels and capabilities for the Air Miles Collector base. Caroline was Corporate Manager of Customer Relationship Marketing at Sears Canada before joining Air Miles. She is former Chair of the Canadian Marketing Association CRM Council, and a frequent industry speaker on marketing and customer relationship management topics. She is Contributing Editor of COLLOQUY (www.colloquy.com), a provider of loyalty marketing publishing, research, educational and consulting services. She can be contacted at: [email protected], or Tele: (416) 228-6408.

Ongoing measurement So that’s where the Air Miles brand is going: from a functional to an emotionally engaging experience, back to our roots of providing the bridge between the anticipation of achieving worthy goals and the warm feelings that follow success. Quantifying our success in this evolution will be a challenge; while the Air Miles Reward Program today has a 97 percent awareness rating in Canada, we still need to understand how our re-branding efforts are helping us tell our brand story to our Collectors. Is a Collector’s attitude toward the brand positive, negative or neutral? Can we move naysayers from a negative attitude to a positive one? Are we building comprehension about the program structure so that Collectors have realistic expectations about how to earn and redeem for rewards? Are we seeing incremental activity go up as a result of our efforts? These measures will help us benchmark success. But the hardest thing to measure will always be the emotional engagement with the brand, especially for a free

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384

Life is not a shopping cart: three keys to building brands and improving customer loyalty Bryan Pearson Air Miles Reward Program, Toronto, Canada Abstract Purpose – With the average consumer receiving six million marketing messages each year, it is increasingly difficult for marketers to achieve their desired impact. The purpose of this article is to provide ideas on sustaining brand loyalty in a crowded marketplace. Design/methodology/approach – The paper cites a recent study to explain the reason for a disconnect between consumers and marketers, and why some brand loyalty programs fall short. It then provides the author’s ideas for rebuilding a relationship with consumers, citing examples from the Air Miles Rewards Program. Findings – By incorporating the three ideas presented within the article, it is believed that marketers can create deeper, more meaningful, relevant and mutually beneficial relationships with their customers. Practical implications – Although consumers are assaulted by six million marketing messages each year, marketers can still take steps to keep their customers loyal. The paper reminds marketers that a brand is a promise and offers ideas on sustaining brand loyalty among consumers. Originality/value – The paper examines results of a recent survey which shows a disconnect between marketers and their marketplace. It then provides ideas on repairing that disconnect. Keywords Brand loyalty, Brand management, Customer relations, Loyalty schemes Paper type General review

pervasiveness and popularity might suggest otherwise. Most of these programs attempt to buy customer affection through discounts and giveaways, incentives and special offers. Given that many customers simply want to get the best deal, such a tactic may be reasonable over the short term. Over the long term, however, it’s not a sustainable strategy. Loyalty can’t be bought, sold or auctioned. In today’s market, loyalty is really about creating value-based, preferred relationships. Marketers must turn up the volume on relevance, emotional connectivity and customer experiences. It’s in this arena where loyalty programs can and do affect consumer behavior. Best-practice organizations recognize that the emotional aspects of their brand are reflected in how customers experience that brand across every touch point. When you create moments of truth over time through a series of small, personal actions, you dial up the relevance for customers. Today’s loyalty programs enable companies to execute against these opportunities by helping them better understand customer motivations, and then deliver on these unique insights through mass customization – not mass communication.

According to a recent study by the American research firm Yankelovich, the average consumer sees about six million marketing messages per year. That’s 16,000 marketing impressions per day. That same study also found that 65 percent of surveyed consumers are annoyed by the sheer volume of messages flooding everyday life. Interestingly, less than 10 percent of marketers believe consumers feel this way. Worse still, almost 60 percent of consumers feel these marketing contacts have no relevance to their lives. Yet less than 15 percent of marketers believe consumers feel this way. Simply put, there’s a dangerous disconnect between marketers and the marketplace. These findings suggest brands are simply no longer achieving the desired consumer impact. Since brand loyalty is vital to enduring and profitable growth, this trend spells trouble for us all. Is this the end of the world as we know it? Are brands no longer able to elicit and sustain customer loyalty? Of course they still can. But many efforts to instill brand loyalty have nonetheless fallen short. In their efforts to feed consumers’ insatiable appetite to buy stuff, many of today’s loyalty practitioners have lost their way. Real brand loyalty isn’t derived from the multitude of available programs designed to influence shopping choices by awarding customers points towards an additional rebate or “cash in” for a range of products and services – although their

Building brand loyalty How do companies create brand loyalty in a marketplace populated by demanding and fickle consumers? Frankly, a brand is a promise. And promises are meant to be kept. At the Air Miles Reward Program, we’ve done a lot of thinking about brand loyalty and the significance of customer experiences. We’ve learned that life is not a shopping cart – and we’ve recognized this in the way we look at our brand and the role we play with our collectors. This mindset has helped to sharpen our focus on who we are and what we do. Together

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Journal of Consumer Marketing 23/7 (2006) 385– 386 q Emerald Group Publishing Limited [ISSN 0736-3761] [DOI 10.1108/07363760610712911]

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Journal of Consumer Marketing

Bryan Pearson

Volume 23 · Number 7 · 2006 · 385 –386

with our business partners It has helped us deliver on promises made. Most consumers spend their money on what they need. But very few consumers get the chance to spend their money on who they are – to express themselves with what they’ve earned. We offer AIR MILES reward miles so that everyone has the chance to say, “These everyday purchases are the things that I need, but this reward – well, this is who I am.” The airline tickets, home electronics, gift certificates and other items in our redemption portfolio represent the tangible rewards of program participation. But the intangible rewards – the true rewards through which customer loyalty is fully realized – lie in the stories, photos, memories and emotional experiences the program sets free. Thomas Edison once said, “Vision without execution is hallucination.” In that light, organizations can no longer push brands out into the marketplace and expect them to sell. To sustain brand loyalty, companies must focus on deeper, more meaningful, relevant and mutually beneficial relationships with their customers. Here are a few thoughts to get you started: . Avoid the quick fix: the challenge for the loyalty industry is to move beyond the traditional thinking that has dictated today’s consumer/retailer relationships, and to move away from the quick hits that drive immediate sales at the expense of creating emotional bonds that drive longerterm profitable relationships. The status quo is no longer acceptable – nor is it sustainable. Less customer loyalty means more opportunities for competitors to eat away at market share. . Manage the customer experience: a successful loyalty initiative is predicated first on creating a meaningful consumer experience. The brand promise must be clear, compelling and consistent across all consumer touch points. When managed with the consumer experience as its guiding principle, loyalty marketing offers businesses the opportunity to facilitate deeper, more meaningful, relevant interactions with customers. The financial benefits of this approach are clear: long-term, profitable consumer and partner relationships.

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Derive customer insights: the true power of a loyalty program lies in its ability to glean customer insights through the data obtained as a result of a well-run program. These valuable consumer insights can help any organization manage the way their customer experiences their products and services. Companies that use program data to understand the who, why and how of customer behavior will be well on the road to properly defining and executing against their brand promise.

The most successful loyalty initiatives enable organizations to answer the hard questions. Who are my best customers? How do they feel about my brand? Am I contributing to their overall positive experience? Can I retain their business over the long term? With the resulting data, you can take a sharper, more strategic focus on the comprehensive retail or service experience, and avoid the marketing disconnect evident in the Yankelovich study. That’s how your brand takes on a new meaning to consumers. That’s the stuff that instills loyalty.

About the author Bryan Pearson is responsible for operating, the Air Miles Reward Program, Canada’s premier coalition retail loyalty program, and for operating these marketing services companies: COLLOQUY and ICOM (Information and Communications Inc.). Working directly with sponsor companies, Bryan oversees the development and execution of loyalty and database marketing strategies that build longterm, interactive and value-added relationships with customers. He is a frequent speaker at conferences across North America on the topics of loyalty marketing, customer relationship management and retail marketing. He has a passion for the power of “measured marketing” through the use of database techniques and for coalition marketing by bringing companies together through loyalty programs. Bryan is also a Contributing Editor of COLLOQUY (www.colloquy. com), provider of loyalty marketing publishing, research, educational and consulting services. Bryan can be contacted at: [email protected] or Tele: 416-228-6671.

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The role of loyalty programs in behavioral and affective loyalty Blanca Garcı´a Go´mez, Ana Gutie´rrez Arranz and Jesu´s Gutie´rrez Cilla´n Department of Management and Marketing, Faculty of Business and Economics, University of Valladolid, Valladolid, Spain Abstract Purpose – The aim of this paper is to analyze the behavioral and affective loyalty of retailer customers in order to establish the role played by loyalty programs in the development of these variables. Design/methodology/approach – Research data were taken from a survey carried out on 750 customers from a Spanish supermarket chain. Several ANOVAs are employed to compare the two loyalty dimensions among participants and non participants in loyalty programs. Findings – The results show that participants in loyalty programs are more behavioral and affectively loyal than non participants. Nevertheless, most customers do not change purchase behavior after joining a loyalty program. The strategy is therefore to retain loyal customers and to achieve the reinforcement of affective bonds linking the customer to the retailer. Practical implications – Companies should focus their efforts on developing a reward plan as adapted as possible to concrete needs of each participant in the program to achieve true loyalty. Originality/value – The main contribution of this paper is the completion of an exhaustive analysis of customer loyalty. On the one hand, it is a pioneer in the study of the influence of loyalty programs on affective loyalty and, on the other hand, it confirms results from other researches on behavioral loyalty of program participants. In addition, this is one of the few papers developed in this field using the survey as a source of information. Keywords Loyalty schemes, Consumer behaviour, Customer retention, Customer loyalty, Supermarkets, Spain Paper type Research paper

consolidated programs (Mollet, 2004b; Reinares and Reinares, 2005). The spread of loyalty programs in business circles has promoted the appearance of one research line comprising plenty of studies dealing with different aspects related to this strategy. Our interest in this paper is focused in analyzing the influence of loyalty programs on customers’ loyalty. This matter is of vital importance for companies implementing them for the following reasons. First, due to the nature of loyalty programs themselves, achieving customers’ loyalty is a prime objective of this strategy. Secondly, the availability of a loyal customer’s database offers benefits for the company which have been already widely documented in literature. Loyal customers add profitability to the company (Woolf, 1996; Heskett et al.,1997) and profitability of one individual customer grows constantly during his relationship with the company (Reichheld and Sasser, 1990; Anderson et al., 1994; Reichheld and Teal, 1996). The high profitability added by loyal customer can be explained, firstly, through their lesser price sensitivity towards the products of the company (Sharp and Sharp, 1997; Dowling and Uncles, 1997; Bowen and Shoemaker, 1998a, b), secondly, due to the fact that they require a smaller investment in communication than those people not having previous experience with the company (Rowley, 2000) and finally, thanks to their role of prescribers (Reichheld and Teal, 1996). Besides, true loyalty based on emotional bonds is hard to copy, so it can be a competitive advantage (Palmer et al., 2000). Although the bi-dimensional view of loyalty is commonly accepted in our time, most papers analyzing the influence of this strategy on customer loyalty were focused on behavioral dimension. This dimension refers to purchase behavior repeated over the time.

Introduction Loyalty programs are a marketing strategy based on offering an incentive with the aim of securing customer loyalty to a retailer. Achieving rewards is related with purchasing frequency, so this type of programs are also called frequent purchase programs (Shoemaker and Lewis, 1999; Long and Schiffman, 2000; Bell and Lall, 2002) or reward programs (Kopalle et al., 1999; Kim et al., 2001). Lately, large-scale loyalty programs have been implemented by firms in different industries around the world. In the case of retailers, on whom this paper is focused, an extended use between most of them has led these programs to become one more element in their offer. In fact this can be seen in the way that companies set part of their communication budget aside to promote these programs, especially loyalty cards, as they are another product offered by the company. However, not all countries show the same level of development regarding to implementation of this tool. For example, in Germany, there are over 100 different programs and the percentage of families taking part in at least one of them exceeds 40 percent (Ku¨nzel, 2002). In the USA, the number of programs surpasses 400 with more than 80 percent of families taking part (Colloquy, 2003). Regarding to Spanish data of use of these programs, consumers participate on average in 1.74 loyalty programs – in the USA, this figure rises to 3.34 – and there are more than 40 national The current issue and full text archive of this journal is available at www.emeraldinsight.com/0736-3761.htm

Journal of Consumer Marketing 23/7 (2006) 387– 396 q Emerald Group Publishing Limited [ISSN 0736-3761] [DOI 10.1108/07363760610712920]

387

The role of loyalty programs in behavioral and affective loyalty

Journal of Consumer Marketing

Blanca Garcı´a Go´mez, Ana Gutie´rrez Arranz and Jesus Gutie´rrez Cilla´n

Volume 23 · Number 7 · 2006 · 387 –396

Existing literature on the impact of loyalty programs on behavioral loyalty yields two interesting results. Firstly, participants in these programs show a higher behavioral loyalty than non participants. This becomes evident through indicators like the frequency of visits to the retailer or the number of visited competitor selling points (Dre`ze and Hoch, 1998; Passingham, 1998; Benavent et al., 2000; Meyer-Waarden, 2002). Secondly, papers focused on loyalty programs that compare the consumers’ behavior before and after enrolling themselves in these programs show that there is virtually no difference between the two states regarding to number of visits to the retailer or purchase volume (Sharp and Sharp, 1997, 1998; Wright and Sparks, 1999; Benavent, 2000; Bell and Lall, 2002; Meyer-Waarden, 2002). Taking into account both the two previous results, we are led to think that loyalty programs do not make customers become more loyal, but its main contribution is retaining the already loyal customers. One of the goals of this paper is deepening in this aspect througth the hypothesis proposed. Following with loyalty components, the affective dimension refers to emotional bonds of one individual towards something, in this case, a selling point (McGoldrick and Andre, 1997; Bennett and Rundle-Thiele, 2002). While attitudinal loyalty may be considered a mere mediator of marketing instruments that affect behavioral loyalty, its measurement is a prerequisite for the understanding of how stimuli affect cognitive and affective processes that make customers to become or remain loyal in their deeds (Noordhoff et al., 2004). Literature points customer attitude, satisfaction, trust and commitment as key component in the development of affective loyalty. Loyalty only based on a repeated behavior is fragile. Some authors consider that there is “true loyalty” only when these two dimensions are met in a consumer (Dick and Basu, 1994; Trinquecoste, 1996). In spite the importance of this aspect, research on the influence of loyalty programs on customers’ affective loyalty towards the retailer are scarce and show contradictory results. On one hand, some researchers state that most loyalty programs are in fact saving programs in disguise that do not contribute to the attitudinal component of loyalty, and thus do not create sustained loyalty (McGoldrick and Andre, 1997; Bennett and Rundle-Thiele, 2002). On the other hand, in a comparative research carried out in Netherlands and Singapore by Noordhoff et al. (2004) we can detect a positive relationship between holding a loyalty card and what they call attitudinal loyalty of consumers towards the retailer in both countries. Undoubtedly, most relevant literature confers to the ability of the strategy to influence customer satisfaction (Shoemaker and Lewis, 1999; Stauss et al., 2001; Mueller and Pietrzyk, 2004). Due to this lack of research, another aim of this paper is to determine the influence of loyalty programs on affective loyalty. The methodology used to fulfill the goals of our research consists in comparing behavioral and affective loyalty of participants and non participants in loyalty programs. Besides, we also take into consideration participants’ change of behavior. Data collection was complete by a personal survey of 720 consumers of one supermarket chain placed in a medium-sized Spanish city running two loyalty programs. The paper is organized into the following sections. Firstly, we propose several hypotheses about loyalty of participants in

loyalty programs. Then, we explain in detail the methodology used in this research and the results obtained from the carried out analysis. The paper ends with the presentation of conclusions based on results, and some recommendations for business management.

Behavioral and affective loyalty of participants in loyalty programs Our research on the effect of loyalty programs on customer loyalty comprises the analysis of three aspects: behavioral and affective loyalty of participants on loyalty programs and their change of behavior after enrolling themselves in the program. Behavioral loyalty of participants in loyalty programs In order to carry out the analysis of behavioral loyalty of participants in loyalty programs we distinguish two aspects in purchase behavior: that produced in the retailer and that taking place in other competitors’ retailers. We use the following indicators for the first aspect: frequency of visits to the retailer, purchases and percentage of purchases per customer. Regarding to the role played by loyalty programs in purchase frequency, Dre`ze and Hoch (1998), Passingham (1998) and also Meyer-Waarden (2002) state that participants in loyalty programs make a higher number of visits to the retailer than non participants. Benavent et al. (2000) emphasize this aspect concluding that promotional actions related to loyalty programs encourage to users of these programs to have smaller interpurchase times than those lacking this incentive. In support of the positive impact of participation on loyalty programs on the purchase volume in the retailer, Benavent et al. (2000) and Meyer-Waarden (2002) conclude that owners of loyalty cards purchase more than people without them. Another indicator of behavioral loyalty is the percentage or share of purchase, defined as the ratio of the total expenses of one consumer made in one specific retailer. A high value in this variable points out that the consumer scarcely buys in other retailers, so he shows a loyal behavior to that point of sale. In this sense, there is plenty of research material which proves that participants in one loyal program have a higher share of purchase in that retailer (Neslin et al., 1985; Dre`ze and Hoch, 1998; Bell and Lall, 2002; Jorna et al., 2002; Jorna and Bijmolt, 2003; Ayala and Neslin, 2004; Mollet, 2004a). Taking into account the existing literature, we propose the following hypothesis: H1. Participants in one loyalty program show a greater behavioral loyalty to the retailer that has implemented that loyalty program than non participants. The spread of purchases between different points of sale is one of the barriers to loyalty towards one retailer. Loyalty programs aim to remove this spread (Meyer-Waarden, 2002), transforming them into defensive strategies. Literature on the effectiveness of loyalty programs deepens in the research on the influence of these programs on switching costs creation (Jackson, 1985; Klemperer, 1987; Duffy, 1998; Kim et al., 2001; Singh, 2001). Switching costs lead the consumer to visit a limited number of points of sale as they reduce the appeal of other choices. In the finance industry, Perrier et al. (1992) state that one technique used by banks to raise retention ratios is to increase 388

The role of loyalty programs in behavioral and affective loyalty

Journal of Consumer Marketing

Blanca Garcı´a Go´mez, Ana Gutie´rrez Arranz and Jesus Gutie´rrez Cilla´n

Volume 23 · Number 7 · 2006 · 387 –396

switching costs through loyalty programs. Also frequent travel programs use by airlines have a similar effect because they lead customers to perceive that competitors offer higher prices since they are deprived of the discounts offered in return for loyal behavior (Palmer and Beggs, 1998). Meyer-Waarden’s (2002) research is the only paper where the defensive role of these programs is proven empirically and which shows their ability to slowly reduce the spread of purchase between several retailers. According to the above facts, we propose the following hypothesis: H2. Participants in one loyalty program show a less behavioral loyalty to other retailers than non participants.

Tietje (2002) focused on the role of rewards offered by loyalty programs, and concludes that obtaining certain rewards can generate positive feelings towards the retailer implementing the program. These feelings linked to the purchase experience involve a greater satisfaction leading to higher purchase intentions (Price et al., 1995; Oliver et al., 1997). Taking into account the previous statements, we propose the following hypothesis: H4. Participants in one loyalty program show a greater level of satisfaction with the retailer than non participants. Trust, the third key element in creating affective loyalty, appears when one part has in the reliability and integrity of the other part in the interchange (Morgan and Hunt, 1994). The concept of trust as key factor to establish successful relationships in the tertiary sector was introduced by Parasuraman et al. (1985). These authors suggest that customers should be able to trust in service suppliers, to be sure about the behavior maintained with them and to have the certainty that data transmitted to them will have a confidential nature. All these considerations are crucial to obtain customers’ loyalty and they mean a positive contribution so that any company has a stable customer portfolio. Other authors like Macintosh and Lockshin (1997) and Sirdeshmukh et al. (2002) also recognize the role of trust in creating loyalty. There is not a great deal of research which can shed light on the possible relationship between participation in loyalty programs and the creation of trust in the retailer. Kelley and Thibaut (1978) and Macintosh and Lockshin (1997) suggest that a loyalty program allows a relationship between supplier and customer to be built, that favors the concept of trust and commitment. Also Meyer-Waarden (2002) supports this idea when he explains that loyalty programs cause switching costs to consumer as a result of rewards offered by them and also costs arising from the evolution of the relationship established with the retailer. The increase in the number of contacts between both parts coming from participation in loyalty programs leads to an improvement in the customer knowledge, which translates into an increase in consumer trust and commitment to the retailer. Based on these premises, we propose the following hypothesis: H5. Participants in one loyalty program show a higher trust in the retailer than non participants.

Affective loyalty of participants in loyalty programs Most authors agree in identifying the key components for developing affective loyalty: attitude, satisfaction, trust and commitment. We propose several hypotheses for each of these components of affective loyalty of participants on loyalty programs. Attitude was defined by Oliver (1980) as a consumer’s relatively lasting affection towards an object or an experience. The role of attitude in customer loyalty is vital, since it is required a previous positive attitude to consider a repetitive behavior as true loyalty (Day, 1969; Jacoby and Chestnut, 1978; Assael, 1987; Solomon, 1996; Huang and Yu, 1999). Regarding the relationship between participation in loyalty programs and attitude, there is virtually no empirical research that discusses in greater depth the strength and sense of this relationship. We can quote Ayala and Neslin (2004) when they assert that the reward obtained from a loyalty program can increase the subsequent purchase behavior as long as the rewarded customer develops a positive attitude towards the retailer. In spite of the shortage of literature on this matter and taking into account that loyalty schemes are aimed to create loyalty in consumer, we venture to suppose that they also obtain a favorable attitude from consumer: H3. Participants in one loyalty program show a more positive attitude to the retailer than non participants. The second element that should be studied in the development of affective loyalty is the satisfaction. Satisfaction is defined as an affective condition resulting from a global assessment of all aspects comprised in the relationship in which the consumer takes part (Severt, 2002). There are plenty of researches proving that satisfaction is an important antecenden of consumers’ intention of behavior (Rust and Oliver, 1994; Taylor and Baker, 1994; Bhattarcherjee, 2001; Szymanski and Henard, 2001; Anderson and Srinivasan, 2003, among others). Therefore, it is one of the variables considered as a key to the creation of customer loyalty. At this point, it is advisable to investigate the conclusions of research which analyzes one possible relationship between participation in loyalty programs and satisfaction. In this field, authors like Shoemaker and Lewis (1999), Stauss et al. (2001) and Mueller and Pietrzyk (2004) establish the outstanding ability of programs to raise customer satisfaction and also to reduce the consumer dissatisfaction when a problem arises in the relationship with the supplier (Bolton et al., 2000; Meyer-Waarden, 2002).

Commitment is widely considered as a key component to achieve successful relationships at long term (Dwyer et al., 1987; Morgan and Hunt, 1994). A high level of commitment appears when there is a rational bond (net profit) and an affective bond (emotional link) in the relationship. According literature, the commitment is a required condition to true loyalty (Day, 1969; Bloemer and Kasper, 1995; Oliver, 1999). There are authors that go even beyond stating that commitment and loyalty are the same (Assael, 1987; Price and Arnould, 1999; Pritchard et al., 1999; Too et al., 2001). It is also necessary to investigate the relationship between loyalty programs and creation of commitment from consumer to the retailer running the strategy. Regarding to this aspect, we should recall that when we tried to justify the relationship between participation in plans and trust we quoted Kelley and Thibaut (1978) and Macintosh and Lockshin (1997), who 389

The role of loyalty programs in behavioral and affective loyalty

Journal of Consumer Marketing

Blanca Garcı´a Go´mez, Ana Gutie´rrez Arranz and Jesus Gutie´rrez Cilla´n

Volume 23 · Number 7 · 2006 · 387 –396

propose that a loyalty program is a means of favoring the customers’ trust and commitment to the retailer. On the other hand, and taking as starting point the concept of leveled commitment, literature on the effectiveness of loyalty programs concludes that those create switching costs and, therefore, leveled consumer commitment (Klemperer, 1987; Caminal and Matutes, 1990; Bolton et al., 2000; Kim et al., 2001; Meyer-Waarden, 2002). From another point of view, one of the benefits derived from the availability of loyal customers is the advising role they play that is widely recognized in literature (Deming, 1982; Anderson, 1998; Bowen and Shoemaker, 1998a, McIlroy and Barnett, 2000, among others). Consumers who purchase repeatedly recommend the product to other people, representing a great source of word-of-mouth advertising. Some of the literature has regarded this advisory role as an example of consumer commitment to the retailer. In the field of loyalty programs, Benavent et al. (2000) explain that the goal of these programs is achieving a bigger income thanks to the cross selling and recruitment of new consumers with lesser cost through word of mouth advertising. For this reason, customer commitment to the retailer can be presumed. According to the above facts, we propose the following hypothesis: H6. Participants in one loyalty program show greater commitment to the retailer than non participants.

that plans should be addressed just to a limited group of consumers who may change their behavior due to their participation in them. According the above, we suggest that loyalty programs cannot modify behavior patters in most consumers taking part in them. Quoting Ma¨gi (2003), it is possible that some consumers change their purchase behavior in one program and other consumers participate in programs rewarding purchasing patterns already established. Thus, our hypothesis is the following: H7. Participation in loyalty programs does not cause a change in most consumers.

Methodology Data collection Collection of data was made through personal surveys carried out at the exit of several retailers belonging to a supermarket chain located in a medium-size Spanish city. A stratified selection by simple affixing was used. The sample consists of 720 people from whom 180 were participants in the retailer frequent shopper program that was in force at that moment, 180 in the card program and 360 not participant in any loyalty program at that retailer. The makeup of this sample allows a comparison between participants and non participants in loyalty programs and the analysis of possible differences between the effectiveness of the two types of programs used. Technical data relating with the sample can be found in Table I.

Influence of loyalty programs on behavior change At this point, we study if consumers change or not the purchase behavior because of their participation in loyalty programs. This aspect is fundamental to discovering if loyalty programs make customers more loyal. Existing research does not offer consistent results in this area. Nevertheless, many researchers coincide in proving the inefficiency of loyalty programs in causing behavior change in the majority of participating consumers. On this line, Sharp and Sharp (1997, 1998) do not find evidence to demonstrate an increase in depth or purchase frequency in consumers through the incentives offered by loyalty programs. These authors believe that it is hard to change repeated purchase models of consumers, and Dowling and Uncles (1997) agree with this. They also conclude that most consumers do not show increased loyalty, which is the final goal of this type of marketing actions. Long and Schiffman (2000) reach a similar conclusion when they recognize that only a small group of consumers change their purchase patterns due to their participation in a loyalty program. Wright and Sparks (1999), meanwhile, explain that the majority of consumers polled declare that they purchased regularly in the retailer before belonging to the reward program. Customers go beyond recognizing their intention to continue purchasing in the future in the retailer, regardless what happens with the loyalty program. In the papers of Benavent (2000) and Meyer-Waarden (2002) we can observe that the time between purchases and purchases spread between retailers hardly varies from card’s possession and that the program only modifies the behavior of a small part of consumers: the large consumers of the retailer. This justifies the emphasis of the authors on the importance of a proper customer segmentation to achieve effective plans. On the same line, Bell and Lall (2002) recognize that the program can obtain an increase in sales although they believe

Measurement of variables In order to draw up the questionnaire, we completed an exhaustive review of research from different study areas supplying the measurement scales of variables used in this paper. In this way, we aim to fulfill the requirements of reliability and validity of scales as far as possible. Nevertheless, the items obtained needed to be corrected in order to adapt them to our specific context. Furthermore, we created new items from theoretical concepts found in relevant literature. Before reaching the final design and in parallel with tasks of concreting and defining the questionnaire, we conducted a series of detailed interviews with managers of some companies belonging to the national grocery retailing. Finally, the initial questionnaire was cleaned up using a pretest. The measurement scales employed in the research are shown in Table II. Table I Sampling technical data

390

Characteristics

Survey

Sample element Sample procedure Sample size Sampling error Trust level Time Scope Source of information

Retailer consumers Random stratified. Same affixing 720 people þ 2 3.6% for p ¼ q ¼ 50% 95% October-November 2005 Valladolid (Spain) Personal survey

The role of loyalty programs in behavioral and affective loyalty

Journal of Consumer Marketing

Blanca Garcı´a Go´mez, Ana Gutie´rrez Arranz and Jesus Gutie´rrez Cilla´n

Volume 23 · Number 7 · 2006 · 387 –396

Table II Measurement scales Name of the construct (Cronbach Alpha or correlation coefficient, composite reliability and average variance extracted) Measure of the construct Attitude towards the retailer: Alpha 5 0.89; CR 5 0.89; AVE 5 0.74 Trust in the retailer: Alpha 5 0.76; CR 5 0.77; AVE 5 0.47

Trust in the retailer staff: Alpha 5 0.85; CR 5 0.86; AVE 5 0.68 Commitment to the retailer: Alpha 5 0.75; CR 5 0.70; AVE 5 0.45

Purchase behavior in the retailer

Purchase behavior in competitor retailers

Satisfaction

Change in purchasing behavior

Reflective scales I like shopping in the retailer It’s a nice, comfortable and friendly retailer In general, I consider it as a good retailer I think that the retailer acts in my best interest The retailer is concerned with my welfare, not only with obtaining profit The retailer is honest with their customers, it does what it promises The retailer makes an effort to know its customers The retailer staff are competent and professional The retailer staff are friendly and helpful I trust the retailer and its staff I like the relationship I have with retailer staff I usually recommend the retailer to my friends and family I intend to purchase in the future in the retailer I consider myself as loyal to the retailer Formative scales Times you go usually to the retailer in one month Types of products purchased in the retailer Average amount earmarked for purchasing drug retailer and food products Percentage of money for purchasing drug retailer and food products that you spend in the retailer Average amount spent per visit to the retailer Number of competitors retailers of the same type you visit regularly Other types of retailer where you purchase: Hypermarket Fruit shops Butchers Fish shops Drug retailers Retailer has good prices Retailer has a large variety of products Section distribution in the retailer is comfortable Parking and access are easy Retailer responds to any product problem I feel comfortable when I go to the retailer Opening hours are adjusted to my needs In general, I am satisfied with the retailer Stopping purchasing in other retailers (Loyalty card) Stopping purchasing in other retailers (Frequency program) Increased purchase frequency at the retailer (Loyalty card) Increased purchase frequency at the retailer (Frequency program)

In this paper we used two different types of scales in respect of the type of relationships between observed and non observed variables: reflective and formative scales. In the case of the first type of scales, it is considered that observed variables with which we try to measure each theoretical concept are a representation or reflection (effect) of this concept. Therefore, we can expect that reflective indicators used to capture the

Average (SD) Measurement scales 3.85 (1.03) 3.92 (0.96) 3.96 (0.92) 2.64 (1.18) 2.60 (1.06)

Five-point Likert

Five-point Likert

3.34 (1.12) 3.08 (1.14) 4.09 (1.01) 4.10 (1.03) 3.85 (1.03) 3.59 (1.10) 2.50 (1.37) 3.56 (1.17) 2.89 (1.34)

Five-point Likert

Five-point Likert

2.65 (1.18) 2.28 (0.79) 3.13 (0.93)

1 to 4 1 to 3 1 to 4

2.20 (2.06)

1 to 4

2.3 (1.01) 1.41 (0.85)

1 to 4 Ratio (open)

1.81 (0.64) 1.92 (0.69) 1.90 (0.76) 1.98 (0.76) 1.72 (0.61) 3.24 (1.11) 3.67 (1.03) 3.96 (0.95) 3.46 (1.42) 4.00 (1.74) 4.00 (0.97) 4.34 (0.90) 3.97 (0.95) 1.83 (0.39) 1.79 (0.40) 2.33 (1.28) 2.37 (1.32)

1 (never) to 3 (always)

Five-point Likert

Five-point Likert

essence of one specific theoretical concept are highly related between them. One alternative in the measurement process that has been less used but that is beginning to spread gradually in the academic world is the use of formative or causal indicators. These involve the creation of composite indexes more than the development of scales (Diamantopoulos and Winklhofer, 2001; Jarvis et al., 2003). 391

The role of loyalty programs in behavioral and affective loyalty

Journal of Consumer Marketing

Blanca Garcı´a Go´mez, Ana Gutie´rrez Arranz and Jesus Gutie´rrez Cilla´n

Volume 23 · Number 7 · 2006 · 387 –396

In this case, one variation in any of the observed variables causes changes in the structure – which is multidimensional by nature – and not in reverse. In other words, the indicator is one of the causes or antecedents of the underlying variable to which it is related. From this point of view, the conceptual and empirical meaning of one underlying composite variable is determined by the set of observed variables used, which are not necessarily inter-related (Jarvis et al., 2003). In our case we measure using reflective scales three dimensions of affective loyalty: attitude, trust and commitment. Satisfaction and behavioral loyalty have been considered as a formative scale. Trust and behavioral loyalty are structured into two parts derived from the factors obtained in the previous exploratory factor analysis. In order to measure purchasing behavior change we used two variables: “stopping purchasing in other retailers” and “increased purchase frequency at the retailer”. For these two variables we distinguish between participants in loyalty programs and participants in the frequent shopper program (see Table II). According to the above considerations, the use of reflective scales requires the completion of a reliability and validity analysis, contrary to formative scales, over which there is no unanimity regarding the methodology that should be applied. For the last scales there is no clean-up process, we simply create one variable that represents the structures examined, to obtain the average of the values obtained for each of variables involved. Reliability and validity analyses are carried out through an exploratory and confirmatory factor analysis of proposed structures. Table II shows the scales resulting from the above clean-up process and their reliability and validity indicators.

into which behavioral loyalty has been divided for the two groups of retailer consumers. That is to say that participants in loyalty programs, independently of the type of program, demonstrate greater behavioral loyalty towards the retailer and lesser behavioral loyalty to competitors than customers who do not take part in any program. According the above, we can accept hypotheses H1 and H2. In order to contrast the hypotheses relating to affective loyalty of participants in loyalty programs, we completed an analysis similar to the previous one: an ANOVA test for the difference of averages. In this case we try to determine the existence of different values for the different constructs of affective loyalty between participants and non participants in the retailer loyalty programs. The values of dependent variables are obtained in different ways, depending on the type of measurement of these variables: formative or reflective scales. Satisfaction was measured through the formative scale. In the same way as in the case of behavioral loyalty, for satisfaction we created an average variable from responses given by consumers to the seven items designed to measure the different aspects of individual satisfaction with the retailer. In the case of the other aspects of affective loyalty – attitude, trust and commitment – and since we are dealing with reflective scales, in order to carry out the analysis we used the consumer marks in the factors obtained in the factor analysis of key components previously carried out. The ANOVA results (see Table IV) coincide in showing significant differences between the average values obtained from the dimensions into which affective loyalty has been divided in both samples – participants and non participants in loyalty programs. These facts lead us to accept H3, H4, H5 and H6, i.e. it can be asserted that participants in loyalty plans show a more positive attitude, and greater satisfaction, trust and commitment towards the retailer than non participants. The last proposed hypothesis (H7) analyzes whether participation in a loyalty program can modify the consumer behavior in the retailer implementing the program. The analysis of frequency distribution made for variables employed to measure change in consumer behavior (see Tables V and VI) shows that a high percentage of participants in retailer loyalty programs claim not to have changed their purchase behavior, that is, they affirm that they do not shop more frequently at the retailer. Furthermore, they state that they do not stop purchasing in competitor retailers as a result of their participation in the loyalty program implemented by one retailer. In order to statistically contrast the results obtained with the contingency tables, we propose proportion tests for each of them. The aim is to check if most consumers taking part in one loyalty program in one retailer, that is, more than 50 percent, do not give up purchasing in other selling points as a result of

Results In order to contrast the hypotheses we divided the sample into two independent sub-samples: on the one hand, the set of people taking part in one loyalty program, whether frequent shopper or card programs, and, on the other hand, the group of consumers who do not take part in any retailer loyalty program. In order to contrast H1 with H2, that is, the hypotheses comparing the behavioral dimension of loyalty between participants and non participants, we use an ANOVA model. It should be recalled that, in this case, we are working with formative scales; so, the values of dependent variables are built up from the average of punctuation of polled individuals for behavioral loyalty variables. We work specifically with two scales, one used to obtain the average loyalty through the purchase behavior of people in the retailer and other measuring the purchase behavior in competitor retailers. The ANOVA analysis results, shown in Table III, are clear, since they reflect significant differences in the two dimensions Table III ANOVA analysis results for behavioral loyalty

Average Average part. non part.

Addition of squares

df

Square average

Intergroups Intragroups

76.317 290.123

1 636

76.317 0.456

167.300 0.000

2.86

2.17

Behavioral loyalty: purchase behavior in competitor retailersa Intergroups Intragroups

6.674 118.484

1 639

6.674 0.185

35.996 0.000

1.70

1.90

Behavioral loyalty: purchase behavior in the retailera

Note: aLevene test reveals equality of variances

392

F

Sig.

The role of loyalty programs in behavioral and affective loyalty

Journal of Consumer Marketing

Blanca Garcı´a Go´mez, Ana Gutie´rrez Arranz and Jesus Gutie´rrez Cilla´n

Volume 23 · Number 7 · 2006 · 387 –396

Table IV ANOVA analysis results for affective loyalty

General satisfaction

a

Trust in the staff Attitude Commitment Trust in the retailer

Addition of squares

df

Square average

F

Sig.

Average part.

33.599 198.814 11.086 517.914 43.267 485.733 41.805 487.195 15.788 513.212

1 490 1 528 1 528 1 528 1 528

33.599 0.406 11.086 0.981 43.267 0.920 41.805 0.923 15.788 0.972

82.808

0.000

4.06

11.302

0.001

0.146

2 0.142

47.032

0.000

0.290

2 0.281

45.306

0.000

0.285

2 0.276

16.243

0.000

0.175

2 0.170

Intergroups Intragroups Intergroups Intragroups Intergroups Intragroups Intergroups Intragroups Intergroups Intragroups

Average non part. 3.53

Note: aLevene test reveals equality of variances

Table V Descriptives for change in purchansing behavior (give up purchasing in competitors) To give up purchasing in competitor retailers Yes No Total

Loyalty card

Absolute frequency Frequency program

32 148 180

Total

Total relative frequency (%)

69 291 360

19.16 80.84 100

37 143 180

Table VI Descriptives for change in purchasing behavior (higher purchase frequency) Higher purchase frequency Completely disagree (1) 2 3 4 Completely agree (5) Total

Loyalty card

Absolute frequency Frequency program

67 30 50 17 14 178

69 28 44 25 14 180

Total

Total relative frequency (%)

136 58 94 42 28 358

37.99 16.20 26.25 11.73 7.83 100

A key matter related to loyalty programs is research into its contribution to customers’ loyalty towards a specific company. Throughout the paper, we have made a detailed analysis of loyalty of participants in one loyalty program of a supermarket chain. The results we have obtained are the following. First, participants in loyalty programs show a greater behavioral loyalty to the retailer that has implemented that loyalty program and, at the same time, less behavioral loyalty to competitors of that company, than non participants. These results can lead us to believe that loyalty programs contribute to an increase in behavioral loyalty of participants, but yet most of these people claim that their purchase behavior varied very little, if at all, since they joined up. With this data, the strategy can at least be attributed with the ability to retain the loyalest of customers. The results obtained are consistent with results achieved by other research referred to in the justification of the proposed hypotheses. Another result is related to affective loyalty. In this regard, participants in loyalty programs show higher levels of positive attitude, satisfaction, trust and commitment than non participants.

participation in the program. In this case, sample and population values are 80.84 percent and 50 percent, respectively. This test leads us to reject the hypothesis of behavioral change on most participants Z ¼ 11,70; p , 0.000). The second contrast focuses its analysis on the percentage of people recognizing that they do not shop more frequently at the retailer as a result of their participation in the loyalty program. In the sample, we consider inside the percentage the people scoring 3 or less in the variable, meaning 80.44 percent of those. Results show that there is a negligible possibility of accepting the null hypothesis, according which over 50 percent of participants in the loyalty program shop more often at the retailer after adopting the loyalty program (Z ¼ 11,52; p , 0.000). All these facts allow us to accept H7 – loyalty programs do not modify the behavior of the majority of consumers.

Conclusions The mass implementation of loyalty programs is a reality, strengthened over time. Our paper is focused on grocery retailing companies, one of the industries with the largest number of current loyalty programs around the world. 393

The role of loyalty programs in behavioral and affective loyalty

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Blanca Garcı´a Go´mez, Ana Gutie´rrez Arranz and Jesus Gutie´rrez Cilla´n

Volume 23 · Number 7 · 2006 · 387 –396

The analyses carried out do not allow us to establish the extent to which loyalty programs have led to a change in the magnitude of these variables. Nevertheless, we can affirm that among participants in loyalty programs we can detect the two factors required to refer to true loyalty: this group of consumers demonstrate favorable feelings towards the retailer and behave in accordance with these feelings. Therefore, loyalty programs attract most loyal retailer customers. Out of this group, non participants are those sporadic customers characterized by a lack of emotional bonds with the company. The size of each group varies from one company to another and the most suitable strategy for each group is different.

differences relating to its management and it is possible that this has an impact on the effect that it is able to obtain.

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Managerial implications Loyalty programs are not able to modify the behavior of consumers towards retailers running them. Their main role is retaining customers already showing loyalty to the company. The strategy is also useful as a means of reinforcing the emotional bonds that link the customer to the point of sale. Therefore, when a retailer is chosen by a consumer as the point of sale at which he will make most of his purchases; loyalty programs play a secondary role. Other services such as variety, prices, location or employees are more important, and the retailer must be focused on these in order to attract potential consumers and, after that, maintain a base of loyal customers. Once this goal is achieved, it is necessary to take into account that loyalty seen through a mere repetitive behavior is weak. True loyalty needs the existence of a feeling consistent with this behavior. True loyalty based on emotional bonds is really a source of competitive advantage and is very hard to copy. The implementation of a loyalty program with a proper reward plan is crucially important in retaining loyal customers and reinforcing emotional bonds with the retailer. Rewards are obtained by participants in the program as a prize for their purchase behavior and they contribute to strengthen the relationship with the retailer not just from a behavioral point of view, but also from an affective one. The defensive role of this strategy therefore becomes evident, supported by a significant part of literature. Since some programs, like shopping cards, supply plenty of data about customers, it is possible to adapt the rewards to the customer’s needs. These are very varied and can include, for example, offers on products, reductions on prices or a special bonus. In this process of strategy customization according to the needs of each group of consumers, some supermarket chains have begun to implement loyalty programs that differ from one used until that time (loyalty card or frequent shopper programs). These new schemes are customers’ clubs, a type of program only available for the retailer’s best customers. Members of customers’ clubs are subject to a special treatment in terms of the type of rewards they receive in comparison with other customers. (Even the card identifying them inside the program has a yellow color similar to gold that symbolizes the value that the customer has for the retailer). We do not wish to close this paper without suggesting a future field of research that is closely related to our objective and that has not yet been developed; the analysis of the influence of different types of loyalty programs (for example, loyalty card, frequent shopper programs or customers’ clubs) on behavioral and affective loyalty. Each of these tools shows significant 394

The role of loyalty programs in behavioral and affective loyalty

Journal of Consumer Marketing

Blanca Garcı´a Go´mez, Ana Gutie´rrez Arranz and Jesus Gutie´rrez Cilla´n

Volume 23 · Number 7 · 2006 · 387 –396

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Blanca Garcı´a Go´mez, Ana Gutie´rrez Arranz and Jesus Gutie´rrez Cilla´n

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Further reading Bagozzi, R.P. (1994), “Structural equations models in marketing research: basic principles”, in Bagozzi, R. (Ed.), Principles of Marketing Research, Blackwell Publishers, Oxford, pp. 317-85. Castan˜ eda, J.A. (2005), “La fidelidad en Internet”, in Gutie´rrez, A.M. and Sa´nchez, M.J. (Eds), Marketing en Internet: Estrategia y Empresa, Pira´mide, Madrid. Drawkins, J. and Reichheld, F.F. (1990), “Customer retention as a competitive weapon”, Directors and Boards, Vol. 14 No. 4, pp. 42-7. Jones, T.O. and Sasser, W.E. (1995), “Why satisfied customers defect”, Harvard Business Review, Vol. 73 No. 6, pp. 89-99. Meyer-Waarden, L. and Benavent, C. (2002), “Loyalty programs and their impact on repeat purchase behavior: a replication and extension on the Behaviorscan Panel”, available at: http//christophe.benavent.free.fr/article. php3?id_article=161 Uncles, M.D., Dowling, G.R. and Hammond, K. (2003), “Customer loyalty and customer loyalty programs”, Journal of Consumer Marketing, Vol. 20 No. 4, pp. 294-316. Wansink, B. and Deshpande, R. (1994), “Out of sight, out of mind: panty and brand-usage frequency”, Marketing Letters, Vol. 5 No. 1, pp. 91-100.

About the authors Blanca Garcı´a Go´mez is an Assistant Professor in the Department of Management and Marketing, Faculty of Business and Economics, University of Valladolid, Valladolid, Spain. She is the corresponding author and can be contacted at: [email protected] Ana Gutie´rrez Arranz is an Assistant Professor in the Department of Management and Marketing, Faculty of Business and Economics, University of Valladolid, Valladolid, Spain. Jesu´s Gutie´rrez Cilla´n is a Full Professor in the Department of Management and Marketing, Faculty of Business and Economics, University of Valladolid, Valladolid, Spain.

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Lasting customer loyalty: a total customer experience approach Oswald A. Mascarenhas, Ram Kesavan and Michael Bernacchi College of Business Administration, University of Detroit Mercy, Detroit, Michigan, USA Abstract Purpose – Understanding and delivering total customer experience (TCE) in order to sustain lasting customer loyalty (LCL) is increasingly important given the pressures of commoditization, globalization and market saturation in developed countries. The purpose of this paper is to review the concepts of TCE and LCL. Design/methodology/approach – The concepts of TCE and LCL are discussed and defined and their combined importance for marketers is outlined and few key cases of their best practices are analyzed in order to derive a set of managerial frameworks for strategizing TCE to achieve LCL. Customer loyalty as a hierarchical ladder starting from random casual awareness in the bottom rung to high bonding loyalty of brand communities in the topmost rung is derived Findings – TCE is captured in its three essential interactive elements: physical moments, emotional involvement moments, and its value chain moments. Accordingly, a typology of customer loyalties is proposed as a function of high vs low levels of the three constitutive elements of TCE. Practical implications – The loyalty ladder is a useful classification tool to monitor customer loyalty and dollar-effectiveness of customer loyalty programs. Each rung offers a managerial challenge to ascend to the next rung of loyalty. Originality/value – Linking TCE with LCL is unique and challenging. Adding the third dimension of value chain moments makes TCE more focused and loyalty-driven. The typology of TCE-based customer loyalty is new and offers a broad strategic canvas for marketers. The loyalty ladder with each rung buttressed by differentiated value, interactive relationship and TCE makes it credible, viable and a strategic destiny. TCE and LCL are also distinguished from related concepts in marketing to derive managerial implications. Keywords Customer loyalty, Customer relations, Customer satisfaction, Buyer-seller relationships, Value chain Paper type General review

Traditionally, marketing activities have focused on success in the product marketplace by examining the physical aspects of products and services such as quantity, quality, functionality, availability, accessibility, delivery, price and customer support. More recently, marketing managers have shifted their emphasis to creating value for their customers (e.g. Clutterbuck and Goldsmith, 1998; Fudenberg, 2000; McAlexander et al., 2002). The current trend in marketing is to create engaging and lasting experiences for the customers (Macmillan and McGrath, 1997; Carbone, 1998; Pine and Gilmore, 1998; Rowley, 1999; Wyner, 2000; Calhoun, 2001; Arussy, 2002; Berry et al., 2002; Gilmore and Pine, 2002; Lamperes, 2002). About 85 percent of senior business leaders interviewed in a recent study agreed that differentiating solely on the traditional physical elements such as price, delivery and lead times is no longer an effective business strategy (Shaw and Ivens, 2002). The new differentiator today is customer experience. The competitive battleground of differentiators is also changing. In the 1970s, the differentiator was quality or functionality; in the 1990s it has been brand and price; in the early 2000s, it is service, information and delivery (Shaw and Ivens, 2002, p. 2). All these attributes are considered as givens

today; that is, customers take them for granted and feel entitled for them. Currently, in the mid-2000s, it is customers’ emotional attachment with the brand, the brand community and the brand company via customer experience that is gaining importance in the literature (Anderson et al., 2006; Barber and Strack, 2005; Bendapudi and Bendapudi, 2005; McGrath and Macmillan, 2005; Mascarenhas et al., 2004; Narayandas, 2005; Selden and Macmillan, 2006). Delivering total customer experience (TCE) goes beyond mere customer satisfaction and is a relatively new concept since satisfied customers could still defect (Jones and Sasser, 1995). In the past, companies have primarily focused on the physical aspects of the product, while totally neglecting the emotional and value aspects and hence, losing many customers in the long run (Nunes and Cespedes, 2003). To compete successfully in this customer experience territory, a growing number of organizations are systematically applying the principles and tools of TCE to generate, strengthen and sustain enduring lasting customer loyalty. Marketers today believe that engineering TCE and lasting customer loyalty (LCL) are important for maintaining customer focus and creating customer preference. In this paper, we review the concepts of total customer experience (TCE) and lasting customer loyalty (LCL), discuss their related importance for marketers, analyze a few key cases of their best practices and as a result, derive a set of theoretical and managerial frameworks for strategizing TCE for LCL. We also distinguish TCE and LCL from related concepts of marketing to generate managerial implications for attracting and retaining customers.

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Journal of Consumer Marketing 23/7 (2006) 397– 405 q Emerald Group Publishing Limited [ISSN 0736-3761] [DOI 10.1108/07363760610712939]

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Lasting customer loyalty: a total customer experience approach

Journal of Consumer Marketing

Oswald A. Mascarenhas, Ram Kesavan and Michael Bernacchi

Volume 23 · Number 7 · 2006 · 397 –405

Importance of TCE and LCL

anxiety Avis installed monitors showing flight departure times and gates and placed them at the door of the car-return facility. Avis also established a new communication center to make calls, send faxes or just plug-in laptops. Within 18 months, Avis rolled out the experience design to other key locations. By 2001, Avis had moved from a bottom rank to rank number one in the Brand Keys customer-loyalty recognition survey of more than 140 national representative brands of 26 industries (Berry et al., 2002).

Brands are at the heart of marketing and business strategy. Brand loyalty is a fundamental concept of strategic marketing and is generally recognized as an intangible asset (Wernerfelt, 1991, p. 229). Successful brands create wealth by attracting and retaining customers as certain loyal customers may be willing to pay more for a brand. Brand loyalty discourages brand switching to competing brands (Dick and Basu, 1994). But at the same time, the increase of competing new products, the competitive prices of new brands, and attractive promotions of new brands can quickly erode customer loyalty (Schiffman and Kanuk, 1997, p. 224). Hence, it is all the more important to investigate the determinants of long-term customer loyalty. Engineering TCE and LCL is an important strategy for establishing and sustaining customer focus for an institution. It should, therefore, be in the capability portfolio of any firm investing in long-term customer relationships (Carbone and Haeckel, 1994). An organization should be successful only when TCE and LCL are the focus of its improvement (Calhoun, 2001).

Apple’s iMac computer The iMac is an all-in-one computer that requires little wiring or setup. The base model needs little upgrading, software comes preloaded and the process of selecting a model has more to do with picking a color than understanding processor speed. It reinforces the message with packaging and graphics and backs its products with excellent customer support. Television ads of iMac communicate how simple it is to do a task with an iMac. The purchasing experience enables the customer to undertake a comprehensive comparisonshopping by models, price and other features. The iMac attracted many first-time buyers, including the elderly, by providing positive TCE (Cuffaro et al., 2002).

Understanding total customer experience We review a few of the many cases from the literature to develop our definition of TCE.

Blyth Industries Blyth Industries, a candle manufacturer, differentiated and re-differentiated its products to suit the entire consumption chain and to create additional positive customer experiences. Blyth grew from a $2 million US producer of candles for religious purposes to a global candle and accessory business with nearly $500 million in sales and a market value of $1.2 billion. This is an excellent performance in an industry that has been declining over the last 300 years (Macmillan and McGrath, 1997).

Disney World As a pioneer in experience management, Disney is dedicated to the delivery of unique customer experiences. Disney theme parks with their hundreds of engineered cues are all coordinated and networked to generate that consummate mix of excitement, entertainment and adventure that ensure a TCE. Disney has a holistic approach to TCE – every adventure, every Disney character, every employee, every shop, and even the long waiting lines systematically manage positive sensory and emotional experience in a commercial setting that achieve a level of differentiation far beyond the commodity zone (Carbone, 1998). Experiences have always been at the heart of Disney’s entertainment business (Pine and Gilmore, 1998).

Analyzing total customer experience The five TCE cases outlined above have some features in common: . Anticipating and fulfilling customer needs and wants better than competitors. All five providers anticipated and understood the specific needs, wants and desires of their target customers and fulfilled them uniquely and way beyond the normal call of duty. Too many companies put all their marketing efforts on the selling side of the product life cycle, forgetting that long-term loyalty requires attention to customers’ needs throughout their experience with a product. Handling things when the product does not work out can be as powerful as meeting the need that motivated the initial purchase (Macmillan and McGrath, 1997, p. 140). . Providing real consumer experiences. All five firms provided customers with real experiences that competitors did not. The experiences they provided were not an amorphous construct but something as real as any service, good or commodity. . Providing real emotional experience. All five products/ services generated customer experience that was beyond physical attributes such as quality, quantity, delivery, price-product bundling, safety, security and privacy. They also triggered an emotional experience of meaning, value, entertainment, friendly and caring service, belongingness,

The American Girl Place Here, mothers and daughters (and occasionally, fathers and grandparents) spend the better part of a day, together. They can enjoy The American Girls Revue at the American Girls Theater, a 70-minute stage production; or they can go to The Cafe´ for a grown-up dining experience. Girls can pose for a photo shoot and take home a copy of American Girl Magazine with their picture on the cover. Others can have their American doll’s hair styled in the Hair Salon. The American Girl Place is no more the store paradigm. It is where experiences are grown, enjoyed and repeated (Gilmore and Pine, 2002). Avis Rental Car With a pronounced decline in its customer-ratings, Avis applied the TCE technique at one of its largest operations, Newark International Airport. After an experience audit, Avis developed an experience motif focused on relieving customer stress and anxiety, both of which are commonplace at airports. For instance, customers dropping off cars were worried about making their flights, so to reduce this flight 398

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Oswald A. Mascarenhas, Ram Kesavan and Michael Bernacchi

Volume 23 · Number 7 · 2006 · 397 –405

brand community, and memorable and engaging experience (Shaw and Ivens, 2002). Experiences as distinct market offerings. All cases offered experiences that were distinct economic offerings. For instance, although iMac’s design was critical to its success, it was just one component of its TCE. The overall strategy was simplicity. The message was that the product would enable users to do high-tech things in a simple way. Economists have typically lumped experiences with services, but consumer experiences are a distinct economic offering, as different from services as services are from goods (Arussy, 2002). Experiences as interactions. These experiences arose from the value-adding interactions of customer involvement and producer participation (Berry et al., 2002, Hoch, 2002). Disney theme parks thrive on customer interactions. Several interactive experiences occur at the American Girl Place. Avis Rental customers interact with various stress and anxiety reduction services offered in the rental premises. These strategies paid off handsomely. Experiences as engaging memories. These experiences engage the customers to create memories within them (Gilmore and Pine, 2002, p. 5; Hoch, 2002). An experience occurs when a company intentionally uses services as the stage and goods as props, to engage individual customers in a way that creates a memorable event (Pine and Gilmore, 1998, p. 98). Thus, Disney keeps customers engaged and excited for days on end; mother-daughters are often repeat buyers at the American Girl Place; Avis customers are lifetime loyalists; iMac buyers swear customer loyalty, and Blyth has candles to capture engaging memories for almost every customer event.

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The resulting TCE has an internal or subjective component (emotional, intellectual and social experience), and an external or objective component (distinct and real product offering, real experience potential, producer-customer interaction potential along all points of the production-consumption chain). While economic offerings such as commodities, goods and services are external and impersonal to the customer, involvement and experiences are inherently internal and personal. They exist only in the minds of customers who are engaged on an emotional, physical, intellectual or even spiritual level (Carbone, 1998). The customer cherishes such an enduring experience before, during and long after product use. Hence, the distinct market offerings that generate such experiences must have sustainable competitive advantages over most competing products (Gilmore and Pine, 2002). TCE as an emotional and subjective experience is uniquely personal and changeable with the customer, product or service. Even the same person may experience a different quality and level of TCE with the same product/service at a different time. The product is staged to provide engaging, memorable, and lived moments; that is, it is highly personalized. In this sense, the provider stimulates the experience while the customer must undergo it (Hoch, 2002). Some customers may also internalize and customize it.

Defining lasting customer loyalty In marketing, customer loyalty is often associated with a brand. Conceptually, a brand is a name, term, sign, symbol or design, or a combination of these, intended to identify and differentiate the goods or services of one seller from those of competitors. Operationally, a brand conveys its identity (name, fame) that embodies a specific set of unique features, benefits and services to the buyers. Currently, brand building is a major marketing cost and undertaking to attract customer loyalty. Brand loyalty gives sellers some protection from competition and greater control in planning marketing programs (Kotler, 2003). Brand loyalty is a “deeply held commitment to re-buy or re-patronize a preferred product/service consistently in the future, thereby, causing repetitive same brand set purchasing, despite situational influences and marketing efforts having the potential to cause switching behavior” (Oliver, 1999, p. 34). This definition helps us to distinguish loyalty as behavioral, attitudinal and situational (Chaudhuri and Holbrook, 2001; Uncles et al., 2003). Behavioral loyalty is mainly expressed in terms of revealed purchase and usage behavior, often conditioned on customer satisfaction, and is measured by historical purchasing of one’s brand and competing brands (this is divided loyalty or polygamous behavior). Attitudinal loyalty is often expressed as an ongoing relationship to a brand, often conditioned on positive customer preferences towards the brand, and is strongly influenced by significant others (this is strong loyalty or monogamous behavior). Lastly, situational loyalty is often expressed as a contingent relationship to the brand (e.g. I will buy it if it is available, or if it is on sale) that is often determined by the shopping and purchasing situation (weak loyalty or promiscuous behavior). All three types of loyalty are important, even though the first two are more critical for long term sales and market share. In

Defining total customer experience From these cases we could extract a workable definition of TCE: it is a totally positive, engaging, enduring, and socially fulfilling physical and emotional customer experience across all major levels of one’s consumption chain and one that is brought about by a distinct market offering that calls for active interaction between consumers and providers. This definition has several implications: . TCE is generated by two components: a distinct market offering that invites and thrives by high involvement between consumers and providers. TCE must have a right blend of both physical and emotional elements along all the stages of the customer experience and value chain, that is, all moments of customer contact with the producer. What defines TCE is the joint interactive participation of the provider and the customer. The higher the interaction and its quality, the higher is TCE and, consequently, the higher is LCL. . TCE is a powerful form of product/service augmentation. Graphically, if we can locate the core product in the innermost circle, and its service component in the circle that surrounds it, then experience belongs to the outermost circle and is dependent upon the inner two circles of product and service, but clearly transcends it. This is because experience is created by the active involvement and interaction between provider and the customer. 399

Lasting customer loyalty: a total customer experience approach

Journal of Consumer Marketing

Oswald A. Mascarenhas, Ram Kesavan and Michael Bernacchi

Volume 23 · Number 7 · 2006 · 397 –405

the face of tough competition, having brand loyal customers not only ensure sales, but also significantly reduce marketing costs (Datta, 2003). In this paper, we include both behavioral and attitudinal aspects of loyalty.

associated costs include monetary and non-monetary (time, effort, anxiety) inputs that are needed to maintain the loyalty relationship (Zeithaml, 1988). Values, in turn, are a function of one’s underlying goals that consumers expect to attain through loyalty relations. Some of these goals are superordinate or terminal (e.g. happiness, love, self-actualization) while others are instrumental values (e.g. product quality, security, privacy, immediate product/service gratification, satisfaction, best value for the dollar, and store convenience). Building customer value through market offerings is a consumer-value-centric competence that should be the driving obsession of an organization (Srivastava et al., 1999, p. 172). Customer value is the fundamental basis for all marketing activity.

Creating TCE to optimize LCL Table I captures the customer experience process as a blend of the physical, emotional and value aspects of the search, purchase, use and post-use stages. TCE spans across all moments of customer-seller contact. Table I implies that TCE occurs when the sellers and manufacturers create a product/ service system that consistently exceeds the physical, emotional and value expectations of its target customers. The traditional business strategy was primarily focused on delivering the physical elements (column 2 in Table I) to the customers. But given that the markets are becoming increasingly crowded and more competitive than before, this strategy is currently unsustainable. Saturated markets and tough competition have leveled the differences between brands to their physical elements. That is, most of today’s products and services have become commoditized. Imitation has become commonplace. Hence, the TCE strategy focuses on the blend of the physical elements and the emotional elements (columns 2 and 3 in Table I) in delivering customer experience. While this is promising, it may not be lasting because emotions are fleeting and vacillating. Hence, if TCE should build lasting customer loyalty (LCL), we need to add a third and necessary dimension to TCE – the value dimension. What business strategy should aim is a TCE that builds LCL by blending the physical, emotional and value elements (columns 2-4 in Table I) of the target customers. This is because consumer loyalty is a function of one’s perception of congruence in values with the product or service provider. The higher the congruence in values, the higher is customer loyalty. Value is the consumer’s perception of the benefits minus the costs of maintaining an ongoing relationship with a provider (Zeithaml, 1988). Relational benefits include the intrinsic and extrinsic utility provided by the ongoing relationship (Gwinner et al., 1998), while

A typology of TCE and LCL Following Table I, we submit that when marketers offer products and services that consistently have strong physical attributes-based satisfaction, provide high emotional experience, and high perceived value summing to a high TCE, they will automatically generate high and lasting customer loyalty (LCL). Conversely, when market offerings are low on physical experience, emotional involvement and customer perceived values, they fail to generate LCL. Between these extreme positions there may be other contingent circumstances that will generate partial TCE and, therefore, partial LCL. This dynamic categorization is captured in Table II. Incidentally, Table II depicts a typology of TCE and LCL, and offers marketers several strategy options when generating varied levels of TCE and LCL.

Managerial implications Given Tables I and II, Figure 1 suggest a multidimensional loyalty ladder as a function of major TCE variables: value differentiation, provider-interaction, and engaging experiences. These three TCE variables interact both horizontally (by rows) and vertically (by columns) to impact each rung of the loyalty ladder bottom-upwards. Loyalty ladders feature in the marketing literature. For instance, Raphael and Raphael (1995) proposed a five-rung

Table I Capturing the customer experience process Experience stage

Physical moments

Emotional involvement moments

The value chain moments

Searching

Print media search Audio-visual media The Website Search The place- in store Availability The product The brand The solution Delivery Brand community Maintenance Support service Complaints Referral Replacement Repeat buy

What do I dream? Seeking information via ads Viewing radio, TV, Internet Seeking advice and direction Salesperson interaction/accessibility Touching, feeling, seeing, believing Color, shape, texture, material Perceived problem solution Excitement, surprise, curiosity Personal satisfaction-delight Visibility, prestige, status Brand community belongingness Satisfaction-dissatisfaction Displeasure, anger, rage Positive or negative referrals Commitment, lifetime loyalty

The right The right The right The right The right The right The right The right The right The right The right The right The right The right The right The right

Finding

Using

Post-usage

400

motivation product advice shop/location price package solution financing use-experience social visibility community warranty feedback complaint re-purchase lifetime brand

Lasting customer loyalty: a total customer experience approach

Journal of Consumer Marketing

Oswald A. Mascarenhas, Ram Kesavan and Michael Bernacchi

Volume 23 · Number 7 · 2006 · 397 –405

Table II A typology of customer experiences and loyalties Product’s physical experience High

Product’s emotional experience

Product’s value experience

High

High

Low

Low

High

Low

Low

High

High

Low

Low

High

Low

Combined characterization

Customer loyalty

High performance zone Consumer actualization Customer delight Market challenge zone Customer high expectations Customer ambivalence Functionality zone Customer rationality Benefits quantification Commoditization zone Consumer conformance Consumer alliance Personalization zone Consumer passion Country of origin Mass customization zone Mass emotions appeal Consumer Indifference

Platinum loyalty Lifetime loyalty Family loyalty Plastic loyalty Precarious loyalty Unpredictable loyalty Performance-based loyalty Reasoned loyalty Objectified loyalty Standardized loyalty Compliance loyalty Acceptance loyalty Subjective loyalty Fleeting loyalty Ethnocentric loyalty Multi-brand loyalty Emotional loyalty Polygamous loyalty Trend-based loyalty Poverty-based loyalty Needs-based loyalty Indigent loyalty Platinum disloyalty Lifetime disloyalty Global disloyalty

Low customer expectations Low self-esteem Low-buying power Disaster zone Customer disgust Customer agony

highly standardized products and marketing messages. For instance, Disney’s adventures are standardized; The American Girl Place has standardized product assortments and marketing promotions; Avis has neither customized cars nor marketing messages; iMac is not a customized product, and Blyth has candles for every occasion but not customized products. What generates great TCE in all the five cases is the unique customer-provider interaction, it is an engaging experience that the product-service stimulates. Customization emphasizes the role of the provider, but not so much of the customer. TCE emphasizes total customer focus. TCE enables the customers themselves to customize the product-service and marketing promotions. Marketers cannot sell packaged or canned experiences across all target customers. Marketers must dovetail TCE with specific target customer needs and times (Macmillan and McGrath, 1997). The test of the quality and reliability of TCE is its capacity to leverage re-buy, exchange positive word-of-mouth (referrals), and generate LCL.

ladder of prospects (people who may be interested in your products), shoppers (people who visit your business), customers (people who purchase one or more of your products or services), clients (people who regularly patronize your business) and advocates (people who give positive referrals to your products). Banks and Daus (2002, p. 107) suggest another rung after advocates: evangelizers. Loyal customers evangelize, often trying to convert friends who use competitors’ products. Narayandas (2005, p. 136) associates a certain hierarchy of customer behaviors with the loyalty ladder. For instance, loyalty may be initiated by some random first-time brand purchases and reinforced by repeated purchases if net benefits remain consistently positive. If repeat purchases are also followed by frequent purchases and volume purchases, they may indicate higher levels of loyalty that prompts lifetime endorsements. Figure 1 reflects loyalty ladder hierarchy as suggested by Narayandas (2005). Real lasting loyalty means faithfulness, an unswerving devotion, despite doing so may run counter to your own interests (Nunes and Dreze, 2006). Lasting customer loyals should overlook company’s faults of commission or omissions. For example, the quality of Chevrolet cars may not compare well with competing Toyota cars (see Consumer Reports), but some Chevrolet loyals may still prefer to patronize the brand.

Understanding the TCE-LCL dynamic Figure 1 implies several new concepts and managerial implications: 1 Loyalty is not a one-step process, but a long ascending process consisting of many sequential steps. 2 Each rung of the ladder is partial or quasi loyalty (e.g., repetitive behavior, brand interest). 3 Loyalty can move upwards or downwards, depending upon how the customer experiences the impact of the TCE variables at a given point in time.

TCE and customization TCE transcends standardization, mass customization and personalization, as may be deduced from Table II. Regardless of best customizations, some customers may capture TCE in 401

Lasting customer loyalty: a total customer experience approach

Journal of Consumer Marketing

Oswald A. Mascarenhas, Ram Kesavan and Michael Bernacchi

Volume 23 · Number 7 · 2006 · 397 –405

Figure 1 The ladder of customer loyalty as a function of total customer experience

4

Loyalty, therefore, is an interactive and interdependent process, a buyer-seller relational process generating relational equity. Higher in the ladder, the stronger is customer loyalty. Conversely, lower in the ladder, more vulnerable is customer loyalty. Loyalty is an accumulative process, a step-by-step function. Given the volatility of consumer preferences and lifestyles, an ascending loyalty is a slower process than a descending one.

should have an emotional appeal, a value statement, and a personal identity. Customers should own their brand. They should proudly and fondly talk about it at home, in the workplace, at the bar and at various sports stadia and arena. These are great positive referrals for your product. Any product or service triggers a series of physical, emotional and value contact points that marketers should be aware of. Table I indicates most of these customer contact points. That is, real TCE should build a brand community of lasting loyal customers (McAlexander et al., 2002).

TCE and LCL result when organizations build themselves around what is good for the customers, and change their organizational structures, systems and processes to build great customer experiences – this is an outside in approach, while the conventional approach has been an inside out strategy that defines companies by what is good for themselves, rather than what is good for the customer (Shaw and Ivens, 2002, pp. 8-9). The reason for this new approach is that the customer is progressively becoming an emotional equity holder in the brand. There is, of course, no substitute for offering a product with best value to the customer dollar. In short, your product

Lasting customer loyalty as an intangible asset Strategy scholars define an asset broadly as any physical, organizational, or human attribute that enables the firm to generate and implement strategies that improve its efficiency and effectiveness in the marketplace (e.g. Barney, 2001). Assets can be tangible or intangible, on or off-the-balance sheet, and internal or external to the firm. Regardless of the type of asset, however, the value of any asset ultimately is realized, directly or indirectly, in the external market place (Srivastava et al., 1999). Developing total customer experiences that are engaging and lasting are intangible

5 6 7 8

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Oswald A. Mascarenhas, Ram Kesavan and Michael Bernacchi

Volume 23 · Number 7 · 2006 · 397 –405

assets that add immeasurably high brand equity and are extraordinarily valuable in terms of customer loyalty, referrals and the lifetime brand value they create. The locus and focus of creating a competitive advantage has moved from physical assets to intangible service assets to managing long-term buyer-seller interfaces (Srivastava et al., 1999). It is no longer even ownership of capabilities that matters but rather a company’s ability to control and make the most of critical capabilities (Gottfredson et al., 2005, p. 134). This is what makes modern successful TCE enterprises, such as Build-a-Bear, Harley Davidson, Hard Rock Cafe´, Panera Bread, and Starbucks Coffee, surpass their rivals.

experiences a firm’s top investment priority (Crosby and Johnson, 2002). TCE and LCL must optimize value along the total consumption chain TCE is realized when the manufacturer-seller optimizes customers’ perceived values via differentiation at every stage of the consumption-chain. A total consumption-chain is all the points the customer encounters with the product and its environment (see Table I). If companies react to the customers’ entire consumption-chain experience they can uncover opportunities to position their offering in ways that they and their competitors would never have considered as possible (Macmillan and McGrath, 1997). Customers are not pure rational decision makers; they combine mind and heart, reason and emotion (Wind and Mahajan, 2002). Opportunities for differentiation in relation to the consumer search process include making your product available when competitors do not. Examples include: 24-hour print shops or drug stores; offering your product in places competitors do not offer (e.g. the McDonald outlets in Wal-Mart stores, Krispy Kreme donut booths in gas stations, cellular phone shops in remote villages). Other examples include making your product ubiquitous (e.g. Coca-Cola) and your service available all the time (e.g. GE has an enormously popular 800 number that is available 24 hours a day to help people with problems regarding all their products). These strategies make the search process less complicated, less expensive, more convenient and more habitual – something that renders customer-shopping experience total and customer loyalty lasting (Wyner, 2000). TCE can also engage the customer at the production-stage (e.g. Lands End). For instance, the customer could be an active co-conceiver, co-designer, co-producer, co-packager, co-pricer, co-promoter and co-distributor of the product (Lengnick-Hall, 1996). The same strategy underlies customerization (Wind and Mahajan, 2002). The more the producer engages the prospective customer, the higher is the potential for TCE and customer loyalty. Obviously, by definition, TCE is customer-dependent and hence, is different for each customer. Compared with service outlets such as restaurants, hotels or banks, there is potential for great diversity in customer experience, because the customer may seek a wide variety of different services or products. Each stage of this consumption-journey involves an experience that the provider must try to optimize and the customer must capitalize. If and when one’s product or service does not match the competitor’s in quality (which is often the case:, e.g. Ford vs Toyota), then all is not lost; one can still maximize the TCE with non-product related emotional and value attributes, benefits and services, such that TCE leads to LCL. This could be a blue ocean strategy that avoids head on competition when it cannot successfully fight it (Kim and Mauborgne, 2004).

TCE and LCL need more than customer orientation The centrality of customer orientation is the backbone of the new marketing concept proposed by Webster (1994). The true mission of the firm is to create value for three key constituencies: customers, employees, and investors. But, according to the marketing concept one must treat customers as “first among equals” because their loyalty is the most fluid (Reichheld, 1994). Customer orientation means organizational commitment to customers such that customers and firms share interdependencies, values, and strategies over the long term. TCE and LCL are outcomes of customer orientation but go beyond it. Customer orientation as a strategy is a necessary but not a sufficient condition for TCE and LCL. Customer orientation does not necessarily imply high and interactive producer-involvement along all the production-consumption stages as TCE and LCL do (see Figure 1). Minimally, TCE requires that the provider focuses on the customer along all the rungs of the loyalty ladder depicted in Figure 1. Everyone in the firm must be charged with responsibility for understanding customers and contributing to developing and delivering value for them. To do this, firms must foster direct customer contact, collect information from customers about their needs and then use customer-supplied information to design and deliver products, services and positive branding experiences. TCE and LCL demand more than customer relationship management Customer relationship management (CRM) refers to the relationship the customer has with the firm, stores or with individual sales associates. But in the final analysis, customers build bonds of trust and expectations with company employees in general, and sales associates in particular but not directly with the firm (Reichheld, 1994). TCE and LCL should be enterprise-wide strategies like CRM. TCE and LCL require strong CRM, but they transcend CRM. Minimally, CRM requires that there is a low to medium provider involvement in the backend of production points, but a high involvement and interaction with customers at the front end of all marketing and consumption points. Achieving TCE and LCL implies an integrated business strategy that goes well beyond single-point solutions in areas such as branding or customer service. The TCE-LCL strategy should consider all the elements that go into a relationship and how they fit together. To do this, the organization must manage complex relationships holistically and over an extended period. This strategy calls for a top-down commitment to make deep and enduring customer relationships and

Concluding remarks Consumers unquestionably desire experiences, and more and more businesses are responding by explicitly designing and promoting them. From now on, leading-edge companies, whether they sell to consumers or businesses, will find that the next competitive battleground lies in delivering lasting 403

Lasting customer loyalty: a total customer experience approach

Journal of Consumer Marketing

Oswald A. Mascarenhas, Ram Kesavan and Michael Bernacchi

Volume 23 · Number 7 · 2006 · 397 –405

experiences (Pine and Gilmore, 1998). To realize the full benefit of delivering such experiences, businesses must deliberately design engaging experiences in what they produce, design and offer. Companies manage and compete best when they combine functional and emotional benefits in their offerings. Emotional bonds between companies and customers are difficult for competitors to imitate or sever.

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Journal of Consumer Marketing

Oswald A. Mascarenhas, Ram Kesavan and Michael Bernacchi

Volume 23 · Number 7 · 2006 · 397 –405

About the authors

Srivastava, R.K., Shervani, T.A. and Fahey, L. (1999), “Marketing, business processes, and shareholder value: an organizationally embedded view of marketing activities and the discipline of marketing”, Journal of Marketing, Vol. 63 No. 2, pp. 168-79. Uncles, M.D., Dowling, G.R. and Hammond, K. (2003), “Customer loyalty and customer loyalty programs”, Journal of Consumer Marketing, Vol. 20 No. 4, pp. 294-316. Webster, F.E. Jr (1994), “Defining the new marketing concept”, Marketing Management, Vol. 2 No. 4, pp. 22-31. Wernerfelt, B. (1991), “Brand loyalty and market equilibrium”, Marketing Science, Vol. 10 No. 3, pp. 229-34. Wind, J. and Mahajan, V. (2002), Convergence Marketing: Strategies for Reaching the New Hybrid Consumer, Prentice-Hall, Upper Saddle River, NJ. Wyner, G. (2000), “The customer’s burden”, Marketing Management, Vol. 9 No. 1, pp. 6-7. Zeithaml, V.A. (1988), “Consumer perceptions of price, quality and value: a means-end model and synthesis of evidence”, Journal of Marketing, Vol. 52 No. 3, pp. 2-22.

Oswald Mascarenhas is the Charles H. Kellstadt Professor of Marketing in the College of Business Administration, University of Detroit Mercy, Detroit, Michigan, USA, where he teaches and researches marketing ethics, new product development, internet marketing, and business turnaround management. He has published in the Journal of Marketing, Journal of the Academy of Marketing Science, Journal of Consumer Affairs, Journal of Consumer Marketing, Journal of Nonprofit and Public Sector Marketing, and other outlets. Ram Kesavan is Professor of Marketing in the College of Business Administration, University of Detroit Mercy, Detroit, Michigan, USA and specializes in marketing strategy, global marketing, and small business entrepreneurship. His research outlets include the Journal of Consumer Research, Journal of the Academy of Marketing Science, Journal of Consumer of Marketing, and Journal of Nonprofit and Public Sector Marketing. Ram Kesavan is the corresponding author and can be contacted at: [email protected] Michael Bernacchi is Professor of Marketing in the College of Business Administration, University of Detroit Mercy, Detroit, Michigan, USA and specializes in marketing law, marketing communications, social marketing, sports marketing, and corporate social responsibility. His research outlets include Journal of Advertising, Journal of Consumer of Marketing, Journal of Consumer Affairs, and Journal of Nonprofit and Public Sector Marketing.

Further reading Ganesan, S. (1994), “Determinants of long-term orientation in buyer-seller relationships”, Journal of Marketing, Vol. 58 No. 2, pp. 1-19. Kalwani, M.U. and Narayandas, N. (1995), “Long-term manufacturer-supplier relationships: do they pay off for supplier firms?”, Journal of Marketing, Vol. 59 No. 1, pp. 1-16. Rosati, M. and Alban, O.A. (2002), “Measuring the reality of the customer experience”, Customer Inter@Ction Solutions, Vol. 20 No. 11, pp. 38-43.

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Segmenting customer-brand relations: beyond the personal relationship metaphor John Story Idaho State University, Pocatello, Idaho, USA, and

Jeff Hess TNS, Inc., Coto De Caza, California, USA Abstract Purpose – The purpose of this paper is to propose and test segmentation of multi-dimensional customer-brand relationships as a superior method of defining, understanding, and predicting customer loyalty behaviors. Design/methodology/approach – A method of segmenting customer-brand relationships is proposed, based on the development of personal and functions connections. The resulting groups are hypothesized to better define and predict customer loyalty behaviors. The model is tested with an empirical sample. Findings – Customers can be effectively segmented into relationship groups, based on the extent to which they have personal and functional connections with the brand. These relationship groups display different levels of commitment to the brand and engage in significantly different levels of loyalty behaviors. The resulting segments serve to define and measure levels of customer loyalty. Research limitations/implications – The primary limitation of this research is that behaviors were self-reported. However, the impact was limited by the fact that the initial survey was conducted six months before the behavior questionnaire. Practical implications – These results have extensive implications for developing customer-brand relationships that promote, enhance, and expand loyalty behaviors. Originality/value – Measures of loyalty based on behavior in the market or customer satisfaction have proven ineffective at defining, measuring, and predicting loyalty behaviors. Relationship segmentation not only better defines loyalty, but also provides insight into loyalty development, based on personal and functional connections. Keywords Customer loyalty, Customer satisfaction, Brand management, Brand awareness Paper type Research paper

research stream, along with loyalty and satisfaction, rather than as an organizing framework. In fact much of the research in customer relationship management (CRM) has focused on data mining rather than developing personal or emotional connections (Parvatiyar and Sheth, 2001) which impact behavior by transforming functional connections into real relationships. Now that we have adopted and adapted relationships to enhance marketing strategy, the time has come to expand their application. In this paper we propose a series of applications that employ relationship segments and test a subset of these.

Introduction Marketers embraced the relationship metaphor to explain the realms of customer behavior that lie beyond the bounds of simple loyalty (Hess, 1995; Fournier, 1998). Relationships have now become the dominant paradigm in marketing strategy, eclipsing transactional perspectives (Gronroos, 1997; Gummeson, 2002). Meanwhile, the basic relationship metaphor has been expanded to include multiple dimensions (Hess and Story, 2005) and encompass diverse classes of relationships. The impact of relationships extends beyond choice decisions to encompass additional outcomes of loyalty, such as testimonials, price insensitivity, willingness to expend additional effort, and advocacy. Yet, while we have embraced relationships as the ultimate manifestation of customer-brand connections, customerbrand relationship concepts, beyond euphemisms for CRM or traditional loyalty ideas, have seen little direct application to marketing strategy. The application of relationships has been much more descriptive, rather than prescriptive. In many ways, relationships have been introduced as a parallel

Conceptual background Personal relationships were introduced as a metaphor for the interactions and bonds that form between customers and brands in order to better explain consumer behavior. Loyal behaviors in the market, such as repurchase, share of purchase, and positive testimonials have long been recognized as desirable, yet until recently, there has been little consensus on defining, measuring, or promoting these behaviors. A relationship framework also allows us to explain other behaviors that have an apparently less direct, but equally powerful impact on profitability such as price sensitivity and willingness to forgive failure. Satisfaction is a poor predictor of loyal behaviors (Oliver, 1999) and fails to capture the full breadth and depth of consumers’ brand experiences. Not only is satisfaction not synonymous with loyalty, current loyalty is not even a reliable

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predictor of future loyalty. The literature is replete with examples of satisfied customers switching brands and seemingly loyal customers defecting (Oliver, 1999; Reichheld, 2003). These occurrences are inherent in a system where loyal customers are virtually always satisfied, but not the converse. Although it has long been recognized that satisfaction alone is not a reliable determinant of consumer loyalty, it was not until recently that this gap began to be filled and the reasons for the impotence of satisfaction have been elaborated. Relationships between customers and brands perform much better as predictors of loyal behaviors over time than satisfaction alone. There have been many approaches to defining and measuring customer loyalty, with varying levels of overlap across studies. For example: . the loyalty index approach that includes satisfaction and intention to behavior along with other randomly collected “loyalty” indicators such as recommend; . the pseudo-attitude approach that asks your intention; and . the actual behavior approach that relies on reported behavior or, more rarely, tracks behavior. Relationships provide a richer definition of loyalty than conventional, behavior-based models. Much, if not all, previous work on loyalty can be categorized into one of three approaches to loyalty based on the methods of definition and measurement. Loyalty behaviors easily fall into two groups – purchase (primary) behaviors and non-purchase (secondary) behaviors. Primary loyalty behaviors such as frequency, volume, share, and retention, are relatively easily measured and translate directly into revenues and profits, but are not reliable predictors of future behavior. Secondary loyalty behaviors, such as referrals, endorsements, advocacy, and selective exposure to alternative brands, are somewhat more difficult to measure and their impact on revenues and profits is less direct, even if greater in magnitude. A third approach to defining loyalty is even less direct and potentially more difficult to measure – attitudinal measures. Oliver (1999) proposed adapting a tripartite loyalty model that incorporates beliefs, feelings, and intentions toward the brand to drive actions. While advancing our understanding of the depth and breadth of loyalty, this model still leaves us measuring loyalty based primarily on purchase behaviors or intentions. In addition, given the mercurial preferences of satisfied customers, there is little direct guidance on how firms can effectively develop and nurture loyalty among customers. We know what marketers want from their customers. They want primary loyalty behaviors (share, frequency, retention, etc.) and secondary loyalty behaviors (advocacy, referrals, selective exposure, response to market influences, etc.). However, in order to really understand these behaviors and craft strategies that initiate and support these behaviors we have to develop full definitions and measures of all component constructs.

customers who behave differently is trust in the brand. In the Trust-Based Commitment Model, satisfaction primarily leads to functional connections between customers and brands, but it also contributes to trust. If brands behave appropriately, trust builds into personal connections (see Figure 1 for process model). The combination of functional and personal connections results in committed relationships. Hence, customers in committed relationships with a brand are a subset of satisfied customers. While merely satisfied customers may be relatively likely to change purchase patterns or even brand affiliation, those satisfied customers who are also in a committed relationship with the brand are much more likely to continue to exhibit loyal behaviors. Though many, perhaps even most, satisfied customers may exhibit loyal behaviors in the checkout lane, committed customers have formed a deeper relationship. There are two main differences between committed customers and customers who are simply exhibiting loyal behaviors. The first is the motivation, the underlying rationale for the behaviors. Committed customers not only exhibit loyal behaviors, they are also emotionally invested in a continuing relationship. There is a strong personal connection between the customer and the brand. Conversely, merely satisfied customers may exhibit loyal behaviors, such as repurchase, share of purchase, or exclusive purchase, merely because of convenience, lack of alternatives, or inertia (Schulz, 2005). Satisfaction removes the motivation to seek out other solutions, but does not act as a barrier to brand switching behaviors as commitment does. Though satisfied, customers who lack multidimensional relationships with brands may engage in variety seeking, or may easily be lured away by competing brands. Satisfaction may merely indicate that needs or requirements are fulfilled, not that these needs or requirements must be fulfilled by the target brand. While satisfaction is required for true commitment, satisfaction alone cannot drive commitment, as there may be many brands capable of delivering similar utility. Commitment requires satisfaction, but does not result unless trust is also present. Trust-based commitment supports at least two dimensions of loyal behaviors. There is behavioral loyalty – commonly measured in the marketplace. Yet, beyond behavior, there is attitudinal loyalty – comprising beliefs, feelings, and intentions toward a brand (e.g. Oliver, 1999). While satisfaction may be sufficient for behavioral loyalty, those customers who are “merely satisfied” are fair game for the competition. However, when loyalty goes beyond observable behaviors to include trust-endowed personal connections, substitutability declines and committed relationships develop. Perhaps the most significant contribution of extending the concept of loyalty to committed relationships is the ability to explain why, within a group of customers who exhibit consistently loyal behaviors, there may be a significant number who will easily switch brands. Where satisfaction fails to predict continuing loyalty, commitment may succeed.

Loyalty and satisfaction, the broken link The fact that some satisfied customers remain behaviorally loyal while others defect provides strong evidence of variance within groups of satisfied customers. The question is, whether satisfaction and loyalty are unrelated or whether other factors moderate the relationship. Hess and Story (2005) proposed that the significant differentiator between groups of satisfied

Relationships as explication of behaviors Neither behavioral loyalty nor satisfaction adequately explain or predict customer behaviors. Loyal customers often defect (Schultz and Bailey, 2000) and satisfied customers are often disloyal to begin with. One logical question is whether this is because we have poorly defined loyalty or whether our measures of loyal customers are flawed. 407

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Figure 1 Commitment process model

In the first case, poorly defined loyalty, we find that most loyalty research focuses on behaviors in the market – repeat purchase, share (of stomach, wallet, or other receptacle), or exclusivity. If we consider a more holistic definition of loyalty, that encompasses emotional attachments, we find that those customers who have strong affective connections with a brand often display loyal market behaviors. (Even historical attitudinal loyalty is often merely propensity to behave, and a weak one at that.) However, should we examine groups of customers defined as loyal simply by their purchase behavior, we should almost certainly find a large number of consumers who purchase repeatedly and/or exclusively based on contextual constraints such as convenience, availability, or lack of alternatives (Reichheld, 2003) yet do not reflexively have personal attachments to the subject brands. Based on the evidence, it seems clear that marketers’ definitions of loyalty may do well in encompassing behaviors that result in outstanding current financial performance, but perform poorly in predicting future behaviors. When we question whether the loyalty definition is flawed or whether the measures of loyal customers are inaccurate, the answer is inherently confounded, since the measures derive from the definition. Defining loyalty as measurable behaviors leads to measures limited to purchase intentions, actions, or history. Relationship commitment, as a measure of multidimensional loyalty avoids the natural confound between loyalty and satisfaction, since trust is the primary differentiator. Committed relationships encompass a broad range of loyal behaviors, which result from satisfaction and commitment, along with personal connections that go beyond satisfaction. The difference between measuring customer loyalty strictly by behaviors, which may result from chance or circumstance and measuring loyalty based on commitment to a brand relationship may differentiate between a short-term phenomenon and a durable brand franchise.

loyalty, by incorporating behaviors that extend beyond the purchase environment supported by multiple dimensions. Inherent in a multidimensional relationship model, as a function of the different dimensions, are groupings of customers who have a broad range of relationships and exhibit a variety of loyalty behaviors. Valid measures of these relationship dimensions result in groups of customers who share, not only behaviors, but also propensities for continued behavior and responses to new stimuli. These customer groups better define loyalty and predict loyal behaviors because they reflect measures of functional, affective, and relational components of customer-brand interaction. Those customers who score high on functional connections may behave similarly to those who score high on personal connections, as long as no external stimuli intervene. However, when faced with new brands, product/service failure, or other intervening stimuli, they may respond quite differently.

Application: segmentation and behaviors Though there may be many ways to segment customers based on relationships, including relationship depth, scope (number of products or services consumed), or exclusivity, the Trust-Based Commitment model provides a multidimensional approach. Segmenting customers based on the relative strengths of personal and functional connections with the brand increases both the information content of segment membership and the probability that members of different segments will behave differently in the marketplace. The Trust-Based Commitment model measures two dimensions, functional and personal. The functional dimension focuses on satisfaction and the basic utility of consumption. Functional connections form when needs are satisfied and products or services perform as expected. Personal connections, on the other hand, result from beliefs and feelings that go beyond basic product and service functions. When customers believe that the brand has their best interests at heart, that the brand will go above and beyond the call of duty, personal connections may begin to develop. In addition to brand activities, customers may enhance personal connections by incorporating brands into their self-concept and deriving pleasure from relational experiences. Each of these dimensions could be scaled into multiple levels, but for the purpose of this research we have limited each dimension to two levels, resulting in four segments (Figure 2). The first segment, those consumers who rate low on both functional and relational connections, are in a transactional relationship, at best. They do not perceive the brand as

Definition, measurement, and nurturing Marketing research on customer loyalty should allow us to effectively define, accurately measure, and ultimately influence the relationships between customers and brands. One potential problem encountered in discussions of loyalty is the breadth of the construct. Loyalty can be used to mean anything from repeat purchase behavior to emotional commitment to a brand. As previously stated, simple behavioral measures have limited value since behaviorally loyal customers often defect. The solution to providing an effective definition is not to constrain loyalty to only certain meanings, but to provide an expanded model that encompasses multiple dimensions of customer loyalty. The relationship metaphor can provide this expanded definition of 408

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Figure 2 Relational groups

Finally, the customers represented by the upper-right quadrant, committed customers, have both functional and personal connections with the brand. These customers are both behaviorally and attitudinally loyal to the brand. Their behavior is influenced, and perhaps constrained, by both functional satisfaction and relational components of consumption. Though many customers in functional and personal relationships with the brand may be deeply satisfied and exhibit behavioral loyalty, those in committed relationships are the customers who will continue their loyal behaviors for long periods of time. It is the combination of functional and personal connections that provides the continuing impetus for loyal behaviors. The differences between these segments provide an explanation for loyal customers who defect and satisfied customers who are disloyal. While the framework was originally proposed to expand our understanding of customer-brand relationships, segmenting customers based on relationship groups derived from the Trust-Based Commitment model may provide better measurement, understanding, and prediction of both behavioral and attitudinal loyalty.

providing significant value, relative to other brands, and do not experience personal connections with the brand. Demand among this group will be highly price elastic, its members may engage in frequent brand switching and, on the whole, they will exhibit neither behavioral nor attitudinal loyalty. Satisfaction levels within this group vary, but none are significantly more satisfied with the target brand than with others. Or else, satisfaction is not a significant driver of their choice in this product/service category. Any perceived loyal behaviors result from inertia or external constraints, rather than any relationship component. Those customers in a functional relationship with the brand have a true relationship, but it is primarily based on satisfaction. Value, convenience, performance, and other functional attributes of brand encounters drive the behaviors of this group. Though less price sensitive than those in transactional relationships, customers in functional relationships may switch brands if the value equation is altered by price competition. The value of the relationship is strictly a function of the price-utility tradeoff that is made to consume the brand. Members of this group are behaviorally loyal, but may make up a majority of those loyal customers who often defect. Those customers represented by the lower right-hand quadrant, the personal relationships, are motivated by attitudinal factors related to personal connections. Beliefs about brand motives, the role of the brand in self-definition, the importance of relationships may all be components of personal connections. These customers may also exhibit loyal behaviors, but these behaviors result from attitudinal loyalty rather than functional outcomes. Though personally connected with the brand, the basic utility of consumption is no greater, if not less than, other brands in the market. These customers may be relatively price insensitive, but do not have strong functional ties to the brand. While not necessarily prone to switch, these customers are at risk to brands that offer significantly more value. Customers with only personal connections may appear loyal, yet switch if offered higher functionality by a competitor in the market.

Market behaviors One problem inherent in defining or measuring loyalty based on marketplace behavior is that there is a broad range of behaviors associated with the construct. Purchase, repurchase, purchase frequency, and share of purchase may all be used to indicate loyalty. Recommending, advocacy, resistance to competitive advertising, willingness to expend additional effort may all result from a loyalty orientation. Narrowing the behaviors used to define and measure loyalty may artificially constrain the construct, yet not result in more viable measurement. What we are seeking is a method that will effectively measure an underlying construct that will provide a means to predict and promote loyal attitudes and behaviors. Moving beyond measures of loyalty and satisfaction, segmentation by the nature and strength of relationships with the brand may predict a broad range of market behaviors. Both primary (purchase, share, etc.) and secondary (recommending, advocacy, etc.) loyalty behaviors are expected to vary with relationship category. Repurchase, share of purchase, visit frequency, price insensitivity, recency of purchase, and advocacy are all expected to correlate with relationship type and strength. However, unlike with simple behavioral measures, relationship segmentation predicts these behaviors and provides an underlying rationale. Even more important, relationship segmentation discriminates between behaviors based on convenience, inertia, or external constraints and behaviors resulting from commitment. Committed customers have more at stake and are more likely to continue loyal behaviors.

Empirical tests of relationship segmentation We propose that segmentation based on relationship type and strength provides a measure of what really matters to firms that is superior to simply recording behaviors. In order to test the efficacy of segmenting customers based on their relationships with brands, consumers were queried concerning a broad cross-section of brands and their behaviors toward the brands were measured. 409

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Selected behaviors represent both primary and secondary loyalty behaviors. Primary behaviors include willingness to pay more for the brand, dollars spent on the brand in the last seven days, self-reported share of purchases, and number of visits within the last seven days. Secondary loyalty behaviors included likelihood of recommending the brand, willingness to go out of your way to purchase from the brand, and willingness to purchase from the brand on the web.

H2.

While predicting the behaviors of committed and disconnected customers is relatively straightforward, predicting the behaviors of customers in personal and functional relationships is somewhat more problematic. Customers in functional relationships resemble many of the customers previously identified as loyal, based on marketplace behaviors. Their relationship is based on the utility of the product or service, and this may result in shared loyalties or switching. Customers in personal relationships, on the other hand, behave as a result of personal connections with the brand. In the short term, customers in either of these groups may prove profitable for the firm. Customers perceiving high levels of functionality may even appear more loyal, at the cash register, than those in strictly personal relationships. Particularly in terms of share, number of visits, and recommendations, customers in functional relationships may score higher than those in strictly personal relationships. In general, we would not predict significant differences between the loyalty behaviors of customers in personal relationships and those in functional relationships. For instance, share of purchase should be higher among those in personal and functional relationships than for disconnected customers, but there is little support for a difference between the two groups. However, one measure represents a direct trade-off between cost and value – willingness to pay more for the brand. Since customers buying based on functionality are trading off utility for price, they would be severely limited in their willingness to pay more. In this case we would expect customers in personal relationships to score higher than those simply buying for functionality. H3. Customers in personal relationships with a brand will be more willing to pay higher prices for the brand than those in functional relationships.

Relationship segments Respondents were categorized into four relationship segments – none (disconnected), functional, personal, and commitment based on their responses to a battery of relational items (see Hess and Story, 2005 for a detailed background). The items were designed to clearly discriminate between personal and functional connections in order to differentiate between the two groups and identify those respondents in strong, committed relationships with the brands. Table I provides a sample of functional and personal connection measures. Those respondents rating lowest on both personal and functional connections were classified as disconnected, with no brand connection. Those with relatively strong personal and functional connections were classified as committed to the brand. Those with only personal or functional connections were classed as having personal or functional relationships. Behaviors or intentions were then measured at two different points in time. Past behavior and intentions were measured during the same session as relationship indicators – willingness to pay more, likelihood of recommending, and willingness to go out of the way to patronize are examples. Six months later, respondents were contacted for a follow-up study that recorded actual behaviors. Based on previous studies, we hypothesized several basic relationships between relationship category and behaviors. Customers who rate low on both personal and functional connection engage strictly on a transactional basis. These customers may be transients, who are in relationships with competing brands, or may not engage in relationships with brands in the product category. Regardless of the motives or causes for their disconnection, they were expected to exhibit fewer loyalty behaviors. H1. Customers lacking both functional and personal connections with the brand will display fewer loyalty behaviors than those with personal, functional, or committed relationships.

Their willingness to pay more for the brand may also translate into customers in personal relationships spending more on the brand than those in functional relationships. Much of the effect of relationship value will be captured only in the behavior of committed customers, since they score highest on personal connections. Yet, we also predict that customers in personal relationships spend more on the brand in any given time period than those in functional relationships. H4. Customers in personal relationships with a brand spend more in a given time period than those in functional relationships.

Customers in committed relationships are satisfied with the brand, have functional connections with the brand, and are personally connected to the brand. Based on the breadth and depth of these relationships, these customers were expected to engage in significantly more loyalty behaviors, both primary and secondary, than customers with only a functional or personal connection to the brand.

Procedure For the baseline survey, 1,988 respondents were randomly selected from a nationwide online panel. Panel participants are screened to participate in no more than four surveys per year. In the baseline survey, respondents’ attitudes toward two retail brands and their expected behavior were assessed. They rated brands with which they were at least somewhat familiar and had visited at least once in the last 30 days. Most respondents were very familiar and visited the store more than once in 30 days. Surveys were all conducted via e-mail invitation online and required approximately 20 minutes to complete. All ratings questions used a seven-point semi-anchored Likert scale. The

Table I Samples of personal and functional connection measures Personal

Functional

I have an emotional connection I have a personal connection I feel a sense of loyalty

They carry a wide variety of products They carry products I’m looking for They meet my basic needs

Customers in committed relationships engage in more loyalty behaviors than those in personal or functional relationships.

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responses range from Completely Agree to Completely Disagree with the following statement. Approximately 51 percent of respondents were female and there were no gender effects on the attributes of interest. The panel from which the sample was drawn is carefully designed to represent a broad range of demographic profiles. Age, Income and Occupation are all distributed among panel members to reflect the US adult population. Additionally the data are weighted to account for demographic biases that may result from online sampling. Approximately six months after the baseline survey was fielded, respondents were re-contacted to determine how they actually behaved in the intervening time between the initial attitude assessment and their most recent visit to the target retail stores. Specifically, respondents were asked only about their behavior at store brands they rated six months previous. In the follow-up survey customers were asked how much money they spent, what proportion of their spending was at a given store and how frequently they visited the stores they rated previously, among other behaviorally-oriented metrics. Of the 1,988 baseline respondents 978 responded to the follow-up survey.

Figure 3 Functional connections, personal connections, and commitment

the behavioral differences supported the hypothesized relationships. These results are organized by hypotheses. As expected, strong support was found for H1. Customers who had neither functional nor personal connections with a brand generally did not exhibit loyal behaviors. While not unexpected, this result does provide additional insight into loyal behaviors. It is important to note that if a meaningful number of customers were behaviorally loyal through simple inertia, the contribution of this effect was insufficient to match the behaviors of customers in functional, personal, or committed relationships. Figures 4 and 5 provide a graphical summary of the results. In both cases, data was rescaled so that the result for committed customers equals one hundred to facilitate the comparison across relationship categories. Disconnected customers (“None” in the figures) rated lower on all behaviors, including primary and secondary loyalty behaviors, than any of the other groups. This result serves to validate the relationship between the measures of personal and functional connections and loyalty behaviors. Further, this indicates that loyalty is not simply a matter of satisfaction with performance. Even customers who have relatively low functional connections display loyal behavior if they perceive a personal connection. Our second hypothesis was also strongly supported by the results. Customers who were classified in committed relationships were more likely to engage in all loyalty behaviors measured than those in the other groups. Figure 4 summarizes the results for primary behaviors – willing to pay more for the brand, actual spending in the previous seven days, brand share of total purchases in the category, and number of visits. Figure 5 summarizes the results for secondary loyalty behaviors – likelihood of recommending the brand, willingness to travel out of the way to patronize the brand, and willingness to purchase the brand online. Taken in the context of previous loyalty research, these results for committed customers are particularly interesting. These results suggest that customers who have strong functional relationships become more “loyal” if they also develop personal connections with the brand. Loyal behaviors and the resulting financial impacts cannot be optimized through satisfaction alone. Conversely, customers with strong personal relationships become more “loyal” if they also develop functional connections with the brand. In other words, augmenting personal connections with strong functional connections optimizes loyal behaviors and the resulting financial impacts. Our third hypothesis proposed that customers in personal relationships, rated high on personal connection, but low on

Results Several different analyses were performed in order to test the hypothesized relationships between category of relationship and behavior. Seven loyalty behaviors (see Table II) were tested for differences among groups of consumers. In addition, a separate set of commitment measures were used to verify the efficacy of defining commitment as resulting from a combination of personal and functional connections with a brand. Two general analyses were performed to validate the overall framework of the hypothesized relationships. First, commitment was regressed on personal connection, functional connection, and their interaction. The overall model was significant (p , 0.001), as were the parameter estimates for personal connection (p , 0.001), functional connection (p , 0.001), and their interaction (p , 0.05). Not only are customers with personal or functional connections more likely to be committed to the brand, and by extension – loyal, the influence of either type of connection is enhanced by the presence of the other type of connection (Figure 3). In the second general analysis, each of the behaviors was regressed on relationship classification. The levels for all seven behaviors were significantly different across relationship groups (p , 0.01). This overall result suggests that personal and functional dimensions of relationships correlate with the incidence of loyal behaviors. In addition, behaviors were examined for individual groups in order to determine whether Table II Loyalty behaviors Primary behaviors (purchase related) Willing to pay more for the brand Dollars spent during the last week Brand share of purchases in the category Visits during the previous week

Secondary behaviors Recommendations Willing to go out of the way Willing to purchase on the internet

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Figure 4 Primary loyalty behaviors and relationship segments

Figure 5 Secondary loyalty behaviors and relationship segments

In addition, relationship segmentation based on personal and functional connections provides guidance on developing loyalty. Past experience has shown that even successful pursuit of satisfaction does not guarantee continued customer loyalty. However, we have found support for the proposition that developing personal and functional connections between brands and customers promotes a broad range of profitable loyalty behaviors. Finally, it is important to note that the behavioral measures were collected six months after the relationship measures. Not only did relationship category correlate well with loyalty behaviors, the relationship persisted over the intervening six months.

functional connection, would be willing to pay higher prices for the brand than those in strictly functional relationships. This was measured using a seven-point Likert scale. Disconnected customers averaged 2.2, those in functional relationships averaged 2.5, and those in personal relationships averaged 3.6. Those customers in personal relationships were significantly more willing to pay more for the brand. As anticipated, price was significantly less important for those customers with a personal connection with the brand. Given that customers with personal connections to the brand are less price sensitive, we hypothesized that, within a given time period, they would spend more overall on the brand than those in functional relationships. This hypothesis was not supported. While both groups, personal and functional, spent more than disconnected customers and less than committed customers, there was no significant difference in spending levels between the two groups.

Managerial implications There is no question that customer loyalty, regardless of the definition employed, is valuable to firms. The current focus on customer relationships is driven by the loyal behaviors that derive from the relationships. However, previous attempts to define and measure loyalty have not resulted in reliable predictors of future behavior, nor have they provided viable strategies for building loyalty. As demonstrated time and again in the marketplace, satisfied customers defect and loyal customers drift away. Employing relationship segmentation, based on personal and functional connections, has two major advantages over previous loyalty programs. First, it explains the variance in behaviors of satisfied and behaviorally loyal customers. Understanding why satisfied customers defect is the first step in retaining them. The second advantage is that

Discussion These results, both in the aggregate and separately, provide strong support for our overall thesis that relationship segmentation reliably defines, measures, and predicts loyalty behaviors among consumers. Unlike methods that rely on marketplace behaviors or direct affective measures, relationship segmentation characterizes customers based on the levels of personal and functional connection with the brand. Customers who develop both types of connections tend to develop strong commitment to the brand. Hence, by measuring customer positions that are not directly related to purchase behaviors, we can identify groups with similar loyalty profiles and predict their future behaviors. 412

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trust-based commitment provides a prescriptive model for initiating loyal behaviors and transitioning satisfied customers to committed customers. Based on a series of simple multi-item scales, relationship segmentation provides a process by which firms can identify existing customer groups, predict the probability of future behaviors, and transition customers from less profitable to more profitable groups. This method promises the possibility of increasing primary and secondary loyalty behaviors by a firm’s customers, now and into the future.

Journal of Economic and Social Research, Vol. 3 No. 2, pp. 1-34. Reichheld, F.F. (2003), “The one number you need to grow”, Harvard Business Review, Vol. 82 No. 12, pp. 46-54. Schultz, D.E. and Bailey, S. (2000), “Customer/brand loyalty in an interactive marketplace”, Journal of Advertising Research, Vol. 40 No. 3, pp. 41-52. Schulz, D.E. (2005), “The loyalty paradox”, Marketing Management, Vol. 14 No. 5, pp. 10-11.

References

About the authors

Fournier, S. (1998), “Consumers and their brands: developing relationship theory in consumer research”, Journal of Consumer Research, Vol. 24 No. 4, pp. 343-73. Gronroos, C. (1997), “From marketing mix to relationship marketing – towards a paradigm shift in marketing”, Management Decision, Vol. 35 No. 4, pp. 322-6. Gummeson, E. (2002), Total Relationship Marketing, Butterworth-Heinemann, Oxford. Hess, J. (1995), “Construction and assessment of a scale to measure consumer trust”, paper presented at the American Marketing Association Summer Educators’ Conference. Hess, J. and Story, J. (2005), “Trust-based commitment: multidimensional consumer-brand relationships”, Journal of Consumer Marketing, Vol. 22 No. 6, pp. 313-22. Oliver, R. (1999), “Whence customer loyalty”, Journal of Marketing, Vol. 63 No. 4, pp. 33-44. Parvatiyar, A. and Sheth, J.N. (2001), “Customer relationship management: emerging practice, process, and discipline”,

John Story (PhD from the University of Colorado at Boulder) is an Associate Professor Of Marketing at Idaho State University. He has published work in the Journal of Consumer Marketing, Journal of Product and Brand Management, Journal of Business Research, and International Journal of Internet Marketing and Advertising, as well as in various national conference proceedings. Dr Story’s research interests include customer-brand relationships, cross-cultural marketing, and internet adoption and implementation. He is the corresponding author and can be contacted at: [email protected] Jeff Hess (PhD from the University of Colorado at Boulder) is Senior Vice President of TNS, Inc. He has published in the Journal of Consumer Marketing and marketing industry publications, as well as award-winning papers in various national conference proceedings. Dr Hess’ primary research interest is the formation, management, and implementation of customer-firm relationships.

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Look after me and I will look after you! Sharyn Rundle-Thiele Griffith Business School, Griffith University, Nathan, Australia Abstract Purpose – Loyalty has been widely researched and while we know the ways that consumers demonstrate their loyalty and the factors that influence loyalty, research reporting consumer and marketer views of loyalty is scant. To date there has been no attempt to compare and contrast marketer’s views on loyalty with their own consumers’ views. The purpose of this paper is to report research that responds to this omission. Design/methodology/approach – An Australian beer category was selected for this research because beer is a category where consumer loyalty exists. A qualitative research approach was used to provide detailed marketer and consumer views about loyalty to gain strategic insight. Company documents were also used to gain additional insight and increase the validity of the research findings. Findings – A detailed view suggests that many marketing initiatives employed are eroding rather than contributing to consumer loyalty. Consumer views suggest that loyalty must be earned and that a consumers’ loyalty cannot be bought. Research limitations/implications – A single case study was used in this research to provide rich and detailed insight into consumer loyalty. Further research is needed to test the validity of the research findings by using a larger sample of consumers, but also in other beer categories and product and service categories. Practical implications – Practices where companies profit at the expense of their own consumers will diminish rather than build consumer loyalty. Marketers must consider loyalty as a reciprocal concept. This research suggests that to gain a consumers loyalty a company must adopt “a look after me and I will look after you” philosophy. Keywords Customer loyalty, Consumer behaviour, Marketing strategy Paper type Case study

attainment of quarterly sales targets and bottom line performance may be a company’s primary concern. Furthermore, this announcement suggests consumers can expect to pay more than $100 each for choosing to maintain an account at this bank. Can companies realistically expect consumer loyalty when they behave unfaithfully towards their consumers? In the twenty-first century, when consumers are faced with more choice than ever (Trout and Rivkin, 2000), and “polygamy is readily apparent” (Dowling and Uncles, 1997, p. 74) loyalty may appear to be a quaint and outdated notion. Yet a strategy that is underpinned with a loyalty or a “look after me and I will look after you” philosophy may set a brand apart from its competition and transform brands into successful and profitable icons (Wee and Ming, 2003). Consider, Harley Davidson, where managers interact with consumers, have high levels of brand loyalty and their consumers demonstrate pro-loyal behaviours such as word-of-mouth and advocacy (McAlexander et al., 2002). This research employs a dyadic research design using one category where loyalty exists. A dyadic research design allowed loyalty to be considered from a relational perspective. Furthermore, qualitative methods were employed to provide strategic insight based on a detailed understanding of loyalty. This research is important to determine the degree of similarity and differences perceived by marketers and consumer because marketers who attach a different meaning to loyalty than their consumers may be misdirecting limited marketing resources.

Introduction “In interpersonal contexts loyalty is shown when persons do not undermine others by what they say or do. A person who has undisclosed misgivings, yet still behaves supportively, is seen as loyal” (East et al., 2005, p. 11). While Fournier (1998) noted that loyalty is a relational construct, researchers have ignored the relational aspect of loyalty. For example Oliver (1999) defines consumer loyalty as a “deeply held commitment to re-buy a preferred product consistently in the future . . ., despite situational influences and marketing efforts having the potential to cause switching behaviour.” This definition requires a commitment from the consumer without any requirement for a company to reciprocate. On 17 March, 2006 a major bank announced they would charge two million consumers an extra $30 per year for having an account. In addition, interest rates on about $23 billion worth of term deposits owned by hundreds of thousands of other consumers were lowered. Following the banks announcement, a leading investment analyst upgraded the banks stocks from a “hold” to a “buy” rating based on the expectation that the increased fees and lowered rates would add $240 million to the bank’s already bulging bottom line. The bank is tipped to make $3.35 billion in 2006, up 19 per cent on last year (Rolfe, 2006). This announcement suggests The current issue and full text archive of this journal is available at www.emeraldinsight.com/0736-3761.htm

Journal of Consumer Marketing 23/7 (2006) 414– 420 q Emerald Group Publishing Limited [ISSN 0736-3761] [DOI 10.1108/07363760610712957]

The author thanks ANZMAC anonymous reviewers for their helpful comments on earlier versions of this work. This research was supported by a Griffith University research grant.

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Research questions This study will be guided by the following three research questions. RQ1. What do marketers mean by loyalty? RQ2. What do consumers mean by loyalty? RQ3. Do marketer’s perceptions of loyalty differ from their consumers?

1971 Jacoby developed a conceptual definition of loyalty that is still used today by some researchers (Beerli et al., 2004; Quester and Lim, 2003; Taylor et al., 2004). The next major step forward in loyalty research occurred in 1994 when Dick and Basu (1994) proposed a conceptual framework to assist marketers to: distinguish between the various types of loyalty; identify the possible drivers of loyalty; and to identify the consequences of loyalty for marketers. Following Dick and Basu’s (1994) loyalty framework, research emerged where multiple dimensions were used to measure loyalty (Bloemer et al., 1999; Narayandas, 1999; Yu and Dean, 2001; Zeithaml et al., 1996). Since Dick and Basu’s (1994) loyalty framework, loyalty theory has continued to broaden as researchers introduce loyalty dimensions and measures into the literature (Athiyaman, 1999; Bloemer et al., 1999; Bloemer and Kasper., 1995; Bloemer and Lemmink, 1992; Butcher et al., 2001; Chaudhuri and Holbrook, 2001; Dekimpe et al., 1997; Lee and Cunningham, 2001; Rundle-Thiele, 2005a). Rundle-Thiele (2005b) provides a comprehensive summary of survey-based measures of loyalty. In addition to research efforts that describe the ways that consumers are loyal research substantial efforts have been directed towards understanding the factors that promote loyalty. Whilst myriad factors have been linked to loyalty, perhaps the two most notable factors relating to loyalty are satisfaction and trust. While many authors have established that satisfaction and loyalty are related (examples include Bolton, 1998; Cronin et al., 2000; Delgado-Ballester and Munuera-Aleman, 2001; Patterson et al., 1997) some authors have indicated that a high degree of satisfaction does not always translate into loyalty (Mittal and Lassar, 1998). Similarly, trust has also been found to influence loyalty (e.g. Chaudhuri and Holbrook, 2001; Sirdeshmukh et al., 2002; Shamdasani and Balakrishnan, 2000). Current conceptions of loyalty in the literature can be considered one-way. While research efforts have both improved our ability to describe and distinguish the different ways that consumers are loyal, and second, identified myriad factors that promote loyalty they have failed to consider the interactive nature of the loyalty construct. Studies have not been conducted that examine marketer’s perceptions of loyalty. Given that marketers who affect loyalty it is important to determine whether the marketer’s perception of loyalty is similar to (or the same as) their consumers. Despite acknowledgement in the literature that loyalty is a relational construct (Fournier, 1998) researchers have ignored the relational aspect of loyalty. Dyads have not been used previously in the literature concerning loyalty. Nor has a study been conducted to examine what marketers mean by loyalty.

Loyalty Prior to 1997 loyalty research considered three different types of loyalty, namely: attitudinal loyalty; behavioural loyalty; and composite loyalty. The composite loyalty category stated that both attitudinal and behavioural loyalty had to exist in order for a consumer to be considered loyal (Day, 1969; Jacoby and Chestnut, 1978). In (1944) Guest first defined the concept of attitudinal loyalty and later measured attitudinal loyalty as a constancy of preference over time (Guest, 1955, 1964). In a personal sense loyalty is a feeling or an attitude of devoted attachment and affection. This feeling of loyalty tends to imply that a person feels an obligation to persevere with a personal relationship through good and bad times. As noted by East et al. (2005, p. 11) “a person who has undisclosed misgivings, yet still behaves supportively, is seen as loyal”. A commercial setting involves a subtle change for the term “loyalty”. One of the main reasons for this change is consumers can behave in a loyal fashion without a feeling or an attitude of devoted attachment. To put this in context think about a consumable product that you commonly purchase such as baked beans or a newspaper. Do you feel attached or committed to a can of baked beans or a newspaper? There would be few who would agree that they are attached or committed to a can of baked beans or a newspaper. Yet, when asked if you repeatedly purchase a brand of baked beans or a newspaper you may answer yes. This loyal behaviour has also been an enduring interest for both academics and marketers alike. The concept of behavioural loyalty was first defined in the 1950s and measured using the proportion of total purchases for one or two brands (Cunningham, 1956). This behavioural approach was developed further by researchers (Bass, 1974) and a stochastic view of consumer behaviour emerged. The stochastic view of consumer behaviour proposes that consumer behaviour is characterised by randomness and not rational thought (Bass, 1974; Hoyer, 1984). Behavioural loyalty by its very nature is observable mainly in consumer goods markets where technology enables purchasing data to be captured and predictive models of behavioural loyalty, e.g. Negative Binomial Distribution (NBD) and Dirichlet (Ehrenberg, 1968; Ehrenberg, 1988) to be estimated. These models describe and predict repeat purchase rates, and hence behavioural loyalty for brands with astonishing accuracy across a wide range of conditions (see Ehrenberg and Uncles, 2000; Goodhardt et al., 1984). In other words stochastic models describe loyal behaviour helping marketers to understand how people buy primarily in markets where data is readily available. In 1969, Day proposed that loyalty should be viewed as a composite concept. This view suggested that loyalty should comprise both attitudinal and behavioural components. The underlying rationale for the composite view of loyalty was to improve construct validity by measuring more aspects of the construct. Day’s (1969) proposition received support and in

Methodology Marketing research could benefit from the use of in-depth case research as many issues can not be studied out of their natural context. A qualitative research approach was selected for this research as qualitative methods allow complex issues, such as loyalty, to be investigated in some depth (Yin, 2003) through building an understanding of the processes and dynamics within particular settings (Eisenhardt, 1989). A single case study method was selected for this research. Specifically, one organisation and one product category were studied in this research. 415

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The sample Following the recommendations of Yin (2003), Dubois and Gadde (2002), and Eisenhardt (1989), data for this investigation was collected from a variety of sources, including secondary sources (e.g. company documents, press articles and government reports), interviews with marketers and consumer focus groups. Archival data was used gather to additional insight, thus increasing the internal validity and reliability of the evidence. Four focus groups were held and 45 beer consumers participated in the four groups. Multiple groups were conducted to generate different perspectives on loyalty and to ensure that the findings were objective. The researcher had a semi-structured interview guide containing an outline of the topics to be covered, with suggested questions that were designed to keep the flow of conversation going. This format allowed the researcher some freedom to pursue insightful focus group responses (Steinart, 1996). The focus groups commenced with a warm up exercise that involved consumers detailing their own mid-strength beer consumption to establish a safe atmosphere for consumers to talk about their own experiences (Steinart, 1996). Consumers were then given a blank sheet of paper where they were asked to write any thoughts that came to their mind for the word “loyalty”. Discussion then progressed to understanding why (or not) loyalty applied to beer and how marketers could possibly build consumer loyalty to mid-strength beer. Finally, consumers were asked to consider categories where they felt they were loyal and the reasons for this loyalty. Open-ended questions were used and consumers were encouraged to follow their thought patterns. Focus groups averaged 90 minutes and the group discussions were recorded and transcribed for analysis. Semi-structured interviews were held with the key personnel in the organisation who were directly responsible for marketing mid-strength beer. Once again topics were outlined in the interview guide and questions were suggested to maintain conversation flow. The interviews commenced with a warm up exercise that involved marketers detailing their role and responsibilities. Discussion then focussed on exploring the marketers understanding of consumer loyalty in the mid-strength beer category, with marketers discussing the brands consumers were loyal to and their opinions on why this loyalty existed. Marketer interviews averaged 45 minutes. In addition to the interviews with marketers company documents were analysed. Company documents included Roy Morgan Research Reports which reported purchase intentions and consumption patterns for the mid strength beer category, internal documents describing consumer behaviour, AC Nielsen Scan Data which reported longitudinal purchasing patterns and commercial in confidence market research on alcohol consumption patterns and trends.

coding procedures retained the essential meaning of the information provided about loyalty, but constant comparison with other statements and previously used codes ensured a reduction of the variety and details of descriptions. Case data The organisation studied is a publicly listed global multibeverage company employing 9,000 people worldwide. The company delivers a total portfolio of beer, wine, spirits, cider and non-alcohol beverages. The mid-strength beer category The mid-strength beer category was selected for this study because consumers are generally loyal to a beer brand drinking on average from a repertoire of four brands (Allen, 2003). Some characteristics in the mid strength beer category worthy of note: first, it is the fastest growing beer category; second, brand X, a rival brewers brand, commands a 70 percent share in the market studied. Brand X initially a line extension of an iconic beer brand is the largest brand in the mid-strength beer category, with a behaviourally and to a lesser extent, attitudinally loyal base of consumers. Brand X is continuing to grow strongly in line with category growth. The parent company of Brand X has changed ownership resulting in the brand moving from a locally owned company to foreign owned company. Brand Y, the second largest brand, has a smaller base of behaviourally and attitudinally loyal consumers. Brand Y is currently exceeding category growth rates. Analysis of the behavioural data identified a promiscuous group of consumers who purchase beer according to price. The data confirmed both the consumers’ reports of their own beer consumption and the marketers understanding of the category.

Results This section presents the results of this investigation. First a description loyalty from a consumer perspective is provided followed by a description of how senior marketers consider loyalty. This section concludes by comparing and contrasting the key features of loyalty from a consumer’s perspective with loyalty from a marketer’s perspective. A consumer’s view of loyalty After gaining background information on the consumer’s beer consumption, the interviews sought to determine the consumers own meaning of loyalty. Three themes emerged following data analysis and each theme will be discussed in turn. Theme 1: a consumer’s loyalty is earned The first theme identified in the analysis is that a consumers’ loyalty can not be bought. While many marketer efforts are directed towards buying a consumers’ loyalty the analysis of consumer views on loyalty suggest that loyalty must be earned rather than bought.

Data analysis In addition to the company reports and data a total of 96 pages of focus group transcripts and 36 pages of marketer interview transcripts were analysed. The data analysis employed qualitative procedures aimed at uncovering themes. Statements were coded firstly using an open (or initial meaning code) and secondly an axial (or categorisation of open codes) coding scheme recommended by Miles and Huberman (1984) and also Strauss and Corbin (1998). The

Loyalty is vital in a relationship, not so in choice of beer. Loyalty is earned not a given.

Theme 2: loyalty is reciprocal The second theme identified in the analysis was the idea that for loyalty to be earned marketers must consider that loyalty is a reciprocal or two-way concept. To gain and keep a consumer’s loyalty companies need to demonstrate loyalty 416

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towards their consumers. Practices that act to diminish loyalty were mentioned frequently by consumers and a degree of cynicism towards marketing practice was evident based on the consumers own experience over time.

This is a very mature duopoly, so we are all chasing numbers and market shares and profit figures at various times. I guess it helps that our end-offinancial years are separate, and half years are separate. We are both publicly listed companies and we are really under scrutiny from investment analysts. So you might say, well if we are going to sell the product anyway why do you care if it is sold this month or next month? This month is reporting period and you want to show a volume number and a market share number to the market that you are going well. So you chase that a bit.

Seems to be one way. Loyalty is certainly not something I have to any beer company. Loyalty is often asked for, rarely demonstrated.

They would probably outspend us 2 to 1 to support that brand in this market.

I think loyalty should be a two-sided thing. I am happy to buy my beer, the one I have chosen as the right taste and the right price while they deliver. But if they changed the beer by making it heavier (higher alcohol content) or more expensive then I don’t have to stick with that beer. We have a big choice.

One marketer made reference to “playing the game” and the data indicated that a lot of marketer thinking tended to be tactical rather than strategic in nature. A lot of reference during interviews was made by marketers to tactical implementation, e.g. promotions, advertising campaigns, sponsorship agreements and pricing approaches that had been used in the past. A comparison of consumer and marketer views of loyalty suggests there are areas where consumers and marketers agree and importantly there are areas where marketers are thinking differently to consumers (see Table I).

Many consumers commented that companies often ask for loyalty but rarely demonstrate loyalty in return. Management practices such as reducing pack sizes were described as practices that act to reduce consumer loyalty. How can they expect loyalty when we are getting less and less? We notice when they are dropping the size of the stubbies [glass bottles] and raising the prices! The breweries should give something back, give people what they want – a decent sized stubby at a reasonable price. I spend more on beer than their dividend cheques.

Implications for marketers

Theme 3: loyalty may be a thing of the past Interestingly, consumers perceive that while loyalty used to be important it is gradually diminishing. Some consumers considered loyalty to be a thing of the past. Product proliferation and increased competition were cited as primary causes of the decreased importance of loyalty:

While consumers in this research questioned the relevance of loyalty to a product category such as beer, some older consumers reflected on times past where loyalty was demonstrated by companies towards employees and local communities. By demonstrating loyalty companies received consumer loyalty in return. This would suggest that companies in order to build customer loyalty companies need to adopt a loyal or “look after me and I will look after you” philosophy. As share prices and hence shareholder returns increase with expectations of higher future profits returning increasingly higher bottom lines is an essential requirement in business today. Yet, this practice cannot continue at the expense of consumers. Consider the bank example cited earlier; can the bank realistically expect consumers to pay more than $100 for choosing to maintain their bank account this year followed by a further $110 increase next year, and so on? As it stands this bank has opened itself to an attack from a savvy competitor. A competitor could use this bank’s action to openly target consumers faced with the $100 fee increase. An awareness campaign that promotes both expected fee increases and a service to simplify the time-consuming bank switching process would assist the competitor to grow at the expense of the bank raising fees as consumers would be more likely to consider switching given the $100 incentive. According to Rigby and Bilodeau (2005) at present 36 percent of companies around the globe report using loyalty measurement tools while 76 percent report using outsourcing. A shift in management thinking may be required. In order to build customer loyalty managers may need to reduce the emphasis on cost cutting measures such as outsourcing and supply chain management and attaining budgets. A loyalty management or “look after me and I will look after you” philosophy would involve centring management thinking on achieving revenue and profit growth through retaining consumers, employees and investors. The competitor bank that had the foresight to use its competitor’s cost cutting announcement may offer an exemplar. This research suggests the actions of marketers may need to be carefully reconsidered. Actions such as blocking strategies

Loyalty is affected by too many choices, e.g. brands, number of sporting teams and money. There is greater choice of full strength beers than there is for mid-strength beers so loyalty is less of an issue with full strength beer. While I nearly always buy Brand X, there is no great loyalty there. When the company changed ownership consumers went off drinking Brand X and the breweries market share went down. Yes I am loyal to a beer but only because I like the taste and I own shares in the company. All I am saying is that loyalty was a huge factor in the old days and I think it is less important now than it was. There used to be loyalty to Brand X in the old days when they were major employers in the region because the brewery used to create a lot of work. The brewery got involved in sporting clubs and gambling and everything so people pledged their loyalty to their product. That is how the brewer built up a such a big base of customers. They do bugger all now.

A marketer’s view of loyalty When questioned about loyalty in the mid strength beer category marketers referred to market share, growth rates, the competition and customers (hotels, retail bottle shops, sporting venues). Managers emphasised the attainment of budgets and the requirements of quarterly reporting rather than consumers and this suggests that consumers are not the focal point of management thinking. When questioned about why consumers are loyal to various brands in the market place marketers referred to above, through and below the line marketing expenditure and the importance of price promotions. Marketers indicated the importance of over investing in the marketplace to build consumer loyalty: We don’t invest anything except some point of sale material for a brand like Brand Z, but if you go into a region where Brand Z was at one stage dominant, or one of the dominant beers in the segment, you know it had about a 25% share, its still got some equity, because people remember. They think what the brand was like, what it represented. So there is still people buying on price, and it’s still a well-priced product. There are still people buying because they are loyal to that brand.

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Table I Consumers and marketers Consumers say . . .

Marketers say . . .

“Loyalty must be earned not bought” “Consumer loyalty should only be expected when the product you are supplying meets the customers demand. If a product or service is offered for a similar or better price suppliers should expect the consumer to change. In a climate of super competition and the availability of a vast range of goods and services loyalty to a product cannot be assumed”

“You spend money through the line to develop loyalty” “There is always something on special at some time, and people . . . with the proliferation of detached bottle shops that there is, don’t have to drive too far to get a decent price on a product that they like”

“The beer companies are selling product now. They spend mega dollars convincing people the same beer is in a new can”

“I think their above the line has really tapped into the hearts and minds (of customers)” “The real measure of success is to grow the brand on a sustainable basis . . . We are over investing there with a view to seeing some fairly positive share gains”

“Multiple brands confuse the issue because it gets people thinking about trying other beers”

“We have a stated strategy of keeping Brand X below Brand Y so that you will see Brand X $1.00 below a carton of Brand Y. That’s trying to play on a customer going ‘OK they are both mid-strength products and I’ll just buy the one that is cheaper.’ You know, we have grown market share of Brand Y by 15-20%, probably over three years but Brand X hasn’t dropped by that amount. We have been cannabilising (mentioned some own brands)”

(where a company secures distribution of its brands for an event or a certain premise thus blocking competitor brands), pack size reductions and cost cutting measures are likely to be eroding consumer loyalty. Importantly, to build consumer loyalty marketers need to be market focussed and not competition focussed. To build and maintain loyalty marketers must consider how they can look after their customers and consumers. A consumers’ loyalty must be earned and importantly managers must remember that loyalty can not be bought. These findings may explain why loyalty programmes have had limited success in building consumer loyalty (Dowling and Uncles, 1997; Uncles, 1994). Marketers must be aware they cannot buy consumer loyalty with above, through and below the line marketing expenditure and price promotions. Marketers need to look after their consumers if they expect their consumers to look after them.

information for managers seeking to build consumer loyalty. For example, low levels of consumer attitudinal loyalty may be easily explained by a low commitment to develop win/win solutions from the company.

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Implications for research According to the majority of loyalty research, loyalty exists when consumers have strong positive attitudes towards the brand, and/or the consumers systematically purchase brands. Furthermore, loyalty may also exist when a consumer is less sensitive towards inducements offered by competing brands with typical lines of questioning directed towards understanding a consumers’ intention to purchase in light of competitor activities. This research identified that loyalty is a reciprocal concept that requires managers to demonstrate loyalty to consumers if consumer loyalty is expected in return. For managers seeking to build consumer loyalty, consideration of consumer loyalty alone, will limit their ability to fully understand how consumer loyalty can be built. Loyalty measurement will require a dual focus where both consumers and employees are surveyed. Research efforts need to be directed towards developing a valid and reliable employee measure to enhance a companies’ ability to assess loyalty. Lines of questioning will need to build an understanding of the companies’ commitment to develop win/win solutions, earn consumer loyalty, and reward loyal consumers. This would provide important diagnostic 418

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Jacoby, J. and Chestnut, R. (1978), Brand Loyalty: Measurement and Management, John Wiley & Sons, New York, NY. Lee, M. and Cunningham, L.F. (2001), “A cost/benefit approach to understanding service loyalty”, Journal of Services Marketing, Vol. 15 No. 2, pp. 113-30. McAlexander, J.H., Schouten, J.W. and Koenig, H.F. (2002), “Building brand community”, Journal of Marketing, Vol. 66 No. 1, pp. 38-54. Miles, M. and Huberman, A.M. (1984), Qualitative Data Analysis, Sage, Thousand Oaks, CA. Mittal, B. and Lassar, W.M. (1998), “Why do customers switch? The dynamics of satisfaction versus loyalty”, The Journal of Services Marketing, Vol. 12 No. 3, pp. 177-94. Narayandas, D. (1999), “Measuring and managing the benefits of customer retention”, Journal of Service Research, Vol. 1 No. 2, pp. 108-28. Oliver, R.L. (1999), “Whence customer loyalty?”, Journal of Marketing, Vol. 63, pp. 33-44. Patterson, P.G., Johnson, L.W. and Spreng, R.A. (1997), “Modelling the determinants of customer satisfaction for business-to-business professional services”, Journal of the Academy of Marketing Science, Vol. 25 No. 1, pp. 4-17. Quester, P. and Lim, A.L. (2003), “Product involvement/ brand loyalty: is there a link?”, Journal of Product and Brand Management, Vol. 12 No. 1, pp. 22-38. Rigby, D. and Bilodeau, B. (2005), “The Bain 2005 management tool survey”, Strategy & Leadership, Vol. 33 No. 4, pp. 4-12. Rolfe, J. (2006), “Westpac hikes up fees”, The Courier-Mail, 16 March, p. 7, available at: http://news.com.au/couriermail Rundle-Thiele, S. (2005a), “Exploring loyal qualities: assessing survey-based loyalty measures”, Journal of Services Marketing, Vol. 19 No. 7, pp. 492-500. Rundle-Thiele, S.R. (2005b), “Elaborating customer loyalty: exploring loyalty to wine retailers”, Journal of Retailing and Consumer Services, Vol. 12 No. 5, pp. 333-44. Shamdasani, P.N. and Balakrishnan, A.A. (2000), “Determinants of relationship quality and loyalty in personalized services”, Asia Pacific Journal of Management, Vol. 17, pp. 399-422. Sirdeshmukh, D., Singh, J. and Sabol, B. (2002), “Consumer trust, value, and loyalty in relational exchanges”, Journal of Marketing, Vol. 66 No. 1, pp. 15-37. Strauss, A. and Corbin, J. (1998), Basics of Qualitative Research: Techniques and Procedures for Developing Grounded Theory, Sage Publications, Thousand Oaks, CA. Steinart, K. (1996), An Introduction to Qualitative Research Interviewing, Sage, Thousand Oaks, CA. Taylor, S.A., Celuch, K. and Goodwin, S. (2004), “The importance of brand equity to customer loyalty”, Journal of Product and Brand Management, Vol. 13 No. 4, pp. 217-27. Trout, J. and Rivkin, S. (2000), Differentiate or Die: Survival in Our Era of Killer Competition, Wiley, New York, NY. Uncles, M.D. (1994), “Do you or your customers need a loyalty scheme?”, Journal of Targeting, Measurement and Analysis, Vol. 2 No. 4, pp. 335-50. Wee, T.T.T. and Ming, M.C.H. (2003), “Leveraging on symbolic values and meanings in branding”, Journal of Brand Management, Vol. 10 No. 3, pp. 208-18. Yin, R.K. (2003), Case Study Research, Design and Methods, Sage Publications Inc., Thousand Oaks, CA. 419

Look after me and I will look after you!

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Sharyn Rundle-Thiele

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brand relationships”, International Journal of Research in Marketing, Vol. 14 No. 5, pp. 451-72.

Yu, Y.-T. and Dean, A. (2001), “The contribution of emotional satisfaction to consumer loyalty”, International Journal of Service Industry Management, Vol. 12 No. 3, pp. 234-50. Zeithaml, V.A., Berry, L.L. and Parasuraman, A. (1996), “The behavioral consequences of service quality”, Journal of Marketing, Vol. 60 No. 2, pp. 31-46.

About the author Sharyn Rundle-Thiele is a Senior Lecturer at Griffith University Australia and has a PhD in loyalty. Sharyn lectures in marketing principles at undergraduate and graduate levels and she regularly consults in the services sector. Sharyn has co-authored two marketing principles textbooks and has recently published eight articles on loyalty. She can be contacted at: [email protected]

Further reading Fournier, S. and Yao, J.L. (1997), “Reviving brand loyalty: a reconceptualization within the framework of consumer-

To purchase reprints of this article please e-mail: [email protected] Or visit our web site for further details: www.emeraldinsight.com/reprints

420

A strategic approach to building online customer loyalty: integrating customer profitability tiers Dennis Pitta University of Baltimore, Baltimore, Maryland, USA

Frank Franzak Virginia Commonwealth University, Richmond, Virginia, USA, and

Danielle Fowler University of Baltimore, Baltimore, Maryland, USA Abstract Purpose – The purpose of this paper is to present a strategic framework to managing online loyalty. Design/methodology/approach – The paper integrates concepts including a range of recently published (1993-2006) theoretical works in consumer loyalty and ongoing case developments in internet practice. Findings – Provides information and action approaches to consumer marketers that may increase the success providing want satisfying market offerings. Outlines the costs and benefits of some online customer loyalty building practices. By integrating the literature supporting lifetime customer value with the literature concerned with generating online customer relationships, it provides a pathway to profitable relationships. It also exposes the unintended problems that some online customer loyalty initiatives may create. Research limitations/implications – The theoretical concepts that form the foundation of the paper appear to have a significant application to consumer marketing but have not been tested empirically. Practical implications – Uncovers a previously unreported strategy for generating profitable online customer loyalty. Originality/value – This paper describes the nature and application of customer value tiers to an important marketing process. It offers the potential of increasing marketing success by allowing firms to maximize the value of their scarce service resources by serving profitable customers. Keywords Customer loyalty, Customer relations, Internet shopping, Profit maximization, Electronic commerce Paper type Conceptual paper

of interest in getting closer to the customer. The concept is grounded on information technology as a way to capture relevant consumer preferences so that repeat customers might enjoy easier future transactions (Pitta, 1998). The authors note that in a typical service encounter, such as checking into a hotel, numerous questions need to be answered. Thus, guests are conducted through the series of choices that supposedly reveal their true preferences. The genius of the concept is that capturing each of these decisions using appropriate hardware and software identifies the customer and a specific set of preferences. It is assumed that the preferences are somewhat persistent and once they are revealed they tend to remain largely unchanged. In principle, the customer is relieved of the burden of repeating the list of answers each time he or she stays at a hotel in the same chain. Since larger firms with numerous locations realized that each of their units could access the information in their central customer databases, the firms invested heavily in the technology. Thus, technology was a complement, and sometimes a substitute for a personal interaction. The situation is analogous to police forces removing ”cops on the beat,” police officers who walk through their assigned area and get to know the neighborhood, the law abiding people, and threats to their safety. When police departments put officers in automobiles, they lost the intimacy with their neighborhoods and the people they swore to protect and became less effective at recognizing symptoms of crime before

Introduction During the 1980s customer loyalty was on most marketers’ minds. Numerous companies spent millions on customer relationship management programs with the goal of building customer loyalty. It is ironic that the concept of customer loyalty is not something that consumers recognize. For most companies the customer or brand loyalty issue is, in behavioral terms, nothing more than repeat purchasing. In fact, many company efforts aim solely at increasing the percentage of repeat purchases among current customers. Supporting this effort, marketing strategists have developed predictive models that show the important profitability effects of increasing the repeat purchase rate among existing customers. The problem may lay in the current nature of customer relationship management initiatives. The 1-to-1 Marketing concept (Pepper and Rogers, 1993) generated a groundswell The current issue and full text archive of this journal is available at www.emeraldinsight.com/0736-3761.htm

Journal of Consumer Marketing 23/7 (2006) 421– 429 q Emerald Group Publishing Limited [ISSN 0736-3761] [DOI 10.1108/07363760610712966]

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Dennis Pitta, Frank Franzak and Danielle Fowler

Volume 23 · Number 7 · 2006 · 421 –429

crimes occurred. For marketers, losing touch with their customers signals the loss of proactive marketing. Despite the good intentions of trying to regain lost familiarity, there are a few defects in the system. First, the technology did not always perform flawlessly. Some systems failed to reach the “once and for all” standard. The result required customers to answer that initial list of questions, each time they arrived at a destination, even if they stayed at the chain or the specific hotel before. Even the most unaware consumer would be bored and probably irritated by the repetition. Some have complained about the number of a company’s customer service personnel who have asked the same questions over and over. With many companies embarking on programs to get to know their customers, a second flaw emerged. While companies continued to collect customer data, there was an expectation that the exercise was more of a discrete act than a process. Just because someone ordered a smoking room and a Coke did not guarantee that those selections would not change over time. Humans develop during their lifetimes and change their behavior based on numerous factors. To be really effective, such programs require continuous refreshing of customer preferences. They also should be based on a clear understanding of the elements that comprise customer loyalty. Perhaps the most important focus should be the “right” customer. Organizations must not only boost repeat purchases and customer satisfaction, they need to discriminate among their customer base. There is a recent awareness that customers are not only different in terms of their needs but deliver different amounts of profitability over their lifetimes. When firms fail to focus on customers who will increase profit and reduce costs, they run the risk of working hard with little to show for the effort. Any attempts at building customer loyalty must be based on a foundation of customer profitability.

will “make things right.” In some relationships the initial transaction is based on no previous history and, absent positive word of mouth, is essentially based on faith. After one successful interaction, a consumer will have evidence that the vendor delivers as promised, or not. That evidence supplants faith as a basis for interaction. Early in a relationship, elements such as company or brand name assume increasing importance. They serve as surrogate indicators of probable performance. Thus, international brands such as Toyota have brand equity that bolsters consumers’ perception of trust (Ward and Lee, 2000). In fact, in a new relationship, brand or company names and perceptions take on the burden of trust creation. In the online world, trust is still a major concern among online consumers. The prospect of divulging credit card information to an online “stranger” is an e-commerce obstacle to some consumers who in reality could shop securely (Dunn, 2004). It is known that trust is a living element in a relationship and can grow in strength by successful interactions or be diminished to the point of extinction by a failure to deliver. In extreme cases, when trust is damaged, so is the transactional relationship. Schlosser et al. (2006) explored three underlying components of trust: the vendor’s ability to deliver, benevolence and integrity. Ability, benevolence, and integrity beliefs are acknowledged as conceptually distinct measures of trusting beliefs. Schlosser and her coauthors investigated the effect of web site design investments on consumers’ trusting beliefs and online purchase intentions. They found that such investments signal the component of trusting beliefs most strongly related to online purchase intentions: ability. They also found that the effects were strongest when consumers sought to search for products rather than to browse and when purchases involved risk. There are specific activities that combine to bolster trust. They include overtly ethical activities that can be recognized by customers. Integrity and performance are other key elements. Overall, energetic attempts to provide customers with appropriate levels of information about products and services, privacy, prices, security as well as the company’s policies, practices and procedures will increase consumer trust levels (Smith, 2002; Reichheld et al., 2000a). There are ways of operating a business that can be called traditional customer retention practices. They are the bedrock of customer loyalty and include transaction security, trustworthiness, on-time delivery, reasonable prices, product and service performance, as well as follow-up service and support. These elements comprise a set of “trust generating activities.” Without these in place and operating, trust building is not possible. The literature notes that trust is perhaps the one element that must be established early and nurtured throughout the lifetime of the customer relationship. The literature also notes that the economics of an individual transaction are insufficient to insure firm profitability. Customers must buy multiple times for a relationship to be profitable. The lifetime approach takes this economic view into account. The focus on lifetime relationship should not be minimized. Many businesses spend inordinate amounts of time and money trying to attract new customers in contrast to retaining existing customers. For all companies that make serious efforts to attract first time customers, only 40 percent devote equal effort in designing customer retention and repeat purchase strategies. In

The core of customer loyalty Consumer loyalty seems to be based on a collection of factors. The first is trust. Consumers must trust the vendor or product they encounter. Second, the transaction or relationship must have a positive perceived value greater than that supplied by competitors. Third, if marketers build on the first two factors, they may be able to create a level of positive customer emotional attachment. That emotional response may be commitment to their brands that is resistant to change. Each of the factors requires some discussion. Trust Much of the customer loyalty literature has attempted to define what the concept entailed. Numerous researchers have focused on the elements that support and grow loyalty. As a result there is a list of concepts that have been studied in isolation and in combination with others. Trust, has been emphasized as a determinant of customer loyalty (Schlosser et al., 2006; Smith, 2002; Reichheld et al., 2000a) throughout the literature. Trust is vital in situations in which informational cues are incomplete. In a world of perfect knowledge, trust is unnecessary. In the real world, it helps reduce perceived risk by lessening the likelihood that a customer will suffer a loss. Essentially, trust bolsters the customer’s belief in the likelihood of a positive outcome. The effect may be expressed in terms of a feeling that the vendor 422

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addition, some companies try to maximize individual marketing transactions, ignoring the value of establishing and exploiting relationships with customers. The emphasis seems to be misdirected. Marketers know the economic benefits of customer retention and efforts to boost repeat purchase. There are numerous reports of the positive benefits of increasing customer retention rates on profits. For example, Reichheld et al. (2000a) found that a 5 percent increase in customer retention rates could produce a 25 to 95 percent increase in profits for many companies. That multiplier effect merits attention and action. Consequently, Zeithaml et al. (2001) emphasize the lifetime customer value (LCV) concept as a means of increasing profitability and success. They suggest that companies should evaluate each customer on his or her LCV and court those with high scores and ignore those with low scores. There are techniques that can be used to accomplish these goals.

They include monetary costs, the well known price of products and services. Costs also include the amount of time, effort, worry, and uncertainty that are involved with a transaction. The idea of “cutting out the middleman” describes a consumer assuming some of the duties of a channel member to get a lower price. For example, pre-paying for an automobile before it is delivered assumes the financial role of a middleman. Similarly, buying in quantity is a middleman practice that should result in lower prices. The practice does not really lower costs, but shifts them to a different set of non-monetary costs. The shifted costs may be considerable and unattractive to particular buyers but is a choice. The basic conceptualizations not only have elements that pertain to the value a consumer realizes from an interaction but have elements of personal emotional factors as well. Perceived value and emotional factors interact with each other. For example, a consumer who interacts with a retailer can evaluate the outcome using logic. If the interaction is “valuable” in economic or needs satisfaction terms, the consumer may develop a positive emotional response toward the retailer.

Net perceived value The relevant research offers a selection of other factors that affect loyalty. Next after trust in developing loyalty is the consumer’s perceived sense of value resulting from a transaction or relationship. Perceived value has been defined as a customer’s overall evaluation of the benefits versus the costs involved in a marketing context. Specifically, perceived value can be viewed as a broad construct focusing on prices, costs incurred and benefits delivered by a marketer versus competitors. The literature notes that customer satisfaction may be based on a foundation of perceived value but the constructs are different. Different consumers view value in different ways. However, most consider costs and benefits. Looking at benefits and costs helps consider the variety of elements involved in a marketing transaction.

The emotional elements If a marketer has established trust with particular customers by conducting business focusing on trust generating activities, the stage is set for a deeper relationship. The role of commitment has received special attention (Amine, 1998; Reichheld et al., 2000b). While brand loyalty can be assessed by observing consistent purchases of a brand over a period of time, it is really more than a set of repetitive discrete transactions between consumers and brands. It is not only repeat purchase behavior but also a set of motives that underlie that behavior. Those motives can help to distinguish between spurious loyalty which can be described as inertia and true loyalty which signifies a commitment to the brand or company. This differentiation helps to understand whether a given consumer’s repeat purchased will continue (true loyalty) or may stop when a change in the store assortment or the selling conditions occurs (inertia). Inertia may be rather durable since it is formed based on habits or routines that enable consumers to cope effectively with time pressures and search efforts. Inertia repeat purchasing of a brand appears has been described as habitual behavior to reduce two types of work. The first is mental and involves comparing product or company attributes. The second is physical and focuses on information search activities like visiting stores or searching the internet. Without the bond of commitment, marketers run the risk that apparent consumer loyalty may evaporate when there is a change in the habitual supply conditions, encouraging brand switching. How may true customer loyalty develop? One mechanism involves customers making repeat purchases from a vendor with or without any commitment. Over time, if the vendor takes measures to boost trust, perceived value, and emotional connections, then commitment may follow. Other emotional bonds may assume equal importance. Recent work on the emotional bonds between customers and brands highlighted a construct that describes the levels of customer engagement. The idea emerged from research done by the Gallup organization. Gallup developed a set of rating scales that measure four critically important emotional states.

Value For some customers, just finding a product is valuable. For example, in the specialty gift market, finding that one gift that perfectly matches a recipient may be a triumph. For others, finance terms or ability to use credit cards is an expected level of service and valued. Other marketing activities such as home delivery of new products and disposal of old ones can determine whether a purchase takes place. Similarly, setup and service may be critical in product categories with a high technological component such as computer or high definition television. Some but not all customers recognize value in the convenience of their transactions. For some customers, time is a critical element. Thus, mechanisms that reduce the time to search for information about products and vendors, to evaluate that information and make a purchase decision, to speed payment and delivery, and reduce the effort in customer service and satisfaction might be valued. This customer segment may be more sophisticated in its understanding of marketing and its set of preferences and can be targeted for further carefully crafted offers. Specifically, bundling a product of acceptable quality with a set of services aimed at increasing convenience and reducing consumer worry or effort may yield profitable results. Costs The basic value computation would seem to look at benefits versus costs. 423

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Gallup’s research was done professionally and yields efficient and reliable measures. While its strength will vary, emotion is inherent in most consumer decisions. Together, these states represent the strength of the emotional connection existing between a customer and a brand or a company. The measures are labeled: confidence, integrity, pride, and passion. Specifically, confidence is confidence in the brand’s promise of benefits. Every product makes claims about the benefits it will convey but consumers have become jaded by too many claims from too many new and improved products. If consumers feel confident about a product or company’s benefits, that product or company has achieved a difficult to get accolade. Consumers must also believe in the brand or company’s integrity. A recent list of the ten worst automobiles included the 1977 Honda Accord. Today the Accord is the standard of a valuable and reliable car. In 1977 it was a new product and suffered engine and body problems that were severe. They threatened to sink the model and the company in the American market. Honda took ownership of the issues and fixed each one, mostly at its own expense. That significant investment in its product’s reputation paid dividends almost immediately. Honda is a perfect example of a company that stands behind its products and it has a loyal following of customers who believe in the company’s integrity. The higher level emotions are pride in being a customer, and passion for the brand. Pride reflects the degree to which consumers feel appreciated by the company and proud of their personal association with the brand. When consumers seek to wear company logo clothing, or select a company credit card, they manifest that pride in being the company’s customer. The Breitling watch company targets some of its owners for exclusive logo merchandise sales. Like the externally branded clothing of the 1990s, Breitling merchandise billboards the corporate name. Exclusive clothing sales to Breitling owners reinforces their pride in being customers. Passion reflects the belief that the brand is essentially irreplaceable and represents a seemingly perfect fit with the customer’s personal needs. It’s hard to surpass the consumer passion shown by Harley-Davidson motorcycle owners whose behavior may border on obsession. The literature has investigated the underlying dimensions of loyalty and loyalty intentions. Some have attempted to map the dynamic mechanism that forms consumer loyalty over time (Johnson et al., 2006). Analysis of responses to the individual items in this set of measures has revealed that customers develop emotional attachment to a brand in a cumulative way, with confidence as the foundation of a brand relationship and passion as the pinnacle.

profitability. There are numerous variations in terms of transactions. Some, like a visit to a doctor’s office may be highly involving and require deep thought and response from patients. Others, such as using a vending machine, are relatively low involvement interactions. The main classification of interest here is the face-to-face versus online transaction. Face to face transactions in a retail setting The face to face (F2F) transaction environment is an information rich, multimedia communication arena. Potentially, each of the senses can play a part. The typical retail environment may involve informational sources such as point of purchase advertising or video based infomercials, apparent product features, price and warranty information, and human salespeople. Building customer loyalty in a face to face interaction Face to face transactions involve what is arguably the most powerful communicator in marketing, the product or service. Well-engineered products broadcast their benefits. For example, bicycle stores have a varied inventory that might include examples with titanium and steel frames. Customers who examine and compare the different units will understand the differences in weight readily. Salespeople, knowledgeable about their products and those of their competitors can point out differences, highlight benefits and costs and in doing so offer great aid. In computer terms, a customer can pose “random access” questions to a salesperson and get tailored answers without the need to search through literature or a list of frequently asked questions. Moreover, the professional salesperson can use his or her selling skills to diagnose customer wants and needs and even offer information which the customer did not anticipate asking. During the interaction, customers may form a positive bond with the salesperson, an emotional response. Thus, salespeople who demonstrate honesty, integrity, care and concern can foster a feeling of trust. They can also engender a perception of value by their helpful behavior. Face-to-face retail interactions may vary greatly. By definition they include the most basic form, customer interactions with a vending machine without other human contact. Even this kind of transaction has some basic elements of the human touch since new vending machines now display questions and express thanks for the business. However, face-to-face transactions between humans are more complicated. Perhaps the checkout line in a retail store is the least likely human-to-human contact to aid in building a customer relationship. And yet, numerous retailers train their check out personnel to make eye contact with customers, greet them and ask if there is anything further aid they might offer. In repetitive purchase situations like grocery shopping, some customers and check out clerks can develop an acquaintance that is like friendship. High involvement products require increasing levels of salesperson interaction – “consultative interaction.” Helzberg Diamonds, a US jewelry retailer, is known for its high quality gemstones at reasonable prices. Diamonds are high involvement products characterized by high cost, and relatively clear criteria that can aid selection. Helzberg’s sales process is highly personalized and may require a trial and error approach that moves a customer to a final choice. Handling that process may be a salesperson’s ultimate selling

The role of the transaction Another relevant factor in customer loyalty is the interactivity inherent in the buyer-seller relationship (Schlosser, 2000). However, the most valuable views have been multidimensional. Marketers have long recognized the importance of transactional elements in marketing relationships. Notions of convenience and transactional success have been studied over time. Examples such as the marketing use of transaction cost accounting were attempts to increase customer satisfaction and ultimately loyalty while maximizing 424

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Volume 23 · Number 7 · 2006 · 421 –429

challenge. It requires guiding a series of decisions and really understanding the customer’s wants. Salespersons’ personalities play an important role in building a relationship with customers and Helzberg selects its personnel carefully. During that consultative interaction, the customer has the opportunity to evaluate the salesperson’s comments. In general they may be scaled on a number of continua. One relevant scale ranges from “high pressure” to “intelligent advice.” If a salesperson can make a sale while conveying the impression of care, concern and offering intelligent, unbiased advice, a positive relationship may result. That relationship may be the basis for strong customer loyalty and repeat purchases. Some Helzberg salespeople have a stable group of customers who return periodically for repeat purchases.

One or both of these reasons are plausible explanations for the finding that online brand loyalty for high market share brands exceeds that of a traditional shopping environment, with the reverse effect for low share brands. The nature of online transactions Commerce on the internet, or e-commerce, has experienced rapid and continued growth over time. Online shoppers are attracted by the ease with which they can find products on the internet, the detailed product information available, and the variety of choices offered. The economics of e-commerce make it easy for vendors to do business online and too many smaller retailers have started internet operations. The result is retailer information clutter. Consumers have demonstrated a tendency to bypass these problems by relying on branded products or themselves (Pitta and Fowler, 2005). The phenomenon, if widespread, could limit the success of new product introductions via the web. Ernst & Young reported that 69 percent of those surveyed stated that brand names play a significant role in their online buying decisions. Thus, marketing through established brands may be required on the internet, even though consumers’ cost of information gathering seems quite low. There is speculation about the source of the differences between online and offline purchases. Compared to communications with the mass media, internet communications feature enhanced interactivity which empowers customers. They can decide how to approach the information. They can decide what to look at and what to ignore and how to evaluate it. In this context, interactivity is an invitation to choose among different message and product alternatives. While the control is in the hands of the consumer (Schlosser, 2000), one of the glaring contrasts between the online and face-to-face retail environments is the relative lack of interactivity online. In an online transaction there are no face-to-face props to aid in consumer decision making. As a result, consumers tend to rely on substitutes and analogs to help them make choices. Over time, they tend to increase their experience with online companies as well as their search proficiency and need to rely less on brand names (Ward and Lee, 2000).

Differences between the face-to-face and online environments Early in the evolution of e-commerce, in the early 1990s, the literature discussed terms such as “bricks and mortar” versus ”virtual retailers.” It became clear that the bricks and mortar competitors had an advantage over the pure internet retailers. Consumers felt more confidence in dealing with a ”real” company rather than a comparatively intangible virtual organization. Consumer fears that some internet stores were in actuality ghost storefronts with no inventory and little reputation to defend sometimes were realized. Some consumers lost money when undercapitalized retailers went bankrupt. At the time, bricks and mortar retailers who started internet stores had an instant advantage. Their “clicks and mortar” retail operations had the confidence enhancing real store backing up the benefits of their internet operations. The situation demonstrated that trust is easier to generate for an existing store brand name. A similar situation exists for products. Danaher et al. (2003) compared the brand loyalty of grocery products for closely matched samples of brands purchased either online or offline. The results were straightforward. They found that for purchases made offline, brand market share is not related to the differences in brand loyalty. However, for online purchases a comparison of actual with model-estimated brand loyalty shows that “excess brand loyalty” exists. In particular, greater brand loyalty is observed for brands with high market share, and vice versa for low share brands. There is strong evidence of higher brand loyalty for online purchases compared with offline. The findings are consistent with those of Degeratu et al. (2000) who found that brand name was important in the sense that a “strong” brand, characterized by a large market share, did better in an online environment compared with a “weak” brand, characterized by a low market share. Danaher et al. (2003), offer possible explanations for this phenomenon. They include: . online shoppers may infer product quality from the brand name and the greater relative salience of the brand name online compared with offline means that consumers are likely to place more emphasis on the importance of brand name when shopping online (Moore and Andradi, 1996); and . buying a well-known rather than a lesser-known brand online has less perceived risk (Ernst & Young, 1999).

Building customer loyalty online Marketing managers must establish consumers’ trust in a variety of contexts. The specific case of computer-mediated environments such as the internet is particularly difficult (Naquin and Paulson, 2003). One approach involves explicit statements about the security of customers’ personal data. However, evidence on the effectiveness of such statements is mixed. Some research has shown that such statements help instill consumer confidence in e-commerce sites (Palmer et al., 2000). In contrast, others suggest that they are not necessarily the most important predictor of online trust (Montoya-Weiss et al., 2003). Findings from a recent large-scale study suggest that consumers use “surface” elements, such as web site design (Fogg et al., 2002) in judging security and trust. Schlosser et al. (2006) found that investments in web site design can boost trusting beliefs and online purchase intentions. Other web site design elements can aid in relationship building. One innovative relationship-building example comes from the UK. Online travel services have exploited the internet’s 425

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strengths such as ease of search and matched them to the specifics of travel. Travel services require information rich communications ranging from schedules to destination details and terms of service. The information load can be overwhelming to a novice traveler. Online, several major travel services vie for dominance in the marketplace using standard website design features to lure customers. In contrast, a group of UK travel agents has designed its website to deliver immediate primary contact with a real person. The UK travel agents provide a toll free number at the beginning of an online transaction to convey the visitor directly to a helpful human. This helps agencies with a limited internet presence and establishes a human bond immediately. Another example from the travel industry comes from the USA. In the US airline industry, retirees can be the human being at the other end of the line. Technology allows individuals to work from home and be available for extended hours. Southwest Airlines has a number of customer service agents who work from homes all over the USA. They are scattered across the four continental time zones and Hawaii and comprise a six-hour time zone window. The agents can offer extensive around the clock coverage. The retiree agents are well trained and selected for personality and helpfulness. For many travelers, a friendly grandparent on the other end of the line is comforting and relationship building. The travel industry offers yet another example that online marketers should consider. Since trust is necessary for any relationship, third party comments can be valuable. Companies like Orbitz solicit customer evaluations and travel recommendations to boost their credibility. These efforts may involve sponsored online forums that invite seasoned travelers to share their personal experiences for the benefit of others. Such online community forums have been shown to alert consumers to opportunities and help them avoid problems. One recent example focused on Easter Island, part of Chile. Travelers warned others to avoid the largest hotel on the island and instead seek one or two smaller venues. The alternatives did not advertise but were excellent. The main hotel advertised heavily but was deemed a place to avoid. The community forum, reachable from the sponsor’s web site, adds credibility and value to the sponsor and thus helps build a consumer relationship. One other technique is to allow customers to join a vendorsponsored “club.” Online customer “members” can reap the benefits of “club members only” deals. They can get early warning of impending offers and often get preferential pricing. Since they are readily available to retailers through an online database, these customers provide lower transaction costs. Since they are experienced customers, in the sense that they have purchased from the retailers before, they have a higher probability of purchasing again. Therefore, they are more valuable than customers with no purchase history. In summary, online marketers face a different challenge than their face to face competitors. They need to generate trust immediately, offer clearly perceived value and establish a long term relationship with selected customers (Smith, 2002). If online marketers are successful in generating brand loyalty, they should treat it as a valuable commodity not to be wasted.

deals with heavy versus light users of products and services and notes that heavy users are valuable not only for their contribution to volume and revenue but also for their positive effects on profit. Most firms are aware at some level that their customers differ in profitability, and recognize the “80/20 rule” – 20 percent of customers produce 80 percent of sales or profit for the company. Empirical studies have supported that rule of thumb. In particular, long-term studies of bank patrons’ account behavior and perceptions of service quality, the 80/20 rule was shown clearly (Zeithaml et al., 2001). Specifically, the authors noted that the top 20 percent produced 82 percent of the bank’s retail profits, an almost perfect confirmation of the 80/20 rule in this profit setting. Moreover, the more profitable 20 percent held more sophisticated views of service quality. The highest tier described service quality in terms of three factors: attitude, reliability, and speed. The lowest tier viewed only attitude and speed. In addition, when analyzing the drivers of repeat purchases, the highest tier focused on speed; the lowest on attitude. That evidence offers an opportunity to focus on the more profitable customers with customer loyalty building strategies to which they will respond. The customer pyramid framework As an example Zeithaml et al. (2001) postulated a framework for categorizing customers using a four tier system based on different expected levels of profit. They called the classification system the customer pyramid. Using the customer pyramid is helpful whenever the company has customers that differ in profitability but is delivering the same levels of service to all customers. In these situations, the firm is using limited resources to stretch across a wide group of customers, possibly under-serving its best customers. Their framework used the names of four metals to indicate value and included the following four tiers, Platinum, Gold, Iron and Lead. Platinum and Gold customers are valued while the Iron and Lead tiers are less attractive. The Platinum tier describes the company’s most profitable customers. They are often heavy users of a product or service and not overly price sensitive. They show a commitment to the firm and seem willing to invest in and try new offerings. The other valuable tier, the gold tier is still attractive. It differs from the platinum tier with lower but still good profitability levels. Gold customers may seek price discounts that limit margins. Also their commitment to the firm is lower than that of the platinum tier. Typically, they may be heavy users of the product category but may value minimizing risk by multi-sourcing rather than working with one company. The two less attractive tiers, Iron and Lead represent much lower profit potential than others. Iron tier customers are valuable in that they provide the volume needed to utilize the firm’s capacity. They should be retained as customers. However, their spending levels, loyalty, and profitability are not substantial enough for special treatment. In contrast, Lead tier customers represent losses to the company. They tend to cost more than they generate. They may demand more service than they are merit given their spending and profitability. Moreover they may pose a potential threat to the company. Since Lead tier customers expect more than they deserve, they may be dissatisfied with the service they do get and complain to others about the company.

The customer value pyramid Marketing textbooks have recognized differences among consumers in their value to firms. Some of the literature 426

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Journal of Consumer Marketing

Dennis Pitta, Frank Franzak and Danielle Fowler

Volume 23 · Number 7 · 2006 · 421 –429

The usefulness of the customer pyramid classification lies in its focus. While many firms conduct usage segmentation, this approach focuses on a group of variables other than sales that are responsible for profitability of the tiers. It is a robust concept that applies to both online and face-to-face selling situations. In distinguishing a customers’ value, keywords can be important. Labels such as “premier customer,” “preferred customer,” and “valued customer,” can communicate an individual’s appropriate service levels quickly. For example, using its own version of the customer pyramid method, Bank of America classifies its customers into three levels roughly equivalent to excellent, good, and average based on their contributions to bank profitability. Each level has access to a range of bank services, with the most profitable customers having the greatest selection and paying the lowest rates. Thus premier customers may have several no fee checking accounts, a free safety deposit box, no charges for currency conversions or money orders, and even preferred rates on loans. In addition, premier customers also are assigned a private banker, available via telephone, who can offer personalized service, increasing the strength of the customer-bank relationship and the potential for profits. Personal contact with a private banker is an expensive service that represents a scarce resource best restricted to the most profitable clients. The less important preferred customers have to pay for some or all of the services and get higher, less favorable loan rates. They typically do not have access to a private banker. The least important, “valued” customers pay for most services and when applying for loans, get the least favorable, ordinary loan rates. Thus the bank offers a higher level of service to the more profitable customers their own platinum and gold clients. They treat their Iron customers professionally with respect. The Lead clients are missing from the bank’s customer service strategy. Customer pyramid concepts can be applied equally well to online and face-to-face retail situations. The key seems to value both your customers and your products appropriately.

services offered by FedEx ranging from slow and cheaper to fast and expensive. Once customers are identified and classified into profitability tiers, firms can attempt to build relationships to enhance customer loyalty while, more importantly, build profitability. For example, Gold tier customers are valuable but can be even more profitable if they morphed into Platinum customers. The requirement is to know the Gold customer very well to build new product offerings that serve the client better. Some strategies that have been used to enhance the relationship include becoming a full service provider. Home Depot is an example of a company that succeeded in turning its Gold customers into Platinum. It opened high end Expo Design Centers that carried exclusive, extensive, and expensive home renovation items targeted to specific Gold customers. It thereby offered a full service alternative to satisfy their needs and found that their purchase volume increased significantly. Largeart.com, a Maryland-based online provider of large art pieces including murals and sculptures, found that commissioning an artist to create a painting large enough to hang in a bank foyer was the easy part. Getting the piece to an international buyer proved more difficult and became an obstacle to purchase. The solution was to become a full service provider. The company decided to roll the finished painting in a shipping tube, insure it and send it not to the customer, but to a certified framing contractor near the customer. The local framing provider was responsible for unrolling the art at its destination, reframing it, delivering it to the site and hanging the painting for the client. The company now has a network of local experts on contract to provide this technical service around the globe. By providing full service for an unwieldy piece of art, Largeart.com gained valuable customer loyalty and saw improvements in its customers’ purchase volume. Online providers are especially concerned about service problems. Such issues can lead to dissatisfaction and customer relationship extinction. It is imperative that companies learn when service problems occur and resolve them quickly and completely. The Dell computer company sells its systems online. It provides as much information as possible online and uses knowledgeable, well-trained consumer service personnel to guide customers through the design and ordering process. It also offers a service guarantee. Dell assures customers that they will be satisfied or else they receive some form of compensation commensurate with their problem. When a system arrives, Dell service representatives can help with setup via telephone. If there is a problem, such as a defective part, it can be expressed to the customer for installation by a local technician. There are several types of service guarantees. Marketers should choose the type of service guarantee geared to the pyramid tier. The very best customers should get a complete satisfaction guarantee. The worst, Lead tier customers, should get one with fewer features. One of the best examples of building customer loyalty and moving customers from Gold to Platinum levels involves outsourcing. The practice involves providing an entire function that a customer firm formerly performed for itself. BJ’s Wholesale Warehouse is one of the large warehouse discount retailers. It had responsibility for all data processing in each of its stores. While it was successful, an outside vendor consultant offered to integrate each store into a centrally

Using the customer pyramid to build online loyalty We have seen that the first level of loyalty, trust, plus the second level, perceived value, can lay the groundwork for the third level, relationship building. Presumably, customers who have online purchase relationships with companies have already reached a level of trust. Their perceptions of value may differ depending on the tier that they are in. Zeithaml and her coauthors recognize four different articulations of value: value as low price; value as desired product or service benefits; value as the quotient of quality divided by price; and finally value as the sum of all the received benefits minus the costs. The first articulation, low price, probably applies to Lead tier customers who want low price even if they have to accept less. Customers who concentrate on desired product benefits without consideration of cost are probably in the Platinum tier. These customers would be satisfied by firms that provide high value, high margin products and services. In between these two are the Gold and Iron customers who are both service and price sensitive. This view supports the reality that some customers are willing to pay for enhanced services and others are not. To support this view, Zeithaml and her coauthors site the numerous levels of express package delivery 427

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Dennis Pitta, Frank Franzak and Danielle Fowler

Volume 23 · Number 7 · 2006 · 421 –429

processed system based near company headquarters in Massachusetts. The system was much more complex than any BJ’s had run internally. The vendor then offered to run and maintain the more extensive system. In a short time, the vendor became the provider of all the company’s transaction processing and moved BJ’s from a Gold to a Platinum customer. Notably, there was a widespread system failure on Memorial Day, 2006. The vendor’s service team solved the problem in less than an hour. While the time lapse seemed like an eternity, and cost the chain lost sales, it was much shorter than the in-house BJ’s data processing operation could have delivered. BJ’s top management was concerned but upon reflection realized that the glitch would have been a disaster without outside help. Even this problem increased the strength of the customer-vendor bond. One reason for its value is that outsourcing reduces the nonmonetary costs of providing these services to the client firm, freeing it to concentrate on its core competencies. For example, the expertise and effort required to keep pace with new information technology, threats to security and fixing problems, and keeping qualified staff can become a distraction for a client firm. Once a vendor performs well for a customer, it can bind the customer to the organization and make the customer’s business predictable, and increase its value to the company.

pose a danger to profits if applied to lead tier customers. Price discounts may be wasted on them. In contrast, Platinum tier members can be alerted to new purchase opportunities before the general public. If price is not part of the offer, Platinum tier customers may appreciate the advanced notice, which will increase their bond with the marketer. Another example, sponsored online community forums can offer value if restricted to satisfied customers. Presumably, the Platinum and Gold tier customers are satisfied and will appreciate third party recommendations. The danger, again, is focused on Lead tier customers. They will probably enjoy the lowest level of services and if they find a way to join such a forum, may complain about the sponsor and tarnish the experience for others. The final caveat is that discriminating against Lead tier customers makes economic sense. However, it must be done carefully to avoid angering them and causing further damage.

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References Amine, A. (1998), “Consumers’ true brand loyalty: the central role of commitment”, Journal of Strategic Marketing., Vol. 6, pp. 305-19. Danaher, P.J., Wilson, I.W. and Davis, R.A. (2003), “A comparison of online and offline consumer brand loyalty”, Marketing Science, Vol. 22 No. 4, pp. 461-76. Degeratu, A., Rangaswamy, A. and Wu, J. (2000), “Consumer choice behavior in online and traditional supermarkets: the effects of brand name, price, and other search attributes”, International Journal of Research in Marketing, Vol. 17 No. 1, pp. 55-78. Dunn, J. (2004), “Survey shows online security perception gap between experts, users”, Knight Ridder Tribune Business News, Vol. 17, p. 1. Ernst & Young (1999), The Second Annual Ernst & Young Internet Shopping Study: The Digital Channel Continues to Gather Steam, Ernst & Young, New York, NY, available at www.ey.com/publicate/consumer/pdf/internetshopping.pdf (accessed October 18). Fogg, B.J., Soohoo, C., Danielson, D., Marable, L., Stanford, J. and Tauber, E. (2002), “How do users evaluate the credibility of web sites? A study with over 2,500 participants”, available at: http://portal.acm.org/citation. cfm?doid ¼ 997078.997097 (accessed December 6, 2005). Johnson, M.D., Herrmann, A. and Huber, F. (2006), “The evolution of loyalty intentions”, Journal of Marketing, Vol. 7 No. 2, pp. 122-32. Moore, K. and Andradi, B. (1996), “Who will be the winners on the internet?”, Journal of Brand Management, Vol. 4 No. 1, pp. 57-64. Montoya-Weiss, M.M., Voss, G.B. and Grewal, D. (2003), “Determinants of online channel use and overall satisfaction with a relational, multichannel service provider”, Journal of the Academy of Marketing Science, Vol. 31, pp. 448-58. Naquin, C.E. and Paulson, G.D. (2003), “Online bargaining and interpersonal trust”, Journal of Applied Psychology, Vol. 88 No. 1, pp. 113-20. Palmer, J.W., Bailey, J.P. and Faraj, S. (2000), “The role of intermediaries in the development of trust on the WWW: the use and prominence of trusted third parties and privacy statements”, Journal of Computer-Mediated Communication, Vol. 5, March, pp. 1-25.

Implications for marketers While online marketers can make astute decisions that boost consumer trust, increase perceived value and build loyal customer relationships, it should be clear that companies must be selective. In an environment typified by turmoil and scarce resources, marketers must consider whom they wish to serve carefully. In fact, the typical strategies for building online volume may be counterproductive in building attractive customer relationships. In contrast, setting requirements to be a customer may raise a firm’s perceived exclusivity, making existing customers proud. For example, some of the specific techniques for building online loyalty involve electronic coupons and e-mail newsletters that contain special offers. The idea is that customers will respond to special offers that reduce price and thereby increase perceived value. Using a classification system like the customer pyramid offers guidance in the search for profitability. In fact, price reduction offers are a bad idea for lead tier customers. The discounts may be popular with lead customers but they may reduce profitability. There are examples of customers delaying routine purchases in anticipation of a price break, reducing profits further. Moreover, discount offers may be irrelevant to platinum or gold tier customers and should be avoided. The economics of e-commerce make personal contact too expensive to extend to all online customers. Thus, marketers tend to reject the concept out of hand. However, contact with a human can be valuable and boost company profits. Marketers may wish to use personal contact relationship building strategies only with their most profitable customers. By restricting it to the Platinum or Gold customer tiers, human contact can become affordable and the firm can enjoy its profit enhancing benefits. Finally, some of the techniques that foster online volume building should be applied very carefully to the customer pyramid. The examples used above like members only clubs 428

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Pepper, D. and Rogers, M. (1993), The One-to-One Future: Building Relationships One Customer at a Time, Century Doubleday, New York, NY. Pitta, D.A. (1998), “Marketing one-to-one and its dependence on knowledge discovery in databases”, Journal of Consumer Marketing, Vol. 15 No. 5, pp. 468-80. Pitta, D. and Fowler, D. (2005), “Online consumer communities and their value to new product developers”, Journal of Product and Brand Management., Vol. 14 No. 5, pp. 283-91. Reichheld, F.F., Markey, R.G. Jr and Hopton, C. (2000a), “The loyalty effect – the relationship between loyalty and profits”, European Business Journal, Vol. 12 No. 3, pp. 134-9. Reichheld, F.F., Markey, R.G. Jr and Hopton, C. (2000b), “E-customer loyalty – applying the traditional rules of business for online success”, European Business Journal, Vol. 12 No. 4, pp. 173-9. Schlosser, A.E. (2000), “Harnessing the power of interactivity: implications for consumer behavior in online environments”, in Hoch, S.J. and Meyer, R.J. (Eds), Advances in Consumer Research, Vol. 27, Association for Consumer Research, Provo, UT, p. 79. Schlosser, A.E., White, T.B. and Lloyd, S.M. (2006), “Converting web site visitors into buyers: how web site investment increases consumer trusting beliefs and online purchase intentions”, Journal of Marketing, Vol. 70 No. 2, pp. 133-48. Smith, A.D. (2002), “Loyalty and e-marketing issues”, Quarterly Journal of Electronic Commerce., Vol. 3 No. 2, pp. 149-61. Ward, M.R. and Lee, M.J. (2000), “Internet shopping, consumer search and product branding”, Journal of Product & Brand Management, Vol. 9 No. 1, pp. 6-20.

Zeithaml, V.A., Rust, R.T. and Lemon, K.N. (2001), “The customer pyramid: creating and serving profitable customers”, California Management Review, Vol. 43 No. 4, p. 118.

Further reading Pastore, M. (2000), “Internet retailers look toward profitability”, InternetNews.com, August 30.

About the authors Dennis Pitta is the J. William iddendorf Distinguished Professor at the Merrick School of Business, University of Baltimore. He has authored numerous articles in product management and healthcare marketing. His current research interests are new product development, and the effect of new technologies on marketing. Frank Franzak is Associate Professor and Chair of the Marketing Department at the Virginia Commonwealth University. His research interests focus on creativity and new product development. In addition, he is engaged in research detailing the relationship between the creative culture in nations and their new product development success. Danielle Fowler is an Assistant Professor at the Merrick School of Business, University of Baltimore. She is an expert in e-commerce and information technology and its application to business and marketing. Her current research investigates emerging marketing applications for smart cards and other technology.

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The royalty of loyalty: CRM, quality and retention Mosad Zineldin School of Management and Economics, Va¨xjo¨ University, Va¨xjo¨, Sweden Abstract Purpose – The purpose of the study is to examine and develop a better understanding of triangle relationship between quality, customer relationship management (CRM) and customer loyalty (CL) which might lead to companies’ competitiveness (CC). Design/methodology/approach – A research model (5Qs) was designed to measure satisfaction and loyalty. This model is based on two conditions: the customer database and CRM strategy are well structured; and that management control systems have the capacity to produce required data for the analysis. Findings – Changing in quality over time within various segments or related to specific products or categories of products/services can be used as an indicator the level of loyalty. By linking infrastructure, interaction and atmosphere indicators to the quality of object and processes, researchers and managers can document which changes in CRM strategy improve the overall satisfaction and loyalty, hence the ultimate outcomes. Practical implications – Key ways to build a strong competitive position are through customer relationship management (CRM) and product/service quality. A company has to create customer relationships that deliver value beyond the provided by the core products. This involves added tangible and intangible elements to the core products thus creating and enhancing the “product surrounding”. One necessary expecting result of the creation of value added is customer loyalty. This is an important function to ensure the fulfilment of given customer requirements and companies profits, survival and competitive positioning. Originality/value – In this study a new technical-functional 5 qualities model (5Qs) is created and utilized to measure the quality and loyalty. The paper suggests how to incorporate the infrastructure, interaction and atmosphere indicators into the quality of object and processes to identify changes and improvement in CRM strategies. Keywords Customer loyalty, Customer relations, Relationship marketing, Competitive advantage Paper type Research paper

higher resource spending. It is likely that the quality may still be perceived as poor because intangible aspects of the service package (of product and services) are not being addressed. Many companies have found themselves in this position with many of their customers. Quality doesn’t improve unless you measure it (Asser, 1990). Customer relationship and quality presents special problems in measurement both at the level of the economy and the level of the operation (business). One way to resolve some of the dilemmas over measurement is to make the link more obvious between the customer loyalty, CRM and quality. The purpose of the study is to examine and develop a better understanding of triangle relationship between quality, customer relationship management and customer loyalty (CL) which might lead to companies competitiveness (CC). This entails making decisions regarding substantive customer relationship management (CRM) attributes that are known to be important to customers and that relate to product or service performance and availability. It provides also some managerial implications.

Introduction Competition will undoubtedly continue to be a more significant factor. Finding a place in this heating sun becomes vital to the long-range profitability and ultimate survival of the company. Those companies that are not considering the new atmosphere to build and protect their competitive position will likely become victims of that heating sun. According to Porter (1980), there are two generic ways of establishing a competitive advantage, the low-cost supplier or by differentiating the offer in a unique and valuable way. Every company has to consider how to enter a market and then build and protect its competitive position. They are forced to find a new basis for competition and they have to improve the quality of their products/services. Customer loyalty is one way to create a competitive advantage. Evaluation of the relationship between quality, CRM and customer loyalty requires an understanding and examination of the elements of quality relative to the operations strategy (Asser, 1990; Zineldin, 1995, 1996, 2000). Improving the intangible attributes of quality is not necessarily achieved by

Relationship and the added value The current issue and full text archive of this journal is available at www.emeraldinsight.com/0736-3761.htm

Today’s marketing is not a function; it is a way of doing business. Marketing is not a new ad campaign or aggressive promotion. Today’s marketing has to be all-pervasive, part of everyone’s job description, from the receptionists to the board of directors. Marketing is also about how to integrate the customer into the design of the product/service and to design a systematic process for interaction that will create substance

Journal of Consumer Marketing 23/7 (2006) 430– 437 q Emerald Group Publishing Limited [ISSN 0736-3761] [DOI 10.1108/07363760610712975]

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Mosad Zineldin

Volume 23 · Number 7 · 2006 · 430 –437

in relationships. In a competitive world, companies have to work hard to have any added value. They have to work with customers and to discover ways to rum the business more efficiently for themselves and more effectively for the customers. A company has to create customer relationships that deliver value beyond the provided by the core product. This involves added tangible and intangible elements to the core products thus creating and enhancing the “product surround”. Customer delivered value can be defined as the total value offered to a customer less the total cost to the customer. Total customer value can include functional value of the product, service value, emotional value, social value, conditional value, and epistemic value, and image. On the other hand, total customer cost can include monetary price, time, shopping efforts, energy and psychological cost value. To survive in dynamic marketplaces, companies clearly need to establish strategies that can survive the turbulent changes in the market environment. Building added value is the hard work of basic business. Many companies do their best at figuring out how to provide high quality at low cost. But so do competitors. That is the nature of competition. If there are many others who can do what you do, then you don’t have much added value. This dynamic erodes your added value. To protect its added value, a company needs to create and enhance long-term customer relationships (Zineldin, 2000; Gro¨nroos, 2000). A key question is how can the company develop an effective process for establishing and maintaining the added value and relationship with key consumers? The answer is they have to renew or improve their CRM and RM strategies of by producing and delivering high quality core products and supporting services in a more systematic manner. Those companies with the deepest and strongest customer relationships will stand the best opportunity of retaining the customer’s transactions. Many companies are selecting a few key market targets and concentrate on trying to serve them better than competitors. Companies, therefore, should emphasize deeper penetration of the existing customer data base. CRM is an effective way to maintain customer data base which allows a company to best understand customer’s needs – particularly their relationship needs – better than the competitors. Companies must build strong foundations that will not be blown away in the storm. They won’t do that by focusing on advertising and promotions. Rather, they need to gain an understanding of the market structure. Then they must develop long-term customer relationships. Those relationships are more important than low prices, flashy promotions, or even advanced technology. The feedback loop is central to these types of relationships. Changes in the market environment can quickly alter prices and technologies, but close relationships with loyal customers can last a lifetime. Close relationships provide a boost to the added value. The added value creates customer loyalty (Zineldin, 2000, 2005).

marketing and the consumer. Companies have recognized the fact that they must change and restructure their way of establishing and maintaining business relationships. For example, many manufacturers discovered, or more accurately, re-discovered that RM and CRM are invaluable with constantly changing technology and increasing global competition (Galbreath and Rogers, 1999; Valentine, 1999, Zineldin, 2000, 2005). Most managers and marketers would of course agree that establishing long-term business relationships is about development and survival. To clarify the concept CRM, we need to understand the close relationship marketing and customer relationship marketing. Relationship marketing is a concept reflecting a number of differing themes or perspectives. Some definitions of relationship marketing are: Relationship marketing is attracting, maintaining and – in multi-service organizations – enhancing customer relationships (Berry et al., 1983).

Or, according to Christopher et al. (1991): The relationship marketing concept is emerging as a new focal point, integrating customer service and quality with a market orientation.

Relationship management, however, emphasises the organisation of marketing activities around cross-functional processes as opposed to organisational functions or departments. This results in a stronger link between the internal processes and the needs of customers, and results in higher levels of customer satisfaction. CRM evolved from business concepts and processes such as relationship marketing and the increased emphasis on improved customer retention through the effective management of customer relationships. Both RM and CRM emphasize that customer retention affects company profitability in that it is more efficient to maintain an existing relationship with a customer than create a new one (Payne et al., 1999; Reichheld, 1996; Zineldin, 2005, 2000). One good example is CRM strategies implemented by the online dating web sites. According to Smith (2005): The application of CRM principles requires an understanding of one-stop shopping, customer tracking analytics and marketing, customer-based call centres, and timely field service. Several online dating companies have developed a customized business strategy based on CRM principles that focus on varying aspects of each customer’s desires and budget. This CRM-focus allows these firms to customize “the who” and “the how” in a segmented marketing plan, thus developing a more positive and trusting relationship with their clients . . . One of the most important goals of a web site should be to maximize loyalty, and the long-term value of that customer’s purchase.

The idea of linking relationship marketing to CRM is fairly strong and has led others such as Newell (2000) and Zineldin (2005) to explore strategic methods for developing, maintaining or improving customer retention. Another view of CRM is that it is technologically and data mining and database oriented (Sandoe et al., 2001). The increasing use of digital technologies by customers, particularly the internet, is changing what is possible and what is expected in terms of customer management (Peppard, 2000; Tamminga and O’Halloran, 2000). We argue that in reality CRM is a complex combination of business and technological factors, and thus strategies should be formulated accordingly. CRM is a useful tool in terms of identifying the right customer groups and for helping to decide which customers to last and keep. Clemons (2000) estimates there may be a tenfold difference between the most profitable customers and

Relationship marketing (RM) and customer relationship management (CRM) During the 1990s, many organizations and consumers experienced great movements and actions. Some key environmental factors provided the setting whereby companies changed their attention and orientation toward 431

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the average. While the idea that an organization cannot have a profitable relationship with all customers and the practice of targeting customers with a differentiated product or service is already widespread in many financial services (Zineldin, 1996, 1995), it is less established in many other business sectors such as manufacturing. One method for identifying customer groups is the notion of distinguishing between transaction and relationship customers. While transaction customers are highly volatile and have little loyalty Relationship customers have far more potential for loyalty as they are often prepared to pay a premium price for a range of reliable goods or services (Newell, 2000). Once relationship customers are recruited they are less likely to defect, provided they continue to receive quality service. Both RM and CRM should be used to identify the potential loyal customer groups and seriously consider the response required. However, CRM strategies are only effective if they deliver positive outcomes and profit for organizations and competitive value and quality for customer. It is no longer good enough just to argue that an organization is customer focused, but it matters what and how it does. If the CRM strategy is improving the profitability and increasing the quality of the prodserv (Product and service, according to Zineldin (2000)) with more reasonable price than the competitors, then the organization is clearly on the right path and able to have better and stronger market position. As CRM reaches into many parts of the business, organizations should adopt a holistic approach (Girishankar, 2000; Zineldin, 2000). For the purpose of this study, we will examine only the interlink between quality, CRM and customer loyalty (CL).

Customer loyalty and the maintenance of the customer relationship, are in fact dependent on how well a product and service measures up to the customer’s original expectations of quality. While Gro¨nroos (2000) divided the total quality of a product/service into technical quality and functional quality. Zineldin (2006) expended technical-functional quality models into framework of five quality dimensions (5Qs) which impacting the satisfaction and loyalty of a customer (5Qs): Q1. Quality of object – the technical quality (what customer receives). It measures the core prodserv itself. Q2. Quality of processes – the functional quality (how the prodserv provider provides the core prodserv (the technical). It can be used to pinpoint problems in service delivery and to suggest specific solutions. Q3. Quality of infrastructure. Measures the basic resources which are needed to perform the prodserv services: the quality of the internal competence and skills, experience, know-how, technology, internal relationships, motivation, attitudes, internal resources and activities, and how these activities are managed, co-operated and co-ordinated. Q4. Quality of interaction. Q4 measures the quality of information exchange, financial exchange and social exchange, etc. Q5. Quality of atmosphere – the relationship and interaction process between the customer-company are influenced by the quality of the atmosphere in a specific environment where they operate. The atmosphere indicators should be considered very critical and important because of the belief that lack of frankly and friendly atmosphere explains poor quality and less loyalty.

A 5Qs model and the customer relationship management

The 5Qs model is more comprehensive and incorporates essential and multidemonical attributes for CRM which are missing in the other models. Such attributes are the infrastructure, atmosphere and the interaction. Figure 1 illustrates the 5Qs model and its constructs that was used in the project, where the total quality (TQ) of the health care is function of Q1-Q5. The TQ is a f (Q1 þ Q2 þ Q3 þ Q4 þ Q5). Each single quality dimension of the CRM strategy is impacting the level of satisfaction which in turn impacting the loyalty. By using A TRM philosophy which includes the 5Qs (Zineldin, 2000) and viewing an organization as a collection of interdependent systems and processes, managers can understand how CRM problems occur and can strengthen the organization as a whole. By linking infrastructure, interaction and atmosphere indicators to the quality of object and processes, however, researchers and managers can document which changes in CRM strategy improve the overall satisfaction and loyalty, hence the ultimate outcomes.

Today a competitive market position and a good reputation of a company can quickly translate into market share and profit, but that distinction is often earned only through a philosophical commitment to service backed by diligent attention to what customers want and need (Zineldin and Bredenlo¨w, 2001). In an era when intense competition is being greatly facilitated by technology, the need of providing adequate product/service quality will necessitate that companies have to focus attention on issues of improving, measuring and controlling their product/service quality (Sylvestro et al., 1990). One way to measure quality is through customer complaints (Chapman et al., 1997) and customer survey. Quality measurement is of particular importance to be considered by all mangers and marketers of high contact services including banking industry. The inputs or delivery system in a supplier is a combination of human resources, locations and equipment. An effective customer database allows a company to understand better customer’s needs – particularly their relationship needs – better than the competitors. The customer database will also include data about the current and past attitude, state/trend of customers business, market shares, profitability, etc. The data about customer’s needs, attitudes and behaviour enables companies to identify today’s key customers, develop CRM with tomorrow’s customers, and calculate the revenue the customer generates, and estimate own future investment opportunities.

The royalty of loyalty Customer loyalty Central to organization’s relationship management strategy is the ability of that organization to develop and enhance long-term customer relationships and to satisfy its existing customers. The main focus of such organizations is on customer satisfaction (CSAT) and customer loyalty, i.e. retaining customers and generating repeat orders. Indeed, 432

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Figure 1 Zineldin’s 5Qs: a multidimensional model of quality attributes impacting the level of CRM strategy and loyalty level

there is a positive impact of customer loyalty and retention on company profitability. The issue of customer retention must be seen in the context of the existing level of customer loyalty. Retaining existing customers reduces the necessity of attracting new ones (Replacement) and can even reduce offensive marketing cost. Although there is no universally agreed definition of customer loyalty (Uncles et al., 2003), customer loyalty can be defined as a commitment to continue do business with a company on an on-going basis. According to Uncles et al. (2003), loyalty is something that consumers may exhibit to brands, services, stores, product categories (e.g. cigarettes), and activities (e.g. swimming). Loyalty can be also defined as a state of mind, a set of attitudes, beliefs, desires, etc. A company benefits from customer’s loyal behavior, but this results from their state of mind. Loyalty is also a relative state of mind. It precludes loyalty to some other suppliers, but not all of them, as a customer could be loyal to more than one competing supplier. Companies, however, should segment their market by level of profitability and identify groups of customers the company wishes to retain and which are likely to provide the most profitable returns. Reichheld (1996), has identified the following three customer groups: 1 Some customers are inherently predictable and loyal, no matter what company they’re doing business with. They simply prefer stable, long-term relationships. 2 Some customers are more profitable than others. They spend more money, pay their bills more promptly, and require less service. 3 Some customers will find your products and services more valuable than those of your competitors. No company can be all things to all people. Your particular strengths will simply fit better with certain customers’ needs and opportunities.

producing magazines, setting up clubs or introducing cards, in the vague hope that loyalty is generated. Focusing marketing strategy on the existing segments of customer base is referred to as defensive marketing strategy. This strategy permits a company to normally produce most of the required revenue and increase market share without investing in new customers. The basic argument is that the cost of obtaining a new customer exceeds the cost of retaining an existing customer. Investing in new customers can be referred to as offensive marketing strategy. Offensive marketing focuses on obtaining new customers and increasing customers’ purchase frequency. The traditional four Ps, i.e. advertising, pricing, sales promotion, and personal selling are main tools in the offensive marketing. Offensive marketing strategy strives to attract competitors’ dissatisfied customers while defensive marketing strategy is geared to managing the dissatisfaction among a company’s own customers. The business world has become much more aware of defensive marketing in recent years. RM and CRM are concerned with customer retention and how this can be achieved by creating long-term customer loyalty. In other words, companies are seeking to create committed customers, not customers who are “locked in”. A customer who is locked in is a “prisoner” and is unlikely to stay with a company if an alternative supplier makes a satisfactory offer. Promises and commitment are central concepts in RM and CRM (Gro¨nroos, 2000). The promises made by the seller include the obvious ones that are part of any selling contract, such as product quality, delivery and inventory management, attendant services, and others, but for a long-term relationships they must also cover deeper commitments and higher flexibility to the buyer’s success. The promises made by the buyer also must be beyond those of the contractual terms of payment. The buyer’s promises much include its commitment, retention and loyalty:

Managing customer loyalty and retention is a critical factor of CRM. In many organizations; the question “what can we do to increase customer loyalty?” is a recurring theme at board level. Many large organizations have jointed the select band of companies with tried and tested schemes, while many others are experimenting. Consumer exposure to invitations to join this or that club must have reached on all-time high. But loyalty is not about throwing money into marketing programs,

The first step in managing a loyalty-base business system is finding and acquiring the right customers: customers who will provide steady cash flow and a profitable return on the firm’s investment for years to come, customers whose loyalty can be won and kept (Reichheld, 1996).

Thus, switching barriers and customer satisfaction may lead to higher customer retention, higher customer loyalty, lower customer switching costs (search costs, learning costs, emotional costs), higher revenue, and higher profitability. 433

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Also, long-term loyal customers are accustomed to how a company conducts business and so ask fewer questions and cause fewer problems. This reduces the costs of serving them. All of these positive effects of customer loyalty enhance the lifetime value of the customer. Relationships are, sometimes, automatic. After satisfied customers have purchased from a company once, next time they have a natural incentive to buy from the same company again, rather from the competition. And that gives the company some added value, but it may not be quite enough. That company can do more. It can actively promote stronger relationships with its customers. And even it if it is not love at the first sight, the company can help turn the first date with a customer into a lifelong romance. Loyal customers often believe they get better service because they are loyal. A company can create loyalty by rewarding it. Loyal customers feel they are rewarded for their loyalty. This has two implications: 1 Loyalty approaches should seek to differentiate the relationship and Prodserv package provided to loyal customers. 2 Ways of giving “special recognition” at the point of customer contact should be used. Every company should have a loyal or frequent-customer program.

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Trading stamps is one older frequent user incentive. Stamps are dispensed in proportion to the amount of a consumer’s purchase and can be accumulated and redeemed for goods. Stamps are attractive to consumers as long as they do not drive up the price of goods/services. Retailers use trading stamps to attract customers to specific stores. Point-of-sale (P-O-S) materials include such items as outside signs, windows displays, counter pieces, and display racks. P-O-S materials are designed to increase sales and loyalty. IKEA stores offer interactive monitors on which room layouts and color combinations may be tested prior to purchases. These items, which are often supplied by producers, attract attention, inform customers and encourage retailers to carry particular products. With money refunds, consumers submit proofs of purchase and are mailed a specific amount of money. Often, manufacturers demand multiple purchases of the product before a consumer can qualify for a refund. Companies could offer recognition and rewards, to their longtime employees. They could give them large discounts off the companies’ products.

Loyalty will be developed over time if the parameters for the relationship are planned and implemented correctly. To develop effective acquisition (offensive marketing) and retention (defensive marketing) strategies, organizations need a thorough understanding of the needs, behavior, and profiles of different groups of customers. In short, this is a case of strategic segmentation.

The technological advances enable most organizations to treat, cost effectively, the customer as an individual. It is now possible and cost effective to design a loyalty program. The following are some examples: . Some cellular-phone carriers could give their best customers free weekend and evening calls. Or they could give free or sharply discounted voice mail services. . Some health clubs could give loyal customers a guest pass and encourage them to bring someone else along. . Cable TV companies have the ability to give their loyal customers premium services that are currently idle. They could offer loyal customer a movie channel they can view for free. . Many retailers are now able to identify their customers by name, let alone mange a relationship with them. Retailers are now able to print out a customized set of coupons for each customer, not just according to what they have purchased on that particular visit (which is not full relationship marketing), but according to what they have bought in previous visits, or even according to preferences expressed in a questionnaire. . Retailers are able to offer specific incentives to customers who buy branded product to buy the own label equivalent, which gives the retailers a much higher profit margin. . Frequent user incentives. Many companies develop frequent user incentive programs to reward individual consumers who engage in repeat purchases. For example, most major airlines offer a frequent flier program through which customers who have flown a specified number of miles are rewarded with free tickets for additional travel. Supermarket loyalty cards are another popular incentive. . A loyalty card offers discounts or free merchandise to regular customers. Thus frequent user incentives help foster customer loyalty and relationship to a specific company or group of co-operating companies that provides extra incentives for patronage. Car hire companies, hotels and credit card companies have also used frequent user incentives.

Customer retention A retention orientation approach requires companies to be responsive to customer concerns by keeping open dialogues with them. This approach involves effective programs for receiving and responding to complaints, active solicitation and analysis of customer satisfaction data, and the development of long-term strategic relationship with customers by evolving to meet their changing needs. The fundamental issue of this customer retention approach is that when dealing with a customer, a company must consider the lifetime value (LTV) of a satisfied customer, rather than the profit to be gained form any individual transaction. However, lifetime value of customer is not a new concept. It is widely used in consumer goods brand management, where the key calculation is how much to spend to prevent consumers form brand switching. The concept’s pedigree comes from direct marketing (in particular mail order), where long-term customer behavior is the key to success, and calculating the difference between costs of acquiring customers and the benefits costs of retention is the norm. Lifetime value arises from future purchases, referrals, and avoiding negative word-of-mouth. Calculating the value of a customer is logically simple – the key is information. The required process is as follows: . Determine the target (segmentation) customers, e.g. 1,000 customers. . Identify the annual marketing and sales costs of gaining, managing and maintaining those customers. . Identify the costs of selling additional Prodserv to them. 434

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The critical aspect for a company is to obtain a balance between efforts to hold on to existing customers and setting out to find new ones. Whilst both objectives have always been in place, the fact of the matter is that the balance of a company’s activities and costs, has recently been heavily in favor of attracting new customers.

Identify the revenues that have been generated by those 1,000 customers each year (for a period of two, or five years). Subtract costs from the profit to produce a stream of net contribution per year (or over the time period). Use discounted cash flow techniques (net present value) to calculate the LTV for those customers. Divide the total by the number of customers to find out a per customer value.

Complaint management and CRM The complaint management is very important for the value of a customer and for the CRM strategy. It is essential for companies to welcome complaints, and view them as a second chance to satisfy a customer. Unfortunately, there are many companies that still do not understand the value of intelligent complaint management, and many bad business practices exist. A complaint is a really chance to keep a customer from leaving dissatisfied. If a customer is unhappy but doesn’t complain, then the company risks losing that customer, along with the customer’s future profit stream. Recovery of a dissatisfied customer is possible, and the benefits of turning around a complaining customer are dramatic. In some circumstances, 95 percent of complainers will return if the complaint is handled satisfactorily. Companies must also consider the impact of word-of-mouth. A happy customer is likely to tell others that she/he is happy and a dissatisfied customer is likely to tell others that the company is not good. Research indicates that a dissatisfied customer tells an average of 9-10 people about the bad experience, and 13 percent will tell approximately 20 people. Finally, unresolved important problems allows customers to spread bad word-of-mouth and this may hurt the image of a company. Switches due to unresolved complaints, or poor relationship management and marketing by a company, can and should be avoided. Relationship management and marketing that encourages complaints and ensures that complaints are dealt with well is the best defense against this cause of switching.

Let us deal with the following simple example, to clarify the lifetime value concept. Consider a young person buying her/ his first car. That customer has not had much time to build financial strength. The car purchased is likely to be small and inexpensive, and the dealer’s profit from selling the car is likely to be small. It is tempting to conclude that this customer is not very important. But, some companies realize that this is not the case. Volvo, for example, builds its product line and plants its service department to enhance customer loyalty and repurchase. The young customer may buy an inexpensive Volvo 360. But after several years of increased earning power, that same customer may buy a medium-priced Volvo 740. Eventually that same customer may buy Volvo’s luxury car, Volvo V70. The dealer’s profit increase over time, as the customer becomes more established financially. To determine the current potential value of a customer, base solely on future purchases, one must calculate the net present value of the profit stream represented by the future purchases. Let us suppose that the dealer profit on a Volvo 360 is $500 on average, while the profits on a Volvo 740 and V70 are $1,500 and $3,000, respectively. Assume that repurchase occurs every five years, and that the discount rate is 10 percent. The value of the current purchase of the Volvo 360 is $500, the value of the Volvo 740 purchase (Volvo 740) is $931, and the value of the third purchase (Volvo V70) is $1,157. In this case the total value of the customer, based on future purchases, is $500 þ $931 þ $1,157 ¼ $2,588, which is more than five times the $500 that the customer is apparently worth, based only on current purchases. Understanding a customer’s value in this light could affect how the customer is treated. If the customer feels mistreated, which is often the case in new car purchases, repurchase from that dealer becomes unlikely. Thus, the sales staff clearly must be motivated to enhance satisfaction as well as immediate sales. One way to do this is to compensate the sales force based both on sales figures and satisfaction with the sales transaction. The evaluation of customer loyalty and its LTV provides answers to the following questions: . Should a particular type of customer be retained? . How much should a company pay to retain customers? . What methods should be used to develop and enhance relationships with those customers? . What loyalty programs should be applied? . How much credit should loyal customers be given? . What is it worth to the company to reactivate lapsed customers? . Which customers are profitable now, and how profitable are they? . Should a company recruit or acquire new customers? . How much should a company pay to recruit new customers?

Strategies for improving customer retention and loyalty Satisfied customers are not necessarily always loyal customers. Customers can be satisfied, repeating orders, and also likely to buy from the competitors in the future. The relative value of the product and services in respect of the price paid must be taken into account when assessing customer satisfaction (CAST). Thus, instead of using the traditional CAST survey approach, organizations should move towards the application of customer value management (CVM) methodologies and tools. By such application, can a sustained improvement in customer loyalty, and attendant improvement in business results/competitive positioning over time, be achieved. The aim of a CVM strategy must be the provision of products and services to customers that are perceived by the customer to be of greater value than they could expect to purchase/receive form the competitors in similar markets. Customer satisfaction and loyalty are some key elements of business success and profitability. The more satisfied the customer, the more loyal the customer and the more durable the relationship. And the longer this lasts, the more profit the company stands to make and the higher the market share. A comprehensive retention strategy composed of quality, 435

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strategy and tactical programs must be designed and implemented. The following strategies can be applied by companies to improve the customer retention: . Measuring customer retention rates over time and by line of business and in each of the business areas. This results of the measurement must then be communicated to the employees. . Analyzing the root causes of defections. Understanding why customers are leaving the company provides that company with the essential information needed to implement a customer retention program. An effective retention program is likely to increase customer satisfaction and thereby improve customer retention and profitability. . Focusing attention on the most profitable customers and setting clear targets and measuring results as will as identifying switching barriers. . Focusing attention on internal marketing and particularly front-line employees to ensure that they are offering product and service quality that consistently meets the requirements of the target market (segment or segments).

strategies and customer loyalty, enabling the company to identify where improvements are needed from the cusomer’ perspective. . The use of the 5Q dimensions provides both a structure for designing a service quality measurement instrument and a framework for prioritizing results and findings. . The 5Qs results can be used in a variety of ways: understanding current CRM strategy, prodserv quality; comparing performance across different companies; comparing performance across different parts of the service and assessing the impact of improvement initiatives on the customer loyalty. The study confirms that CRM is a complex and holistic concept requiring appropriate business processes and integrated systems. In addition, the study demonstrates the relevance of the need for effective system integration, leadership, information sourcing, targeting and evaluation within CRM strategies. The impact of CRM on CL is real and so are the problems for certain organizations in terms of successful implementation. Thus, there is a great need for additional empirical research within CRM to identify the extent of such issues and for additional insights. This particular research is ongoing and will aim to develop and expand on the issues raised by conducting empirical studies.

As a marketing strategy, companies seek ways to develop ongoing relations with loyal customers in order to ensure repeat transaction and/or ongoing Prodserv supplying (retention). The main advantage to the company of a close and long term relationship is that it knows who its current customers/clients are, what are their needs and wants and, usually, what use they make of the Prodserv offered. This strategy attempts to transform the customers into loyal longterm multiple-product clients.

References Asser, J.R. (1992), “Zero defections: quality comes to services”, in Lovelock, C. (Ed.), Managing Services, Marketing, Operations and Human Resources, Prentice-Hall, Englewood Cliffs, NJ. Berry, L.L., Shostack, G.L. and Upah, G.D. (Eds) (1983), Emerging Perspective on Service Marketing, American Marketing Association, Chicago, IL, pp. 25-8. Chapman, R., Murray, P. and Mellor, R. (1997), “Strategic quality management and financial performance indicators”, International Journal of Quality & Reliability Management, Vol. 14 No. 4, pp. 432-48. Christopher, M., Panyne, A. and Ballantyne, D. (1991), Relationship Marketing: Bringing Quality, Customer Service and Marketing Together, Butterworth-Heinemann, Oxford. Clemons, E. (2000), “Gathering the nectar”, in Understanding CRM, Financial Times publication, Spring Supplement, pp. 24-7. Galbreath, J. and Rogers, T. (1999), “Customer relationship leadership: a leadership and motivation model for the twenty-first century business”, The TQM Magazine., Vol. 11 No. 3, pp. 161-71. Girishankar, S. (2000), “Companies want CRM tools to manage business relationships”, Information Week, No. 17, p. 65. Gro¨nroos, C. (2000), Service Management and Marketing: A Customer Relationship Management Approach, Wiley & Sons, Chichester. Newell, F. (2000), Loyalty.com: Customer Relationship Management in the New Era of Internet Marketing, McGraw-Hill, New York, NY. Payne, A., Christopher, M., Clark, M. and Peck, H. (1999), Relationship Marketing for Competitive Advantage, Butterworth Heinemann, Oxford.

Discussion and managerial implications To sum it up so far we can say that a customer loyalty (CL) and competitive positioning, among other things, can be achieved through product/service (prodserv) quality, CRM and differentiation. Positioning means at least at an operational level that the PRODSERV package has to be designed in a special way to reach and to suit the prospects the company is trying to please. This design will no doubt affect customer satisfaction, productivity and efficiency. The relation between quality, CRM and CL can be measured using different indicators. Overall customer value, satisfaction and loyalty can be measured using the 5Qs model. Changing in quality over time within various segments or related to specific products or categories of products/services can be used as an indicator the level of loyalty. Two conditions are that the customer database and CRM strategy are well structured and that management control systems have the capacity to produce required data for the analysis. High Prodserv quality maintains the existing customers over long time and attracts a new customer and businesses. CRM enables organisations to provide high quality Prodserv with a reasonable cost relative to the competitors. Evidence suggests that higher quality improves margins by helping to create customer loyalty and a competitive edge, thus increasing market share, which in turn leads to higher profitability and scale economies. Quality and CRM provide a company with opportunities to offer the customer something, which is distinctive, and special need to be exploited. This study shows that the 5Qs instrument has a useful diagnostic role to play in assessing and monitoring CRM 436

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Peppard, J. (2000), “Customer relationship management (CRM) in financial services”, European Management Journal, Vol. 18 No. 3, pp. 312-27. Porter, M. (1980), Competitive Strategy: Techniques for Analyzing Industries and Competitors, Free Press, New York, NY. Reichheld, F.F. (1996), The Loyalty Effect, Harvard Business School Press, Boston, MA. Sandoe, K., Corbitt, G. and Boykin, R. (2001), Enterprise Integration, John Wiley & Sons, New York, NY. Smith, A.D. (2005), “Exploring online dating and customer relationship management”, Online Information Review, Vol. 29 No. 1, pp. 18-33. Sylvestro, R. et al. (1990), “Patterns of measurement of service performance: empirical results”, in Teare, R., Moutinho, L. and Morgan, N. (Eds), Managing and Marketing Services in the 1990s, Cassell Educational, London. Tamminga, P. and O’Halloran, P. (2000), “Finding the real value in CRM: leveraging IT solutions through the customer driven approach”, Cutter IT Journal, Vol. 13 No. 10, pp. 4-11. Uncles, M.D., Dowling, D.R. and Hammond, K. (2003), “Customer loyalty and customer loyalty programs”, Journal of Consumer Marketing, Vol. 20 No. 4, pp. 294-316. Valentine, L. (1999), “The First Union tackles CRM with CRMs”, ABA Banking Journal, Vol. 41 No. 10, p. 62. Zineldin, M. (1995), “Bank-company interaction, and relationships: some empirical evidence”, International Journal of Bank Marketing, Vol. 13 No. 2. Zineldin, M. (1996), “Bank strategic positioning and some determinants of bank selection”, International Journal of Bank Marketing, Vol. 14 No. 6. Zineldin, M. (2000), TRM, Studentliteratur, Lund, Sweden. Zineldin, M. (2005), “Quality and customer relationship management (CRM) as competitive strategy in the Swedish banking industry”, The TQM Magazine, Vol. 17 No. 4. Zineldin, M. (2006), “The quality of health care and patient satisfaction: an exploratory investigation of the 5Qs model at some Egyptian and Jordanian medical clinics”, International Journal of Health Care Quality Assurance, Vol. 19 No. 1, pp. 60-92.

Zineldin, M. and Bredenlo¨w, T. (2001), “Performance measurement and management control: quality, productivity and strategic positioning – a case study of a Swedish bank”, Managerial Auditing Journal, Vol. 9 No. 16.

Further reading Deming, E. (1986), Out of the Crisis, Cambridge University Press, Cambridge. Devlin, J. and Ennew, C. (1997), “understanding competitive advantage in retail financial services”, International Journal of Bank marketing, Vol. 15 No. 3, pp. 73-82. Payne, A. and Clark, M. (1996), “Marketing services to external markets”, in Glynn, W. and Barnes, J. (Eds), Understanding Services Management, Wiley, New York, NY.

About the author Mosad Zineldin is Professor of Strategic Management and Marketing, served as a Chairman of the Marketing Department at the School of Management and Economics, Va¨xjo¨ University, Sweden. He taught at the School of Business, Stockholm University for many years. The author is also engaged in a considerable number of research and consulting activities. He has participated in different international conferences as a presenter and a keynote speaker and has written several books and numerous articles. His latest book TRM: Total Relationship Management, (2000) is the first book in the world to outline the framework of relationship management from a holistic totality and multifunctional perspective. His articles have appeared in European Journal of Marketing, International Journal of Bank Marketing for the financial service sector, Supply Chain Management, Journal of Marketing Intelligence & Planning, Management Decision Journal, International Journal of Physical Distribution & Logistics Management, European Business Review, Managerial Auditing Journal and TQM Magazine. Some of his articles have been cited with the Highest quality rating by ANBAR Electronic Intelligence and others positioned in the top 10 list by Emerald’s readers and reviewers. Mosad Zineldin can be contacted at: [email protected]

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Masochistic marketing: Volvo Australia’s not “so safe” strategy Go¨ran Svensson Oslo School of Management, Oslo, Norway, and

Greg Wood and Michael Callaghan Bowater School of Management and Marketing, Deakin University, Warrnambool, Australia Abstract Purpose – The objective is to describe a marketing approach used by Volvo in the Australian marketplace. It appears to be a rare approach and could be perceived to some extent as being “masochistic”. Design/methodology/approach – The research is based upon a case study. The term “masochistic marketing” is introduced. Findings – The “masochistic marketing” approach applied by Volvo in Australia should be seen as a process. It is dependent upon the outcome of a series of cause and effect relationships. Research limitations/implications – The masochistic marketing approach may be divided into four cause-related phases, all of which create a dualistic outcome of either positive or negative effect-chains in respect to the corporate image in the marketplace and society. Practical implications – A masochistic marketing approach is a high-risk venture. It is a challenging and demanding marketing process, because it plays on the humiliation of the corporate image itself. The core idea of the masochistic marketing approach violates, or at least appears to oppose, the fundaments of marketing. Originality/value – Masochistic marketing is not recommended to be used as a common approach, unless a series of events has turned the corporate image in the marketplace into something that is highly undesirable and a stigma. Keywords Automotive industry, Marketing strategy, Advertising effectiveness, Australia Paper type Research paper

affluent customers in the society. In many cases, these individuals were more advanced in years and as such were seen as conservative. At the same time, Volvo’s international reputation for safety was well known in Australia. The myth grew up in Australian popular culture that Volvo drivers were dangerous, older drivers who purchased a Volvo because it gave them a better chance of surviving their own poor driving practices than other vehicles may have done. The cars were viewed by many Australians as boxy and lacking in style (Shoebridge, 2003). Volvo themselves acknowledged that the cars in Australia were seen as:

Introduction In 1990, Volvo executives in Sweden realised that to compete successfully in the future that they would have to reposition Volvo in a manner similar to their main competitors in the luxury car market segment. The cars would need to be seen as “fast, fun to drive and sexy”, yet still maintain, if possible, their distinct competitive advantage as the safety leader (Rix, 2004). Volvo executives were concerned that if they could not achieve this goal then their market share would fall, thus placing the company in jeopardy. The company in Australia in 2003 was faced with just such declining sales across its passenger vehicle market. Its S40 and V40 had gone respectively from 2.8 percent market share in 2002 to 1.0 percent in 2003 and from 1.2 percent in 2002 to 0.7 percent in 2003. The S60 had also suffered a slide in market share from 2.5 percent in 2002 to 1.6 percent in 2003. The Volvo XC 70 had slumped from 10.4 percent of market share in 2002 to 5.7 percent in 2003 (Polk Automotive Intelligence, 2004). The brand was obviously in trouble in Australia. What could Volvo do to arrest this situation? In Australia, Volvo had always suffered from the myths of popular culture as being a product that attracted more

. . . boring, boxy cars driven by erratic, unpredictable drivers . . . (McIntyre, 2004).

These perceptions gave rise to the often throw-away line of “Bloody Volvo drivers”. This phrase was not one of endearment, but one of distinct derision. In September 2003, Volvo Australia launched a controversial marketing campaign that was designed to reposition the brand and to revitalise it in a marketplace, where its sales growth was dropping and its market share was being eroded. The company wanted however to maintain its premier status in the marketplace for safety. The structure of the paper considers the following issues: . Volvo and Safety. . The campaign. . Comments on the campaign. . Results of the campaign. . Theoretical implications. . Managerial implications. . Conclusions.

The current issue and full text archive of this journal is available at www.emeraldinsight.com/0736-3761.htm

Journal of Consumer Marketing 23/7 (2006) 438– 444 q Emerald Group Publishing Limited [ISSN 0736-3761] [DOI 10.1108/07363760610712984]

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Volvo and safety

process consists of components such as the traffic environment, setting requirements, development, test, and production (Volvo, 2000). Volvo pursued safety issues well before they were palatable in other companies. They led the market and the society in terms of making safer cars. This concern led Volvo to implement safety features that not only challenged the market, but that established a positive persona for the product with consumers. Volvo included features in their cars that the market did not as a whole necessarily believe that it needed. They included features in their cars that others only included after there was legislative intervention. For example, safety belts were introduced in Sweden in 1959 by Volvo. The legislative intervention in Sweden for front seat safety belts occurred in 1975 and for back seat safety belts in 1985 (Volvo, 2002). Volvo has been a world leader in a number of safety areas (Volvo, 2002). These areas include a triangular twocircuit brake-system in 1966; day-running lights in 1975; lap-diagonal safety belt in the back middle seat in 1986; Side Impact Protection System (SIPS) in 1991; integrated child cushion in 1992; SIPS-bag in 1994; Inflatable Curtain (IC) in 1998; Whiplash Protection System (WHIPS) in 1998 and Blind Spot Information System (BLIS) in 2005. Volvo established a reputation for safety that in its obvious extension meant a concern for their customers. This focus has benefited the company since its inception. In Australia, Volvo’s greatest strength of safety par excellence in many ways became its Achilles heel. The issue that Volvo faced in Australia was two-fold: to increase sales and to maintain its safety image that had been a brand hallmark since its inception. This task would not be an easy one for the company as the very safety that had been its strength had given rise to the urban myth in Australia of the fact that Volvo drivers were a “problem” for other road users. By association, the brand itself had become tainted. The “Bloody Volvo driver” image needed to be expunged from the minds of the Australian motoring public, but how could they achieve this change in perception? The company decided to take the public perception head on and instead of shying away from the “Bloody Volvo driver” perception, they chose to use the phrase as the cornerstone of their marketing and advertising campaign. They decided to attempt to debunk the urban myth by focussing upon it and showing it as unrealistic and unrepresentative of Volvo, its cars and its customers.

Kuertz (1993) in his review of the Ries and Trout book The 22 Immutable Laws of Marketing: Violate Them at Your Own Risk! wrote that the most powerful concept in marketing is the Law of Focus in which a company owns “. . . a word in the prospect’s mind . . .”. The consumer automatically “. . . associates certain words with certain companies . . .”. One of the three examples given is that of Volvo and safety. Safety has been at the forefront of all that Volvo has done, since its inception. Safety and Volvo are synonymous. Volvo has a long tradition of focussing upon the safety of its products. Volvo was founded in 1927 and the founders of Volvo stated a few years later, when safety issues in the automotive industry were ignored, that: . . . Cars are driven by people. The guiding principle behind everything we make at Volvo therefore, is – and must remain – safety . . . (Volvo, 2002, p. 1).

Volvo’s safety philosophy may be characterised as a “holistic approach”. The objective of Volvo’s safety philosophy is: . . . to design cars which enable you to avoid traffic accidents, theft and threatening situations whenever this is humanly possible – and which protect you and your passengers should an accident prove unavoidable . . . (Volvo, 2001, p. 4).

It is not the individual details that determine car safety, but an overall consideration of the issue (Volvo, 2001, p. 4). For example, car safety has to provide the driver with the technology that enables him or her to drive more safely and avoid accidents. Car safety has to consider the design of cars which give everyone travelling in them effective protection should a collision prove unavoidable. This protection must function in real life and cover the most representative types of accident at a wide range of speeds and involving many different collision objects. The unfortunate trend towards an increasing number of car break-ins and thefts has accentuated the need for effective techniques to keep thieves at bay. The growing problem of threats to the car owner’s person has also stepped up the call for greater personal protection (Volvo, 2002, p. 1). Volvo is one of the few car manufacturers in the world that has its own accident research team. Since 1970, almost 30,000 accidents have been analysed where Volvo cars have been involved. The standards for safety required by Volvo are far more comprehensive than the legal standards that are mandated. They go beyond the law and lead public perception to what is possible in car safety. The aim is to save lives, alleviate the effect of injuries, or preferably, to prevent accidents ever occurring (Volvo, 2001). The Volvo car safety centre employs approximately 100 people. The centre includes the accident research team, technological development, testing, calculation and design. Real-life conditions can be recreated in many different ways. Before the time comes to crash two cars in a full-scale test, the systems have already been tested in the super-computers, in the unique crash sleigh or in one of the components rigs. Volvo applies a systematic method with the aim of constantly enhancing the level of safety in its cars (Volvo, 2001, p. 4). It may be described as a circle, a never-ending process, which starts and ends in the real-life traffic environment. Accordingly, the circle is a process which involves human beings, cars and the environment: everything that happens before, during and after an accident. This

The campaign The campaign comprised three phases. The first phase was to inform current customers of the impending television advertisements. The company sent to them a brochure that led with “. . . sticks and stones can break your bones and you shouldn’t be called names either . . .”. The brochure then went on to outline the advertisements that were to appear. The company used such phrases as: “. . . stealing the initiative . . .”, “. . . attaching new meaning . . .”, “. . . a few things that you can throw back . . .”, finishing with “. . . they wish . . .”. An attached sticker was included that said, “. . . You wish you were a Bloody Volvo Driver . . .”. The brochure explained to Volvo drivers that, “. . . for far too long now Volvo and the people who drive Volvo have, in Australia, been stigmatised . . .” The brochure went on to say that, “. . . we simply do not believe those who make the intelligent choice to drive a Volvo should 439

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Go¨ran Svensson, Greg Wood and Michael Callaghan

Volume 23 · Number 7 · 2006 · 438 –444

be the targets of one-sided, ill-considered humour . . .” The company then highlighted in the brochure that Volvos were Bloody Fast, Stylish, Intelligent, Caring, Innovative and Safe. The sentiments were poignant, forthright and in keeping with the advertising theme that was to emerge on Australian television screens (Volvo, 2004). The objective of Phase One of the advertising campaign was to disempower the “Bloody Volvo driver” stigma by stealing the expression and using it as a catch phrase by Volvo itself about Volvo. The company commissioned 3 £ 15 second commercials to be aired from September 21, 2003. The commercials comprised a motorcyclist in a near miss with a Volvo XC90; a grouchy person shopping who is inconvenienced by another person’s shopping trolley; and a Volvo driver who has to endure another driver’s inattention to detail. The tagline in each advertisement is of course “Bloody Volvo driver”. The advertisements were designed to build awareness and anticipation for what was to come in subsequent advertisements (Volvo, 2004). Phase Two of the advertising campaign was scheduled to be launched in mid-October, 2003. The intent was to transform the phrase “Bloody Volvo driver” into a positive phrase and minimize the old stigma, if not remove it. Four advertisements were featured in this set. Three of the advertisements were of 30 seconds duration and the emporium advertisement was 15 seconds. The first one was a continuation of the Phase One motorcycle scenario where it was now shown that the motorcycle actually cut out from behind a truck into the Volvo’s path and it is only through the safety features of the car and the driver’s cool headedness that the motorcyclist is saved from injury. The second advertisement features a policeman driving a Volvo that intercepts a fast-looking European sports car on a motorway. The intention is to highlight the speed of a Volvo. The third advertisement has a group of customers in a design emporium who are distracted from the chic around them by a Volvo S60 AWD that parks outside of the shop. The message here is one of class and styling to rival the cool design pieces of the emporium. The final advertisement is that of a baby playing with a noisy toy whilst sitting in the booster seat of the mother’s Volvo. The mother reaches around and removes the toy from the child’s possession: safely of course! The tagline in each advertisement is again “Bloody Volvo driver”: even from the baby. The four advertisements highlighted Volvo’s message of safety, speed, style and satisfaction at being a Volvo driver (Volvo, 2004). In December of 2003, the final Volvo product message of the campaign was communicated. It centred on the theme of people wishing to be Volvo drivers. The advertisement featured the new S60R, which had been given the coveted title of “the most beautiful car in the world” by the Italian Press, Automobilia Mondo, 2000 (Walker, 2003), and the copy highlighted the speed and engine capacity of the S60R. The tag line was “YOU WISH YOU WERE A BLOODY VOLVO DRIVER!” From Volvo’s perspective, the transition was under way (Volvo, 2004).

pre-eminent business publication, the Business Review Weekly, was scathing in his condemnation of the concept. He said: The campaign is either a very smart move by Volvo to freshen its image and position the Volvo as a cool brand, or it is a very foolish move that will anger its existing customers, be dismissed by potential customers and turn the Volvo brand into a joke – well, more of a joke than it is today (Shoebridge, 2003, p. 63).

The risk to Volvo was that the campaign may disenfranchise its existing customer base and perhaps lampoon itself into more of an urban myth. Shoebridge (2003) went on to question the mocking of one’s customers. He lambasted Volvo for breaking the rules of marketing and advertising in the segment, which requires one to provide information and pictures of cars, so that the brand is in the mindset of the customers when they go to make a decision about a possible car purchase. In a final parting shot, in which his comments were not a ringing endorsement of Volvo’s strategy he said: The “Bloody Volvo drivers’” campaign does not give consumers any reason to include Volvo on their new-car shopping list. It might however, inspire some people to add the Volvo name to another list. That list would be headed CARS TO AVOID, or, THE SILLIEST AD CAMPAIGN OF 2003 (Shoebridge, 2003, p. 63).

Auto World from South Africa featured the campaign on September 17, 2003 on its web site. Walker (2003) suggested that the campaign was designed to “. . . desensitise the term ‘bloody Volvo drivers’ and is setting out to overcome the stigma inherent in this phrase. Interestingly it is a peculiarly Australian phenomenon . . .” Walker went on to express surprise at the perpetuation of the myth by Volvo itself. The campaign attracted the ire of some Australian consumers who lodged complaints about the toddler advertisement with the watchdog, Advertising Standards Board. The complaints revolved around young children using offensive language and the exploitation of someone so young to sell a product. Volvo responded by citing the third edition of the Macquarie Dictionary that contended that the word “bloody” had moved from a term of profanity to an idiomatic word of contemporary times. The Advertising Standards Board determined that the majority of the community would deem the advertisement to be humorous and that the advertisement did not breach the Advertiser Code of Ethics on the grounds of language or on any other grounds (Advertising Standards Board, 2003).

Results of the campaign From September 20 until November 29, Blue Moon Research and Planning completed a tracking study on the campaign. The sample size was 350 people. They found that 49 percent of people recognized the tagline. In the last four weeks of the program 80 percent of respondents recognized the tagline. Those considering purchasing a Volvo had grown from 22 percent to 34 percent (Auto Web, 2003). Steve Blyth, managing director of Volvo Car Australia, (Auto Web, 2003) said that: . . . the unique advertising campaign has broken through many boundaries for Volvo in Australia . . . I can now confidently say this latest research data vindicates our decision to run the BVD campaign . . .

Comments on the campaign

Todd Hallenbeck, Public Affairs Manager of Volvo Car Australia, released an insight into Volvo sales figures locally and globally. The half-year sales for Volvo in all of its international markets in 2004 showed Volvo increasing in all

Due to its profile and unique approach, the campaign drew attention from consumers to media commentators. Neil Shoebridge, the marketing commentator for Australia’s 440

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of its major international markets. As compared with 2003, Volvo world to date figures at the end of June 2004 were up 11 percent. In Australia they were up over the same period of time by 28.3 percent (Hallenbeck, 2004). From Volvo’s perspective, the campaign had been successful, yet ultimately success should not just be measured by unit sales in the marketplace, as the actual units sold can be misleading. One needs to look at market share as a better method of comparison, as it shows the movement in not only the sales figures but also one’s performance against one’s competition (Belch and Belch, 2004). What do the new car sales figures and market share performances tell us? Table I presents the total new car sales by vehicle model for the Volvo marque in Australia and includes the years 2002-2004. Also included in the table is the percentage of market share for each model of Volvo car and an overall percentage of the market share that Volvo possesses in the market segments of the car market in which its models sell. Table I enables some interesting insights to be suggested about the Volvo new car sales in Australia and by association the success of its advertising campaign. In 2002, the year prior to the advertising campaign, Volvo had 4.99 percent market share of the new car market segments in which it competed. In 2003, the year of the campaign, its market share had dropped to 3.57 percent. It must be recognised that the campaign did not take place until the third quarter of 2003, hence one would have expected to see the positive results of the campaign in the sales figures for the full year of 2004. The figures for 2004 show a market share of 3.38 percent, which is a drop in market share over 2003. In 2004, Volvo had lost about 5.5 percent (3.38 percent/3.57 percent) of its market in the new car sales area from the 2003 figures. As compared to the loss of market share from 2002 to 2003 when Volvo’s market share dropped by 28.5 percent (3.57 percent/ 4.99 percent), then this loss is actually a favourable figure. The campaign may have halted the slide in Volvo’s popularity, but such a halt was not enough to ensure that the 2004 new car sales market share exceeded that of 2003. The market share for 2004 was 67.7 percent (3.38 percent/4.99 percent) of 2002. One could conclude that the Volvo marque in the area of new car sales was still in serious trouble in Australia.

A few bright spots for Volvo in 2004 were in the increasing sales of the XC90, S40 and the V50 models, but will this growth be enough to sustain the brand? The S40 has resonated with buyers turning around its extremely poor performance from 2002 to 2003.

Theoretical implications The most important aspect of developing a successful advertising campaign is in generating the desired response from the consumer, as opposed to not generating any response at all, or worse – generating a negative response. The campaign versus accepted advertising and marketing theory Response process models have been developed in order to represent and explain the process through which the target consumer progresses when effectively influenced (Belch and Belch, 2004). When considering the consumer response process activated by advertising the most widely used model would be the Hierarchy of Effects model developed by Lavidge and Steiner (1961). Essentially this model describes the effect advertising has on influencing consumer attitudes progressively from the cognitive, to the affective and finally the behavioural attitudinal stages (Figure 1). When Volvo’s campaign is considered by way of the Hierarchy of Effect model questions are raised in regard to the effect the campaign may have had. It is clear that the Cognitive component of consumer attitude toward Volvo was developed as a result of the campaign as illustrated by the high levels of recognition found by Blue Moons research. One explanation of high recognition may be that the campaign increased awareness of the “Bloody Volvo” phenomenon rather than the brand itself. Such raised awareness may then have led to the cognitive development and reinforcement of the derogatory elements of the campaign in regard to consumer knowledge. Whilst there is no specific research indicating the effect of the campaign on the affective attitude component, the high recognition coupled with the reported increase in purchase intention, would suggest a favourable effect resulting from the campaign. As the majority of the advertisements featured in the campaign are (ultimately) based on a humour appeal, the

Table I Volvo new car sales in Australia 2002-2004 Model C70 coupe C70 c’nvert S40 S60 S80 V40 V50 V70 XC70 XC90 Total Volvo Total cars re segments

Units sold 11 138 696 746 7 296 0 254 892 0 3,169 63,523

2002 % market segment 0.04 0.5 2.8 2.5 0.02 1.2 0 0.8 10.4 0 4.99

Units sold 2 72 298 559 4 197 0 199 683 622 2,692 75,370

Source: Courtesy of Jim Rutherford, VFACTS Manager, FCAI, 18 January, 2005

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2003 % market segment 0.001 0.2 1.0 1.6 0.01 0.7 0 0.6 5.7 5.2 3.57

Units sold 0 36 506 296 61 97 190 125 568 923 2,802 82,832

2004 % market segment 0 0.11 1.4 0.9 0.2 0.3 0.5 0.4 4.1 6.7 3.38

Masochistic marketing: Volvo Australia’s not “so safe” strategy

Journal of Consumer Marketing

Go¨ran Svensson, Greg Wood and Michael Callaghan

Volume 23 · Number 7 · 2006 · 438 –444

Managerial implications

Figure 1 Hierarchy of effects model

An attempt to formalise the masochistic marketing approach is illustrated in Figure 2. The conceptual model essentially considers two components: the logical cause and, the resultant effects of the application of the approach. These two components are interrelated. Masochistic marketing: the concept The cause-component refers to the corporate behaviour and the business practices applied during the masochistic marketing approach in the marketplace. It may be divided into four cause-related phases (see Figure 2): 1 Information – the first phase intends to inform about the masochistic marketing message under way to current and potential customers in the marketplace and the society. The purpose is to build awareness and anticipation for what is going to come, otherwise, these customers may be taken by surprise and this approach may give rise to an upsurge of doubts and criticism amongst this group. 2 Support – the second phase intends to support the current and potential customers to truly understand and be able to justify or “defend” the masochistic marketing message transmitted in the marketplace and the society. The purpose of this stage is to turn the stigma into a positive idiom by undermining the less attractive option, otherwise, the failure to achieve a transformation of these customers’ meanings and perceptions about the product may backfire against the corporate image that these people hold. 3 Fortification – the third phase intends to finally convince the current and potential customers about the appropriateness of the masochistic marketing message communicated in the marketplace and society. The purpose is to provide an additional injection to overcome a potential “resistance” of transition of the stigmatic meanings and perceptions of the product. 4 Outcome – the last phase determines the success or failure of the masochistic marketing approach undertaken in the marketplace and the society. The key issue to be evaluated in the Volvo-case at hand is whether there has been a change of idiom – from being a “Bloody Volvo Driver” to actually being a “Bloody Good Volvo Driver”.

development of consumer “liking” for the message might be expected. It is important to note that such “liking” may, in fact, be directed toward the message delivery (or worse the derogatory elements) rather than the Volvo brand itself. In turn, the development of preference by the consumer (based on the campaign) and subsequent conviction (as indicated by purchase intention) may in reality be more indicative of the consumer’s acceptance of the superficial (derogatory) message content rather than the more subtle ironic conclusion intended. As such, high purchase intention results may have been increased by acquiescence bias due to the favourable evaluation of the message delivery and appeal. The campaign’s effect on the behavioural component is also questionable as purchase/sales figures (detailed in the previous section) are not congruent with the purchase intention results indicated by Blue Moon. It may also be germane that market share dropped in the year of the campaign and comparatively recovered when the campaign had ceased in the following year. Further analysis of Volvo’s sales by model would seem to indicate that its newly launched 4WD models (the XC70 and XC90) shouldered the burden of maintaining a rapidly declining market share. When it comes to considering the net result of the “Bloody Volvo” campaign in terms of behavioural component of consumers’ attitudes, it is possible perhaps that the campaign may have cultivated a psychologically different customer while, perhaps, alienating traditional (conservative) customers. The Australian passenger vehicle market has changed with a marked increase in sales of 4WDs – the psychology of this new segment appears more tolerant, perhaps even immune, or embracing of the “Bloody Volvo” taunts? Perhaps the tag line of the campaign is evolving toward a new herald of the “Bloody 4WD Volvo Driver!” which is embraced and worn as a badge of pride among Volvo’s “new” loyalists?

These four phases are linked as the ultimate success of the marketing approach is determined by the ability to convince current customers, potential customers and others of the new image of the product or product range. Depending upon the success or failure of the masochistic marketing approach to remedy the persona of the corporate image and its products, one of two effect-chains is likely to be the outcome: either the negative effect-chain or the positive effect-chain (see Figure 2). The components of the effect-chains refer to the impact of their actions on the company’s corporate image in the marketplace and in the society. This impact may be judged as either positive or negative. The positive effect-chain leads to a change and an improvement in the perception of the company’s corporate image in the marketplace and in the society. Four successive and positive effects may be identified that are linked to and derived from the cause-related phases (see Figure 2): 1 Empowerment – an initial positive effect that may be achieved by the information-phase is the one of empowerment among current and potential customers in 442

Masochistic marketing: Volvo Australia’s not “so safe” strategy

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Go¨ran Svensson, Greg Wood and Michael Callaghan

Volume 23 · Number 7 · 2006 · 438 –444

Figure 2 The masochistic marketing approach

2

3

4

order to create a weakening of the impact of the stigma on the corporate image in the marketplace and in the society. Transformation – another positive effect that may be achieved by the support-phase is one of transformation among current and potential customers in order to create a change of meaning and perception of the corporate image in the marketplace and in the society. Persuasion – a further positive effect that may be achieved by the conviction-phase is the persuasiveness of the marketing message among current and potential customers that creates a strengthening of a positive corporate image in the marketplace and in the society. “Bloody Good Volvo Driver” – the ultimate positive effect that may be achieved in the outcome-phase is the one of the perceived success of the campaign with current and potential customers in order to have created a new positive image of the brand in the marketplace and in the society.

3

4

The negative effect-chain creates a downwards spiral that leads to the deterioration of the corporate image in the marketplace and in the society. Four successive and negative effects may also be identified that are linked to the cause-related phases (see Figure 2): 1 Disqualification – a negative effect that may be caused by the information-phase is one of rejection among current and potential customers. This means that they do not accept or agree that the approach will create a weakening of the impact of the stigma on the corporate image in the marketplace and in the society. It does not influence them at all. 2 Reinforcement – another negative effect that may be caused by the support-phase is the one of distrust amongst current and potential customers. This means that the old

stigmatic meanings and perceptions of the corporate image in the marketplace and society are reinforced and may even be strengthened. Stabilisation – a further negative effect that may be caused by the conviction-phase is one of dejection among current customers that is derived from the lack of strength of the corporate image in the marketplace of the product that they have chosen to purchase. In respect to potential customers, they may become more concerned about the lack of desirability of the product. “Bloody Volvo Driver” – the ultimate negative effect that may be caused in the outcome-phase is the failure of potential customers in particular to change their perceptions. In actual fact, the disparaging perceptions of the company’s corporate image in the marketplace that led to the campaign in the first place may be further reinforced, not only to potential customers, but to existing customers as well as to the society at large.

In sum, the masochistic marketing approach may be seen as an appropriate approach when the current corporate image and/or product in the marketplace and in the society have dire consequences for the company. Such an approach may be the only way out in the short term, even though it is a potentially hazardous strategy. If it is successful, the old stigma will change to a new perception of the brand in the marketplace. If it fails, then the old stigma may have further penetrated the perceptions and the psyche of current and potential customers in the marketplace and in the society. Before embarking on such an approach managers need to be cognisant of the fact that they run the real risk of the further alienation of the brand. Instead of bringing the brand to the fore in the consideration set of consumers, the company may in fact 443

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further alienate their product from the marketplace and contribute further to their own demise.

Belch, G.E. and Belch, M.A. (2004), Advertising and Promotion: An Integrated Marketing Communications Perspective, 6th ed., McGraw-Hill, New York, NY. Hallenbeck, T. (2004), “Volvo sales improve locally and globally”, Volvo Sales News, July 8, 2004, available at: www.volvocars.com.au/_Tier3/AboutUs/NewsEvents/ News (accessed November 25, 2004). Kuertz, D.L. (1993), “The 22 immutable laws of marketing: violate them at your own risk!”, The Journal of Consumer Marketing, Vol. 10 No. 4, pp. 70-2. Lavidge, R.J. and Steiner, G.A. (1961), “A model for predictive measurements of advertising effectiveness”, Journal of Marketing, Vol. 24, pp. 59-62. McIntyre, P. (2004), “Rising sales bloody Volvo driver image”, Sydney Morning Herald, July 1. Polk Automotive Intelligence (2004), “Polk news: new vehicle sales 2003”, available at: www.polk.com.au (accessed April 2, 2004). Rix, P. (2004), “Bloody Volvo marketer!”, Marketing: A Practical Approach, 4th ed., available at: www.mcgraw-hill. com.au/vet/mkgpradv/rix4e/stu (accessed February 9, 2004), McGraw-Hill, Sydney. Shoebridge, N. (2003), “Bloody Volvo ads”, Business Review Weekly, October 9-15, p. 63. Volvo (2000), “‘New Safety Centre A–Z Collisions’,: Volvo Cars Special Edition/00”, The international inhouse magazine. Volvo (2001), Safety Down to the Smallest Detail, Volvo Leaflet. Volvo (2002), Knowledge Saves Lives, Volvo Cars Safety Centre, Gothenburg. Volvo (2004), “Sticks and stones can break your bones and you shouldn’t be called names either”, available at: www. volvocars.com.au/Showroom/S60/2004+Drive+away.htm (accessed December 27, 2004). Walker, A. (2003), “Volvo – a surprising brand”, AutoWorld, available at: www.autoworld.co.za/autonews/viewart. asp?id=1388 (accessed December 27, 2004).

Conclusions The marketing approach used by Volvo to debunk the stigma of “Bloody Volvo Driver” in the Australian marketplace appears to be a rare approach and could be perceived to some extent as being “masochistic”. The “masochistic marketing” approach applied by Volvo in Australia should be seen as a process. It is dependent upon the outcome of a series of cause and effect relationships. The masochistic marketing approach may be divided into four cause-related phases, all of which create a dualistic outcome of either positive or negative effectchains in respect to the corporate image of Volvo in the marketplace and in the society. A masochistic marketing approach is a high-risk venture. It is a challenging and demanding marketing process, because it plays on the humiliation of the corporate image itself. The core idea of the masochistic marketing approach violates, or at least appears to oppose, the fundaments of marketing. The underlying idea is to turn a stigmatized image in the marketplace into something useful and valuable in forthcoming marketing and business activities however, in the process the corporate image may deteriorate severely. At worst, the corporate image may be at stake in the marketplace, if the marketing process fails. In addition, it may cause a long-term and severe impact on the future prosperity of the company’s business practices in the marketplace. Masochistic marketing is not recommended to be used as a common approach, unless a series of events has turned the corporate image in the marketplace into something that is highly undesirable and a stigma, such as the idiom of “Bloody Volvo Driver” that is in apposition to Volvo’s corporate image – safety, safety and above all safety. In consequence, it should be used as an “ultimate” attempt to convert an unfortunate and a damaging stigma involving the corporate image in the marketplace into one that attracts rather than repulses potential customers.

About the authors Go¨ran Svensson is a Professor at Oslo School of Management, Oslo, Norway. He is the corresponding author and can be contacted at: [email protected] Greg Wood is an Associate Professor in the Bowater School of Management and Marketing, Deakin University, Warrnambool, Australia. Michael Callaghan is a Lecturer and PhD candidate in the Bowater School of Management and Marketing, Deakin University, Warrnambool, Australia.

References Advertising Standards Board (2003), “Complaint reference number 375/03”, available at: www.advertising standardsbureau.com.au/PDF/375.pdf (accessed 24 November 2003). Auto Web (2003), “‘Bloody Volvo Driver’, campaign a bloody success”, available at: www.autoweb.com.au/cms/ A_1000390/newsarticle (accessed February 9, 2004).

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Customer satisfaction and loyalty in a digital environment: an empirical test Jean Donio’ University of Paris II, Paris, France

Paola Massari and Giuseppina Passiante e-Business Management School, ISUFI, University of Lecce, Lecce, Italy Abstract Purpose – The purpose of this paper is to explore the links between customer loyalty attitude, customer loyalty behaviours (measured by customer purchase behaviours) and profitability. The aim is to define a conceptual framework within which to analyse the relationships between attitudes, behaviour, and profitability of the customers. Design/methodology/approach – Reference was made to earlier studies which argued that loyal customers constitute competitive asset of business organizations. Several authors noted that customers generally vary in terms of loyalty behaviours and attitudes and highlighted that differences about customers’ loyalty levels affect a firm’s profitability results. Customer loyalty, its antecedents and outcomes, and the links between customer satisfaction, customer loyalty and profitability have been analyzed at a customer level. Findings – The results showed support for all but one of the five hypotheses, the exception being H2. Originality/value – The results of the study provide evidence that a Loyalty Index can give managers an adequate support for market segmentation. This means that actual market segment strategies, based on geographical, demographical and/or psychographic variables, should take into account also loyalty measurement models. Keywords Customer satisfaction, Customer loyalty, Electronic commerce, Customer relations, Marketing intelligence Paper type Research paper

differ in current and/or future profitability makes a firm’s strategy more effective, by identifying profitability customer tiers, and offer products and services customized for the specific tier, and therefore capturing its financial value. The purpose of this empirical study is to explore the links existing between customer loyalty attitude (as his consistently favourable set of stated beliefs towards the brand purchase), customer loyalty behaviours (in terms of his pattern of past purchases) and profitability. To this end, customer loyalty, its antecedents and outcomes, and, thus, the links between customer satisfaction, customer loyalty and profitability have been analyzed at a customer level. Specifically, the study has focused on the following issues: . What are the main antecedents and outcomes of customer loyalty? . What are the links between customer satisfaction, customer loyalty and profitability? . How can loyalty be evaluated in a firm’s customer base? Is there any model that can assess customer loyalty based on specific variables and indexes? Do characteristics exist which determine whether customers attain high levels of customer loyalty?

Introduction Research on factors that influence customer satisfaction and loyalty has made considerable progress within the last years (Szymanski and Henard, 2001; Oliver, 1999). Customer loyalty is seen to be crucial to the success of business organizations, since attracting new customers is far more expensive than retaining existing ones (Dick and Basu, 1994; Saren and Tzokas, 1998; Fournier, 1998). It has been suggested by many authors that loyal customers are a competitive asset and that a way of increasing customer retention is through secure and collaborative relationship between buyers and sellers (Chaudhuri, 1999; Chaudhuri and Holbrook, 2001; Fournier, 1998; Oliver, 1999). Several authors pointed out that customers generate different levels of profitability (Cooper and Kaplan, 1991; Peppers and Rogers, 1993; Shapiro et al., 1987; Slywotzky and Shapiro, 1993), and not all customers generate acceptable cost and revenue streams (Carroll, 1991; Storbacka et al., 1994). It has been suggested, therefore, that the firm should actively develop relationships with profitable customers and try to end relationships with unprofitable customers (Jones and Sasser, 1995; Peppers and Rogers, 1993; Shapiro et al., 1987; Slywotzky and Shapiro, 1993). Tailoring marketing efforts to segments that

Our approach is based on a framework put forward by Costabile (2001) to analyse the relationship existing between the act of purchase of the customer, his satisfaction, his trust and commitment and, at the end, his loyalty. In this study, we have extended the single act of purchase to the complete purchase behaviour of the customer, and we have also explored the relationship existing between the customer loyalty and his profitability. The paper is organized as follows. The first section presents the theoretical framework and the main hypotheses. The second section illustrates the method adopted. The main

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Journal of Consumer Marketing 23/7 (2006) 445– 457 q Emerald Group Publishing Limited [ISSN 0736-3761] [DOI 10.1108/07363760610712993]

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Customer satisfaction and loyalty in a digital environment

Journal of Consumer Marketing

Jean Donio’, Paola Massari and Giuseppina Passiante

Volume 23 · Number 7 · 2006 · 445 –457

results are described and discussed in section three. The final section presents conclusions and a future research agenda.

fests (McAlexander et al., 2002), and the classic case of Harley-Davidson bikers (Schouten and McAlexander, 1995). Despite the psychological and sociological richness of the “attitudes drive behaviour” and “relationship” approaches to understand customer loyalty, these conceptualizations of loyalty are not without drawbacks (e.g. Dowling, 2002). They are thought to be less applicable for understanding the buying of low-risk, frequently-purchased brands, or when impulse buying or variety seeking is undertaken, than for important or risky decisions (Dabholkar, 1999). Also, as Oliver (1999) has noted, there is little systematic empirical research to corroborate or refute this perspective of customer loyalty. The examples above are isolated cases, often cited as illustrative of the revenue-effects that might be achieved, rather than the profit impacts that have been achieved.

Theoretical framework and main hypotheses Customer loyalty: antecedents and outcomes Customer loyalty is a concept that has enjoyed wide currency and usage within the field of consumer behaviour for many years. Dick and Basu (1994) viewed customer loyalty as “the strength of the relationship between an individual’s relative attitude towards an entity (brand, service, store, or vendor) and repeat patronage”. Three conceptualizations of customer loyalty have been identified in the literature: 1 loyalty as primarily an attitude that sometimes leads to a relationship with the brand; 2 loyalty mainly expressed in terms of revealed behaviour (i.e. the pattern of past purchases); and 3 buying moderated by the individual’s attitudes.

Loyalty mainly expressed in terms of revealed behaviour This conceptualisation is arguably the most controversial but the best supported by data. The controversy comes out because loyalty is defined mainly with reference to the pattern of past purchases with only a secondary interest in consumer motivations or commitment to the firm (Ehrenberg, 1988; Fader and Hardie, 1996; Kahn et al., 1988; Massy et al., 1970). Researchers have gathered impressive amounts of data about these purchase patterns over many years – across dozens of product categories and for many diverse countries (Uncles et al., 1994). They have found that few consumers are “monogamous” (100 percent loyal) or “promiscuous” (no loyalty to any brand). Rather, most people are “polygamous” (i.e. loyal to a portfolio of brands in a product category). From this perspective, loyalty is defined as “an ongoing propensity to buy the brand, usually as one of several” (Ehrenberg and Scriven, 1999). These researchers tend to adopt a market focus as opposed to an individual focus (e.g. key performance measures are purchase amount and frequencies, repeat-buying – for a defined period). Stochastic modelling techniques describe the observed patterns of customer buying. Given these descriptions, loyalty is inferred to operate in the following manner: through trial and error, a brand that provides a satisfactory experience is chosen. Loyalty to the brand (measured by repeat purchase) is the result of repeated satisfaction that in turn leads to weak commitment. The consumer buys the same brand again, not because of any strongly-held prior attitude or deeply-held commitment, but because it is not worth the time and trouble to search for an alternative. If the usual brand is out of stock or unavailable for some reason, then another functionally similar (or substitutable) brand (from the portfolio) will be purchased (e.g. East, 1997; Ehrenberg et al., 1997, 2004). There is little reason to spend much effort weighing up the alternatives when all are likely to be satisfactory. However, over repeated purchases a weak commitment to the (limited) number of brands bought in a product category can form. All these studies are grounded in considerable amounts of market research data and analysis. But, despite the weight of empirical evidence, controversy persists. Those who subscribe to the “attitudes drive behaviour” and “relationship” approaches expressly rule out revealed behaviour as a dominant measure of loyalty. That, they argue, may merely reflect happenstance. Even combined measures of revealed behavior and satisfaction may not probe deeply enough for us

Loyalty as primarily an attitude that leads to a relationship with the brand Researchers argue that there must be a strong “attitudinal commitment” to a brand for true loyalty to exist (Day, 1969; Jacoby and Chestnut, 1978; Foxall and Goldsmith, 1994; Mellens et al., 1996; Reichheld, 1996). This is seen as taking the form of a consistently favourable set of stated beliefs towards the brand purchased. These attitudes may be measured by asking people how much they like the brand, feel committed to it, will recommend it to others, and have positive beliefs and feelings about it – relative to competing brands (Dick and Basu, 1994). The strength of these attitudes is the key predictor of a brand’s purchase and repeat patronage. This is what Oliver (1997, p. 392) has in mind when he defines customer loyalty as: “A deeply held commitment to re-buy or re-patronize a preferred product/ service consistently in the future, thereby causing repetitive same-brand or same brand-set purchasing despite situational influences and marketing efforts having the potential to cause switching behaviour”. In the fields of advertising and brand equity research this model received some support (e.g. Aaker, 1996; de Chernatony and McDonald, 1998). The approach also appealed to many practitioners in advertising and brand management because it is empathetic with the search for strategies to enhance the strength of consumers’ attitudes towards a brand. Ahluwalia et al. (1999) have shown that attitudinally-loyal customers are much less susceptible to negative information about the brand than non-loyal customers. Also, where loyalty to a brand increases, the revenue-stream from loyal customers becomes more predictable and can become considerable over time – as shown in analyses of cases such as Federal Express, Pizza Hut franchises, and Cadillac dealerships (Gremler and Brown, 1999). An extension of the “attitudes define loyalty” perspective is to suggest that consumers form relationships with some of their brands. A good example of this perspective is provided by Fournier (1998), who sees loyalty as a committed and affect-laden partnership between consumers and brands. It is a partnership that will be even stronger when supported by other members of a household or buying group, and where consumption is associated with community membership or identity. Examples include Skoal smokeless tobacco among some North American cowboys, loyalty to particular European soccer teams (Arnould et al., 2002), the Beanie Babies craze (Morris and Martin, 2000), Jeep brand 446

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Jean Donio’, Paola Massari and Giuseppina Passiante

Volume 23 · Number 7 · 2006 · 445 –457

to be sure there is true loyalty (Arnould et al., 2002; Oliver, 1999).

ongoing propensity to buy one or a product from a specific firm. All these patterns profile customers, not brands per se; that is, consumers of a firm’s products could be distributed across segmentation criteria with respect to their loyalty level to a brand/firm. As shown in Figure 1, trust is considered as an outcome of customer satisfaction and as antecedent of customer commitment and customer loyalty. The reason why many authors regard trust as an antecedent of customer loyalty is underlined by Moorman et al. (1993). According with Schurr and Ozanne (1985) trust has been defined as “the belief that a party’s word or promise is reliable and that a party will fulfil his/her obligation in an exchange relationship”. Commitment expresses the extent to which a partner is willing to maintain a valued relationship (Moorman et al., 1992), and similarly to trust, is “critical to the study and management of customer loyalty” (Morgan and Hunt, 1994, p. 31). Trust is seen as a key determinant to commitment (e.g. Morgan and Hunt, 1994; Gruen, 1995; Geyskens et al., 1996). Morgan and Hunt (1994) state that “trust is so important to relational exchange . . . because relationships characterized by trust are so highly valued that parties will desire to commit themselves to such relationships”.

Buying moderated by the individual’s attitudes This conceptualisation argues that the best conceptualization of loyalty is to allow the relationship between attitude and behaviour to be moderated by time. The reasons for incorporating buyers’ attitudes into a definition of loyalty have been put forward by various authors over the past 20 years are two: 1 Distinguishing between attitudinally loyalty and non-attitudinally loyal customers is useful because it indicates who and how many customers are vulnerable to a change in the “spurious” environmental causes of their loyal behaviour. Hence, it gives an indication of how long customers are likely to stay loyal. 2 A purely behavioural definition of loyalty fails to explain the causes of loyal behaviour. The dynamic approach is based on a dynamic model that has been defined in order to interpret the customer-firm relationship life-cycle as a continuum, along which cognitive and behavioural constructs overlap. In this way through successive sedimentation the multidimensional construct of customer loyalty is defined. The model is founded on empirical evidence and experiments realized in the different fields of study. Specifically, it refers to: studies on customer satisfaction, its determinants and consequences (Iacobucci et al., 1992; Oliver, 1997); empirical evidence on trust, as well as studies that are the connection with the propensity to repurchase and the consolidation of the relationship (Bitner, 1995; Blois, 1999); and the studies of the relationship life-cycle and the different forms of loyalty, whose basic configuration is simple repurchase, but with an evolution path towards “true loyalty” on the base of the attitudinal constructs interacting with the behavioural one (Jacoby and Chestnut, 1973, 1978; Ford, 1980, 1998; Iacobucci and Zerrillo, 1997). In Figure 1, the dynamic model of customer loyalty is described. The anchor points are customer trust and customer commitment. In this model, satisfaction with past purchases, and any consequential habit formation, explains most of a person’s

Conceptualising customer profitability Customer profitability is a customer-level variable which refers to the revenues which one particular customer generates over a given period of time. Customer profitability appears in two temporal forms in marketing literature. First, it appears as an historical record. In this sense, a customer profitability analysis is similar to the firm’s analysis of its profits and losses. The main difference is that a customer profitability analysis refers to one particular customer, whereas a profit and loss statement refers to all customers. A history-oriented customer profitability analysis can be made at several levels. A common point of departure is to calculate the contribution margin (gross contribution margin), based on “sales revenue less all product-related expenses for all products sold to an individual customer during one particular period of time” (cf. Wang and Splegel, 1994). Then, depending on the availability of data, sales, general and administrative expenses traceable to the individual customer are subtracted (Cooper and Kaplan, 1991; Howell and Soucy, 1990). The result of this calculation is the operating profit generated by the customer. An extension of this line of thinking is the computation of “customer return on assets”, i.e. customer profitability divided by, e.g. the sum of accounts receivable and inventory (Rust et al., 1996). Second, customer profitability is also referred to in a future sense in the literature. In this case, it often takes the form of the output from a net present value analysis. The output is sometimes referred to as the “lifetime value” of a customer (cf. Heskett et al., 1997; Peppers and Rogers, 1993; Petrison et al., 1993; Rust et al., 1996). It has been defined, for example, as the stream of expected future profits, net of costs, on a customer’s transactions, discounted at some appropriate rate back to its current net present value (Peppers and Rogers, 1997, p. 32). A similar concept is “customer equity” which is seen as a function of the customer’s volume of purchases, margin per unit of purchase and acquisition, development and retention costs traceable to this customer (Blattberg and Deighton, 1996;

Figure 1 A dynamic model of customer loyalty

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Jean Donio’, Paola Massari and Giuseppina Passiante

Volume 23 · Number 7 · 2006 · 445 –457

Wayland and Cole, 1997). Several authors have also noted that customers generally vary in terms of profitability (Cooper and Kaplan, 1991; Peppers and Rogers, 1997; Shapiro et al., 1987; Slywotzky and Shapiro, 1993). It has been argued that one particular customer does not generate the same costs and revenues over time as another customer. Moreover, not all customers generate acceptable cost and revenue streams. For example, in retail banking, some 50-60 percent of customers may be unprofitable (Carroll, 1991; Storbacka et al., 1994). It has been suggested, therefore, that the firm should actively encourage relationships with profitable customers and attempt to terminate relationships with unprofitable customers (Jones and Sasser, 1995; Peppers and Rogers, 1997; Shapiro et al., 1987; Slywotzky and Shapiro, 1993).

marketing’s link to profitability is stressed in the definitions of marketing offered by the Chartered Institute of Marketing and the American Marketing Association (cf. Buttle, 1996). However, attention in the marketing literature has instead been focused on other customer-level variables than customer profitability which provide marketers and market researchers with an easier access to data, particularly in terms of customer surveys, and are assumed to be carriers of information about customer profitability. Customer satisfaction and customer loyalty are a variable of this type. The attention devoted to these particular variables can be seen in the light of the current interest in relationship marketing. It is assumed, in brief, that it is more profitable to keep existing customers than to attract new customers, and it is commonly assumed that customer satisfaction serves as a particularly important antecedent of customer retention and thus long-term customer relationships (cf. Anderson et al., 1994; Buttle, 1996; Rust et al., 1995). However, due to the lack of data on customer profitability, the nature of the satisfaction-loyalty-profitability link has rarely been analyzed in empirical terms.

Customer profitability and customer loyalty An increased focus on profitability at the customer level is a reflection of a movement within the marketing discipline towards a less aggregate view of markets. In other words, the individual customer – rather than segments of customers – is increasingly stressed as the unit of analysis. This movement has given birth to labels such as “one-to-one marketing” and “micro marketing”. Seen from this perspective, customer profitability is emerging as an important dimension in which each (unique) customer can be described. A focus on customer-level profitability can also be conceived of as a reflection of marketing’s changing role within the firm (cf. Webster, 1992). An important aspect of the new role is that “marketing is too important to be left to the marketing department”. Consequently, at least in marketing literature, other departments are encouraged to deal with marketing issues. This can be seen particularly in terms of cost control, in the sense that marketing performance measures are being introduced in cost accounting literature and practice. For example, activity-based costing and balanced scorecard techniques often include dimensions which are highly relevant to marketing (cf. Cooper and Kaplan, 1991; Kaplan and Norton, 1992). In this context, it is worth noting that marketing has traditionally lagged behind other functional areas of business with respect to the implementation of cost control systems (Dunne and Wolk, 1977; Morgan and Morgan, 1980). Another factor behind the interest in customer profitability (and its links to behaviour and attitudes) is the development of information technology, e.g. in terms of “data warehouses”, which allows for a detailed analysis of each customer. Despite the growing interest in customer profitability, identifying profitable customers is likely to be easier said than done for most firms. The main reason is that few firms have an internal accounting system which allows for an analysis of profitability at the individual customer level. At least this is what many academicians claim (Howell and Soucy, 1990; Myer, 1989; Reichheld, 1996; Slywotzky and Shapiro, 1993). However, given that several computerized systems which facilitate an analysis of customer profitability are commercially available on the market, there are reasons to believe that practitioners are experimenting with such data to an extent that is not yet reported in academic journals. In any case, profitability data on the customer level are generally not collected in empirical studies carried out by marketing scholars. This is not likely to advance marketing theory. After all, profitability lies at the heart of the marketing concept (Kohli and Jaworski, 1990; Narver and Slater, 1990). Similarly,

Hypotheses development Figure 2 presents the model that guided our hypotheses development. Following the Bagozzi (1974) holistic construal, the conceptual meaning of our focal concept (loyalty) is obtained through specification of antecedents (purchase behaviour, satisfaction, trust, commitment) and the outcomes (profitability). As shown in Figure 2, following Blattberg and Deighton (1996), Wayland and Cole (1997), we consider customer profitability as a performance outcome (from the supplier’s point of view) of customer’s purchase behaviour. We assume customer’s purchase behaviour to affect profitability by effects on both revenues and costs. First, as the customer continues to purchase from the same supplier, the supplier’s revenues increase. In addition, as the purchases continue, the customer may discover, and purchase, additional products in the supplier’s assortment. In other words, the potential for crossselling may increase over time – which affects revenues positively (Kalwani and Narayandas, 1995). Second, a high level of repeated purchases is likely to go hand in hand with having contacts with the supplier at several occasions. H1. Customer purchase behaviour is positively and significantly related to customer profitability. As pointed out by several authors (Jones and Sasser, 1995; Chaudhuri and Holbrook, 2001; Fournier, 1998; Oliver, 1999), we suggest that customer satisfaction (a mental state) can have an impact on customer profitability: indeed, the University of Michigan found that for every percentage increase in customer satisfaction, there is an average increase of 2.37 percent of return on investment (Keiningham and Vavra, 2001). Moreover, the cost of gaining a new customer is ten times greater than the cost of keeping a satisfied customer (Gitomer, 1998). Following Costabile (2001) we consider satisfaction as a possible antecedent of customer loyalty. Research about influencing factors of customer satisfaction on loyalty has made considerable progress within the last years (Fournier and Mick, 1999; Oliver, 1999; Anderson et al., 1994; Buttle, 1996; Rust et al., 1995; Szymanski and Henard, 2001; Oliver, 1999). Indeed, the most commonly applied conceptual models of loyalty begin from the well-established notion that customers who have satisfying experiences with products will 448

Customer satisfaction and loyalty in a digital environment

Journal of Consumer Marketing

Jean Donio’, Paola Massari and Giuseppina Passiante

Volume 23 · Number 7 · 2006 · 445 –457

Figure 2 Predicted links between customer loyalty attitude, purchase behaviour and customer profitability

buy those products or will intend to buy them again (Jacoby and Kyner, 1973; Szymanski and Henard, 2001; Jacoby and Chestnut, 1973, 1978; Ford, 1980, 1998; Iacobucci and Zerrillo, 1997). Finally, we hypothesize that, as the customer loyalty enhances, customer profitability increases (Reichheld and Sasser, 1990; Kohli and Jaworski, 1990; Narver and Slater, 1990). Improvements in customer loyalty and retention by even a few percentage points have in some cases increased profits by 25 per cent or more (Griffin, 1995). H2. Customer satisfaction is positively and significantly related to customer profitability. H3. Customer loyalty attitude is positively and significantly related to customer profitability. H4. Customer satisfaction is positively and significantly related to customer loyalty attitude.

H5.

Customer satisfaction, trust and commitment are positively and significantly related to purchase behaviour.

Research method In order to test our hypotheses, we have conducted an empirical study in the agri-food sector. The point of departure for the case study was to match customer satisfaction and customer loyalty attitude data (at the customer level) with purchase behaviour and profitability data (also at the customer level). The first step has been to identify a firm which had kept track of costs and revenues over time at a customer level, and was willing to provide access to this data. We identified one firm. For confidentiality issues we cannot name it. The firm is based in Italy and sells food products (pasta, olive oil, wine, vegetables, bread, sauces, cakes, honey and other typical foods) through direct marketing activities. Its current range consists of about 50 different items. Most relevant sales channels used are: telephone, internet, television, catalogue mail. The most important direct marketing instrument is based on the telephone, which develops alone 52 per cent of sales purchase. Television instrument develops 27 per cent of sales, catalogue mail 18 per cent of sales, and internet just 3 per cent of sales. The customer base is spread throughout the national territory. Products are delivered through a national courier directly to the houses of its customers. The cost accounting system allows for a detailed analysis of customer behaviour, as well as analyses of profitability at several levels (customers, products, sales persons, etc). This technology has been complemented with a telephone survey submitted to the customers.

We have also included two variables, which are assumed to be consequences of customer satisfaction and predictors of profitability, as they have been suggested by the literature (Costabile, 2001; Garbarino and Johnson, 1999; Anderson et al., 1994; Peppers and Rogers, 1997; Reichheld, 1996). Variables are trust and commitment that influence reciprocity and co-operation between the firm and its customers (Stern and El Ansary, 1992; Bucklin and Sengupta, 1993; Bitner, 1995; Blois, 1999). We have added to the previous hypotheses the analysis of trust and commitment as determinants that develop as customers gain experience and adopt relational orientations, and their connection with the customer propensity to repurchase and to consolidate of the relationship, following the suggestions of Bitner (1995), Blois (1999) and Garbarino and Johnson (1999). More specifically, trust has been considered as an outcome of customer satisfaction (Schurr and Ozanne, 1985) and as an antecedent of customer commitment and customer loyalty (e.g. Morgan and Hunt, 1994; Gruen, 1995; Geyskens et al., 1996; Scheer and Stern, 1992). 449

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Jean Donio’, Paola Massari and Giuseppina Passiante

Volume 23 · Number 7 · 2006 · 445 –457

Data collection The data used in this study have been derived from two sources: a telephone survey of the firm’s customers; and the firm’s customers’ database. In order to collect data on customer satisfaction and customer loyalty attitude, a telephone survey was developed. The scales used by respondents have been measured on balanced five-point Likert-type scales, ranging from “strongly disagree” to “strongly agree”, where 1 ¼ strongly disagree and 5 ¼ strongly agree. The telephone survey was chosen because of its relevant advantages such as monitoring of interviews for improved quality control, higher response rates, less bias due to non-response, shorter time requirements for completion, reasonably low cost (Dutka, 1993; Leland and Bailey, 1995). Customer satisfaction was measured as the weighted mean of three items[1] defined during a previous the market survey, based on global satisfaction, congruence between expected and perceived value, value perception. Customer Loyalty Attitude was measured with nine items adapted from Morgan and Hunt (1994), Moorman et al. (1992, p. 82), Pearson (1996), Schijns and Schroder (1996), Anderson and Narus (1984, p. 66), Selnes (1993), Crosby et al. (1990), Anderson and Weitz (1989). Two main dimension of customer loyalty attitude were investigated: customer trust and customer commitment. Customer Trust was measured with one item adapted from Hess (1995) and Moorman et al. (1992). Customer Commitment was measured using a multiitem scale adopted and modified from Mowday et al.’s (1979) Organizational Questionnaire, and Beatty and Kahle’s (1988) brand commitment scale. In particular, the following most relevant dimensions of customer commitment were examined: exclusive purchase intention, word-of-mouth[2], expectation of continuity, price sensitivity. Indicators and items capturing customer satisfaction and the attitudinal dimensions of customer loyalty are described in Table I. Data on customer purchase behaviour and profitability were collected from the firm database. Thus, the behavioural

dimension of customer loyalty was measured by ten indicators as shown in Table II. Data on purchase behaviour and customer profitability were collected from the firm database. The firm provided us the access to its records for the period December 2002-June 2004. The sample selection was based on the firm’s retained customers (those customers who made at least one purchase annually after the initial sale), who participated to the telephone survey with a useful response rate (in total, 4,397 customers). Data from these records were then entered into the same database as the survey responses. The client code was the key to matching the purchase behaviour and profitability records kept by Firm A on each customer with the survey responses. Several attempts were made to examine the quality of measurements. Internal consistency of the scales used was ascertained by both calculating Cronbach’s coefficient alpha and conducting an item analysis (through item-whole and inter-item correlation, as suggested by Spector, 1992). First, Cronbach’s alpha results were largely higher than Malhotra’s (1993, p. 308) 0.60 limit for acceptable reliability in terms of internal consistency. The customer attitudinal loyalty measure, consisting of nine items, has an alpha value of 0.910. The customer satisfaction measure, consisting of three items, has an alpha value of 0.798. The customer purchase behaviour measure, consisting of ten items, has an alpha value of 0.92. Content validity for the customer loyalty attitude, customer satisfaction, customer behaviours and customer profitability measures was ascertained by examining the scale composition throughout measure purification. The resulting scales demonstrate good reliability, as evidenced by Table III, in addition to being content valid. Data analysis The data gathered from the customer survey (data capturing attitudinal loyalty and customer satisfaction) and the firm’s data base (data capturing customer behaviours and profitability) were entered into a computer database and

Table I Variables and items capturing attitudinal loyalty and customer satisfaction Variables

Indicators

Survey’s items

Attitudinal

Trust

Trust attitude (1-5)

Loyalty

Commitment

Willingness to invest in the relationship (1-5) As a consumer to this firm/brand, I am willing to put in extra effort to buy product from this firm Exclusive purchase intention (1-5) As long as the product is similar I could just as well be buying from a different firm/branda Word-of-mouth attitude (1-5) I am proud to tell others that I buy product from this firm. I would recommend this brand to others Exclusive purchase Intention (1-5) For me, this brand is the best alternative Expectation of continuity (1-5) I expect to stay with this brand for a long period of time Price sensitivity (1-5) As a consumer to this brand, I feel that I am prepared to pay more for higher quality products Loyalty perception (1-5) I feel very little loyalty to this firm/branda

Satisfaction

Satisfaction

Global satisfaction (1-5) I am completely satisfied with the products of firm A Congruence between expected and perceived Performance expectations after purchasing firm A’s products exceed value (1-5) expectations prior to the purchase Value perception (1-5) Firm A’s products benefits are more important with respect to the costs and sacrifices related to the product purchase

I feel that I completely trust this firm activities and its products

Note: a Reverse coded items

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Jean Donio’, Paola Massari and Giuseppina Passiante

Volume 23 · Number 7 · 2006 · 445 –457

Table II Variables and items capturing customer purchase behaviour and profitability Definitiona

Variables 1. Sales purchases value (1-5)

The amount of sales purchases (Euros) during a period of time of 18 months

2. N. of orders (1-5)

The number of orders during a period of time of 18 months

3. Frequency of purchases (1-5)

The frequency of purchase, expressed in days (n. orders/days)

4. Returns (1-5)

The percentage of products returns with respect to overall sales purchase value (returns/total sales purchase value expressed in %)

5. Debt (1-5)

The % of debt with respect to overall sales purchase

6. Interactions (1-5)

All kind of interaction with the firm, intended ad communications, compliments, complaints

7. Way of payment (1-5)

The way of payment usually chosen by the customer (credit card, anticipated to the courier, at moment of delivery, anticipated through the bank, after 30/60 days)

8. Way of order (1-5)

The order could be done in outbound way (the firm contact the client, during a direct marketing campaign) or in inbound way (the client contact the firm for the order)

9. Loyalty program’s membership (1-5)

The client shares some personal information with the firm in order to participate in Loyalty Programs

Composition of purchase (1-5)

The composition of purchase, expressed in %, could be based more on special offers and discount or could be based more on purchase with normal conditions of price

Customer profitability (1-5)

According with Cooper and Kaplan (1991) and Howell and Soucy (1990), customer profitability was operationalised for each customer in the sample as “ þ sales revenue – all product related expenses for all products sold to an individual customer during one particular period of time, – sales, general and administrative expenses traceable to the individual customer for the same period of time”. The currency is EU currency (Euro) and a period of 18 months is included in the analysis. Thus, the profitability observations for each customer consist of the operating profit generated by each customer during this period of time

Note: a Variables definition is based on literature review (Kelley, 1967; Raj, 1982; Tate, 1961, Farley, 1968; Fournier and Yao, 1997; Kahn et al., 1986; Rao, 1969; Carman, 1970; Enis and Paul, 1970; Goldman, 1977-1978; Jacoby and Chestnut, 1978; Cooper and Kaplan, 1991; Howell and Soucy, 1990; Kaplan and Norton, 2004) and on the results of an Expert Analysis (survey to 30 managers of the agri-food sector)

Table III Scale summary Constructs scale

n

a

N

Std dev.

Satisfaction Customer loyalty attitude Customer purchase behaviour Customer profitability

3 9 10 1

0.798 0.910 0.92 0.944

4,397 4,397 4,397 4,397

0.42 0.54 3.9 4.2

ANOVA analysis summarized the results of variance’s analysis. The sum of squares and mean square were analysed, for two sources of variation, regression and residual. The output for Regression displayed useful information about the variation accounted for by each model. The output for Residual displayed information about the variation that was not accounted for by each model. R, R squared, adjusted R squared, and the standard error were analysed. Among the initial hypothesis, the model that accounted for most of variation in the dependent variable, with a good large regression sum of squares in comparison to the residual sum of squares, was highlighted. Some observations should be made before we examine the outcome with regard to the hypotheses. First, the standard deviations for customer profitability confirm what was claimed about this variable in the introduction. That is to say, customers clearly do vary in terms of the profitability they generate. For example, in this case study, the top ten customers (2.4 per cent of the sample) who ranked highest in terms of customer profitability generated 20 per cent of the total customer profitability in the sample. This is in line with most relevant theoretical approaches and empirical evidences. Second, the analysis of the most relevant behavioural loyalty variables (customer n of orders and sales purchases value) reveals a positive and strong correlation with customer profitability that has been confirmed by the Beta coefficients computation. The detailed results of these computations are summarised in Table IV.

Notes: n ¼ number of items; a = Cronbach’s alpha; N ¼ number of cases; Std dev. ¼ standard deviation

then analyzed using the Statistical Package for the Social Sciences (SPSS). Factor analysis, cluster analysis, ANOVA, canonical correlation analysis, multiple regression, path analysis, and t-tests were employed to test the research hypotheses on the relationships among the variables. A logistic regression analysis was used in order to identify the stronger predictors of customer profitability and customer loyalty, using all available measures, including both behavioural and attitudinal variables. The regression coefficients of each model equation related to the main hypothesis were elaborated. A particular attention was given to standardised coefficients calculation. This, because the magnitude of a regression coefficient isn’t necessary related to how good a predictor the variable is, since the size of the coefficient depends in large part on the units of the measure for the variable. One way to make the coefficients easier to compare is to compute what are known as standardised coefficients (Beta coefficients). 451

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Jean Donio’, Paola Massari and Giuseppina Passiante

Volume 23 · Number 7 · 2006 · 445 –457

Table IV Results of the computations Unstandardized coefficients B Std error

Standardized coefficients Beta

t

Sig.

H1 Dependent variable: profitability (Constant) Customer purchase behaviour (REGR factor score 1 for analysis 1)

0.015 0.736

0.007 0.015

0.964

2.164 48.329

0.031 0.000

H2 Dependent variable: profitability (Constant) Customer satisfaction

0.431 0.446

0.010 0.007

0.684

45.308 62.010

0.000 0.000

H3 Dependent variable: profitability (Constant) Customer loyalty attitude (REGR factor score 1 for analysis 2)

0.018 0.325

0.021 0.035

0.822

0.875 10.022

0.381 0.000

H4 Dependent variable: loyalty attitude (Constant) Customer satisfaction

0.015 0.058

0.002 0.005

0.725

6.138 0.039

0.021 0.000

2.386 0.230 1.254 20.061

0.500 0.060 0.023 0.054

0.760 0.823 0.621

212.430 12.208 32.342 1.778

0.000 0.000 0.000 0.000

H5 Dependent variable: purchase behaviour (Constant) Customer trust Customer commitment Customer satisfaction

as maintaining a price advantage and/or providing additional services to offer value. To this end, in the literature some categorization of customers are suggested, useful for identifying, motivating, serving a customer according to his/ her expected differential levels of profits (e.g. Zeithaml et al., 2001) Customer satisfaction was found to be positively related to customer loyalty attitude, explaining 43 per cent of the variance of the latter. However, multiple linear regression and ANOVA analysis have shown that the model fails to explain a lot of the variation in the dependent variable, and it needs for additional factors that help account for a higher proportion of the variation in the dependent variable. This is in line with many theoretical approaches that highlighted how apparent high levels of satisfaction may not result in a behaviour characterised by high loyalty due to the many intervening variables of customer loyalty development process (Jones and Sasser, 1995; Oliver, 1999). Satisfaction, trust and commitment were found to be positively and significantly associated with purchase behaviour. Three variables were entered the model, but two of them resulted most relevant according with t statistic method: customer trust and customer commitment. Figure 3 shows the links between purchase behaviour and the main customer loyalty attitude enablers. Customer trust and customer commitment resulted the most important variables positively and significatively related to purchase behaviour. In particular, customer commitment, with the large t statistics value, resulted to be the main driver for customer purchase behaviour.

As shown in Table IV, all the hypotheses of conceptual framework were confirmed by the empirical results.

Discussion: the governance of customer loyalty The results showed support for all but one of the five hypotheses, the exception being H2. Purchase behaviour (behavioural dimension of customer loyalty) was found to be positively and significantly associated with customer profitability. This result was confirmed by t statistics method results, that identified sales purchase value and number of orders as some of the best predictors of customer profitability. Customer satisfaction was found to be positively associated with customer profitability, but it was considered not statically significant. The results confirm what was claimed about this variable in the introduction: customer satisfaction (a mental state) has not any direct impact on customer profitability. Thus, our results seem to confirm the hypotheses of Fournier and Mick (1999) and Oliver (1999) that it is the behaviour of the customer, which may follow from a certain level of satisfaction, trust and commitment that affects customer profitability. Customer loyalty attitude was found to be positively and significantly associated with customer profitability. Our model then relates “customer attitudinal loyalty” measures (intent to repurchase, willingness to recommend and other probable market actions) to the expected profitability of each customer: estimating the customer expected profitability, basing on his attitudinal loyalty level, could be extremely useful for a manager for setting-up a customized marketing strategy, such 452

Customer satisfaction and loyalty in a digital environment

Journal of Consumer Marketing

Jean Donio’, Paola Massari and Giuseppina Passiante

Volume 23 · Number 7 · 2006 · 445 –457

Figure 3 Links between customer loyalty attitude enablers and purchase behaviour

interactions, customer retention and longevity, furnish key lagging indicators of customer loyalty.

Our study has identified several significant associations between variables in the customer satisfaction-customer loyalty attitude-purchase behaviour (behavioural loyalty)customer profitability chain. The associations between the two latter types of variables should not be surprising, since it is the actual acts by customers, not their attitudes that affect the firm’s performance (cf. Storbacka et al., 1994). However, the results of our analysis show that customer loyalty variables are related to what customers do in terms of purchase behaviour: these relationships are commonly missing in many parts of the marketing literature. Segmentation literature, and particularly the literature on segmentation of business markets, is one area in which these results are relevant. Many segmentation variables have been described as candidates for the segmentation of business markets, but they are generally related to other characteristics of the buyer than customer loyalty (cf. Shapiro and Bonoma, 1984; Webster, 1984). The results of the study provide evidence that a Loyalty Index can give managers an adequate support for market segmentation. This means that actual market segment strategies, based on geographical, demographical and/or psychographic variables, should take into account also loyalty measurement models. Literature review and empirical results have also shown cost savings associated with a loyalty building strategy in at least six areas (Reichheld, 1996): 1 reduced marketing costs – customer acquisition costs more; 2 lower transaction costs, such as outbound efforts and order processing; 3 reduced customer turnover expenses (fewer lost customers to replace); 4 increased cross-selling success, leading to larger share of customer; 5 more positive word of mouth; and 6 reduced failure costs (reduction in returns, debt, claims and complaints).

As suggested by Kaplan and Norton (1992), without effective leading indicators, it may be difficult to establish how outcomes are achieved. Moreover, an organization lacking leading indicators of key performance outcomes or results has no early warning mechanism to signal the need for corrective action. By relying exclusively on outcomes or results, organizations may not detect the need for action until it is too late. The link between the attitudinal and behavioural indicators pointed out in this paper allow to use attitudinal measures for the purpose of estimating future results, as well as developing models that enable an organization to examine alternative “what-if” scenarios.

Conclusions In this paper we have explored links between variables concerning the customer satisfaction – the customer attitudinal loyalty – the customer behavioural loyalty – the customer profitability chain. We have included both attitudinal (such as intent to repurchase, willingness to recommend and other probable market actions have been included in order to provide the basis for developing leading indicators of customer loyalty) and behavioural measures (such as repeat purchasing, volume or frequency of purchasing, returns, debt, complaints and interactions, customer retention and longevity, have been included as lagging indicators of customer loyalty). Our model also has verified some relations existing between attitudinal measures and behavioural measures, in order to use attitudinal measures for estimating the customer expected profitability; this estimation can be used for setting-up a customized marketing strategy, such as maintaining a price advantage and/or providing additional services to offer value. As a conclusion, some limitations of our study should be emphasised. Firstly, data were collected in one single firm. Secondly, the study focused on a single industry, namely that of agri-food. While useful in controlling for potential extraneous influences unrelated to the study, the limitation involved in studying a single industry constrains the possibility to generalize these findings. Future research should seek to replicate the study into different firms and business sectors in order to assess whether the linkages identified here still exist in different industrial and consumer populations. Another limitation is related to the time periods used in the study. It is not clear to what extent the time periods have provided a

To obtain these cost savings, we believe that is necessary to measure and manage customer loyalty effectively, by using both leading and lagging indicators: . Attitudinal measures, such as customer commitment (intent to repurchase, willingness to recommend and other probable market actions) provide the basis for developing leading indicators of customer loyalty. . Behavioural measures, such as repeat purchasing, volume or frequency of purchasing, returns, debt, complaints and 453

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Jean Donio’, Paola Massari and Giuseppina Passiante

Volume 23 · Number 7 · 2006 · 445 –457

proper context for an analysis of the relationships between attitudinal variables and behavioural variables. However, it does seem clear that attitudinal variables such as customer satisfaction do not remain constant over time (cf. Peterson and Wilson, 1992). Future research should seek to replicate the study into different period of time, more than one. It means, among other things, that the timing of the survey becomes a key issue. One may consider as time unit “the year”, but if customer relationships are viewed as investments, a longer period may be needed to determine the extent to which one particular customer is profitable (cf. Reichheld, 1996).

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Notes 1 Items capturing customer satisfaction have been adapted from Pearson (1996), Oliver (1993), Holbrook (1999), Costabile (2001), Westbrook and Oliver (1991). 2 Word-of-mouth can be defined as “oral, person-to-person communication between a receiver and a communicator” (Arndt, 1967, p. 189).

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Journal of Consumer Marketing

Jean Donio’, Paola Massari and Giuseppina Passiante

Volume 23 · Number 7 · 2006 · 445 –457

Dowling, G.R. and Uncles, M.D. (1997), “Do customer loyalty programs really work?”, Sloan Management Review, Vol. 38 No. 4, pp. 71-82. Ehrenberg, A.S.C., Mark, D.U. and Goodhardt, G. (2004), “Understanding brand performance measures: using Dirichlet benchmarks”, Journal of Business Research., Vol. 57 No. 12, pp. 1307-25. Fournier, S. (1996), “The consumer and the brand: an understanding within the framework of personal relationships”, working paper 97-024, Harvard Business School, Boston, MA. Heskett, J.L., Jones, T.O., Loveman, G.W., Sasser, W.E. and Schlesinger, L.A. (1994), “Putting the service profit chain to work”, Harvard Business Review, Vol. 72 No. 2, pp. 164-74.

O’Brien, L. and Jones, C. (1995), “Do rewards really create loyalty?”, Harvard Business Review, Vol. 73 No. 3, pp. 75-82.

About the authors Jean Donio’, is a Full Professor at the University of Paris II, France. He is the corresponding author and can be contacted at: [email protected] Paola Massari is a Researcher at e-Business Management School, ISUFI, University of Lecce, Lecce, Italy. Giuseppina Passiante is a Full Professor of Innovation Management at the e-Business Management School, ISUFI, University of Lecce, Lecce, Italy.

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Customer loyalty programs: are they fair to consumers? Russell Lacey Department of Marketing and Logistics, University of New Orleans, New Orleans, Louisiana, USA, and

Julie Z. Sneath Mitchell College of Business, University of South Alabama, Mobile, Alabama, USA Abstract Purpose – The purpose of this paper is to examine the fairness of loyalty programs to consumers regarding two emerging criticisms of loyalty programs: discriminating value proposition segmentation and potential exploitation of captured personal information. Design/methodology/approach – Equity theory and exchange theory are the theoretical foundations used for evaluation of the aspects of loyalty program fairness. Findings – First, through the application of equity theory, firms can more effectively recognize and reward more valuable customers without alienating less valuable customers. Second, through the use of exchange theory, firms can secure authorization to collect and use individual customer information from customers in exchange for enhanced value proposition offerings via loyalty programs. Loyalty programs can induce customers to give up their personal information in exchange for benefits they would not otherwise receive. Marketers use the higher level of benefits available through loyalty programs as a form of compensation to customers for sharing personal information. Practical implications – Customer loyalty programs that are equitably administered and thoroughly communicated will be perceived favorably by consumers. Originality/value – This paper marks the first study to examine the issue of consumer fairness as it relates to how firms use loyalty programs to collect proprietary information and differentiate value propositions among customer segments. The findings can be used by managers to strengthen the marketing position of the firm through a loyalty program without compromising on their customers’ perceptions of fairness. Keywords Loyalty schemes, Equity theory, Exchange, Customer relations Paper type General review

customer segments in order to reward customer loyalty will require even greater value proposition differentials among customers and more precise market segmentation than currently exists with most loyalty programs. Firms that utilize these programs are explicitly shifting resources away from non-participating customers in favor of customers who participate in their loyalty programs, which may lead to accusations of discriminatory customer treatment. For customers who participate in loyalty programs, there is potential for increased concern about the misuse of personal information and loss of control over how information is being collected and disseminated (Langenderfer and Cook, 2004), given that current regulatory measures designed to protect consumer information privacy may not be sufficient (Petty, 2000). Meanwhile, the collection and use of information that can favorably impact the longevity and profitability of customer relationships is often dependent upon consumers’ voluntary participation in these programs. To our knowledge, no published work has examined the issue of consumer fairness as it relates to how firms use loyalty programs to collect proprietary information and differentiate value propositions among customer segments. This is a critical gap in the literature because loyalty programs continue to be used by organizations as marketing tools to support their customer relationship management (CRM) strategies. For example, the firm may use loyalty programs to focus directly on building loyalty, cultivate higher retention rates among its most valuable customers, and/or focus on data collection from individual customers. It is not the purpose of this analysis to explain the marketing objectives of individual firms, but rather to examine the fairness of such programs to the firm’s customers at a macro level. This study will briefly describe the

Introduction The popularity of customer loyalty programs has attracted widespread attention among marketing scholars in recent years (e.g. Kivetz and Simonson, 2002; Nunes and Dreze, 2006; Roehm et al., 2002; Uncles et al., 2003). Much of the research has been directed towards investigating how these programs contribute to the firm’s financial and market performance (e.g. Bolton et al., 2000; Kim et al., 2004; Lewis, 2004; Sharp and Sharp, 1997) and their ability to cultivate customer loyalty (e.g. Dowling and Uncles, 1997; O’Malley and Prothero, 2004; Uncles et al., 2003). Despite the abundance of customer loyalty program research – and emerging perspective that firms must move from their traditional position of providing all participating customers with equivalent benefit enhancement offerings – few studies have evaluated aspects of program fairness and privacy from the customer’s vantage point. While critics of loyalty programs argue that marketers should strive to enhance the value proposition for every customer, the nature of customer loyalty programs is such that tiered levels of benefits and customer services are created. Furthermore, programs that target specific customers or The current issue and full text archive of this journal is available at www.emeraldinsight.com/0736-3761.htm

Journal of Consumer Marketing 23/7 (2006) 458– 464 q Emerald Group Publishing Limited [ISSN 0736-3761] [DOI 10.1108/07363760610713000]

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Customer loyalty programs: are they fair to consumers?

Journal of Consumer Marketing

Russell Lacey and Julie Z. Sneath

Volume 23 · Number 7 · 2006 · 458 –464

Tiered value propositions

growing popularity of loyalty programs as a focal marketing tool. Next, it will present the two emerging major criticisms of loyalty programs: discriminating value proposition segmentation; and potential exploitation characteristics regarding personal information disclosure, and examine their respective relationships with equity theory and exchange theory. Finally, key findings and managerial implications resulting from this investigation will be presented.

Marketers faced with the business reality of limited firm resources are challenged by the desire to better serve their most valuable customers without overtly discriminating against less valuable customers. Because all customers are not equally valuable, it may be neither economically nor operationally wise to expand the firm’s value proposition to all of its customers (Reichheld, 1996). Failing to consider customer value may result in firms’ wasting resources oversatisfying less valuable customers, while under-satisfying those with greater value (O’Brien and Jones, 1995). While many marketers aggressively leverage segmentation through loyalty programs that explicitly reward their most valuable customers, such actions may discriminate against other patrons by channeling more resources toward the discriminating needs and desires of its most valued customers. Value discrimination results from firm activities which award select customers with elevated social status recognition and/or enhanced products and services above and beyond what is normally offered to customers. As an expansion of the marketer’s value proposition, loyalty programs can be designed to accommodate individual consumers in the form of added products or enhanced customer service options not generally presented to all of the firm’s customers. For example, personalized customer service can recognize select program members and handle each of the customers belonging to this category on an individual basis. Clearly, loyalty programs can be used to convey prestige to customers and make them feel special, important, and appreciated (Morgan et al., 2000). However, the effect on the firm’s nonparticipating customers can lead to dissatisfaction and alienation with the firm. Moreover, customers who participate in the program might become frustrated, and perhaps even disenfranchised, due to their inability to benefit from these programs (Dowling and Uncles, 1997).

Emergence of loyalty programs Customer loyalty programs are coordinated, membershipbased marketing activities designed to enhance the building of continued marketing exchanges among pre-identified customers toward a sponsoring brand or firm. Loyalty programs use targeted communications and customize the delivery of branded goods and services to build stronger bonds with the sponsoring brand/firm than would result without such programs. Often based on cumulative brand purchases, loyalty programs enhance value proposition offerings to preserve active customer status. Loyalty programs are set apart from other forms of promotions by their long-term nature and deliberate emphasis on preserving customer retention and intensifying purchase frequency (Sharp and Sharp, 1997). The history of loyalty programs can be traced back to 1896, with the introduction of S&H Green Stamps. By the 1960s, S&H Green Stamps was the largest purchaser of consumer goods in the world. Since the decline of popularity of stamp reward programs, newer generations of loyalty programs have gained widespread appeal. The first modern loyalty program was instituted by the airline industry when American Airlines introduced its frequent flyer program in 1981. During the technology boom of the 1990s, loyalty programs grew exponentially in the USA and throughout much of the economically developed world. According to a 2000 Jupiter Research study, 75 percent of US consumers participated in at least one loyalty program (Colloquy, 2000). In a 2005 AC Nielsen survey, nearly all Canadians (97 percent) participate in at least one loyalty program (AC Nielsen, 2005). Other highly mature loyalty program markets outside North America include the UK and Australia. Customer loyalty programs are utilized across a broad spectrum of vertical consumer markets, including hotels, credit card issuers, retailers, airlines, car rental companies, and entertainment firms. Deregulation trends in the cellular telephone and cable television industries suggest that commodity-based firms will increasingly utilize loyalty programs to attract and retain customers. Since firms across different industries share many of the same customers, many loyalty programs have also begun to adopt multiple branding schemes through which customers are able to combine and transfer program benefits. Multiple brand loyalty programs can occur between firms or within a corporate conglomerate (e.g. Starwood Hotels & Resorts, InterContinental Group), with the latter serving as an enterprise-wide program of a customer’s total marketing relationship with an organization. Extremely popular in Canada and the UK, loyalty programs supported by multiple participants offer increased customer value by accommodating a broader scope of business and organizational value due to the sharing of program costs (Swaminathan and Reddy, 2000).

Equity theory Fairness and/or discrimination in customer loyalty programs can be examined using equity theory (Huppertz et al., 1978). According to this theory, equity is viewed as something that is fair, right, or deserving in comparison to other entities, whether real or imaginary, individual or collective, person or non-person (Oliver, 1997). Equity should not be confused with equality, which calls for all customers to receive the same value proposition regardless of individual contributions. According to equity theory, customers form perceptions of the inputs (i.e. money, time, effort, opportunity costs) and outputs (e.g. tangible and intangible benefits) that are associated with an exchange. In this section, the fairness of value proposition discrimination practices commonly found in customer loyalty programs is discussed relative to three wellestablished aspects of this theoretical base, i.e. distributive equity, procedural equity, and interactional equity. A summary of customer perceptions and potential sources of discrimination are presented in Table I. Distributive equity Distributive equity is the extent to which customers perceive that they are equitably rewarded for their input (Oliver and Swan, 1989). Customers assess fairness of an exchange by comparing inputs to outcomes. An exchange is judged to be fair when customer input (what the customer is willing to invest) is proportional to outcomes associated with the exchange. Equity is said to exist when the perceived inputs 459

Customer loyalty programs: are they fair to consumers?

Journal of Consumer Marketing

Russell Lacey and Julie Z. Sneath

Volume 23 · Number 7 · 2006 · 458 –464

Table I Assessing fairness of value proposition discrimination in customer loyalty programs Form of equity

Customer perceptions of equity

Sources of perceived inequity

Distributive equity

Customers perceive there is fairness in the rewards they receive in return for their input

Needs and desires of different market segments Usage rate often linked to rewards Observing how other customers are treated Rising expectations and demands for greater rewards

Procedural equity

Customers perceive there is fairness in the process or means by which reward allocation decisions are made

Nonstandardization of loyalty programs Changing rules Nonportability of programs Inaccurate and/or inadequate consumer information Variability in response to individual requests

Interactional equity

Customers perceive there is fairness in the exchange of information and communication of outcomes

Treatment of customers Delivery of the value proposition Inconsistency across interactions Communication of mechanics and program criteria

and/or outcomes received by an individual consumer are psychologically consistent with the perceived inputs and/or outputs of similar customers (Huppertz et al., 1978). A major motivation for behavior is the anticipation of maximizing one’s own outcomes. This helps to explain, in part, why customer loyalty program participants often forgo freedom of choice in brand, firm, or service provider selection – or even pay some form of entry fee – in exchange for the benefits available from the loyalty program. Program benefits are commonly grouped into two categories: tangible or “hard” benefits and intangible or “soft” benefits. Hard benefits are comprised of tangible rewards, such as product offerings, gifts, special deals, price discounts, and cash incentives. Soft benefits are intangible and relationship-oriented, dominated by various forms of customized communications and preferential treatment. Examples of preferential treatment might include special access to private lounges for travelers, priority service at restaurants, special retailer events invitations, expedited check-in and check-out at hotels, and rental car upgrades. Soft benefits hold greater potential for program distinction for many successful loyalty programs (Hennig-Thurau et al., 2002). Customers’ perceptions of distributive equity increase when it is perceived that the firm is exerting extra effort by offering enhanced value propositions. Customers will consider the balance of their own inputs and outcomes compared to those received by other customers who are deemed to be of similar stature (Xia et al., 2004). As such, customers’ criteria for evaluating distributive equity come from observations of how other customers are treated. Although many loyalty programs strive to achieve distributive equity, the application of such programs may create perceptions of inequity and dissatisfaction and negative consequences can result. This is more likely to occur in settings where customers regularly interact with other customers and are able to observe superior value propositions being awarded. Through the separation of value proposition variables, firms are essentially transferring value from nonparticipants to program participants and rewarding loyalty program members at the expense of nonmember customers. Since many loyalty programs are targeted towards customers in the heavy-user segment of a particular product or service, heavy users often stand to gain the most from these programs. In contrast, light users generally do not benefit from loyalty programs (Kim et al., 2001).

Procedural equity Procedural equity involves the fairness of the process or means by which reward allocation decisions are derived. It represents the fairness of the process that leads to the outcome (Thibaut and Walker, 1975), including consistency of application in policies, procedures, and other criteria used to determine the results (Blodgett et al., 1997). According to Shugan (2005), customers can be discouraged from actively participating in loyalty programs due to their nonstandardized nature, especially when they are difficult to evaluate, have changing rules and regulations, and lack portability. It is of critical importance to procedural equity that reward allocation decisions be based on accurate customer information. While allocation processes should be unbiased, Tax and Brown (1998) argue that procedural equity should consider circumstances and requirements of individual customers. However, equity for one consumer group resulting from program membership may create sentiments of inequity among nonparticipating customers. Some researchers have advised marketers to use caution in their flexibility to individual requests to ensure that such actions do not create feelings of inequity for other customers (Tax and Brown, 1998). On the other hand, if nonmember customers perceive that the value proposition to qualifying members as unbiased, impartially delivered, and consistent with the standards of the program, they are likely to perceive procedural equity. Indeed, in such cases, nonmembers may perceive that outcomes directed to program members are unfair, yet still assess the procedures used to derive such treatment as equitable. Interactional equity Interactional equity considers the fairness associated with the exchange of information and communication of outcomes (Goodwin and Ross, 1992). Customers evaluate how processes are implemented and the way in which the processes and outcomes are explained. Associated with interpersonal treatment (Maxham and Netemeyer, 2003), interactional equity focuses on the two-way flows between customers and marketers, including the manner in which the customer is treated in terms of respect, interest, friendliness, honesty, and politeness. Even when customers perceive that outcomes and procedures are equitable, they may still feel mistreated if they discern unfair status treatment due to the manner in which the marketer delivers the value proposition. 460

Customer loyalty programs: are they fair to consumers?

Journal of Consumer Marketing

Russell Lacey and Julie Z. Sneath

Volume 23 · Number 7 · 2006 · 458 –464

A danger of loyalty programs is that consumers may feel slighted when their interactions with the firm are inconsistent with what they believe is deserved. Potential for interactional inequity can be reduced through open and extensive communications. Loyalty programs frequently engage in recognition and personalization through extensive communications with participating members. For some firms, loyalty program communications occur as one component of a more comprehensive communications effort (Roehm et al., 2002). For other firms, loyalty programs are the primary vehicles used to create a sense of community and establish meaningful dialogue with its best customers in order to develop customer relationships. With many loyalty programs, members are regularly sent newsletters, direct mail, and e-mails. It is often through loyalty program-sponsored web sites that the marketer has its best opportunity to engage in ongoing communications with its customers through account information and customized content.

discretionary personal information depends on the existence and strength of a marketing relationship with the firm (Sheehan and Hoy, 2000), and is influenced by the purpose for which information will be circulated within and outside the firm. Although personal privacy concerns depend largely on the type of information being collected and how it will be used (Nowak and Phelps, 1992), customers routinely cooperate with marketers’ request for personal information that they perceive is necessary to complete the marketing exchange.

Exchange theory Customers and firms engage in exchange for a variety of reasons, including those that are economic and social in nature. Marketing exchanges typically involve the transfer of resources such as goods, services, and/or money (utilitarian exchange), as well as the symbolic aspects of exchange, e.g. social rewards (Levy, 1959; Bagozzi, 1975). Historically, information disclosure has been linked to exchange theory (Hirschman, 1980) whereby consumers are willing to exchange their personal information in order to obtain other resources, such as monetary savings or enhanced services. Exchange theory also posits that individuals will trade personal information for other resources during marketing transactions, including love, status, money, goods, and services (Brinberg and Wood, 1983). Resources being exchanged must be valued by the parties to the exchange, with scarce resources having greater value than those that are readily obtained (Brinberg and Castell, 1982). In the case of loyalty programs, participating customers are offered an enhanced value proposition, and in return firms will be given access to personal information that can be used to further refine strategies and tactics. In general, members to the exchange will attempt to coordinate efforts and cooperate with each other (Stern, 1969; Wilkinson, 1974). Although individual consumers have shown a general willingness to disclose information about themselves when they believe they will receive benefits in return (Milne and Gordon, 1993), they also consider the nature of the benefit being offered when deciding whether a request for information violates their personal privacy (Westin, 1967). In the paragraphs that follow, the fairness of capturing and using customer information via customer loyalty programs is discussed in terms of three types of exchange, i.e. restricted exchange, generalized exchange, and complex exchange. Perceptions of equity and sources of inequity concerning the collection and use of personal information are summarized in Table II.

Capturing individual consumer information The ultimate marketing objective behind many loyalty programs is their use as a primary data-gathering platform that can help to improve the efficiency and effectiveness of a firm’s marketing initiatives (Uncles et al., 2003). Often the firm’s most robust source of behavioral data, loyalty programs allow marketers to capture detailed transactional and preference customer databases. These databases can be used to determine customer value, define specific marketing strategies for finite customer segments, and model customer attrition and intervention strategies. Recent developments in telecommunications and customer database technology have provided unprecedented power to marketers attempting to build and aggregate customer information systems and assemble in-depth, enterprise-wide portraits of individual consumer purchasing behaviors (Nunes and Dreze, 2006). Perhaps the greatest benefit obtained from loyalty programs resides in the data mining and knowledge base that firms can use to develop statistical models to improve customer loyalty, support customer service, and develop new offerings to help reduce defection and increase customer lifetime value (Wansink, 2003). Discriminating value proposition offerings demands individualized customer information. Armed with customer-specific information, firms are able to direct and tailor their communications and optimize product mix offerings. Thus, loyalty programs represent an alternative to mass-market promotion since firms have the ability to more precisely target an increasingly fragmented customer base, and communicate customized and relevant value propositions and marketing messages to individual customers. At the same time that firms are engaging in an unprecedented collection of individual customer information, consumers are becoming increasingly concerned about their privacy and how personal information is being used and disseminated (Zabin and Brebach, 2004). Information privacy exists when an individual can limit accessibility and control the release of information about oneself (Westin, 1967). Invasions of privacy occur when there is loss of control resulting from marketing exchanges (O’Malley and Prothero, 2004). Recent corporate mismanagement scandals and fears of technology abuse have only fueled personal privacy concerns (Sarathy and Robertson, 2003), and some customers resist trading in their privacy and data security unless compelling benefits are offered. Furthermore, consumers’ propensity to divulge

Restricted exchange In a restricted exchange, two parties are engaged in a reciprocal relationship. Both parties seek to maintain equality and balance in their dealings, particularly when the exchange relationship is ongoing (Bagozzi, 1975). Implicit to this type of exchange is the notion that undue advantage or deception by one party may limit subsequent exchange opportunities. In addition, it is assumed that both parties will exchange something of value (“quid pro quo”) and that benefits being exchanged should be similar (Brinberg and Castell, 1982). By agreeing to join a loyalty program, customers are, in effect, giving the firm their approval to collect and use discretionary personal information in order to execute the enhancement of products or services that may result from participating in the loyalty program. Marketers essentially use the higher level of benefits achieved through loyalty programs as compensation 461

Customer loyalty programs: are they fair to consumers?

Journal of Consumer Marketing

Russell Lacey and Julie Z. Sneath

Volume 23 · Number 7 · 2006 · 458 –464

Table II Assessing fairness in data collection and use in customer loyalty programs Type of exchange

Customer perceptions of equity

Sources of perceived inequity

Restricted exchange

Customers perceive there is fairness in the exchange when both parties are equal and directly benefit from the relationship

Customers who wish to maintain privacy Misuse of information Insufficient compensation for personal information

Generalized exchange

Customers perceive there is fairness in the exchange when all parties to the exchange indirectly benefit from the relationship

“Opt-out” standard Sharing personal information with outside parties Insufficient “quid pro quo”

Complex exchange

Customers perceive there is fairness in the exchange when all parties to the exchange directly or indirectly benefit via a system of interconnected relationships

Lack of coordination Incomplete information Dissimilar benefits being exchanged Individualistic behavior

actively soliciting customers’ permission in advance of transferring data to other firms for use in direct marketing.

to customers who share personal information (Schultz and Bailey, 2000). In return for personal information, it is important that customer loyalty programs offer benefits that will lead to continued exchanges and disclose how information being collected will be used. Unless both parties are exchanging something that is perceived as having equal value, future attempts to engage in exchange will be limited. Consumers are most likely to participate in programs they believe offer equitable relationships and will, ultimately, base their decision to participate in loyalty programs according to their perceptions of fairness. Most loyalty programs offer their members assurance that the firm will take reasonable precautions to prevent the misuse of personal information, and indicate they will not suffer negative consequences as a result of disclosing proprietary information. Even so, some customers may choose to sacrifice membership – and membership rewards – to protect their personal information. Unless rewards of sufficient value can be offered in return for information, there is also a risk these customers may perceive that the firm is penalizing nonparticipating customers who wish to maintain their privacy (Introna and Pouloudi, 1999).

Complex exchange The final form of exchange, complex exchange, involves a system of three or more exchange partners, each of whom is involved in one or more direct, or indirect, sequential exchanges (Bagozzi, 1975). In complex exchanges, coordination among the parties to the exchange is important, since parties may receive direct or indirect benefits from more than one member in the system. For multiple brand loyalty programs – those that are supported by multiple firms or within a corporate conglomerate – the rewards are often provided by someone other than the party involved in the original exchange. In addition to the potential for perceived imbalance in the exchange, customers may not understand that personal information will be shared with other exchange partners, even though direct benefits may never be sought, let alone used, by each of the parties. Furthermore, since complex exchanges often include indirect exchange, it is possible that benefits being exchanged may be dissimilar, and/or that one or more parties to the exchange will act in their own self-interest. Consequently, firms may offer loyalty program benefits that are not valued, causing some customers to reject efforts to engage in future exchange and others to feel under-compensated for information they have made available.

Generalized exchange Generalized exchanges involve three or more parties, in which benefits from the exchange are realized indirectly. Unlike restricted exchange, it does not involve the direct exchange of one thing of value for another. A party to the generalized exchange may give up something of value to another party, but will receive benefits from someone else. Generalized exchanges such as the sale of electronic databases across firms and/or industries enables firms to develop and communicate relevant value propositions and marketing messages, increasing efficiencies and reducing costs for all parties to the exchange. However, it is important that the firm discloses its information practices and informs customers how the data will be collected, used, and shared. With few exceptions the default assumption for information collected through loyalty programs is the “opt-out” standard, in which firms use individual consumer marketing information unless the customer says otherwise. By contrast, under an “opt-in” standard, data are kept private unless the customer explicitly gives permission to use and/or share his or her personal information. Because it is often the firm and not than the consumer that controls personal information (Langenderfer and Cook, 2004), firms pursing long-term relationships through customer loyalty programs might wish to consider

Summary Loyalty programs are facing mounting pressure concerning their use as a facilitator of specific customer information and potential to discriminate against non-member customers because of greater marketing resource allocations shifted toward selective customers. This study has examined how firms may be in a position to manage these perceptions through the practical application of equity theory and exchange theory. In doing so, the authors have attempted to address a notable gap in customer loyalty program literature, namely the lack of research concerning fairness of loyalty programs in terms of their potential for value proposition discrimination and personal information exploitation. Previous studies have focused on assessing loyalty programs’ effects from the firm’s perspective, typically on the value of customer relationships and strengthening financial performance. Grounded on seminal marketing theories, this paper has presented two important research contributions through an examination of the fairness of loyalty programs from the customer’s point-of-view. First, 462

Customer loyalty programs: are they fair to consumers?

Journal of Consumer Marketing

Russell Lacey and Julie Z. Sneath

Volume 23 · Number 7 · 2006 · 458 –464

through the application of equity theory, we offer instruction to marketing managers for how they can effectively manage different tiers of customers, driven by customer equity, without alienating less valuable customers. Second, through the use of exchange theory, we have attempted to shed insight regarding how firms can secure authorization to collect and use individual customer information from consumers in exchange for enhanced value proposition offerings via voluntary loyalty programs. By taking full advantage of these lessons, managers can use loyalty programs to strengthen their marketing positions without compromising on their customers’ perceptions of fairness.

scrutiny, firms that have already secured consumers’ permission to collect personal information, and are providing appropriate benefits in return for that information, appear to be better positioned should a major shift occur in privacy laws or attitudes toward loyalty programs.

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Managerial implications Despite the emerging criticisms highlighted concerning loyalty programs, the application of equity theory to marketing managers suggests that customers’ perceptions of distributive equity can, in fact, be strengthened by enhanced value propositions made available through such programs. But it is paramount for marketing managers to use open and extensive communications to manage the perceptions of procedural, interactive, and distributive equity among all customers of the firm. Perceptions of distributive inequity are likely to be influenced when members enjoy superior treatment and/or amenities that are visible to non-member or non-qualifying customers. However, if the procedures used to determine enhanced value propositions are based on criteria that are unbiased, impartially delivered, and consistent with the standards of the loyalty program, all customers, including those who do not choose and/or are ineligible to participate in the loyalty program will more readily accept the firm’s employment of such programs and perceive them favorably. Another hallmark of an equitably administered loyalty program is that its participating customers recognize the level of consistency in how they are treated relative to how they believe they deserve to be treated. Thus, the fairness of discriminating value propositions delivered through loyalty programs must be in proportion to customer inputs with clear and straightforward information about the mechanics of its selection criteria and program rewards. Marketing managers should also consider the potentially valuable role loyalty programs play in not only collecting customer information but also how these programs can be used as the formal system for marketing managers to induce consumers to voluntarily share their personal information in exchange for benefits they would not otherwise receive. Much like equity theory’s relationship to value proposition discrimination, exchange theory provides guidance to marketing managers by suggesting that information gathering and use may be “fair” as long as there is perceived equity across exchange relationships. The membership-based attribute of loyalty programs can be devised to secure customer permission that allows the firm to assemble an individual customer profile that can then be used to assess the potential value of each member and help determine marketing’s efforts to realize their potential value. The managerial implications of exchange theory to assess fairness of loyalty programs is also key to reducing privacy concerns in the current regulatory climate (as well as in the future) should firms become subject to mandatory opt-in regulation requirements. Though it is often not necessary that firms receive explicit customer approval to collect and use individual-specific information, these activities continue to attract criticism from privacy advocates. Due to this increased 463

Customer loyalty programs: are they fair to consumers?

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Russell Lacey and Julie Z. Sneath

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Sheehan, K. and Hoy, M. (2000), “Dimensions of privacy concern among online consumers”, Journal of Public Policy & Marketing, Vol. 19 No. 1, pp. 62-73. Shugan, S. (2005), “Brand loyalty programs: are they shams?”, Marketing Science, Vol. 24 No. 2, pp. 185-93. Stern, L. (1969), Distribution Channels: Behavioral Dimensions, Houghton Mifflin, New York, NY. Swaminathan, V. and Reddy, S. (2000), “Affinity partnering: conceptualization and issues”, in Sheth, J.N. and Parvatiyar, A. (Eds), Handbook of Relationship Marketing, Sage, Thousand Oaks, CA, pp. 381-406. Tax, S. and Brown, S. (1998), “Recovering and learning from service failure”, Sloan Management Review, Vol. 40 No. 1, pp. 75-88. Thibaut, J. and Walker, L. (1975), Procedural Justice, Erlbaum, Hillsdale, NJ. Uncles, M., Dowling, G. and Hammond, K. (2003), “Customer loyalty and customer loyalty programs?”, Journal of Consumer Marketing, Vol. 20 No. 4, pp. 294-316. Wansink, B. (2003), “Developing a cost-effective brand loyalty program”, Journal of Advertising Research, Vol. 43 No. 3, pp. 301-9. Westin, A. (1967), Privacy and Freedom, Antheneum, New York, NY. Wilkinson, I. (1974), “Researching the distribution channels for consumer and industrial goods: the power dimension”, Journal of the Market Research Society, Vol. 16 No. 1, pp. 12-32. Xia, L., Monroe, K. and Cox, J. (2004), “The price is unfair! A conceptual framework of price fairness perceptions”, Journal of Marketing, Vol. 68 No. 3, pp. 1-15. Zabin, J. and Brebach, G. (2004), Precision Marketing: The New Rules for Attracting, Retaining, and Leveraging Profitable Customers, John Wiley & Sons, Hoboken, NJ.

About the authors Russell Lacey is an Assistant Professor at the University of New Orleans. He earned his PhD at the University of Alabama, Tuscaloosa in 2003, preceded by 11 years of corporate marketing experience at Blue Cross & Blue Shield of Kansas City and Baylor Health Care System. His research interests include relationship marketing, customer loyalty, and services marketing. He has published in Marketing Health Services, co-wrote a book chapter on customer loyalty (Relationship Marketing: Springer), as well as published in both marketing and management conference proceedings. Russell Lacey is the corresponding author and can be contacted at: [email protected] Julie Z. Sneath is an Associate Professor of Marketing at the University of South Alabama. She earned her BSBA from the University of Arkansas, and PhD in Marketing from Georgia State University. Her research interests include consumer behavior, sponsorship, event marketing, and services marketing. She has presented her research at national and international meetings, and has authored publications that appear in the Journal of Advertising Research, Journal of Customer Service in Marketing and Management, Marketing Health Services, Journal of Targeting, Measurement, and Analysis for Marketing, and numerous conference proceedings.

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464

Can a brand outperform competitors on cross-category loyalty? An examination of cross-selling metrics in two financial services markets Kerry Mundt, John Dawes and Byron Sharp Ehrenberg-Bass Institute for Marketing Science, University of South Australia, Adelaide, Australia Abstract Purpose – Many service organisations seek to grow by selling additional different products to their existing customers. Many managers are evaluated on the level of customer loyalty in terms of cross-product holdings – for example, the average number of bank products or insurance policies held per customer. The purpose of this paper is to provide managers and researchers with some contextual knowledge and norms concerning “cross-category” loyalty. Design/methodology/approach – In order to compare the levels of loyalty for competing brands, five relevant loyalty metrics were used in the analysis, with data sourced from two service industries, banking and insurance. Findings – The results show little variation in loyalty scores between competing brands, and what variation there is can be explained by historic factors, without reference to CRM strategies. This suggests that investments into CRM and cross-selling initiatives seem to have less effect on loyalty metrics than many marketing textbooks and CRM advocates have assumed. Practical implications – Marketers should be very cautious of setting ambitious goals for increasing loyalty to their brand at a cross-category level. Originality/value – Very few research papers have explored the issue of cross-category loyalty. This is despite the value of the specific loyalty metrics as key performance indicators in service industries such as financial services and insurance. Keywords Customer loyalty, Financial services, Cross-selling, Australia Paper type Research paper

Introduction

The logic of cross-selling

Many service organisations operating in subscription type markets offer a range of products over multiple product categories. In such markets managers place less attention on encouraging share loyalty, which is already very high (Sharp et al., 2002), instead they focus on reducing defection and cross-selling products from different, but related categories. For example, insurance companies will encourage their home insurance customers to also take out car insurance with them (i.e. switch brands). While there is long-running literature reporting loyalty metrics and empirical generalizations concerning loyalty metrics in packaged good and other repertoire categories (Copeland, 1923; Ehrenberg and Goodhart, 2004), there is no parallel literature for crossselling related loyalty metrics covering several product categories, nor has there been much research documenting loyalty metrics for service brands. This article starts to address this gap by specifically looking at how much competing service brands vary in their cross-selling related loyalty metric scores.

There are two main strategies for market share growth that have been suggested in the marketing and strategy literature, dating back to Ansoff (1965). Either a brand can increase its sales from existing customers or it can obtain additional sales from new customers, who are either new to the market or have been previously buying another brands (Anschuetz, 2002; Ehrenberg et al., 1990; Reichheld and Sasser, 1990). Achieving brand growth by increasing cross-category loyalty is achieved by increasing sales to existing customers, over numerous categories. In seeking to cross sell different products to existing customers, marketers hope to both grow revenue and discourage customer defection though deepening the relationship and increasing the costs of switching. Investments in CRM, product discounts and staff training are often made in anticipation of improved cross-selling related loyalty metrics. We examine such loyalty metrics in two major subscription markets, retail financial services and domestic insurance, for brands that operate in multiple categories (i.e. categories such as credit cards, home loans and cheque accounts in financial services; or motor, home and personal insurance in the insurance market). Many financial institutions are now convinced that increasing cross-category loyalty will lead to increased profitability (Mattson, 1998). In mature markets in the developed world, service marketers have been moving towards a focus on loyalty related strategies. Over recent decades, relationship marketing has been advocated as the most

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Journal of Consumer Marketing 23/7 (2006) 465– 469 q Emerald Group Publishing Limited [ISSN 0736-3761] [DOI 10.1108/07363760610713019]

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Can a brand outperform competitors on cross-category loyalty?

Journal of Consumer Marketing

Kerry Mundt, John Dawes and Byron Sharp

Volume 23 · Number 7 · 2006 · 465 –469

relevant strategy for financial service institutions worldwide to achieve growth (Berry, 1979; Perrien et al., 1992). Firms that adopt this strategy focus less on customer acquisition and large sums of money are often invested in CRM systems designed to facilitate cross-selling. Cross-category loyalty metrics have also started to be used as “key performance indicators” sometimes with management salaries tied to these metrics. Though, unfortunately for managers embracing this philosophy, there is a lack of descriptive research providing benchmarks and norms for cross-category loyalty metrics. Consequently it is difficult for marketers to interpret and set targets using these metrics, and so managers are largely guessing when they set targets for improvement in these metrics. Similarly it is difficult to interpret variations in performance, as there are no benchmarks to indicate what sort of difference would be surprising – an indication of success or failure. In order to address this situation we examine brand loyalty metrics across multiple categories in two major subscription markets: retail financial services and domestic insurance.

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the incidence of being named as the customer’s main financial institution. Note that this last measure was not used in the analysis of insurance brands because it was not relevant to the insurance industry.

All the data were acquired through telephone interviews from randomly selected members of the general public, using a professional market research team. In total, 1,116 respondents were incorporated into the research relating to financial services with an additional 1000 respondents used for the insurance replication. All interviews were conducted by professional interviewers operating under IQCA (Interviewer Quality Control Australia) standards. For simplicity and consistency, we used five common banking products for the financial services analysis: savings/ transaction account, cheque account, credit card, home loan and personal loan. For each product mentioned, the name of the respective brand that was used was obtained. To reduce sampling error we focused on the results for the largest five brands. For banking these were the Commonwealth Bank, National Australia Bank, ANZ Bank, Westpac Bank and Bank SA (with the national body of this institution referred to as St.George Bank). These collectively make up 65 percent of the market according to our sample. These brands comprise some of the largest banks in the world with collective market capitalization exceeding $180 billion. For the insurance analysis, subscription to three common types of insurance was measured: home building, home contents and car insurance (up to two car policies were allowed for). Four of the five loyalty measures that were used for financial services were also used in this analysis. “Main financial institution” was not included, as this is a measure generally only associated with financial service providers. The five largest national insurance brands were used: RAA, CGU, SGIC, AAMI and Australian Pensioners Insurance Agency (APIA). These five largest brands account for approximately 55 percent market share.

Methodology The purpose of this research is to provide some insight into cross-category loyalty for brands operating in multiple, related, product categories. A common aim of CRM programs and cross-selling initiatives is to increase a brand’s level of customer loyalty on a cross-category basis. Directly assessing the success of such strategies is difficult as data specifying the efforts that have been made by financial institutions are rarely available in a reliable form in the public domain. However, without this information it is still possible and very worthwhile to measure cross-category loyalty in order to identify if any brands have actually achieved greater success in increasing their loyalty metrics in contrast to their competitors. Our logic is to firstly determine whether variations exist – this in itself provides managers useful contextual information, i.e. just how much inter brand variation in loyalty metrics seems reasonable to expect. Secondly we seek simple obvious explanations for the variations in terms of public domain factors unrelated to CRM or cross-selling. If differences in loyalty metrics can be not convincingly be explained by other factors then this suggests that CRM strategies might be successfully enhancing brand’s cross-selling performance. In order to compare the levels of loyalty for competing brands, five relevant loyalty metrics were used in the analysis. Multiple loyalty measures, all of which measure loyalty in a slightly different way, were chosen to decrease the error or bias that can be associated with single measures. If one or more of the brands analysed were able to over-perform at cross-category loyalty, higher levels of loyalty (that could not be attributed to other factors) would be exhibited in at least one of the different loyalty metrics displayed below. Also additional metrics provide greater ability for diagnostic interpretation. These metrics were: . incidence of sole loyalty among an institution’s/brand’s customer base; . average number of competing institutions/brands used; . average number of products held with the institution/ brand; . share of wallet, proportion of total requirements; and

Results: financial services The results of data analysis from the financial services industry are displayed in Table I. Of the five different loyalty measures we found no statistically significant difference between the brands for the first three measures: Incidence of sole loyalty, Average number of institutions used; and Share of wallet. All brands performed relatively equally on these three loyalty measures. A statistically significant difference was found for National Australia Bank (NAB) for the measure “Average number of products”. For this measure, it seems that NAB has a slightly higher level of average product holdings than two other brands, Commonwealth Bank (CBA) and Bank SA (ANOVA: df 4,1024, f ¼ 2:3, p ¼ 0:06, post-hoc test significant at p , 0:10). That is, National Australia Bank users tend to have slightly more banking products with it compared to the users of those other two brands. A statistically significant difference was also found for ANZ for “Incidence of MFI status”. For this measure, ANZ has a lower incidence of being considered the MFI among its user base (64 percent actual, 71 percent expected, chi-square test: df 4, x2 7.9, p , 0.10) – the difference while statistically significant, is not large. In the next section we undertake some 466

Can a brand outperform competitors on cross-category loyalty?

Journal of Consumer Marketing

Kerry Mundt, John Dawes and Byron Sharp

Volume 23 · Number 7 · 2006 · 465 –469

Table I Five cross-category loyalty measures: Australian financial institutions

Financial institution Commonwealth Bank Bank SA/St George ANZ Bank Westpac Bank National Aust. Bank Average Average absolute deviation

Penetration (%)

Sole loyalty (%)

No. of institutions used

Share of wallet (%)

No. of products

% of users stating this is their MFI

Sample n

29 19 18 14 13 19 –

47 43 37 40 45 42 3.2

1.7 1.8 1.8 1.8 1.8 1.8 0.02

72 70 65 68 70 69 2

2.0 2.0 2.1 2.2 2.3 2.1 0.1

75 74 64 71 72 71 3

318 209 198 159 145 – –

additional analysis to determine the reasons that underlie these apparent differences in loyalty.

Table II ANZ credit card analysis

Explanation of variations In order to seek explanations for the identified variances we drew on prior contextual knowledge (in line with recommendations from Sharpet al. (2000) and Ehrenberg (1988)). Such information is often lack in quantitative academic analyses and yet is vital to give context and meaning to data. The main exception was ANZ. As was seen in the results, ANZ Bank’s scores were generally lower than other brands for most of the loyalty measures, but particularly for the proportion of their customers who consider it to be their main financial institution, or “MFI”. It appears that this apparent weakness is actually due to particular success in recruiting customers. It has been widely reported in the financial services industry that the ANZ Bank tends to perform particularly well in the credit card market, which could have some effect on these scores. For example:

Financial institution Commonwealth Bank Bank SA ANZ Bank National Australia Bank Westpac Bank Average

Proportion of CC customers with just a CC at that institution

Proportion of CC customers with a different MFI

15 8 31 19 23 19

20 13 35 22 22 22

Note: more of ANZ Bank’s credit card customers have only one product with ANZ and have a different MFI

again, this time with ANZ’s customer base made up of fewer “single product” credit card customers (13 of these respondents were excluded from the analysis). The average proportion of credit card customers who now had just the one product with ANZ was still higher than the average (ANZ ¼ 24 percent, average across all brand ¼ 19 percent). We then ran another chi-square test. The overall chi-square statistic was now non significant (df 4, x2 4.9, p ¼ 0.3). Therefore with this mild anomaly in the customer base accounted for, there now appears to be no significant difference between the brands. The other single exception was that customers of the National Australia Bank hold slightly more products with that bank than to the customer bases of other banks. Additional analysis found that the NAB’s higher average product holdings compared to CBA and Bank SA coincides with its users having slightly higher product holdings in general across all providers. NAB users have 3.7 banking products overall (2.3 of which are held with NAB), while the average of the sample overall is 3.4. The difference is statistically significant (df 4,1024, F ¼ 3:6, p , 0:01). Therefore it seems logical that the higher product loyalty for NAB is an artifact of its user base being slightly heavier users of banking products in general. We controlled for this by taking a random subset of only five of NAB’s heaviest category users and excluding them from the analysis. This still left NAB with a somewhat higher category usage rate (3.5) than the average of the other brands. The resultant analysis of variance for brand by products without these heavy users was not statistically significant (df 4,1020, F ¼ 2:18, p ¼ 0:13). The post-hoc test showed the difference between the scores for the NAB and CBA and

The most popular card in the market, the Qantas ANZ Visa (Hughes, 2003). The biggest card issuer, ANZ Banking Group . . . (Kahler, 2003).

Success in this particular category could potentially skew the loyalty results for this brand. For example, it is possible that many customers, who consider another bank to be their main institution, may use ANZ Bank for just their credit card. If this were the case, there would be a lower proportion of ANZ Bank’s customer base that could consider that bank their main financial institution. In order to see if this affected the cross-category loyalty scores for the ANZ Bank, further analysis was undertaken to determine the proportion of each bank’s credit card customers who have just one product at that institution. Table I displays the results. On average, there are a significantly higher proportion of credit card customers that have just that one product with ANZ Bank, compared with other institutions. Of ANZ’s credit card customers, 31 percent have only that product with that institution. Table II shows that a greater proportion of ANZ Bank’s credit card customers consider another bank their Main Financial Institution. Therefore there is a greater proportion of ANZ Bank’s credit card customers who have just that one product with ANZ, and a series of other products with a different institution. In order to confirm the effect that this pattern has had on the cross-category loyalty results, we undertook some further analysis. The cross-category loyalty measures were calculated 467

Can a brand outperform competitors on cross-category loyalty?

Journal of Consumer Marketing

Kerry Mundt, John Dawes and Byron Sharp

Volume 23 · Number 7 · 2006 · 465 –469

Bank SA were now non-significant with a p-value of 0.29 in both cases. Therefore, once this higher category usage rate for NAB is even partially controlled for, the excess in product holdings with the NAB disappears. Perhaps NAB’s marketing strategy is to target heavier users of financial services, or perhaps there is some historical reason for its skew to heavier users. This is the main difference in its customer base however, it is not a loyalty difference.

For “Average number of products”, the overall ANOVA was statistically significant (df 4, 565, F ¼ 3:5, p , 0:01). A post-hoc test indicated there was a statistically significant difference between RAA and two other brands: SGIC (p ¼ 0:05); and CGU (p ¼ 0:025). Therefore, we conclude the RAA has a lower level of loyalty on this measure compared to two other brands, and this cannot be simply attributed to sampling error. For Share of wallet, the ANOVA test statistic was significant (df 4,565, F ¼ 4:1, p ¼ 0:003). The post hoc test indicated there was a statistically significant difference between RAA and CGU (p ¼ 0:01) and RAA and APIA (p ¼ 0:01). In both cases the RAA has a lower share of wallet.

Research conclusions: banking The variations that were identified for the National Australia Bank and the ANZ Bank appeared to be largely explainable through additional data analysis. With these variations reasonably accounted for, it seems that no brand has been able to achieve higher cross-category loyalty scores than its competitors when comparing the five main financial services products. What it does suggest is that all institutions are doing equally as well or equally as poorly, in this area. In the spirit of using multiple sets of data as a basis for empirical generalisations we replicated the analysis on a similar market, namely domestic insurance. This replication will enable us to see if results from the financial service research extend into another category. Similar to the financial services market, providers in the insurance market offer multiple, related products and focus on generating crosscategory loyalty as a means for brand growth.

Explanation of variations These results suggest that the RAA tends to under-perform on two of the four measures of customer loyalty. This appears explained (albeit in a post hoc manner) by the fact that RAA is a road-service organisation (Royal Automobile Association) that is recognised mainly for car insurance. Indeed its insurance-related advertising has historically emphasized car insurance. We surmise that this advertising emphasis, along with its long-held position as the only road-service organisation in the geographic region means that RAA acquires customers quite readily for car insurance, but due to its “car” positioning it suffers from an inability to cross-sell home buildings and contents insurance. Table IV shows the proportion of the sample that use a particular provider for each different type of insurance. Here we can see that the RAA does tend to underperform on home and contents insurance for a brand of its size.

Results: insurance Insurance is another example of what are called “subscription-type” markets in which consumers tend to use a particular brand for extended periods, where there is little “switching” in time periods of a year or more (Sharp et al., 2002). The same methodology was used for the replication. Four of the five cross-category loyalty measures were examined using the insurance data and the differences in scores between brands compared. Table III shows the results for the four cross-category loyalty measures. A glance at the data suggests there is very little difference between the brands with the possible exception of the RAA with a lower level of sole loyalty for several of the measures. Analysis of the results found there were statistically significant, though small, differences between brands for two of the four different loyalty measures: “Average number of products” and “Share of wallet” – both relating to RAA. All brands performed relatively equally on Incidence of sole loyalty, and Average number of brands used.

Table IV Proportion of sample using provider for specific insurance products

Provider

% of the sample using provider for . . . Home Home Car building contents insurance insurance insurance

RAA CGU SGIC AAMI APIA Average

17 11 10 10 5 11

8 12 11 5 5 8

9 12 12 5 7 9

n 155 137 128 86 64 114

Note: The RAA, which is the one brand with apparently lower loyalty, leads in car insurance but does not do well in home buildings or contents insurance

Table III Insurance – four cross-category loyalty measures Provider RAA CGU SGIC AAMI APIA Average Average absolute deviation

Penetration (%)

Sole loyalty (%)

No. of brands used

Share of wallet (%)

No. of products

n

16 14 13 9 6 12 –

52 66 59 57 67 60 5

1.5 1.4 1.5 1.5 1.4 1.5 0.04

70 81 77 74 84 77 4

1.7 2.0 2.0 1.8 2.0 1.9 0.1

155 137 128 86 64 114 –

Note: There was very little difference for any measure between brands

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Can a brand outperform competitors on cross-category loyalty?

Journal of Consumer Marketing

Kerry Mundt, John Dawes and Byron Sharp

Volume 23 · Number 7 · 2006 · 465 –469

Summary, conclusions and future research

Berry, L.L. (1979), “Service strategies in the 1980s”, Journal of Retail Banking, Vol. 1 No. 2, pp. 1-10. Copeland, M.T. (1923), “Relation of consumers’ buying habits to marketing methods”, Harvard Business Review, Vol. 2 No. 2, pp. 282-9. Ehrenberg, A.S.C. (1988), “Data reduction and prior knowledge”, Chance, Vol. 1 No. 3, pp. 37-42. Ehrenberg, A.S.C. and Goodhardt, G.J. (2004), “Understanding brand performance measures: using Dirichlet benchmarks”, Journal of Business Research, Vol. 57 No. 12, pp. 1307-25. Ehrenberg, A.S.C., Goodhardt, G.J. and Barwise, T.P. (1990), “Double jeopardy revisited”, Journal of Marketing, Vol. 54 No. 3, pp. 82-91. Hughes, A. (2003), “American Express joins the credit card fight with the banks”, Sydney Morning Herald, 2 October. Kahler, A. (2003), “Fees head for the sky as reforms begin to bite”, Australian Financial Review, 11 December. Mattson, B. (1998), “Cross-selling gains momentum”, The Business Journal, 25 May. Perrien, J., Filiatrault, P. and Ricard, L. (1992), “Relationship marketing and commercial banking: a critical analysis”, International Journal of Bank Marketing, Vol. 10 No. 7, pp. 25-9. Reichheld, F.F. and Sasser, W.E. Jr (1990), “Zero defections: quality comes to services”, Harvard Business Review, Vol. 68 No. 5, pp. 105-11. Sharp, B., Riebe, E. and Tolo, M. (2000), “Explaining retail brand performance: an application of ‘prior knowledge’”, presented at the Australian New Zealand Marketing Academy Conference, Griffith University, Gold Coast. Sharp, B., Wright, M. and Goodhardt, G. (2002), “Purchase loyalty is polarised into either repertoire or subscription patterns”, Australasian Marketing Journal, Vol. 10 No. 3, pp. 7-20.

We analysed two markets, retail financial services and domestic insurance. Overall we found that cross-selling related loyalty levels differed little between competing brands. Indeed the largest difference was for a brand that under-performed compared to its competitors in domestic insurance, and it is reasonable to attribute this to a historical positioning as a road service provider rather than a generalist insurance provider. Our results indicate that regardless of the investments made into CRM and cross-selling initiatives, no brands seem to markedly out-perform their direct competitors in terms of cross-category loyalty when comparing loyalty for the major products in each market. This indicates that setting ambitious objectives to engender more loyalty (for these major products) in the customer base as a basis for growth may not be realistic – there is an absence of evidence that this can actually be accomplished. Some providers may believe that a way of gaining an advantage over competitors in terms of cross-category loyalty metrics is to offer more products/services, e.g. a bank offering income protection insurance. Logically, firms offering twice as many products should expect substantially higher crosscategory loyalty scores. However, when the number of products that are offered is controlled for (as we have done in this research), there is very little variation in cross-category loyalty metrics between the brands. Given that such massive efforts go into cross-selling in order to increase cross-category loyalty, more research in this area is needed. There are potentially two streams of future research that could be fruitful. Firstly, do these results hold in other categories, or similar categories in other countries? Secondly, why does brand performance in terms of cross-category loyalty does not vary very much between competing brands? Investigation of consumer purchase behaviour may help reveal the answer to this question. We know that financial services is a market in which there are “start-up” costs in opening an account or initially finding out about the offerings provided by the brand. These are a form of switching cost, and so perhaps once a relationship is established, when consumers have a need for a new financial product they tend to buy from their existing brand. This already high loyalty perhaps constrains the extent to which loyalty can vary between brands. Considering the large sums invested in CRM systems and accompanying great hopes of cross-selling success more research is urgently needed. Meanwhile our results suggest managers should take a very cautious conservative approach to setting cross-selling targets, particularly when benchmarking against competitors.

Further reading Garland, R. (2003), “Share of wallet’s role in customer profitability”, Journal of Financial Services Marketing, Vol. 8 No. 3, pp. 259-68.

About the authors Kerry Mundt is a research associate at the Ehrenberg-Bass Institute for Marketing Science at the University of South Australia. She holds a Master of Business (Research) degree and is currently working on a PhD focusing on the area of cross-category loyalty. She is the corresponding author and can be contacted at: [email protected] John Dawes is an Associate Professor at the Ehrenberg-Bass Institute at the University of South Australia. His research interests centre on buyer behaviour, competitive market structure and pricing. Byron Sharp is the Director of the Ehrenberg-Bass Institute for Marketing Science, at the University of South Australia. He has a long-standing interest in customer loyalty, brand performance metrics and the development of empirical generalisations in marketing.

References Anschuetz, N. (2002), “Why a brand’s most valuable customer is the next one it adds”, Journal of Advertising Research, Vol. 42 No. 1, pp. 15-21. Ansoff, H.I. (1965), Corporate Strategy: An Analytic Approach to Business Policy for Growth and Expansion, McGraw-Hill Book Company, New York, NY.

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469

Does parent satisfaction with a childcare provider matter for loyalty? Timothy L. Keiningham IPSOS Loyalty, Parsippany, New Jersey, USA

Lerzan Aksoy College of Administrative Sciences and Economics, Koc¸ University, Istanbul, Turkey

Tor W. Andreassen Department of Marketing, Norwegian School of Management, Oslo, Norway, and

Demitry Estrin IPSOS Loyalty, Parsippany, New Jersey, USA Abstract Purpose – The purpose of this study is to investigate the relationship between parent satisfaction and child retention at a childcare provider. Design/methodology/approach – The survey data used in the analyses involves a sample size of 1,003 respondents, all clients of a regional childcare provider in the USA. Logistic regression was used to test the propositions. Findings – The results indicate that parent satisfaction is most important to child retention when the child is very young (birth to one year of age). As children increase in age, however, parent satisfaction becomes increasingly less predictive of children’s continued enrollment at a childcare facility. Research limitations/implications – One of the limitations of this research is that it tests the propositions within a single firm. Future research should attempt to replicate these findings across several childcare providers. Practical implications – Emphasizing improvements in different attributes for different age groups has implications for increasing retention for childcare providers, in addition to ultimately increasing the satisfaction of parents. Originality/value – While all would agree that childcare services are of extremely high importance (at both a national and individual level), no research to date has examined the role of parent satisfaction to the continued enrollment of a child at a childcare facility. Our findings show that the presumed relationship between satisfaction and retention varies greatly by the age of child. Keywords Customer satisfaction, Customer retention, Child care, Customer service management, Day care, United States of America Paper type Research paper

Keiningham et al., 2005; Keiningham et al., 2003) and on financial performance (Anderson et al., 1994; Leung et al., 1998; Rust and Zahorik, 1993). Currently, however, the relationship between parent satisfaction and retention of a childcare provider is accepted as a truism without rigorous empirical research to support the relationship. Therefore, the need exists for an examination of the relationship between parents’ levels of satisfaction and the retention of their children with a childcare provider. This research addresses this need by examining this relationship for parents of a regional US childcare provider.

Introduction How important is parents’ satisfaction with a childcare provider and their repurchase behavior? Governments often study parents’ satisfaction levels to guide their policy-making efforts. Similarly, researchers study the drivers of parent satisfaction with the implicit belief that satisfaction impacts parents’ ultimate behavior. In general, both practitioners and academics have accepted the premise that customer satisfaction results in customer behavior patterns that positively impact business results (Kotler, 1994; Rust and Oliver, 1994). A number of studies support the validity of such a linkage. Research has found that customer satisfaction has a measurable impact on purchase intentions (Bolton and Drew, 1991), on customer retention (Mittal and Kamakura, 2001), on share of spending (Baumann et al., 2005; Bowman and Narayandas, 2004;

The US childcare market In the USA, 62.8 percent of mothers of children (ages 0-5) are members of the labor force, more than double the 30.3 percent level of the 1970s (Blau, 2003a). Someone other than their parents regularly cares for over 70 percent of children with employed mothers, with figures ranging from 22 to 32 percent in center-based childcare arrangements (Capizzano et al., 2000; Ehrle et al., 2001). As a result of the dramatic increase in demand, issues surrounding childcare are of great

The current issue and full text archive of this journal is available at www.emeraldinsight.com/0736-3761.htm

Journal of Consumer Marketing 23/7 (2006) 470– 479 q Emerald Group Publishing Limited [ISSN 0736-3761] [DOI 10.1108/07363760610713028]

All authors have contributed equally to the paper.

470

Does parent satisfaction with a childcare provider matter for loyalty?

Journal of Consumer Marketing

Timothy L. Keiningham, Lerzan Aksoy, Tor W. Andreassen and Demitry Estrin

Volume 23 · Number 7 · 2006 · 470 –479

interest to families, employers, and policy makers in the USA and other countries. Because few topics are as important to most families as finding the proper balance between caring for children and work, numerous studies by government entities, think tanks, non-profit organizations, and academic researchers have focused on childcare issues (Gain, 1999; Mitchell, 1992). These studies, as is the case with this research, almost exclusively focus on center-based care. This likely occurs because outside of center-based care, childcare primarily takes place in the homes of parents or relatives, both contexts less likely to be studied. The Urban Institute reports that childcare arrangements of working mothers for children under the age of three were handled as follows: 27 percent Parents, 27 percent Relatives, 22 percent Center-based care, 17 percent Home-based care, and 7 percent Nanny care (Ehrle et al., 2001). With regard to center-based care, research finds that parents consistently express high levels of satisfaction with their childcare (Bogat and Gensheimer, 1986; Britner and Phillips, 1995; Erdwins et al., 1998). One plausible explanation for this may be found in satisfaction as relief (Oliver, 1997): that is the satisfaction of finding a suitable place for the child. Objective assessments of the actual quality of childcare facilities in the USA however are distressing. Research conducted in the 1990s by four different universities found that only 15 percent of childcare facilities could be classified as “excellent,” the bottom 15 percent were “abysmal” and the middle 70 percent were barely adequate (Brownlee et al., 1997). To resolve this paradox, Brownlee et al. (1997) argue that parents want to believe that they have made the right choice for their children, and are therefore in a state of denial in order to cope with the fact that most are sending their children to “barely adequate” or worse childcare facilities. The disconnect between parent satisfaction and objective assessments of childcare quality is lessened, in part, by findings of the National Child Care Survey that despite the stated levels of high satisfaction, 26 percent of parents surveyed indicated that they wanted to change their childcare arrangements. This would appear to be supported by the fact that in studies of parents who actually switched childcare providers, service failure is frequently cited as the impetus behind their switching behavior (Grace and O’Cass, 2001a, b; O’Cass and Grace, 2001). It is clear that the link between satisfaction and retention in the context of childcare services is not straightforward. While researchers have examined the predictors of parent satisfaction with childcare (Britner and Phillips, 1995; Britner, 1999), currently no research exists examining the relationship between parent satisfaction and retention of their children with a childcare provider. The next section reviews the relevant services literature, culminating in the hypotheses to be tested.

H1.

Parent satisfaction will be positively related to childcare provider retention.

Studies on child development suggest that there are multiple stages of human development from birth to five years of age (Child Development Institute, 2005). Erik Erikson, Sigmund Freud, and Margaret Mahler all theorize that children in fact do go through distinct developmental phases starting from birth (Childstudy.net, 2005) and hence their needs and behavior during the various stages could differ. For example, studies propose that while not perfect, after the second birthday, children’s social and emotional development can tend to be clustered into annual milestones (AAP, 2005; Child Development Institute, 2005). This suggests that parents may have different expectations regarding their childcare facilities based on the developmental stage of the child and changes in its needs. Therefore, with regard to the relationship between parent satisfaction and childcare provider retention, we would expect the following: H2. The relationship between parent satisfaction and childcare provider retention will vary by the developmental stage of the child. To test these hypotheses empirically a study was designed and a survey was conducted. The following section elaborates upon the research design.

Methodology The research for this study was conducted in two parts: an exploratory phase designed to aid in the creation of a questionnaire; and a quantitative phase consisting of the administration and analysis of a parent/guardian satisfaction survey. In an effort to ascertain the components of service important to parents of a childcare facility, the authors first engaged in several methods of exploratory research. The first step was to conduct an extensive literature review regarding childcare in the USA. A large body of research was available, as it is a subject heavily researched by both government and non-profit organizations (for example: The Government Accounting Office, www.gao.gov; The Urban Institute, www.urban.org; The National Network for Child Care, www.nncc.org). Additionally, researchers have examined issues of objective quality (Blau and Hagy, 1999; Blau, 2003a) and parent search behavior (Grace and O’Cass, 2001a, b; O’Cass and Grace, 2001). The second phase was to conduct in depth, one-on-one interviews with staff at the childcare facilities regarding their perceptions of the critical service attributes (Carlzon, 1987) that determine parents’ ultimate satisfaction with the service. In all, ten one-on-one interviews were conducted either in person or via telephone to randomly selected staff members from around the USA. Interviews lasted between one and two hours, and were recorded. A team of researchers then went over each of the interviews to compile a list of attributes determining satisfaction. The final step was to conduct two focus groups with parents of children enrolled in the childcare facility. Each group was comprised of ten parents and lasted between 1.5 and 2 hours. These groups were videotaped. As with the one-on-one interviews, a team of researchers then went over each of the focus group videos to compile a list of attributes.

Hypotheses development Given that satisfaction has been shown to be positively related to customer retention across a variety of industries (for example: Anderson and Sullivan, 1993; Bolton, 1998; Ittner and Larcker, 1998; Jones and Sasser, 1995; Loveman, 1998; Mittal and Kamakura, 2001), we would expect the following with regard to a parent’s satisfaction and the retention of a childcare provider. 471

Does parent satisfaction with a childcare provider matter for loyalty?

Journal of Consumer Marketing

Timothy L. Keiningham, Lerzan Aksoy, Tor W. Andreassen and Demitry Estrin

Volume 23 · Number 7 · 2006 · 470 –479

From the literature review, in-depth interviews, and focus groups, a list of parent needs was compiled. The list exceeded 300 needs. These needs were then organized into a smaller number of managerially relevant groupings using K-J analysis[1] (Bossert, 1991). In addition to the list of attributes, the exploratory research confirmed what the literature suggests about how age (particularly school-age versus non-school-age) affects parents’ needs from a childcare facility. The difference in needs for school-age versus non-school-age children was largely because school-age children needed less center-based care due to their school schedule. The authors found during in-depth interviews with childcare staff and focus groups conducted with parents that parents held different expectations from their childcare provider based upon the age/developmental stage of their children, even before children reach school age (e.g. expectations differed for parents of very young children vs parents of two, three, fouryear-olds, etc.). This likely results from the differing needs and capabilities of children at various stages of child development (Child Development Institute, 2005).

Approximately six months after the fielding of the survey, which followed the beginning of a new school year in the USA (early Fall), the childcare provider provided the authors with information regarding the continued enrollment of respondents’ children with the childcare facility. This data was then appended to the survey data. To eliminate the possible impact of government assistance on the decision to continue to use the childcare provider, subsidized respondents were removed from the analyses, as subsidies have been found to directly impact choice options and behavior with regard to childcare (Blau, 2003b; CDF, 2002). The focus of this study was on parents of children ages one to five. The reason for this focus is that information gathered through the all phases of the exploratory research revealed that parents’ needs differed significantly with regard to childcare after children became of school age. Likewise, needs differed significantly with regard to the care of infants. Therefore, parents of children less than one or greater than five years of age were removed from this analysis. Officers of the childcare provider confirmed the validity of this conclusion in their discussions with the authors. Furthermore, examination of children ages less than five years of age is typical of research into childcare since after children reach school age, childcare arrangements tend to become more informal (Blau, 2003a). Additionally, this study specifically addresses the impact of the developmental stage of a child on the relationship between parent satisfaction and childcare provider retention. Therefore, to uniquely address this issue and eliminate bias stemming from experience with the childcare service for a different child, it was necessary to remove households with children of multiple ages receiving care from the childcare provider from the analysis. As a result, the total usable sample for this investigation is 1,003 respondents.

Questionnaire development The list of attributes derived through the K-J analysis was used as the foundation for the creation of a questionnaire. The questionnaire contained 54 closed-end questions regarding various aspects of the service at their childcare facility and an overall satisfaction measure. To minimize order bias, the service attribute questions were randomly ordered in creating the final attribute list. To further mitigate order bias, two versions of the questionnaire were created, each with opposite ordering of the service attribute questions. All closed-end questions used a 1 to 10, end-anchored scale to assess the level of satisfaction with the provider. The questionnaire was then pre-tested with a small number of parents for understandability and readability.

Analyses and results To test the existence of a relationship between parent’s overall satisfaction and childcare provider’s retention, we tested the correlation between the two variables. Correlations were examined for parents as a whole, in addition to each child age segment separately. Table I shows that satisfaction is positively associated with retention when looking across all child age groups (1-5) thus confirming H1. When looking at parents as a whole the association is relatively weak but statistically significant explaining 5 percent of the variance in the data. H2 stated that the relationship between parent satisfaction and childcare provider retention should vary by the

Survey data In order to test these hypotheses empirically, data from aregional national US childcare provider were used. Facilities are located in several states in the USA. The firm faces competition from several other national and regional care facilities. The vast majority of competitors, however, are small, single facility operations. It is important to note that even relatively small towns tend to offer a wide array of childcare facilities. For example, the state of Indiana recognizes 181 different childcare facilities available to parents in the city of Evansville, population 121,582 according to the US Census (State of Indiana Division of Family and Children, 2005). In the context of this research, parents would appear to have discretion in the choice of care facilities chosen. As a result, while the firm is large relative to the vast majority of its competitors, it does not exceed a five percent share of the center-based childcare market in any of the markets it serves. A random sample of 10,000 parents was then drawn from the childcare provider’s customer database (i.e. all parents had an equal likelihood of being selected). Questionnaires were mailed to parents’ homes. In all, approximately 10,000 surveys were mailed. Of those 2,020 were returned (a 20 percent response rate).

Table I Pearson correlations: overall parent satisfaction and childcare provider retention

Ages 1-5 Age 1 Age 2 Age 3 Age 4 Age 5

r

Sig.

N

R square

0.234 0.445 0.251 0.221 0.187 0.130

0.000 0.000 0.000 0.000 0.006 0.076

991 165 203 222 215 186

0.05 0.20 0.06 0.05 0.04 0.02

Note: Italicized results indicate significance at p , 0.05

472

Does parent satisfaction with a childcare provider matter for loyalty?

Journal of Consumer Marketing

Timothy L. Keiningham, Lerzan Aksoy, Tor W. Andreassen and Demitry Estrin

Volume 23 · Number 7 · 2006 · 470 –479

developmental stage of the child. To conduct a preliminary test, correlation analysis was performed. When segmenting parents based upon child age, the relationship differed significantly. Interestingly parent satisfaction had a much stronger link with childcare provider retention for parents of children one-year of age than for parents of children ages 2-5. In fact, the variance explained was more than three times greater for parents of one-year olds than for any other group (R2 ¼ 0:20 versus R2 ¼ 0:06). Additionally, the strength of the relationship between parent satisfaction and childcare provider retention declined with each increase in age of the child (child). For parents of five-year olds, the correlation was not significant at the 95 percent confidence level. Obviously for experienced parents, childcare satisfaction is less a driver of retention. One explanation may be that changing the childcare provider after some years represent switching costs (pre-contractual search costs and the childcare provider’s unique understanding of the child) thus satisfaction has less of an impact on whether the parent exits the relationship. Paired t-tests on the correlations, after using Fisher’s r to z transformation, revealed that the difference in correlations for the one-year of age group relative to all other age groups was significant at the 95 percent confidence level (see Table II). Although directionally it appears that the strength of the relationship between parent satisfaction and childcare provider retention declines as the child increases in age, the difference in the correlations was not statistically significant for age groups 2-5. While correlation analysis is useful in establishing a positive relationship[2] more robust tests should be performed before drawing conclusions regarding hypotheses. While we are examining links between satisfaction levels to retention, it is a known fact that linear approximations are not appropriate for two reasons. First, retention is a binary variable (i.e. 0 or 1),

typically quasi-likelihood methods based on generalized linear models should be used (Wedderburn, 1974). Second, the impact of satisfaction on customer behavior has frequently been demonstrated to be non-linear. Mittal and Kamakura (2001) for example find that the relationship between satisfaction and repurchase is non-linear. Likewise, Anderson (1998) finds the relationship between customer satisfaction and word-of-mouth and Keiningham et al. (2003) find the relationship between satisfaction and share-of-wallet to be non-linear. For these reasons, logistic regression analysis was conducted to develop predictive models of the relationship between changes in parents’ overall levels of satisfaction and childcare provider retention. The corresponding specification of the logistic regression model is: P ¼ expðb0 þ b1 x1 Þ=ð1 þ expðb0 þ b1 x1 ÞÞ where P is the probability of the actual retention with the childcare provider ¼ “yes”, exp is the exponential function and is written as exp(x) or e(x) (where “e” is the base of the natural logarithm and is approximately equal to 2.7183, b0 is the intercept, b1 is the coefficient for the predictor variable and x1 is the value of the predictor variable (satisfaction). Table III shows the results of the logistic regressions. The coefficient estimates, the Wald statistic and the model chi-square statistic are presented to examine overall model fit. Because several model specifications are being compared, the odds ratio (i.e. exponential beta) and the Nagelkerke R2 (Nagelkerke, 1991) statistics are presented to compare model performance[3]. As with the initial findings based on correlations, when looking at all age groups, satisfaction is positively associated with retention, in this case explaining 8 percent of the variance in the data based upon the Nagelkerke R2. When segmenting parents based upon the age of the child, the strength of the relationship differed significantly. Again, parent satisfaction was a much stronger predictor of childcare provider retention for parents of children one-year of age than for parents of children ages 2-5, with the variance explained being more than three times greater for parents of one-year olds than for any other group (Nagelkerke R2 ¼ 0.28 versus Nagelkerke R2 ¼ 0.09). Figure 1 shows the probability of childcare provider retention by the parents’ level of satisfaction for the various age groups under investigation. In all cases the relationship is positive although the shape of the curves varies considerably by age group. The pattern that emerges from the figure is one where incremental increases in satisfaction levels appear to have a stronger impact on retention for age 1 group. This is especially the case for increases at lower levels of satisfaction (1-6) and the incremental effect appears to lessen with higher satisfaction levels (7-10). As older age groups are examined, the steepness of the curve diminishes indicating decreasing impact of satisfaction on retention with increases in age. The results appear to support H1 and H2, that parent satisfaction will be positively associated with childcare provider retention, and that this relationship would vary by the developmental stage of the child. It is important to note, however, that a statistically significant relationship between parent satisfaction and childcare provider retention is not universal; for parents of children age 5, satisfaction was not a significant predictor of retention.

Table II Paired t-tests on the overall parent satisfaction and childcare provider retention correlations using Fisher’s r to z transformation Age (i)

r

N

1

0.445 165

2

0.251 203

3

0.221 222

4

0.187 215

5

0.130 186

Age (j)

Dif. (i-j)

Z

Sig. (two-tailed)

2 3 4 5 1 3 4 5 1 2 4 5 1 2 3 5 1 2 3 4

0.194 0.224 0.258 0.315 2 0.194 0.030 0.064 0.121 2 0.224 2 0.030 0.034 0.091 2 0.258 2 0.064 2 0.034 0.057 2 0.315 2 0.121 2 0.091 2 0.057

2.10 2.42 2.77 3.22 2 2.10 0.32 0.68 1.23 2 2.42 20.32 0.37 0.94 2 2.77 20.68 20.37 0.58 2 3.22 21.23 20.94 20.58

0.036 0.016 0.006 0.001 0.036 0.749 0.497 0.219 0.016 0.749 0.711 0.347 0.006 0.497 0.711 0.562 0.001 0.219 0.347 0.562

Note: Italicized results indicate significance at p , 0.05

473

Does parent satisfaction with a childcare provider matter for loyalty?

Journal of Consumer Marketing

Timothy L. Keiningham, Lerzan Aksoy, Tor W. Andreassen and Demitry Estrin

Volume 23 · Number 7 · 2006 · 470 –479

Table III Logistic regression analyses: overall parent satisfaction as a predictor of childcare provider retention

Ages 1-5 Age 1 Age 2 Age 3 Age 4 Age 5

IV

B

S.E.

Wald

Sig.

Exp(B)

Cox and Snell R Square

Nagelkerke R Square

X2

OV Sat OV Sat OV Sat OV Sat OV Sat OV Sat

0.29 0.54 0.32 0.26 0.25 0.16

0.04 0.12 0.10 0.08 0.09 0.09

48.43 20.09 11.13 9.90 6.98 3.06

0.00 0.00 0.00 0.00 0.01 0.08

6.03 1.72 1.38 1.30 1.28 1.17

0.05 0.14 0.05 0.04 0.03 0.02

0.08 0.28 0.09 0.07 0.06 0.03

47.00 24.31 10.96 9.56 6.61 2.93

Note: Italicized results indicate significance at p , 0.05

Figure 1 Probability of agency retention by overall satisfaction level of parent

We also tested to determine whether satisfaction levels varied between age groups. To test this empirically, an ANOVA was conducted to assess differences in overall satisfaction between age groups. The results indicate no significant differences between groups (Fð4; 986Þ ¼ 0:32, p ¼ 0:86). To further prove a lack of difference among groups in satisfaction levels, post hoc mean comparisons using the Tamhane T2 test (Tamhane, 1977) were conducted. Table IV summarizes the post hoc comparison test results for each of the groupings. There were no statistically meaningful differences in satisfaction levels among the various groups. In fact, mean satisfaction levels for all groups were relatively high and almost identical amongst age groups (M ¼ 8:06, 7.96, 7.85, 7.99 and 8.01 for age group 1, 2, 3, 4 and 5 respectively). As parents gain expertise with the childcare provider, their ratings of overall satisfaction seem to remain positive.

Table IV Post hoc mean comparison tests of overall parent satisfaction levels by each child age group AGE (i) Mean sat. Std dev. N AGE (j) Mean dif. (i-j) Std error Sig.

Investigating specific service attributes driving overall satisfaction Although the result for overall satisfaction levels shows no variation amongst age groups, this result does not say much about the drivers of these overall levels of satisfaction within age groups. In fact, it is possible to observe changing importance of specific attribute’s performance on overall satisfaction with age membership. It has been shown that customers’ evaluation criteria change as they gain experience 474

1

8.06

1.93

165

2 3 4 5

0.10 0.21 0.07 0.06

0.20 0.20 0.20 0.21

1.00 0.97 1.00 1.00

2

7.96

1.84

203

1 3 4 5

20.10 0.10 20.03 20.05

0.20 0.19 0.18 0.19

1.00 1.00 1.00 1.00

3

7.85

1.98

222

1 2 4 5

20.21 20.10 20.13 20.15

0.20 0.19 0.19 0.20

0.97 1.00 1.00 1.00

4

7.99

1.90

215

1 2 3 5

20.07 0.03 0.13 20.02

0.20 0.18 0.19 0.20

1.00 1.00 1.00 1.00

5

8.01

1.99

186

1 2 3 4

20.06 0.05 0.15 0.02

0.21 0.19 0.20 0.20

1.00 1.00 1.00 1.00

Does parent satisfaction with a childcare provider matter for loyalty?

Journal of Consumer Marketing

Timothy L. Keiningham, Lerzan Aksoy, Tor W. Andreassen and Demitry Estrin

Volume 23 · Number 7 · 2006 · 470 –479

(Mittal et al., 2001). This is understandable, given that expectations are shaped by experience and therefore change over time (Rust et al., 1994). Experts were found to have more developed and complex cognitive structures compared with novices (Alba and Hutchinson, 1987), and use more attributes and more attribute levels to differentiate between offerings (Moorthy et al., 1997). Hence, it is necessary to understand the drivers of overall satisfaction for different age groups. Because novice parents lack experience, when evaluating the quality of the service offering, it is hypothesized that they will focus more on observables or tangible elements with the service and the provider (e.g. facilities and equipment, the way the facility-parent interaction is handled, etc.). For parents with high degree of usage experience with the child, output elements of the childcare provider’s services rather than the tangibles elements of the childcare provider’s services are expected to be the key drivers of parent satisfaction. Therefore the following hypothesis is proposed: H3. As parents gain expertise with the childcare provider’s service, i.e. the child stays with the facility for a longer period, we will see significant differences in antecedents to satisfaction. For those with longer (shorter) experience, output (tangibles) will be a more important driver of satisfaction.

Table V Scales created based on performance attributes in questionnaire

Preliminary analyses included the creation of six scales using Factor Analysis via parent components (see Table V). The initial items were purified into the final 35 items making up the six scales by eliminating cross loadings. Alpha tests were conducted on each scale to evaluate goodness of fit for those items with factor loadings of 0.5 or higher. Cronbach’s Alpha was above the acceptable range (i.e. greater than 0.70) for all scales (Nunnally, 1967). The underlying attributes for each scale were all intuitive. Labels for each dimension were manually assigned based upon common themes for variables associated with each scale. The common themes of the scales are: . Child development: a ten-item scale (alpha ¼ 0.96). . Caregivers: a seven-item scale (alpha ¼ 0.94). . Business relationships: a six-item scale (alpha ¼ 0.93). . Facilities and equipment: a five-item scale (alpha ¼ 0.89). . Scheduling: a three-item scale (alpha ¼ 0.72). . Fees: a four-item scale (alpha ¼ 0.84).

Child development Basic learning skill development Social skill development Self-help skill development Values learned Cooperative development Happiness children experience Child’s progress assessments Frequency of progress assessments Parent-caregiver communication Learning environment

Caregivers Qualifications Turnover Caregiver absenteeism Supervision Fair treatment Attentiveness to child Handling of accidents

Business relationship Parent-manager communication Management competence Openness to feedback Responsiveness Sense of importance Understands your needs

Scheduling Schedule flexibility Operating hours Application of sick policy to child

Facilities and equipment De´cor of facility Toy quality Cleanliness of facility Layout of facility Furniture in facility

Fees Fee type 1 Fee type 2 Fee type 3 Fee type 4

Note: attribute groupings created through factor analysis (via parent components). Headings were manually assigned by the authors based upon common themes for the attribute groupings. Attributes listed are those with loadings of 0.5 or greater

The contention proposed in H3 was that as parents gain expertise with the childcare provider, they would focus more on output (child development) rather than tangibles as drivers of overall satisfaction. The results demonstrate that child development is in fact significant and important for all age groups. When the relative rankings of importance are examined in Table VII, we observe that child development while ranked 5 in age group 1 increases in importance as a driver with greater expertise. For age groups 2, 3, 4 and 5, while not ranked the top driver, the relative ranking increases from 5 to 4 to 2 and 3. As for the remaining drivers classified as tangibles, all were significant within the age groups. Contrary to expectations however, one attribute – facilities and equipment – was not significant for age group 1. These results therefore lend partial support to H3. Further examination of the rankings indicates that in fact caregivers seem to be the most important attribute driving overall satisfaction. It is consistently ranked first or second place within all age groups. Business relationships also appear important (ranked 1 or 2 for age groups 1, 2 and 5). Scheduling becomes less important as a driver as expertise with childcare provider increases. While ranked 3 for younger age groups like 1 and 2, it drops last to 6th place for older age groups 3, 4 and 5. Finally, facilities and equipment is ranked lower (5 or 6) except for age group 4 and is non significant for age group 1.

The specific attributes measured in this study fall under the two broad categories. The output group consists of child development, whereas business relationship, facilities and equipment, scheduling, and fees would fall under tangibles. Parent groups were segmented based upon the age of child. To determine the potential impact of collinearity on the regression coefficients, the variance inflation factor (Belsley et al., 1980; Hair et. al., 1992) and condition index (Pedhazur and Schmelkin, 1991) were calculated. Collinearity levels were well under the thresholds supported by Pedhazur and Schmelkin (1991) (condition index , 30), and Hair et al. (1992) (VIF , 10), with the maximum condition index ¼ 1.8, and the maximum VIF ¼ 1.2 for any of the regression models. OLS regressions were then run using the six factor scores as independent variables on overall satisfaction. Table VI summarizes the relative importance in the regression of each of the attributes shown in Table V. 475

Does parent satisfaction with a childcare provider matter for loyalty?

Journal of Consumer Marketing

Timothy L. Keiningham, Lerzan Aksoy, Tor W. Andreassen and Demitry Estrin

Volume 23 · Number 7 · 2006 · 470 –479

Table VI OLS regression analyses: attribute satisfaction as a predictor of overall satisfaction by child age group IV

Age 1 (Constant) Child develop. Business relationship Caregivers Facilities 1 equipment Scheduling Fees Age 2 (Constant) Child develop. Business relationship Caregivers Facilities 1 equipment Scheduling Fees Age 3 (Constant) Child develop. Business relationship Caregivers Facilities 1 equipment Scheduling Fees Age 4 (Constant) Child develop. Business relationship Caregivers Facilities 1 equipment Scheduling Fees Age 5 (Constant) Child develop. Business relationship Caregivers Facilities 1 equipment Scheduling Fees

B

SE

7.93 0.51 0.75 0.56 0.17 0.46 0.48

0.12 0.15 0.11 0.13 0.13 0.10 0.13

8.04 0.58 0.69 0.91 0.47 0.61 0.35

0.07 0.07 0.06 0.07 0.08 0.07 0.07

7.85 0.59 0.61 0.71 0.55 0.47 0.55

0.10 0.09 0.11 0.10 0.10 0.11 0.09

8.04 0.60 0.58 0.79 0.60 0.21 0.46

0.09 0.08 0.09 0.09 0.08 0.10 0.08

8.10 0.68 0.86 0.64 0.36 0.10 0.39

0.10 0.10 0.10 0.09 0.09 0.10 0.10

Beta

t

Sig.

0.21 0.41 0.27 0.08 0.27 0.21

63.79 3.45 6.73 4.45 1.29 4.39 3.63

0.00 0.00 0.00 0.00 0.20 0.00 0.00

0.32 0.41 0.51 0.23 0.34 0.19

110.97 7.93 10.68 13.27 5.92 8.59 4.92

0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.31 0.27 0.35 0.27 0.21 0.28

82.60 6.55 5.80 7.32 5.67 4.28 5.84

0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.34 0.29 0.40 0.36 0.10 0.26

89.63 7.17 6.14 8.54 7.70 2.15 5.57

0.00 0.00 0.00 0.00 0.00 0.03 0.00

0.34 0.44 0.34 0.20 0.05 0.20

80.14 6.59 8.76 6.78 3.83 1.00 3.85

0.00 0.00 0.00 0.00 0.00 0.32 0.00

R

R square

Adjusted R square

0.69

0.48

0.46

0.84

0.71

0.70

0.72

0.52

0.51

0.74

0.55

0.54

0.74

0.55

0.54

Note: Italicized results indicate significance at p , 0:05

Discussion and implications

decision is being made and the child is separated from home, it is possible that satisfaction becomes more important. Furthermore, research regarding uncertainty and perceived risk may help to explain why parents of older children are less impacted by satisfaction on the retention of their children with the childcare service. Rust et al. (1994, p. 48) note:

In line with the findings of the literature, the results indicate that parent satisfaction has a positive impact on childcare services retention. However, this impact is especially pronounced earlier in the relationship. Only for parents of children one year of age did satisfaction explain more than 10 percent of the variance in retention (explaining 28 percent of the variance). The economic implication for childcare providers is that parent satisfaction is a more critical concern with regard to child retention for parents of very young children (children 1 year of age). Since parents with very young children are more involved due to the uncertainty in the decision and most likely it is the first time such a

Under some circumstances it is perfectly rational for an individual to choose an option that actually is expected to be worse (on average) if the downside risk for that option is less. One thing that tends to reduce uncertainty, and thus worry, is experience. As experience increases, knowledge about product or service increases, and the expected distribution of expected outcomes tightens . . . Downside risk is reduced, and probability of repurchase therefore increases, even if the perceived quality is only what is expected. This helps explain why customers often appear loyal. They are being rational and avoiding risk.

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Does parent satisfaction with a childcare provider matter for loyalty?

Journal of Consumer Marketing

Timothy L. Keiningham, Lerzan Aksoy, Tor W. Andreassen and Demitry Estrin

Volume 23 · Number 7 · 2006 · 470 –479

Table VII Comparison of OLS regression results across different age groups: rankings of attribute satisfaction as a predictor of overall satisfaction 1 Sig. p p p

2 Rank

Sig. p p p p p p

Rank

Child development 5 1 Business relationship Caregivers 2 Facilities & equipment £ 6 p Scheduling 3 p Fees 4 p Notes: ¼ p , =0.05 level; the rankings were determined in order of

4 2 1 5 3 6

Age group 3 Sig. Rank p 2 p 4 p 1 p 5 p 6 p 3

4 Sig. p p p p p p

5 Rank 3 4 1 2 6 5

Sig. p p p p £ p

Rank 3 1 2 5 6 4

magnitude of importance

Another attribute determined to be important is business relationships. For several of the age groups, this attribute ranked 1st or 2nd place. Consequently, childcare service providers should strive to keep an open relationship with parents and provide regular feedback in a professional manner. Finally, although statistically significant, scheduling seems to be one of the least important attributes impacting overall satisfaction, especially with increased experience.

Therefore, parent satisfaction may be overridden by the perceived potential downside risk and switching costs associated with removing children from a childcare service who has worked with parents’ children for an extended period, i.e. asset specificity (Williamson, 1975). Although switching costs may be one potential explanation for the results – based on the competitive landscape for childcare services described earlier – parents in fact do have alternatives they could switch should they deem necessary. It is also interesting to notice from Figure 1 that the slope of the curve for age 1 is significantly different than the other age groups for lower levels of satisfaction. There seems to be an asymmetric effect where for this group, minor changes in overall satisfaction level given low degrees of satisfaction have a major impact on the retention probability. However when overall satisfaction level for this group improves beyond six, the impact on retention probability approaches that of the other groups. This finding implies that for age group 1 it is absolutely essential for the childcare provider to avoid low degrees of parent satisfaction in order to retain the parent’s contract. The lack of significant differences in overall satisfaction between age groups also indicates that there are no significant changes in satisfaction with this childcare provider given time and experience. This result however did not preclude some attribute determinants gaining relative importance in determining overall satisfaction compared to others with time. It was expected that output attributes should contribute to overall satisfaction to a greater extent later in the relationship as opposed to tangibles, which should be more influential earlier on. Since childcare services have credence properties, the parent could look to more tangible cues at the beginning of the relationship. With time, as child development becomes more observable, this attribute was expected to become more influential. This proposition was partially supported by the data. Although child development was less important in the age 1 group and gained importance with parent experience, it was not the top determinant of overall satisfaction. In this childcare services context, the quality of the caregiver seems to be the primary determinant of overall satisfaction. The qualifications of the teachers, supervision, absenteeism of the caregiver and related issues seem to be paramount to parents within all age groups. Hence, although all other attributes were mostly significant in predicting overall satisfaction, service providers in this context should focus particularly on developing and maintaining the quality of issues related to their caregivers.

Limitations Despite the rewarding results from this study the authors acknowledge that there are reasons other than satisfaction alone that affect retention. Switching costs in this industry could be especially high given the waiting lists for childcare at some facilities and the risks associated with the learning curve of a new provider. Nevertheless, it is important to remember that parents do have alternatives with regard to childcare (i.e. childcare facilities face a host of competitors). Therefore, based upon examination of a host of other industries one would reasonably expect satisfaction to play an important role in retention. The reader is reminded that the purpose of this paper is to provide the first examination of the relationship between parent satisfaction and actual retention of a childcare provider. This single focus of satisfaction on customer behavior or financial outcomes is typical of so many other papers in the marketing literature (for example, Anderson, 1998; Anderson et al., 1997; Keiningham et al., 2003; Rust and Zahorik, 1993). This research seeks to continue in that vein by providing insight into an as yet uninvestigated area of research. Nonetheless, this research offers valuable insight into the role that parent satisfaction plays into the retention of children with caregivers: a topic all would agree to be of significant importance not only to parents, but also to society as a whole.

Notes 1 K-J is a Japanese management technique designed to generate a hierarchical tree diagram of data. In this exercise, a team organizes a list of needs by group consensus. It uses a bottom-up approach, organizing the most detailed needs, and then seeing higher levels of organization in those groupings. 2 Because correlations analysis is a measure of the linear relationship in the data. 477

Does parent satisfaction with a childcare provider matter for loyalty?

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Timothy L. Keiningham, Lerzan Aksoy, Tor W. Andreassen and Demitry Estrin

Volume 23 · Number 7 · 2006 · 470 –479

3 Nagelkerke’s R-square is the most-reported of the R-squared estimates. It is a modification of the Cox and Snell coefficient to assure that it can vary from 0 to 1. That is, Nagelkerke’s R2 divides Cox and Snell’s R2 by its maximum in order to achieve a measure that ranges from 0 to 1. Therefore Nagelkerke’s R-square will normally be higher than the Cox and Snell measure (Nagelkerke, 1991).

markets”, Journal of Marketing Research, Vol. 41 No. 4, pp. 433-47. Britner, P.A. (1999), “What leads to satisfaction for child care providers and parents?”, National Network for Child Care, Ames, IA, June, available at: www.nncc.org/Research/ satisfaction.html Britner, P.A. and Phillips, D.A. (1995), “Predictors of parent and provider satisfaction with child day care dimensions: a comparison of center-based and family child care”, Child Welfare, Vol. 74 No. 6, pp. 1135-68. Brownlee, S., Miller, M., Fox, S., Saltzman, A. and Koerner, B.I. (1997), “Lies parents tell themselves about why they work”, US News & World Report., Vol. 122 12 May, p. 58. Capizzano, J., Adams, G. and Sonenstein, F. (2000), Child Care Arrangements for Children Under Five: Variation Across States, Series B, No. B-7, 15 March, available at: www. urban.org/url.cfm?ID=309438, The Urban Institute, Washington, DC. Carlzon, J. (1987), Moments of Truth, Ballinger, Cambridge, MA. CDF (2002), “Low income children bear the burden of state child care cutbacks”, September, Children’s Defense Fund, Washington, DC, available at: www.childrensdefense.org CDI (2005), Normal Stages of Human Development (Birth to 5 Years), Child Development Institute, Orange, CA, available at: www.childdevelopmentinfo.com/development/normal development Childstudy.net (2005), “Margaret Mahler, Sigmund Freud & Erik Erikson: a hypertext overview”, available at: http:// childstudy.net/cdw.html Ehrle, J., Adams, G. and Tout, K. (2001), Who’s Caring for Our Youngest Children? Child Care Patterns of Infants and Toddlers, Occasional paper number 42, 1 January, available at: www.urban.org/url.cfm?ID=310029, The Urban Institute, Washington, DC. Erdwins, C.J., Casper, W.J. and Buffardi, L.C. (1998), “Child care satisfaction: the effects of parental gender and type of child care used”, Child & Youth Care Forum, Vol. 27 No. 2, pp. 111-23. Gain, L. (1999), Using Consumer Views in Performance Indicators for Children’s Services: Anntated Bibliography, Consultancy Report prepared for the Steering Committee for the Review of Commonwealth/State Service Provision, 9 November, available at: www.pc.gov.au/gsp/consultancy/ childservices/bibliography.pdf Grace, D. and O’Cass, A. (2001a), “Attributions of service switching: a study of consumers’ and providers’ perceptions of child-care service delivery”, Journal of Services Marketing, Vol. 15 Nos 4/5, pp. 300-21. Grace, D. and O’Cass, A. (2001b), “Child care services: an exploratory study of choice, switching and search behavior”, European Journal of Marketing, Vol. 37 Nos 1/2, pp. 107-32. Hair, J.F. Jr, Anderson, R.E., Tatham, R.L. and Black, W.C. (1992), Multivariate Data Analysis, Macmillan Publishing, New York, NY. Ittner, C. and Larcker, D.F. (1998), “Are non-financial measures leading indicators of financial performance? An analysis of customer satisfaction”, Journal of Accounting Research, Vol. 36, Supplement, pp. 1-35. Jones, T.O. and Sasser, E.W. Jr (1995), “Why satisfied customers defect”, Harvard Business Review, Vol. 73 No. 6, pp. 88-99.

References AAP (2005), Children’s Health Topics: Developmental Stages, American Academy of Pediatrics, Elk Grove Village, IL, available at: www.aap.org/healthtopics/stages.cfm#inf Alba, J.W. and Hutchinson, J.W. (1987), “Dimensions of consumer expertise”, Journal of Consumer Research, Vol. 13, March, pp. 411-54. Anderson, E.W. (1998), “Customer satisfaction and word-ofmouth”, Journal of Service Research, Vol. 4 No. 1, pp. 1-14. Anderson, E.W. and Sullivan, M.W. (1993), “The antecedents and consequences of customer satisfaction for firms”, Marketing Science, Vol. 12, Spring, pp. 125-43. Anderson, E.W., Fornell, C. and Lehmann, D.R. (1994), “Customer satisfaction, market share, and profitability: findings from Sweden”, Journal of Marketing, Vol. 58, July, pp. 53-66. Anderson, E.W., Fornell, C. and Rust, R.T. (1997), “Customer satisfaction, productivity, and profitability: differences between goods and services”, Marketing Science, Vol. 16 No. 2, pp. 129-45. Baumann, C., Burton, S. and Elliott, G. (2005), “Determinants of customer loyalty and share of wallet in retail banking”, Journal of Financial Services Marketing, Vol. 9 No. 3, pp. 231-48. Belsley, D.A., Kuh, E. and Welsch, R.E. (1980), Regression Diagnostics, John Wiley &Sons, New York, NY. Blau, D.M. (2003a), “An economic perspective on child care policy”, Supplement to Journal of Population and Social Security (Population), Vol. 1 No. 1, available at: www.ipss. go.jp/English/WebJournal.files/Population/WebPopulation. html Blau, D.M.b. (2003b), “Child care subsidy programs”, in Moffitt, R. (Ed.), Means-tested Transfer Programs in the United States, University of Chicago Press for the National Bureau of Economic Research, Chicago, IL. Blau, D.M. and Hagy, A.P. (1999), “The demand for quality in child care”, The Journal of Political Economy, Vol. 106 No. 1, pp. 104-46. Bogat, G.A. and Gensheimer, L.K. (1986), “Discrepancies between the attitudes and actions of parents choosing day care”, Child Care Quarterly, Vol. 15, pp. 159-69. Bolton, R.N. (1998), “A dynamic model of the duration of the customer’s relationship with a continuous service provider: the role of satisfaction”, Marketing Science, Vol. 17 No. 1, pp. 45-65. Bolton, R.N. and Drew, J.H. (1991), “A longitudinal analysis of the impact of service changes on customer attitudes”, Journal of Marketing, Vol. 55 No. 1, pp. 1-10. Bossert, J.L. (1991), Quality Function Deployment: A Practitioner’s Approach, ASQC Quality Press, Milwaukee, WI. Bowman, D. and Narayandas, D. (2004), “Linking customer management effort to customer profitability in business 478

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Timothy L. Keiningham, Lerzan Aksoy, Tor W. Andreassen and Demitry Estrin

Volume 23 · Number 7 · 2006 · 470 –479

Keiningham, T.L., Perkins-Munn, T., Aksoy, L., Estrin, D. and Evans, H. (2003), “The impact of customer satisfaction on share-of-wallet in a business-to-business environment”, Journal of Service Research, Vol. 6 No. 1, pp. 37-50. Keiningham, T.L., Perkins-Munn, T., Aksoy, L. and Estrin, D. (2005), “Does customer satisfaction lead to profitability? The mediating role of share of wallet”, Managing Service Quality, Vol. 15 No. 2, pp. 172-81. Kotler, P. (1994), Marketing Management: Analysis, Planning, Implementation and Control, 8th ed., Prentice-Hall, Englewood Cliffs, NJ. Leung, K., Li, W.K. and Au, Y.F. (1998), “The impact of customer service and product value on customer loyalty and purchase behavior”, Journal of Applied Social Psychology, Vol. 28 No. 18, pp. 1731-41. Loveman, G.W. (1998), “Employee satisfaction, customer loyalty, and financial performance: an empirical examination of the service profit chain in retail banking”, Journal of Service Research, Vol. 1 No. 1, pp. 18-31. Mitchell, A. (1992), Consumers and Child Care: An Annotated Bibliography, National Center for Children in Poverty, New York, NY, June, available at: www.nccp.org/media/ cac92-text.pdf Mittal, V. and Kamakura, W. (2001), “Satisfaction, repurchase intent and repurchase behavior: investigating the moderating effect of customer characteristics”, Journal of Marketing Research, Vol. 38, February, pp. 131-42. Mittal, V., Kamakura, W., Katrichis, J.M. and Kumar, P. (2001), “Attribute performance and customer satisfaction over time: evidence from two field studies”, Journal of Services Marketing, Vol. 15 Nos 4/5, pp. 343-56. Moorthy, S., Ratchford, B.T. and Talukdar, D. (1997), “Consumer information search revisited: theory and empirical analysis”, Journal of Consumer Research, Vol. 23, March, pp. 263-77. Nagelkerke, N.J.D. (1991), “A note on a general definition of the coefficient of determination”, Biometrika, Vol. 78 No. 3, pp. 691-2. Nunnally, J.C. (1967), Psychometric Theory, McGraw-Hill Publishing, New York, NY. O’Cass, A. and Grace, D. (2001), “Exploring childcare services: studying the service switching and choice issues”, Services Marketing Quarterly, Vol. 23 No. 2, pp. 21-48. Oliver, R.L. (1997), Satisfaction: A Behavioral Perspective on the Consumer, McGraw-Hill Co., New York, NY.

Pedhazur, E.J. and Schmelkin, L.P. (1991), Measurement, Design, and Analysis: An Integrated Approach, Lawrence Erlbaum, Hillsdale, NJ. Rust, R.T. and Oliver, R.L. (1994), “Service quality: insights and managerial implications from the frontier”, in Rust, R.T. and Oliver, R.L. (Eds), Service Quality: New Directions in Theory and Practice, Sage Publications, Thousand Oaks, CA. Rust, R.T. and Zahorik, A.J. (1993), “Customer satisfaction, customer retention, and market share”, Journal of Retailing, Vol. 69 No. 2, pp. 193-215. Rust, R.T., Zahorik, A.J. and Keiningham, T.L. (1994), Return on Quality: Measuring the Financial Impact of Your Company’s Quest for Quality, Probus Publishing, Chicago, IL. State of Indiana Division of Family and Children, Bureau of Child Development (2005), available at: www. childcarefinder.in.gov Tamhane, A.C. (1977), “Multiple comparison in model I: one-way anova with unequal variances”, Communications in Statistics, Series A, No. 6, pp. 15-32. Wedderburn, R.W.M. (1974), “Quasi-likelihood functions, generalized linear models, and the Gauss-Newton method”, Biometrika, Vol. 61, pp. 439-47. Williamson, O.E. (1975), Market and Hierarchies: Analysis and Antitrust Impications: A Study in the Economics of Internal Organizations, Free Press, New York, NY.

Further reading Mittal, V., Kamakura, W., Katrichis, J.M., Kumar, P. and Tsiros, M. (1999), “Attribute-level performance, satisfaction, and behavioral intentions over time: a consumption-system approach”, Journal of Marketing, Vol. 63 No. 2, pp. 88-101.

About the authors Timothy L. Keiningham is Senior Vice President & Head of Consulting, IPSOS Loyalty, Parsippany, New Jersey, USA. Lerzan Aksoy is Assistant Professor of Marketing in the College of Administrative Sciences and Economics, Koc¸ University, Istanbul, Turkey. She is the corresponding author and can be contacted at: [email protected] Tor W. Andreassen is Associate Professor of Marketing in the Department of Marketing, Norwegian School of Management, Oslo, Norway. Demitry Estrin is a Vice President of IPSOS Loyalty, Parsippany, New Jersey, USA.

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479

Misplaced marketing

Movie theaters’ suicide-by-advertising with income from abusing customers Herbert Jack Rotfeld Auburn University, Auburn, Alabama, USA Abstract Purpose – The purpose of this article is to examine US cinema audiences’ reactions to advertising. Cinema advertising and other failures of customer relations management by movie exhibitors explains how consumers are discouraged from going to a cinema to see newly released movies. To avoid commercials, consumers increasingly turn to VCR rentals, DVD purchases and computer downloads, all of which feed production company profits at the expense of the movie theater owners. Design/methodology/approach – Historical observations on the origins and growth of cinema advertising in the USA, coupled with notes on the overall decline of the cinema viewing experience. Findings – Cinema advertising swiftly grew from “underused” to commonplace in the early 1980s, while losing track of theater owners’ early concerns for potential harm to the viewing experience for ticket purchasers. The presence of advertising is not in itself the cause for consumer dislike of the theater experience, but the increasing quantity of messages are often poorly written or produced, previously seen by audiences ad nauseam on television and written for a small target group of cinema audiences while boring or offending the rest. Practical implications – There are limits to consumer tolerance of ambush media vehicles, and a failure to take this consumer abuse into account contributes to a loss of customers. In a similar vein, over commercialization of over-the-air radio encourages consumers to use subscription systems, satellite radio or other forms of in-car entertainment. Increasing television advertising clutter is a major factor in declining ratings for programs, as well as decreased attention to advertising messages by audiences that remain. Originality/value – A call to action for movie theaters to see ticket sales as a function of factors other than the appeal of the latest blockbusters, with overuse of advertising discouraging repeat customers. Keywords Customer relations, Cinema, Advertising, Customer retention, Advertising media, Target audience Paper type Viewpoint

passes that were given to the Evaniers with his apologies while directing an employee to “Get the letters for the front and the ladder. I want to change something.” Riding by the theater the next day, Mark saw that comedian Dick Shawn replaced Jonathan Winters on the marquee. Modern movie audiences learn much more about movies than trailers and marquees before the release date, so such consumer deceptions wouldn’t succeed today. More noteworthy was the manager’s concern to retain customers, instead of the more common modern view in which they apparently believe that an audience gathered for the main feature will endure all sorts of distractions or abuse undertaken to increase profits for the exhibitor. Of course, some elements of the declining movie theater experience are outside any manager’s control, such as ubiquitous cell phones, uncontrollable children and generally rude audience members. A renewed tradition of cinema ushers would only be able to remove such problems if they had the physical presence, training and salaries of bouncers working at the popular metropolitan night clubs. Rising ticket prices and overpriced candy, popcorn and drinks are driven by contract costs with movie producers and distributors, though claims of refreshments as a movie theater’s main profit center would intuitively direct design of a better counter system than one that is less efficient than an overworked and understaffed government post office in the

In his weblog for March 22, 2006, entertainment writer Mark Evanier noted the upcoming showing of Penelope on Turner Classic Movies, coupled with his personal memories of seeing the movie in 1966 with his father[1]. They were initially enticed by a movie trailer from weeks earlier: the teenaged Mark by scenes of Natalie Wood running around in her underwear; his father by seeing Jonathan Winters announced in a starring role. But despite the star billing with his name on the theater marquee as large as Natalie Woods, the less-thanenjoyable movie had Mr Winters on screen for only a few minutes. The elder Evanier told his son that he felt swindled. On the way out they encountered the manager who expressed a hope that they’d come back soon, so Mark’s father blurted out his huge dissatisfaction with a movie whose marquee contradicted the delivery of a much-loved-yet-absent star. To their surprise, the manager quickly whipped out four free The current issue and full text archive of this journal is available at www.emeraldinsight.com/0736-3761.htm

Journal of Consumer Marketing 23/7 (2006) 480– 482 q Emerald Group Publishing Limited [ISSN 0736-3761] [DOI 10.1108/07363760610718069]

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Movie theaters’ suicide-by-advertising

Journal of Consumer Marketing

Herbert Jack Rotfeld

Volume 23 · Number 7 · 2006 · 480 –482

midst of the December holiday season. Another audience problem of dark movies is simply an error caused by too many owners’ or managers’ mistaken beliefs that running the projector light at a reduced level lengthens the life of the expensive bulbs. The most often cited major source of audience ire noted by both film critics and the ticket buyers comes from the product advertising that now precedes each showing, so a scheduled 8 p.m. movie does not start till 8:15, 8:30 or even later. Rarely do audiences voice objections to the trailers for current or upcoming films, which have taken up a few minutes at the start of every movie from the earliest days of the medium. While cinema advertising for unrelated products or services possesses an equally long tradition in other countries, it is relatively new in the USA. And while the European cinema owners always have been conscious of the need to provide messages audiences want to see, the increasing quantity of boring or poorly targeted advertising per movie in the USA encourages audiences to seek an advertising-free venue.

Trade Commission declared it to be unfair and misleading to run the cinema advertising without first warning patrons. Initially, some theater companies set their own limits on non-trailer minutes of advertising, also demanding higher production values than television spots and that commercials have a lengthy first-run in theaters before being adapted for the small screen. (For a more detailed review of this history of cinema advertising, see Rotzoll, 1987.)

Boiling the frog Advertising has become so ubiquitous in the twenty-first century such that modern audience dislike of cinema advertising can’t be attributed to the advertising itself, but also to the quantity and quality of the messages. Regardless of the announced start time for a movie, everyone knows it is “really” later, but how much later is uncertain. It could be a few minutes or it could be a half-hour, so the audience desires to come on time to not miss the start of the movie. Some product advertising looks grainy with chaotic sound tracks, as if it was produced for television and badly converted to the wide-screen, complete with a stretched out center that gives the audience a headache. Other commercials already approach audience burn-out from prior showings in other theaters or even television. And while early cinema advertisers showed concern for matching of their products with theaters in certain locations or fitting the movie style (e.g. Johnson, 1981; Rotzoll, 1987), the lack of such matching is painfully obvious when PG-rated animated movies from Pixar are the forum for clothing and grooming product advertising whose presentations are designed to appeal to young adults in the throes of raging hormones. During the past year, an often-repeated metaphor from Al Gore’s book and movie on global warming, An Inconvenient Truth, tells the tale of a frog in a pot of water that is increasingly heated up. The frog is comfortable, and as the water is gradually heated, the amphibian does not realize its life is in danger from being boiled until it is too late. Cinema advertising has been increasing the quantity of advertising to the point of driving away their audience, in a fashion akin to the equally destructive myopic practices of commercial television and radio. Up until the mid-1980s, the US Federal Communications Commission (FCC) had rules aimed to prevent the overcommercialization of television and radio stations with specific limits on the number of advertising minutes that could be broadcast per hour. The National Association of Broadcasters (NAB), an industry trade group, had similar rules in its code of good practices. The NAB dropped its code in 1982, and the FCC dropped their rules in 1984 and 1985. At that time, the common assertion was that “market forces” of competing stations or networks would discourage any broadcaster from becoming over-commercialized. The loss of these restrictions made it possible for television stations to broadcast the now-ubiquitous infomercials, an entire half hour that television stations or cable networks sell an advertising period (Wicks, 1997). But aside from infomercials, commercial clutter has expanded well beyond the limits of two decades ago while market forces has not proven to be a restraint since all competitors are making the comparable increases in the number of commercial minutes per hour of programs.

Conflicting forces of greed and fear Describing media buyer information phrased in terms of the potential benefits of the vehicle for advertisers, plus reporting results of audience advertising recall from small-scale tests, Johnson (1981) labeled theater screens as an “underused” advertising medium. Few audience complaints were registered during the research in the Okemos, Michigan, and the article also noted that “infrequent complaints” came to a movie theater company in Farmington, New Mexico, that had been running advertising for several years. A corporate study described in the article of consumer attitudes toward advertising in cinema versus television did not reveal stronger negative audience reactions for the former and another company’s study of a single commercial found stronger consumer memory of the advertising in the cinema than from the television showing. The article could be read as advocacy of potential benefits from using the cinema as an advertising vehicle. Advertisers’ efforts in US theaters other than movie trailers were minimal at that time and the advertising tests reported were all limited in scope, sample and location. Advertising researchers know that comparing the number of complaints relative to total customers is always a misleading indicator of potential problems, yet the picture was encouraging for advertisers to direct more spending toward the cinema owners in the future. However, the major limitation to cinema advertising at that time was not the dearth of potential advertisers, but rather, the reluctance of cinema owners to sell the time. After all, the advertising audience pays a hefty price to see the movie in a theater while other advertising media can claim the programs are free, such as television or radio, or greatly price-reduced, such as magazines or newspapers. They were properly concerned of adverse audience reactions. Movie exhibitors were facing declining revenue and profits in the late 1970s and early 1980s, so potential advertising income seemed like an easy alternative to even larger ticket price increases. The potential revenue source seemed so attractive that it also created political fighting between theater owners and distributors as to who should get the get the money. The advertising trade press of the period reported numerous audience complaints wherever theaters first started to run the advertising, while the Seattle office of the Federal 481

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To a degree, the ever-increasing quantity of advertising minutes per hour is a function of media vehicle economics. With the decline of the former mass demand popular programs into more segmented and targeted options, the total audience size of even the largest broadcast vehicles is greatly reduced. There are limits to how much stations or networks could hope to increase their cost-per-thousand prices to advertisers, so with smaller audiences, the vehicles need to sell more time to make the same amount of revenue. Yet it should be intuitively obvious that a plethora of radio commercials would encourage commuters to select a different form of entertainment while driving, such as prerecorded music, subscription services or even talking on the phone, causing a further decline in radio ratings. Even with a remote control, flipping, zapping and zipping gets tedious, causing audiences to seek other entertainment alternatives and reducing the effectiveness of commercials in programs they watch. Last March, the trade magazine Advertising Age reported an offer by Philips Electronics to buy four minutes of cinema advertising time and run a 15-second spot that said, “We could have run a four-minute commercial. Instead we chose simplicity. Sometimes, simplicity means getting you to your movie quicker.” Unfortunately, Screenvision, the primary seller of cinema ads, rejected the idea on the grounds that it poked fun at cinema advertising (Kerwin, 2006). Of course it did. That is why Philips thought the advertising would have been effective. Film critic Roger Ebert has repeatedly expressed the view that overuse of cinema advertising does more to discourage movie going than any other single factor under control the exhibitors. Yet the companies remain steadfast, ignorant or myopic, with Ebert’s column and web pages noting their responses to letters of complaint with claims that the audiences see advertising as an enjoyable part of the theater experience, something to do before the movie starts. This claim might be true if the commercials ran before the movie’s posted start time instead of causing the movie to start late.

The claim might also have been true when the advertising first started, when it was minimal, well produced and better targeted. In 1966, the senior Evanier probably thought he was simply venting his ire, yet the manager immediately responded. Through most of the 1980s, the audience’s decision became a trade off of desires to see the movie versus the pain of enduring a few minutes of commercials. Today, there are numerous cinema alternatives to seeing a movie unedited: premium cable or satellite networks, pay-per-view, VCR/DVD purchase or rentals, all of which can be seen on large-screen home televisions that mimic a theater experience. The audience is no longer captive, and as the cinema companies increase their advertising time sales, they become frogs that are turning up the heat on their own cooking pot.

Note 1 Mark Evanier is a writer of cartoon shows, television programs and comic books and his News From Me is what he describes as “a weblog about TV, movies, comics, theater, news, politics and other forms of fantasy” at http://newsfromme.com

References Johnson, K.F. (1981), “Cinema advertising”, Journal of Advertising, Vol. 10 No. 4, pp. 11-19. Kerwin, A.M. (2006), “The water cooler”, Advertising Age, March 13, p. 34. Rotzoll, K.B. (1987), “The captive audience: the troubled odyssey of cinema advertising”, in Austin, B.A. (Ed.), Current Research in Film: Audiences, Economics and Law, Vol. 3, Ablex Publishing, Norwood, NJ, pp. 72-87. Wicks, J.L. (1997), “Which factors primarily influence the number of infomercial hours a commercial station airs?”, Journal of Media Economics, Vol. 10 No. 1, pp. 29-38.

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2006 Awards for Excellence The following article was selected for this year’s Outstanding Paper Award for

Journal of Consumer Marketing

‘‘Collaborative structure between Japanese high-tech manufacturers and consumers’’ Yuichi Washida Massachusetts Institute of Technology, Cambridge, Massachusetts, USA Purpose – This is a study that aims to explore a new conception of marketing management based on the analyses of the demand side in Japanese high-tech industries. Currently, due to the rapid development of technologies, conventional marketing and management methodologies sometimes cannot explain why emerging technologies and new usage diffuse epidemically among consumers in a short time. Design/methodology/approach – As a major example of successful technological development, this study focuses on a collaborative structure between Japanese high-tech manufacturers and two types of consumer communities, ‘‘otaku’’ and ‘‘kogal’’. The paper explores a hypothesis that each of the two consumer communities gives a different type of feedback to the manufacturers, and thus Japanese manufacturers can develop and improve their products very efficiently. Findings – Japanese management has been understood as ‘‘kaizen’’ – a management way to improve the efficiency of the supply side. However, today’s Japanese high-tech companies focus relatively on the demand side and have found interesting dynamics of consumer behaviors which can make one technology more valuable and useful in the daily lives. The paper also shows a comparative framework from the viewpoint of the user-collaboration to contrast the basic difference of management styles among the USA, Europe, and Japan, and suggests that each company in each region can use other region’s collaboration dynamics to develop its products, or build a technological standard more efficiently. Originality/value – The hypothesis and framework in this paper can be expected to fill a vacuum in studies on Japanese management after the 1990s, as a successor to ‘‘kaizen’’ methodology. Keywords Consumer marketing, Customer relations, Innovation, Japan www.emeraldinsight.com/10.1108/07363760510576527 This article originally appeared in Volume 22 Number 1, 2005, pp. 25-34, of Journal of Consumer Marketing

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The following articles were selected for this year’s Highly Commended Award

‘‘An assessment of strategic corporate philanthropy on perceptions of brand equity variables’’ Joe M. Ricks Jr This article originally appeared in Volume 22 Number 3, 2005 of Journal of Consumer Marketing

‘‘Store visits and information sources among urban Chinese children’’ Kara Chan

‘‘Shades of green: linking environmental locus of control and pro-environmental behaviors’’ Mark Cleveland Maria Kalamas Michel Laroche These articles originally appeared in Volume 22 Number 4, 2005 of Journal of Consumer Marketing

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