A key challenge facing business marketers surrounds developing a deeper understanding of customer needs. We conceptualize that challenge as having three dimensions: calculating, creating and claiming value. We discuss key problems, new developments and research challenges in each of these three domains and note the desirability for a deeper collaboration between academics and practitioners to address the research challenges.

Global CRM software spending was $7.8 billion in 2007 and is projected to reach $8.9 billion in 2008. Further, CRM software sales will touch $13.3 billion by 2012. These software and processes have made it possible for companies to gather and analyze large amounts of data on their existing and prospective customers. This article shows how customer-level data can lead to increased customer profitability through (a) selection of the right customers by using the Customer Lifetime Value (CLV) metric, (b) the nurturing of those right customers and, (c) reallocation of resources to the profitable customers. Due to this approach profitable management of individual customers is the basis for growth in firm profitability. A case study will show how IBM used CLV as an indicator of customer profitability and allocated marketing resources based on CLV.

Can a marketer drive the stock price of the firm? Yes, it should be possible. Toward this endeavor, the authors develop a framework to link customer equity (CE) (as determined by the customer lifetime value metric) to market capitalization (MC) (as determined by the stock price of the firm). The authors test the framework in an empirical field experiment with two Fortune 1000 firms in the business-to-business and business-to-consumer contexts, respectively. The findings show that (1) a CE-based framework can reliably predict the MC of the firm and (2) marketing strategies directed at increasing the CE not only increase the stock price of the firm but also beat market expectations. Furthermore, the results indicate that the relationship between CE and MC is moderated by risk factors in the form of volatility and vulnerability of cash flows from customers. By accounting for these factors, the authors improve the association between CE and MC. The findings broaden the scope and role of marketing while reinforcing the importance of the marketer to any organization.

The firm-customer exchange process consists of three key parts: (1) firm-initiated marketing communications, (2) customer buying behavior, and (3) customer product return behavior.To date, the literature in marketing has largely focused on how marketing communications affect customer buying behavior and, to some extent, how past buying behavior affects a firm's decisions to initiate future marketing communications. However, the literature on product returns is sparse, especially in relation to analyzing individual customer product return behavior. Although the magnitude of the value of product returns is known to be high ($100 billion per year), how it affects customer buying behavior is not known because of a lack of data availability and understanding of the role of product returns in the firm-customer exchange process. Given that product returns are considered a hassle for a firm's supply chain management and a drain on overall profitability, it is important to study product return behavior. Thus, the authors empirically demonstrate the role of product returns in the exchange process by determining the exchange process factors that help explain product return behavior and the consequences of product returns on future customer and firm behavior. In addition, the authors demonstrate that product returns are inevitable but by no means evil.

Many firms have experienced greater success through implementing relationship marketing strategies. This is achieved by gaining knowledge about their own customers through database marketing and about the general marketplace through marketing research. Over time, this has led firms to adopt a general framework which we call the conventional path to profitability. This conventional framework suggests that new product innovation leads to acquisition, acquisition combined with a rich experience leads to satisfaction, satisfaction leads to loyalty and customer retention, and loyalty/retention leads to profitability. However, we show that some of the links in the framework are weak based on both academic research and marketplace realities. Consequently, we reverse the logic of the conventional path to profitability. We introduce a new approach that starts the customer relationship management strategy with customer profitability and the notion that different customers should be rewarded and satisfied differently. In addition, we outline a strategy that relationship marketing firms can implement, leading to higher levels of customer profitability and offer directions for future research.

Procter & Gamble (P&G) Asia-Pacific is interested in managing value growth. Only after fully understanding the true effects of the marketing-mix variables can P&G managers make strategic decisions answering questions such as the following: (1) Are the P&G brands in the detergent market inelastic or elastic with respect to price? How has the price elasticity changed over time? Can P&G increase the price of its brands to gain value growth? (2) What are the price, distribution, and sizing combinations needed to achieve the desirable value growth? (3) How can P&G gain market share from its competitors without cannibalizing its own brands? P&G Asia-Pacific approached us to develop a value growth framework to answer the above questions. To generate the answers for the above questions, we develop a three-step weighted random coefficient estimator that captures the heterogeneity across cross sections (different stock-keeping units and states) and the endogeneity of distribution. Based on the parameter estimates, we provide strategic recommendations to P&G for a field test to validate our suggestions. We developed a simulator for P&G managers so that they can generate appropriate marketing-mix strategies for achieving the desired value growth. As a result, P&G gained over $39 million in value growth over a one-year period by implementing the recommendations from our modeling approach.

Loyal customers cost less to serve, pay more than other customers, and attract more customers through word of mouth. If you agree with these three claims, it is time to revisit them and find out why they may not be true. Our research has shown that loyal customers know their value to the company and demand premium service, believe they deserve lower prices, and spread positive word of mouth only if they feel and act loyal. Then why do companies pursue the claims listed above, and what is their logic in doing so? The answer lies in the premise that loyalty equals profitability. With this premise as the base, companies maximize backwardlooking metrics such as RFM (Recency of purchases, Frequency of purchases, and Monetary value of purchases), PCV (Past Customer Value), and SOW (Share of Wallet). Managing customers for loyalty, however, does not amount to managing them for profitability. On the contrary, the loyalty-profitability link must be managed simultaneously. How is this achieved? We propose that measuring and maximizing Customer Lifetime Value (CLV) will help companies address this issue. When using the CLV paradigm, companies can make consistent decisions over time about which customers and prospects to acquire and retain, as well as those not to acquire and retain, and also determine the level of resources to be spent on the various micro-segments. Further, we have found that selecting and nurturing customers based on the CLV approach increases future profitability of the customers.

The purpose of this paper is to summarize key challenges and knowledge gaps in understanding the choices that both firms and customers make in a personalization/customization environment. We start with a summary of personalization and customization in practice, and then draw on research in economics, statistical, and consumer behavior to identify what we know and do not know. We conclude with a summary of key research opportunities.

Through field experiments conducted in two business-to-business firms, the authors evaluate the financial and relational consequences of adopting a customer focus in sales campaigns. In both the experiments, salespeople adopting the customer-focused sales campaign coordinated their sales calls with the objective of selling all the products that a customer was predicted to purchase only at the time the customer was expected to purchase. The authors compare this strategy with the current practice in the organization in which salespeople for each product category independently contacted the customers who were expected to purchase in that category without any guidance on the expected timing of customer purchase. The experiments show that adopting a customer-focused sales campaign can significantly increase firm profits and return on investment. The total incremental profits obtained from implementing the customer-focused sales campaign was more than $1 million. High-revenue customers were the source of improvements in the efficiency of marketing contacts, whereas low-revenue customers were the source of improvements in the effectiveness of the marketing contacts. A customer-focused sales campaign also improved the relationship quality between the customer and the firm. This research provides empirical evidence for theoretical expectations of the benefits provided by a customer-focused sales campaign. Organizations can use the field experiments illustrated in this study as a template for implementing the first step in migrating to a customer-centric organization.

Cross-selling is a popular marketing tool for selling additional products to existing customers to boost revenue and profits. However, our findings show that not all profitable customers necessarily buy more products, and not all customers who buy more products are necessarily profitable. Consequently, firms need to exercise caution while cross-selling to customers. Furthermore, cross-selling decisions should be evaluated vis-a-vis up-selling and notselling decisions as well. The framework proposed here can help managers make optimal selling decisions for long-term profitability.

Customer management activities at firms involve making consistent decisions over time, about: (a) which customers to select for targeting, (b) determining the level of resources to be allocated to the selected customers, and (c) selecting customers to be nurtured to increase future profitability. Measurement of customer profitability and a deep understanding of the link between firm actions and customer profitability are critical for ensuring the success of the above decisions. We present the case study of how IBM used customer lifetime value (CLV) as an indicator of customer profitability and allocated marketing resources based on CLV. CLV was used as a criterion for determining the level of marketing contacts through direct mail, telesales, e-mail, and catalogs for each customer. In a pilot study implemented for about 35,000 customers, this approach led to reallocation of resources for about 14% of the customers as compared to the allocation rules used previously (which were based on past spending history). The CLV-based resource reallocation led to an increase in revenue of about $20 million (a tenfold increase) without any changes in the level of marketing investment. Overall, the successful implementation of the CLV-based approach resulted in increased productivity from marketing investments. We also discuss the organizational and implementation challenges that surrounded the adoption of CLV in this firm.

Marketing managers are being required to demonstrate the profitability of their marketing actions down to the level of their individual customers and on an ongoing basis. At the same time, customers expect firms to increasingly customize their products and services to meet their demands. Firms still need to produce superior products, sell smarter, and understand the markets as a whole, but the ability of firms to orient themselves to interact successfully with their individual customers will differentiate them in the future. Advances in technology have resulted in increasing opportunities for interactions between firms and customers, between customers, and between firms. An interaction orientation reflects a firm's ability to interact with its individual customers and to take advantage of information obtained from them through successive interactions to achieve profitable customer relationships. First, the authors identify the components of interaction orientation: (1) customer concept, (2) interaction response capacity, (3) customer empowerment, and (4) customer value management. Second, they relate interaction orientation to both customer-level and aggregate-level performance measures. Third, they identify the antecedents of interaction orientation. Fourth, they examine the moderating effects of customer-initiated contacts and competitive intensity on the interaction orientation-performance linkage. The results are based on a survey of top marketing managers. The commonly held view that customer-based relational performance is related to customerbased profit performance is not supported. However, both customer-based relational performance and customerbased profit performance affect aggregate business-level performance positively. Interaction orientation is a phenomenon observed in both business-to-business and business-to-consumer firms. The extent of customerinitiated contacts moderates the interaction orientation–performance relationship.

Various customer selection metrics are available for managing loyalty programs, but the traditional ones don’t consider (1) the probability of customers being active in the future, (2) the future marketing costs, and (3) the future contribution margin. Customer lifetime value (CLV) incorporates all these aspects in the calculation. Firms can harness three key strategies to maximize CLV: optimal allocation of resources, pitching the right product to the right customer at the right time, and acquiring and retaining profitable customers.

This study addresses significant challenges that practitioners face when using customer lifetime value (CLV) for customer selection. First, the authors propose a Bayesian decision theory-based customer selection framework that accommodates the uncertainty inherent in predicting customer behavior. They develop a joint model of purchase timing and quantity that is amenable for selecting customers using CLV. Second, the authors compare performance of the proposed customer selection framework (1) with the current customer selection procedure in the collaborating firm and (2) with different customer-level cost allocation rules that are necessary for computing CLV. The study finds that given a budget constraint, customers selected by means of a Bayesian decision theory-based framework (i.e., using the maximized expected CLV of a customer and the corresponding optimal marketing costs as an estimate of future costs) provide the highest profits. The study provides guidelines for implementation and illustrates how the proposed customer selection framework can aid managers in enhancing marketing productivity and estimating return on marketing actions.

The technology for managing customer relationships has gotten fairly sophisticated. Companies can draw on databases that tell them how much each customer has purchased and how often, which they may supplement with detailed demographic profi les. By applying statistical models, they can predict not only when each customer is likely to make a future purchase but also what he or she will buy and through which channel. Managers can use these data to estimate a potential lifetime value for every customer and to determine whether, when, and how to contact each one to maximize the chances of realizing (and even increasing) his or her value.

The authors explore the drivers of multichannel shopping and the impact of multichannel shopping on customer profitability. Through a longitudinal analysis, the authors provide evidence that multichannel shopping is associated with higher customer profitability. Using the social exchange theory, they develop hypotheses regarding the impact of several customer-firm interaction characteristics on customer channel adoption duration. They propose a shared-frailty hazard model for testing the proposed hypotheses. They use the customer database of an apparel manufacturer that sells through three distinct channels for the empirical analysis and find that frequency-related interaction characteristics have the greatest influence on second-channel adoption duration. In contrast, proportion of returns, a purchase-related interaction characteristic, has the greatest influence on third-channel adoption duration. Variation across customers in purchase-related attributes has a greater impact on the duration to adopt the second channel than the duration to adopt the third channel. In contrast, variation across customers in the channel-related attributes has a greater impact on the third-channel adoption duration than on the second-channel adoption duration. The customer-firm interaction characteristics identified in this study and the proposed model framework allow for forward-looking allocation of multichannel marketing resources.

Customer equity, the asset value of customers, can be measured using different aggregate- and disaggregate-level approaches. The authors compare how customer equity is measured and maximized under various approaches. We find that, in the disaggregate-level approach, customer lifetime value is maximized by implementing customer-level strategies such as optimal resource allocation, purchase sequence analysis and balancing acquisition and retention spending. At the aggregate-level, improving the drivers of customer equity maximizes customer equity. A comparison of different aggregate approaches shows that, while an emphasis on retention is a common feature across approaches, conceptual differences in terms of accounting for existing customers and prospects, acquisition, and the projection period exist across the different approaches. The authors propose a hybrid approach, which addresses the issues and challenges in existing approaches and helps firms to measure and manage customer equity.

It is becoming increasingly clear from the literature that there is a need for a metric that can objectively measure future profitability of the customer to the firm. This paper traces the emergence of such a metric–the customer lifetime value (CLV) and discusses the two measures of computing CLV-the aggregate approach and the individual level approach. Subsequently, eight strategies that are available to firms for maximizing CLV are discussed. These strategies assist firms in deciding how to: select the best customer, make loyal customers profitable, optimally allocate the resources, pitch the right product to the right customer at the right time, link acquisition and retention to profitability, prevent customer attrition, encourage multi-channel shopping behavior, and maximize brand value. Each of these strategies was successfully implemented by different firms across various industries, resulting in significant increases in the bottom-line. Further, the challenges in implementing a CLV-based framework in a B-to-C organization are also discussed with an illustration.

This article provides an introduction and overview of this special issue on "Managing Customers for Value." The field of customer management has grown rapidly in recent years, with significant research examining approaches with which firms can manage customers as key assets. Starting with the notion that firms seek to maximize customer lifetime value and customer equity, the authors identify eight key challenges that firms and researchers face in understanding, managing, and implementing successful customer management strategies. The authors then present an organizing framework and use it to offer an overview of the rest of the special issue, outlining how each of the remaining articles in the special issue (each addressing one of the key challenges) fits in the framework. Based on a synthesis of the overall insights from the articles, the authors conclude with a research agenda that highlights potentially fruitful avenues for further investigation.

As modern economies become predominately service-based, companies increasingly derive revenue from the creation and sustenance of long-term relationships with their customers. In such an environment, marketing serves the purpose of maximizing customer lifetime value (CLV) and customer equity, which is the sum of the lifetime values of the company's customers. This article reviews a number of implementable CLV models that are useful for market segmentation and the allocation of marketing resources for acquisition, retention, and cross-selling. The authors review several empirical insights that were obtained from these models and conclude with an agenda of areas that are in need of further research.

Predicting customer behavior is so difficult that companies spend millions inundating-and alienating-customers. Here's a way to crunch the data that makes it possible to offer customers what they want, when they want it.

It is becoming increasingly apparent from the literature that marketers need to consider customer-level information when they generate a marketing strategy for the firm. In this article, the authors develop a customerfocused framework that uses a marketing strategy with an overall objective of maximized financial performance. This strategy is driven by seven customer-level marketing tactics and shows how actual customer data can be used to generate an actionable marketing strategy leading to optimal levels of profitability, customer equity, and shareholder value. In addition, the authors discuss a successful implementation of this strategy for several business-to-business and business-to-consumer firms and offer insights as to how to customize an implementation strategy for any firm, along with presenting potential challenges a firm may encounter during the implementation process. Several suggestions for future research are offered to explore and harness this newly available evidence.

We develop a conceptual framework, which identifies the customer-level characteristics and supplier factors that are associated with purchase behavior across multiple channels. We also propose that multichannel shoppers provide benefits as measured by several customer-based metrics. We conduct an empirical analysis of our propositions using the customer database of a high technology hardware and software manufacturer. We find that customers who buy across multiple product categories, initiate more contacts with the firm, have past experience with the supplier through the online channel, have longer tenure, purchase more frequently, are larger and receive communication from the supplier through multiple communication channels, especially through highly interpersonal channels. We also find evidence for a nonlinear relationship between returns and multichannel shopping, and that there is a positive synergy towards multichannel shopping when customers are contacted through various communication channels. Customers who shop across multiple transaction channels provide higher revenues, higher share of wallet, have higher past customer value, and have a higher likelihood of being active than other customers. We derive several implications for managers who wish to target customers for a multichannel strategy.

In this research, the authors present a modeling framework for balancing resources between customer acquisition efforts and customer retention efforts. The key question that the framework addresses is, “What is the customer profitability maximizing balance?” In addition, they answer questions about how much marketing spending to allocate to customer acquisition and retention and how to distribute those allocations across communication channels.

The authors evaluate the usefulness of customer lifetime value (CLV) as a metric for customer selection and marketing resource allocation by developing a dynamic framework that enables managers to maintain or improve customer relationships proactively through marketing contacts across various channels and to maximize CLV simultaneously. The authors show that marketing contacts across various channels influence CLV nonlinearly. Customers who are selected on the basis of their lifetime value provide higher profits in future periods than do customers selected on the basis of several other customer-based metrics. The analyses suggest that there is potential for improved profits when managers design resource allocation rules that maximize CLV. Managers can use the author’s framework to allocate marketing resources efficiently across customers and channels of communication.

For too long, marketers have not been held accountable for showing how marketing expenditures add to shareholder value. As time has gone by, this lack of accountability has undermined marketers’ credibility, threatened the standing of the marketing function within the firm, and even threatened marketing’s existence as a distinct capability within the firm. This article proposes a broad framework for assessing marketing productivity, cataloging what is already known, and suggesting areas for further research. The authors conclude that it is possible to show how marketing expenditures add to shareholder value. The effective dissemination of new methods of assessing marketing productivity to the business community will be a major step toward raising marketing’s vitality in the firm and, more important, toward raising the performance of the firm itself. The authors also suggest many areas in which further research is essential to making methods of evaluating marketing productivity increasingly valid, reliable, and practical.

ROI is usually calculated in isolation by comparing a company’s ability to generate revenue in any given year in relation to how much it costs to generate that revenue. However, this is not necessarily an optimal method for measuring and then maximizing ROI. Companies need to consider other factors that drive firm performance when considering ideal investment strategies. Additionally, companies must look at the relationship between profitability and ROI and decide how to manage each metric simultaneously.

Each customer varies in his/her lifetime value to a firm. A firm would like to estimate the lifetime value of each customer and use this information in planning differential marketing initiatives targeted at each customer. Customer lifetime value computations require different approaches depending on the business application that a firm is looking at. The authors present two approaches of computing customer lifetime value and offer some best practice applications. The authors also address challenges that firms typically face in implementing the customer lifetime value approach to marketing.

Stable, healthy growth is built on the profitability of customers, not on their raw numbers or their loyalty. New techniques allow companies to focus their marketing dollars precisely where the profits are. Clearly, companies that focus only on customers who are easy to acquire and retain are not allocating their resources as efficiently as they might.

The terms relationship marketing (RM) and loyalty have been extensively promoted in marketing literature. Advocates of RM and loyalty have argued that RM leads to loyalty and loyalty leads to profitability. However, currently available evidence questions these arguments. We propose a term relationship intention. Relationship intention is willingness of a customer to develop a relationship with a firm while buying a product or service attributed to a firm, brand, and a channel. We build a multi-item scale for measuring relationship intention. We propose a framework, wherein we argue that the relationship intention is influenced by the customers’ perceived firm equity, perceived brand equity, and perceived channel equity. We propose the consequences of relationship intention as being low cost to serve, price premium, word-of-mouth promotion, and company advertisement. We also argue that relationship intention moderates the association between lifetime duration and profitability. Finally, we discuss the managerial implications of relationship intention in terms of transaction and RM

The authors develop a framework that incorporates projected profitability of customer in the computation of lifetime duration. Furthermore, the authors identify factors under a manager's control that explain the variation in the profitable lifetime duration. They also compare other frameworks with the traditional methods such as the recency, frequency, and monetary value and illustrate the superiority of the proposed framework. Finally, the authors develop several key implications that can be of value to decision makers in managing customer relationships.

Loyal customers cost less to serve! They pay more than other customers, and attract new customers through word-of-mouth. These loud claims prompted one high-tech service provider to launch a $2 million-per-year customer-loyalty program. Five years later, the company made distributing discoveries: Half of its loyal customers barely generated a profit. And half of its most profitable customers bought high-margin products once-then disappeared. What happened? As recent research reveals, the loyalty-equals-profitability equation is surprisingly weak-and complicated. Not all loyal customers are profitable and not all profitable customers loyal. Managing customer for loyalty doesn't automatically mean managing them for profits. To strengthen the loyalty-profitability link, you manage both-simultaneously.

The last decade has seen rapid advances in telecommunications technology in an increasingly deregulated and competitive market place. Companies operating in these various markets have relied on demand forecasts to justify the considerable investment needed to ensure capacity availability at the right time. These new markets are typically composed of new consumers taking up a product or service for the first time, established users changing their usage patterns, users of competing services shifting to the alternative service and those exiting from this segment of the market altogether. This paper describes various models that have been used to understand market dynamics. Markets discussed include both established and new: mobile, the internet, and PSTN (public switched telephony network). Cross-sectional choice models of the mode of accessing the service are discussed along with models for usage in established markets. These models typically include price (and perceived price) differentials and use standard econometric methods, focusing on price elasticity estimation. Forecasting accuracy has been neglected. New product models may include additional 'drivers' such as aspects of service quality and the attributes of the products themselves. Both choice models of adoption of new products and Bass-type diffusion models have been used in forecasting. Because of the complexity of the 'drivers' of the adoption process, the successful modelling of these new markets has been limited, not least by inadequate data. Simulation models have been proposed to structure the problem more completely and overcome these inadequacies. Both these classes of model have not been effectively validated, researchers having been content just to propose a new approach without thoroughly testing it against alternatives. The only class of telecommunications forecasting problem that has been more thoroughly analysed are those needed to support operations such as call centres. This review paper describes the research that has been carried out on the three problem areas of established products, new products and operations, highlighting areas where further research is needed. The paper also serves as an introduction to the Special Issue on Telecoms Forecasting by describing how the papers contribute to the developing research agenda.

This article develops a framework for assessing how marketing actions affect customers’ lifetime value to the firm. The framework is organized around four critical actions that firms must take to effectively manage the asset value of the customer base: database creation, market segmentation, forecasting customer purchase behavior, and resource allocation. In this framework, customer lifetime value is treated as a dynamic construct, that is, it influences the eventual allocation of marketing resources but is also influence by that allocation. By reviewing customers as assets and systematically managing these assets, a firm can identify the most appropriate marketing actions to acquire, maintain, and enhance customer assets and thereby maximize financial returns. The article discusses in detail how to assess customer lifetime value and manage customers as assets. Then, it identifies key research challenges in studying customer asset management and the managerial challenges associated with implementing effective customer asset management practices.

In an unpredictable global marketplace, managers face the constant challenge of trying to determine where their company is headed next. They must respond quickly and effectively, using only a handful of levers to help build sales and marketing efficiency and market share. This is where decision models can play a key role. By providing headlights for predicting future short and longer term sales response activity, they can help managers make better decisions. By definition, decision models are used to predict the outcomes of marketing-mix decisions. At the most basic level, decision analysis models input sales response variables – such as market-size/opportunity, demographics, and/or firmographics, marketing communications, price, and promotional offers – into a database. Then, by using typical mathematical techniques, they predict how the market will respond to changes in the analyzed set of variables. The result can help measure the effectiveness of marketing efforts and provide decision support for actual decisions. Decision models are most useful when they’re backed with relevant, timely, and accurate data and variables. With a good mix of variables, decision models can deliver useful information to help managers reach decisions. Decision models can be used to answer several “if this-then-that” questions. Just as in descriptive models, they can find a regular pattern to predict how a market will react to changes in marketing-mix variables. With this information, the manager has a better chance of finding the correct solution than by depending on intuition or non-empirical data.

This study attempts to lend empirical evidence to the relevance of the arbitrate pricing theory in providing economic interpretation to stock market factors. A multistage model to explain the stock returns of a representative set of U.S. companies is developed. Monthly returns data for individual securities are obtained and the cross-sectional interdependencies between securities are identified. The returns of the securities are found to be related to at least three, and possibly four, factors. The hypotheses related these factors to broad economic aggregates such as cost and be supply of money, in addition to the market return index. The presence of idiosyncratic industry effect in the market is also demonstrated. The replication of the analysis with another sample from a different time period yields similar results. Marketing implications are drawn based on the findings of this study.

Relationship marketing emphasizes the need for maintaining long-term customer relationships. It is beneficial, in genera, to serve customers over a longer time, especially in a contractual relationship. However, it is not clear whether some of the findings observed in a contractual setting hold good in noncontractual scenarios: relationships between a seller and a buyer that are no governed by a contract or membership. The authors offer four different propositions in this study and subsequently test each one in a noncontractual context. The four propositions relate to whether (1) there exists a strong positive customer-lifetime-probability relationship, (2) profits increase over time, (3) the costs of serving long-life customers are no less, and (4) long-life customer pay higher prices. The authors develop arguments both for and against each of the propositions. The data for this study, obtained from a large catalog retailer, cover a three-year window and are recorded on a daily basis. The empirical findings observed in this study challenge all the expectations derived from the literature. Long-life customers are not necessarily profitable customers. The authors develop plausible explanations for findings that go against the available evidence in the literature and identify three indicators through discriminant analysis that can help manager focus their efforts on more profitable customers. The authors draw several marketing implications and acknowledge the limitations of the study.

Starting with Bass’s (1969) article, diffusion researchers have predominantly focused on modeling category-level sales growth and issues surrounding it. In this article, the authors propose a brand-level diffusion model and demonstrate its managerial use by applying it to the following issue: If a brand enters a category that has not attained its peak sales, how can a practicing manager evaluate its impact on the category and on the incumbent brands? The proposed model helps the manager diagnose whether the late entrant affects the market potential and/or the diffusion speed of the category and of the incumbent brands. The authors test the model using brand-level sales data from the cellular telephone industry in multiple markets.