Return on Marketing Using Customer Equity to Focus Marketing Strategy

Return on Marketing: Using Customer Equity to Focus Marketing Strategy Author(s): Roland T. Rust, Katherine N. Lemon and Valarie A. Zeithaml Reviewed work(s): Source: Journal of Marketing, Vol. 68, No. 1 (Jan., 2004), pp. 109-127 Published by: American Marketing Association Stable URL: http://www.jstor.org/stable/30161978 . Accessed: 20/11/2012 21:53 Your use of the JSTOR archive indicates your acceptance of the Terms & Conditions of Use, available at . http://www.jstor.org/page/info/about/policies/terms.jsp . JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org. . American Marketing Association is collaborating with JSTOR to digitize, preserve and extend access to Journal of Marketing. http://www.jstor.org This content downloaded by the authorized user from 192.168.72.228 on Tue, 20 Nov 2012 21:53:13 PM All use subject to JSTOR Terms and Conditions Roland T. Rust, Katherine N. Lemon, & Valarie A. Zeithaml Return on Marketing: Using Customer Equity to Focus Marketing Strategy The authors present a unified strategic framework that enables competing marketing strategy options to be traded off on the basis of projected financial return, which is operationalized as the change in a firm's customer equity rel- ative to the incremental expenditure necessary to produce the change. The change in the firm's customer equity is the change in its current and future customers' lifetime values, summed across all customers in the industry. Each customer's lifetime value results from the frequency of category purchases, average quantity of purchase, and brand-switching patterns combined with the firm's contribution margin. The brand-switching matrix can be esti- mated from either longitudinal panel data or cross-sectional survey data, using a logit choice model. Firms can ana- lyze drivers that have the greatest impact, compare the drivers' performance with that of competitors' drivers, and project return on investment from improvements in the drivers. To demonstrate how the approach can be imple- mented in a specific corporate setting and to show the methods used to test and validate the model, the authors illustrate a detailed application of the approach by using data from the airline industry. Their framework enables what-if evaluation of marketing return on investment, which can include such criteria as return on quality, return on advertising, return on loyalty programs, and even return on corporate citizenship, given a particular shift in cus- tomer perceptions. This enables the firm to focus marketing efforts on strategic initiatives that generate the great- est return. The Marketing Strategy Problem Top managers are constantly faced with the problem of how to trade off competing strategic marketing initia- tives. For example, should the firm increase advertis- ing, invest in a loyalty program, improve service quality, or Roland T. Rust is David Bruce Smith Chair in Marketing, Director of the Center for e-Service, and Chair of the Department of Marketing, Robert H. Smith School of Business, University of Maryland (rrust@ rhsmith.umd. edu). Katherine N. Lemon is Associate Professor, Wallace E. Carroll School of Management, Boston College (e-mail: lemonka@bc.edu). Valarie A. Zeithaml is Roy and Alice H. Richards Bicentennial Professor and Senior Associate Dean, Kenan-Flagler School of Business, University of North Carolina, Chapel Hill (e-mail: zeithamv@bschool.unc.edu). This research was supported by the Marketing Science Institute, University of Maryland's Center for e-Service, and the Center for Service Marketing at Vanderbilt University. The authors thank Northscott Grounsell, Ricardo Erasso, and Harini Gokul for their help with data analysis, and they thank Nevena Koukova, Samir Pathak, and Srikrishnan Venkatachari for their help with background research.The authors are grateful for comments and suggestions provided by executives from IBM, Sears, DuPont, General Motors, Unilever, Siemens, Eli Lilly, R-Cubed, and Copernicus. They also thank Kevin Clancy, Don Lehmann, Sajeev Varki, Jonathan Lee, Dennis Gensch, Wagner Kamakura, Eric Paquette, Annie Takeuchi, and seminar participants at Harvard Business School, INSEAD, London Business School, University of Maryland, Cornell University, Tulane University, Uni- versity of Pittsburgh, Emory University, University of Stockholm, Norwe- gian School of Management, University of California at Davis, and Mon- terrey Tech; and they thank participants in the following: American Marketing Association (AMA) Frontiers in Services Conference, MSI Cus- tomer Relationship Management Workshop, MSI Marketing Metrics Work- shop, INFORMS Marketing Science Conference, AMA A/RfT Forum, AMA Advanced School of Marketing Research, AMA Customer Relationship Management Leadership Program, CATSCE, and QUIS 7. none of the above? Such high-level decisions are typically left to the judgment of the chief marketing or chief executive officers, but these executives frequently have little to base their decisions on other than their own experience and intu- ition. A unified, data-driven basis for making broad, strate- gic marketing trade-offs has not been available. In this arti- cle, we propose that trade-offs be made on the basis of projected financial impact, and we provide a framework that top managers can use to do this. Financial Accountability Although techniques exist for evaluating the financial return from particular marketing expenditures (e.g., advertising, direct mailings, sales promotion) given a longitudinal his- tory of expenditures (for a review, see Berger et al. 2002), the approaches have not produced a practical, high-level model that can be used to trade off marketing strategies in general. Furthermore, the requirement of a lengthy history of longitudinal data has made the application of return on investment (ROI) models fairly rare in marketing. As a result, top management has too often viewed marketing expenditures as short-term costs rather than long-term investments and as financially unaccountable (Schultz and Gronstedt 1997). Leading marketing companies consider this problem so important that the Marketing Science Insti- tute has established its highest priority for 2002-2004 as "Assessing Marketing Productivity (Return on Marketing) and Marketing Metrics." We propose that firms achieve this financial accountability by considering the effect of strategic marketing expenditures on their customer equity and by relating the improvement in customer equity to the expendi- ture required to achieve it. Journal of Marketing Vol. 68 (January 2004), 109-127 Return on Marketing / 109 This content downloaded by the authorized user from 192.168.72.228 on Tue, 20 Nov 2012 21:53:13 PM All use subject to JSTOR Terms and Conditions Customer Equity Although the marketing concept has reflected a customer- centered viewpoint since the 1960s (e.g., Kotler 1967), mar- keting theory and practice have become increasingly customer-centered during the past 40 years (Vavra 1997, pp. 6-8). For example, marketing has decreased its emphasis on short-term transactions and has increased its focus on long- term customer relationships (e.g., ffakansson 1982; Stor- backa 1994). The customer-centered viewpoint is reflected in the concepts and metrics that drive marketing manage- ment, including such metrics as customer satisfaction (Oliver 1980), market orientation (Narver and Slater 1990), and customer value (Bolton and Drew 1991). In recent years, customer lifetime value (CLV) and its implications have received increasing attention (Berger and Nasr 1998; Mulhern 1999; Reinartz and Kumar 2000). For example, brand equity, a fundamentally product-centered concept, has been challenged by the customer-centered concept of cus- tomer equity (Blattberg and Deighton 1996; Blattberg, Getz and Thomas 2001; Rust, Zeithaml, and Lemon 2000). For the purposes of this article, and largely consistent with Blat- tberg and Deighton (1996) but also given the possibility of new customers (Hogan, Lemon, and Libai 2002), we define customer equity as the total of the discounted lifetime values summed over all of the firm's current and potential customers .1 Our definition suggests that customers and customer equity are more central to many firms than brands and brand equity are, though current management practices and met- rics do not yet fully reflect this shift. The shift from product- centered thinking to customer-centered thinking implies the need for an accompanying shift from product-based strategy to customer-based strategy (Gale 1994; Kordupleski, Rust, and Zahorik 1993). In other words, a firm's strategic oppor- tunities might be best viewed in terms of the firm's opportu- nity to improve the drivers of its customer equity. Contribution of the Article Because our article incorporates elements from several liter- ature streams within the marketing literature, it is useful to point out the relative contribution of the article. Table 1 shows the contribution of this article with respect to several streams of literature that influenced the return on marketing conceptual framework. Table 1 shows related influential lit- erature streams and exemplars of the stream, and it high- lights key features that differentiate the current effort from previous work. For example, strategic portfolio models, as Larreche and Srinivasan (1982) exemplify, consider strate- gic trade-offs of any potential marketing expenditures. How- ever, the models do not project ROI from specific expendi- tures, do not model competition, and do not model the behavior of individual customers, their customer-level brand switching, or their lifetime value. Our model adds to the 'For expositional simplicity, we assume throughout much of the article that the firm has one brand and one market, and therefore we use the terms "firm" and "brand" interchangeably. In many firms, the firm's customer equity may result from sales of several brands and/or several distinct goods or services. strategic portfolio literature by incorporating those elements. Three related streams of literature involve CLV models (Berger and Nasr 1998), direct marketing-motivated models of customer equity (e.g., Blattberg and Deighton 1996; Blat- tberg, Getz, and Thomas 2001), and longitudinal database marketing models (e.g., Bolton, Lemon, and Verhoef 2004; Reinartz and Kumar 2000). Our CLV model builds on these approaches. However, the preceding models are restricted to companies in which a longitudinal customer database exists that contains marketing efforts that target each customer and the associated customer responses. Unless the longitudinal database involves panel data across several competitors, no competitive effects can be modeled. Our model is more gen- eral in that it does not require the existence of a longitudinal database, and it can consider any marketing expenditure, not only expenditures that are targeted one-to-one. We also model competition and incorporate purchases from com- petitors (or brand switching), in contrast to most existing models from the direct marketing tradition. The financial-impact element of our model is foreshad- owed by two related literature streams. The service profit chain (e.g., Heskett et al. 1994; Kamakura et al. 2002) and return on quality (Rust, Zahorik, and Keiningham 1994, 1995) models both involve impact chains that relate service quality to customer retention and profitability. The return on quality models go a step farther and explicitly project finan- cial return from prospective service improvements. Follow- ing both literature streams, we also incorporate a chain of effects that leads to financial impact. As does the return on quality model, our model projects ROI. Unlike other mod- els, our model facilitates strategic trade-offs of any prospec- tive marketing expenditures (not only service improve- ments). We explicitly model the effect of competition-an element that does not appear in the service profit chain or return on quality models. Also different from prior research, our approach models customer utility, brand switching, and lifetime value. Finally, we compare the current article with a recent book on customer equity (Rust, Zeithaml, and Lemon 2000) that focuses on broad managerial issues related to customer equity, such as building a managerial framework related to value equity, brand equity, and relationship equity. The book includes only one equation (which is inconsistent with the models in this article). Our article is a necessary comple- ment to the book, providing the statistical and implementa- tion details necessary to implement the book's customer equity framework in practice. The current work extends the book's CLV conceptualization in two important ways: It allows for heterogeneous interpurchase times, and it incor- porates customer-specific brand-switching matrices. In sum- mary, the current article has incorporated many influences, but it makes a unique contribution to the literature. Overview of the Article In the next section, on the basis of a new model of CLV, we describe how marketing actions link to customer equity and financial return. The following section describes issues in the implementation of our framework, including data options, model input, and model estimation. We then present 110 I Journal of Marketing, January 2004 This content downloaded by the authorized user from 192.168.72.228 on Tue, 20 Nov 2012 21:53:13 PM All use subject to JSTOR Terms and Conditions TABLE 1 Comparing the Return on Marketing Model with Existing Marketing Models Type of Model Strategic portfolio CLV Direct marketing: customer equity Strategic Trade- offs of Any Marketing Exemplars Expenditures Larrech6 and Srinivasan (1982) Berger and Nasr (1998) Blattberg and Deighton (1996); Blattberg, Getz, and Thomas (2001) ROI Modeled and Calculated? Explicitly Models Competition? Calculation of CLV? Can Be Applied to Most Industries? Net Present Value of Revenues and Costs? Brand Switching Modeled at Customer Statistical Level? Details? No Yes Yes Yes Yes No No No No No Yes No No Yes Yes No No No Yes Yes No Yes Yes Yes Longitudinal database marketing Bolton, Lemon, and Yes Verhoef (2004); Reinartz and Kumar (2000) No, unless panel data No, unless panel data Yes Yes No Yes Yes No No No No Yes Yes No No No Yes Yes Yes No Yes Yes Yes No No No No No Yes Yes Yes Service profit chain Return on quality Customer equity book Heskett et al. (1994); Kamakura et al. (2002) Rust, Zahorik, and Keiningham (1994,1995) Rust, Zeithaml, and Lemon (2000) Return on Current paper Yes marketing Yes Yes Yes Yes Yes Yes Yes Return on Marketing / 111 This content downloaded by the authorized user from 192.168.72.228 on Tue, 20 Nov 2012 21:53:13 PM All use subject to JSTOR Terms and Conditions an example application to the airline industry, showing some of the details that arise in application, in testing and validat- ing our choice model, and in providing some substantive observations. We end with discussion and conclusions. Linking Marketing Actions to Financial Return Conceptual Model Figure 1 shows a broad overview of the conceptual model that we used to evaluate return on marketing. Marketing is viewed as an investment (Srivastava, Shervani, and Fahey 1998) that produces an improvement in a driver of customer equity (for simplicity of exposition, we refer to an improve- ment in only one driver, but our model also accommodates simultaneous improvement in multiple drivers). This leads to improved customer perceptions (Simester et al. 2000), which result in increased customer attraction and retention (Danaher and Rust 1996). Better attraction and retention lead to increased CLV (Berger and Nasr 1998) and customer equity (Blattberg and Deighton 1996). The increase in cus- tomer equity, when considered in relation to the cost of mar- FIGURE 1 Return on Marketing Marketing investment Increased CLV Increased customer equity Return on marketing investment keting investment, results in a return on marketing invest- ment. Central to our model is a new CLV model that incor- porates brand switching. Brand Switching and CLV It has long been known that the consideration of competing brands is a central element of brand choice (Guadagni and Little 1983). Therefore, we begin with the assumption that competition has an impact on each customer's purchase decisions, and we explicitly consider the relationship between the focal brand and competitors' brands. In con- trast, most, if not all, CLV models address the effects of marketing actions without considering competing brands. This is because data that are typically available to direct marketers rarely include information about the sales or pref- erence for competing brands. Our approach incorporates information about not only the focal brand but competing brands as well, which enables us to create a model that con- tains both customer attraction and retention in the context of brand switching. The approach considers customer flows from one competitor to another, which is analogous to brand-switching models in consumer packaged goods (e.g., Massy, Montgomery, and Morrison 1970) and migration models (Dwyer 1997). The advantage of the approach is that competitive effects can be modeled, thereby yielding a fuller and truer accounting of CLV and customer equity. When are customers gone? Customer retention histori- cally has been treated according to two assumptions (Jack- son 1985). First, the "lost for good" assumption uses the customer's retention probability (often the retention rate in the customer's segment) as the probability that a firm's cus- tomer in one period is still the firm's customer in the fol- lowing period. Because the retention probability is typically less than one, the probability that the customer is retained declines over time. The implicit assumption is that cus- tomers are "alive" until they "die," after which they are lost for good. Models for estimating the number of active cus- tomers have been proposed for relationship marketing (Schmittlein, Morrison, and Columbo 1987), customer retention (Bolton 1998), and CLV (Reinartz 1999). The second assumption is the "always a share" assump- tion, in which customers may not give any firm all of their business. Attempts have been made to model this by a "migration model" (Berger and Nasr 1998; Dwyer 1997). The migration model assigns a retention probability as pre- viously, but if the customer has missed a period, a lower probability is assigned to indicate the possibility that the customer may return. Likewise, if the customer has been gone for two periods, an even lower probability is assigned. This is an incomplete model of switching because it includes purchases from only one firm. In one scenario (consistent with the lost-for-good assumption) when the customer is gone, he or she is gone. This approach systematically understates CLV to the extent that it is possible for customers to return. In another scenario (consistent with t