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.

Survival in today's economic climate and competitive retail environment requires more than just low prices and innovative products. To compete effectively, businesses must focus on the customer's shopping experience. To manage a customer's experience, retailers should understand what "customer experience" actually means. Customer experience includes every point of contact at which the customer interacts with the business, product, or service. Customer experience management represents a business strategy designed to manage the customer experience. It represents a strategy that results in a win–win value exchange between the retailer and its customers. This paper focuses on the role of macro factors in the retail environment and how they can shape customer experiences and behaviors. Several ways (e.g., promotion, price, merchandise, supply chain and location) to deliver a superior customer experience are identified which should result in higher customer satisfaction, more frequent shopping visits, larger wallet shares, and higher profits.

There is an ever-present need for managers to justify marketing expenditures to the firm. This can only be done when we can establish a direct link between marketing metrics and future customer value and firm performance. In this article, we assess the marketing literature with regard to marketing metrics. Subsequently, we develop a framework that identifies key metrics that firms should focus on that can give a firm a better picture of how they got to where they are now and insights towards how they can continue to grow into the future.We then identify several organizational challenges that need to be addressed in order for firms to build the capabilities of collecting the right data, measuring the right metrics, and linking those metrics to customer value and firm performance. Finally, we offer guidelines for future research with regard to marketing metrics to help firms establish successful marketing strategies, measure marketing effectiveness, and justify marketing expenditures to top management.

The phenomenon of cross-buying by consumers enables retailers to cross-sell their products and increase revenue contribution from existing customers. The effectiveness of cross-selling can be greatly improved by identifying the drivers of cross-buy and using them to target the right customers. In this study we identify exchange characteristics such as average interpurchase time, ratio of product returns, and focused buying, and product characteristics such as category of first purchase, as important drivers of cross-buy. The impact of marketing efforts of the firm on cross-buy is also identified. The results of the study have important implications for academicians in understanding what drives cross-buying as well as practitioners to help design more effective cross-selling strategies.

This study examines how customer lifetime value (CLV) can be computed at individual customer level in a retail setting to maximize profitability. The study finds that maximum positive impact to CLV occurs when the customer cross-purchases, shows multi-channel shopping behavior, stays longer with the firm, buys specific product categories and purchases more frequently with the firm. Interestingly, the CLV follows an inverted U relationship with increase in return of prior purchases. Other interesting findings include a surprisingly low correlation between customer loyalty and future profitability and low correlation between stores' historic revenues and future profitability. Several implications are suggested for retailers to manage and maximize customer profitability as well as store profitability.

Coupons account for over two-thirds of all consumer promotional efforts initiated by the manufacturers of consumer goods. In this study, the impact of coupons on brand sales is investigated and how that impact decays over the life of the coupon is demonstrated. Specifically, we present an econometric model that can capture coupon effects in terms of equivalent price reduction, account for coupon effects over time, allow inference of coupon effects when retailers decide to double or triple the coupon value, and provide both self-coupon and cross-coupon elasticities at different levels of aggregation. A widely used sales response model is adapted, and an analytical model is proposed to estimate both the self-coupon and cross-coupon (face value) elasticities of sales at the store level. From the store-level elasticity estimates for a given week, the authors analytically derive the coupon elasticities for the chain level by aggregating across stores, and over the life of the coupon by aggregating over time. The proposed sales response model is estimated with the data obtained from three markets for various product categories, and the coupon elasticities are computed. The proposed framework allows one to demonstrate the hypothetical equivalence of a shelf-price reduction for a given coupon face value in each week. Also, the effect of doubling the face value of a coupon results in more than a proportionate increase in elasticity. The authors find that both self and cross-coupon elasticities are much smaller in magnitude than the average self and cross-price elasticity measures reported in the literature.

The concept of customer loyalty is conspicuous by its ubiquity. Therefore, there is no surprise that is it one of the most widely studies areas by researchers and one of the most widely implemented marketing initiatives by practitioners. This article draws upon past research to review important findings related to customer behavior and attitude in the context of customer loyalty. Further, research related to linking loyalty to profitability and forward looking metric such as the customer lifetime value is reviewed to propose a conceptual framework for building and sustaining loyalty and profitability simultaneously at individual customer level. A two-tiered rewards structure is presented as a means for marketers to operationalize the framework. The conceptual framework hopes to serve as a platform to understand the evolving dominant logic of loyalty programs for building and sustaining loyalty in the twenty first century as well as induce further research in that direction.

This article shows that in a model of exogenously given sales expansion target with a dominant manufacturer, the relative profitability of coupon and price-reduction schemes depends on the values of coupon redemption rates and the equivalence ratio of price reduction to coupon face value. It is also shown that retailer’s margin has an uncertain impact on the compensation constraint on the manufacturer’s choice between the two schemes. However, higher retailer’s margin increases the likelihood that the chosen sales expansion scheme (SES) is the one that generates higher consumer welfare. A sales response model is used to estimate the equivalence ratio and critical coupon redemption rate below which the manufacturer will prefer a coupon promotion. A sensitivity analysis of the manufacturer’s decision reveals that changes in the magnitude of the retailer’s margin have little impact on the manufacturer’s choice between alternative SESs.

How many times have you ignored a banner ad or closed a pop-up window with a vengeance? For most Web users, the answer is "too many." The genesis of this problem lies in a poor marketing strategy that marketers adopt when advertising online. This article describes a framework for developing an effective online advertising strategy, followed by information on how to track the performance of online advertisements.

Retail stores are segmented using socio-economic characteristics of the trade area and it is shown that the effects of store environment on store performance vary across segments. Store performance is measured by a market-based measure – sales and a productivity-based measure – sales per square feet. The internal store environment includes the number of checkout counters per square foot of selling area, the number of non-grocery products sold (extent of scrambled merchandising), whether the store at least doubles manufacturers’ coupons, whether there is a banking facility and whether the store is open for twenty-four hours. The external store environment includes the type of neighborhood it is located in. A methodology for predicting store performance (for existing and new stores) based on the type of environment and store location using aggregate secondary data is demonstrated. The proposed models are estimated and validated using Market Metrics geodemographic data for 646 grocery stores provided by A.C. Nielsen. It is shown how the findings of this retail environment study can be used to offer guidelines to retailers for attaining desired levels of sales and sales per square feet using readily available data.

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.

The impact of internal reference price discrepancy (between actual price and internal reference price), and external reference price discrepancy (between actual price and external reference price) on brand choice is studied in two different contexts: whether the consumer faces a stockout condition at the time of purchase or not, and whether the consumer is a deal-prone consumer or not. Internal reference price based on the past prices paid for the brand by the consumer, and external reference price is dependent upon the prices of all brands in the category at the point of purchase. Hypotheses are tested by estimating brand choice models using IRI scanner panel data for two product categories-saltine crackers and baking chips. Results indicate that, in general, the impact of the external reference price discrepancy on brand choice is greater than that of the internal price discrepancy. However, this varies depending upon the contextual conditions. For consumers facing a stockout condition at the same of purchase, and for deal-prone consumers, the impact of the external reference price discrepancy on brand choice is greater than that of the internal reference price discrepancy. However, for consumers not facing a stockout condition, and for non-deal-prone consumers, there is no difference in the impact of the internal and external reference price discrepancy on brand choice. Also, the impact of both the external and internal reference price discrepancy on brand choice is greater for consumers not facing a stockout condition at the time of purchase relative to facing a stockout condition, and for deal-prone and non-deal-prone consumers. Implication for pricing strategies are discusses for retailers, and limitations and extensions of this study are also provided.

The authors focus on their 1990 model (B&K; see Balasubramanian and Kumar 1990), which indicates that market share, market growth, and their interaction are important predictors of the ratio of advertising and promotional costs to sales (A&P/S). In sharp contrast, Ailawadi, Farris, and Parry's (1994; AFP) subsequent replication attempt asserts that market share and market growth are not good predictors of A&P/S. The authors' research documents serious problems in AFP's study, including erroneous model estimates caused by incorrect execution of the SAS TSCSREG procedure, pooling data for analysis in ways that cannot be justified statistically, data preparation errors, and inappropriate operationalizations of market share and market growth. With regard to analyses of Profit Impact of Market Strategy and brand-level data reported by AFP, the authors again find that AFP have analyzed data in inappropriate ways, given the conceptual framework of the B&K model. Because these problems provide compelling evidence to disregard AFP's results, the authors conclude that the criticisms advanced by AFP against the B&K model are not valid.

This research hypothesizes and tests the following propositions: (1) competitive performance of a brand varies across retails chains, driven by differences in retailers' promotion decisions for the brand, and (2) type, timing, frequency, and magnitude of retail promotions affects brands' competitive performance. Further, we illustrate the presence of asymmetry in brand competition and test the expectation that differences in market shares and price levels are major factors contributing to competitive asymmetry. A model of brand competition is formulated at the retail level. The results indicate that the competitive performances of brands are not uniform across pairs of brands and the retails chains in a market.

The variation in the impact of a brand's retail price promotion on its own sales (promotional price elasticity) and the sales of its competitors (promotional cross-price elasticities) during the period of the price promotion is studied. Brand characteristics and retailer policies significantly explain the variation in promotional price elsaticities and cross-price elasticities across brands. Results are used to offer guidelines to retail managers for planning promotions in terms of what brand to promote, and how and when to promote them under three different retailer objectives: (1) to liquidate inventories; (2) to maximize product category sales; and (3) to maximize product category profits. These guidelines can be used by retailers in taking actions that will result in desirable levels of responses to price promotions of a brand-promotional price elasticity and promotional cross-price elasticities.

This study focuses on the short-term sales response to price promotions in retail grocery stores and attempts to explain its variation using frequency of price promotions and the consecutive scheduling of price promotions. Retail managers' expectations and tenets from behavioral theories provide the basis for the hypotheses that the frequency of price promotions and consecutive scheduling of price promotions affect short-term response to price promotions. The hypotheses are tested on three frequently purchased product categories, using store-level data from retail chains in three major markets. The analysis is validated with additional data on the same product categories and markets. A variety of managerial implications are drawn from the results and suggestions for future research are offered.

The research provides an assessment of the relevant literature on market share models and identifies the need for further research. Additional insights are generated in this study by using point-of -sale scanners for evaluating the forecasting performance of market share models under various conditions. Specifically, weekly store-level scanner data for four frequently purchased product categories-saltine crackers, baking chips, diapers and toilet tissue, and simulated data are used in this study. Consistent with theoretical expectations, the attraction models estimated by GLS produce the best forecasts even (1) at the brand level, and (2) when competitors' actions are predicted. However, the superiority of the attraction models is diminished when systematic errors are introduced to the values of the competitors' predictor variables in the holdout sample. In fact, naïve models outperform all types of econometric models when large errors are present in the competitors' predictor values, and among the econometric models, linear models produce better forecasts than attraction models. The need for estimating the models with GLS (as opposed to OLS) with the use of cross-sectional time-series data is also illustrated. Finally, guidelines are developed for practitioners and researchers on the usefulness of market share models for forecasting.

The authors decompose repeat buying for frequently purchased nondurables. There results are very similar for two categories each over a different city and time period. A factor analysis of 18 measures of repeat buying obtains four principal factors that explain 79-85% of the variance: Preference, Inertia, Coupon Proneness and Impulse Buying. A cluster analysis of factors on these dimensions yields four segments, with distinct behavioral characteristics.

Merger and Acquisition (M&A) activity in retailing is widespread. Nevertheless, little scholarly research has addressed this subject from the perspective of antecedent conditions present in retail M&A activity. The present study examines a variety of finance-, marketing-, and corporate-related variables that have been proposed as likely antecedent conditions for M&A activity. The results indicate that preconditions for M&A activity can be identified and the probability of becoming a bidder and target retailer can be determined on the bias of these variables. Implications of becoming a target firm are highlighted, as are characteristics of bidder and nonmerging firms.

Prior research assessing the predictive validity of alternate market share models produced conflicting results and often found that econometric models performed worse than naïve extrapolations. However, contributors to IJF's recent issue on market share models suggested that such models are often misspecified, in part because they exclude promotional variables and are estimated on aggregate data. Thus, we used weekly scanner data to assess full, reduced, and naïve form for linear, multiplicative, and attraction specifications across different levels of parameterization. Consistent with specification-based arguments (1) econometric models were superior to naïve models, (2) GLS estimates of attraction models were superior when models were fully specified, (3) OLS estimates of linear models were superior when models omitted important variables, and (4) attraction models predicted best overall. Moreover, in general, unconstrained models yielded superior forecasts relative to constrained models because brand-specific parameters were heterogeneous for the product category tested.

The authors extend prior research on explaining variations in the intensity of marketing communication (i.e., advertising and promotional expenditures/sales) with a simple model estimated on cross-sectional time-series data. Analysis on firm-level data pooled at the industry level, performed for each of several industries belonging to different markets, attest to the model’s explanatory power. Model validation analyses show an impressive similarity of model parameters and goodness of fit across split-half data samples for each industry analyzed. Further, for all industries belonging to a given market, the results indicate remarkable consistency in the signs of the associations between marketing communication intensity and (1) market share, (2) market growth, and (3) their interaction. However, this consistency does not hold across all markets. Plausible explanations for these results are advanced.

Advertising and promotional expenditures in the United States grew from $47.1 billion in 1975 to $ 159.3 billion in 1985 (Bowman, 1986), a 338 percent increase over a decade. Of this increase, promotional expenditures have grown rapidly, exceeding advertising expenditures by over $30 billion in 1987. As increasing dollars are spent on advertising and promotion, advertisers are interested in knowing whether consumers' purchase decisions are influenced by these variables, and more importantly, to what extent these variables differentially affect the consumers. In other words, do different people in the market react differently to advertising and/or promotion? If so, are there groups of people who exhibit similar behavior patterns? This is an example of a market segmentation problem which is important to advertisers. This article proposes a visual approach to searching for market segments.

Using store-level scanner data, the authors investigate the effect of retail store price promotion, featuring, and displays on sales of brands of disposable diapers within a city. A hierarchical, cross-sectional, and time-series modeling procedure is used to identify the competitive structure among retail stores within a test market city. Models are developed for pooled store pairs to investigate the effect of promotion on store substitution. The specification of these models is based on findings from within-store brand substitution, followed by featuring and displays. Similarly, these activities produced store substitution in certain instances. However which specific promotional variables are significant and the magnitude of the effect are a function of the geographic proximity of the stores.

In this paper, we provide theoretical arguments and empirical evidence for how Genetic Algorithms (GA) can be used for efficient estimation of macro-level diffusion models. Using simulations we find that GA and Sequential Search-Based-Nonlinear Least Squares (SSB-NLS) provide comparable parameter estimates when the data including peak sales are being used, for a range of error variances, and true parameter values commonly encountered in the literature. From empirical analyses we find that the forecasting performance of the GA estimates is better than that of SSB-NLS, Augmented Filter, Hierarchical Bayes, and Kalman Filter when only pre-peak sales data is available for estimation. When sales data until the peak time period are available for estimation, SSB-NLS is able to obtain parameter estimates when the starting values provided are the estimates from using GA. The estimates from GA are not biased and do not change in a systematic fashion when postpeak sales data are used, whereas the estimates from SSB-NLS are biased and change in a systematic fashion. Summarizing, we find that GA may be better suited for diffusion model estimation under the three conditions where SSB-NLS has been found to have problems.

The ability to forecast market share remains a challenge for many managers especially in dynamic markets, such as the telecommunications sector. In order to accommodate the unique dynamic characteristics of the telecommunications market, we use a multi-component model, called MSHARE. Our method involves a two-phase process. The first phase consists of three components: a projection method, a ring down survey methodology and a purchase intentions survey. The predictions from these components are combined to forecast category sales for the wireless subscribers market. In the second phase, market shares for the various brands are generated using the forecast of the number of subscribers tat are obtained in Phase I and the share predictions from the ring down methodology. The proposed methodology produces the minimum Relative Absolute Error for each market as compared to the forecasts from each individual component in the first phase. The value of the proposed model is illustrated by its application to a real world scenario. The managerial implications of the proposed model are also discussed.

In order to effectively make forecasts in the telecommunications sector during the growth phase of a new product life cycle, we evaluate performance of an evolutionary technique: genetic algorithms (Gas), used in conjunction with a diffusion model of adoption such as the Bass model. During the growth phase, managers want to predict (1) future sales per period, (2) the magnitude of sales during peak, and (3) when the industry would reach maturity. At present, reliable estimation of parameters of diffusion models is possible, when sales data includes the peak sales also. Cellular phone adoption data from estimates obtained from Gas exhibit good consistency comparable to NLS, OLS, and a naïve time series model when the entire sales history is considered. When censored datasets (data points available until the inflection point) are used, the proposed technique provides better predictions of future sales; peak sales time period, and peak sales magnitude as compared to currently available estimation techniques.

Purchase intentions are routinely used to forecast sales of existing products and services. While past studies have shown that intentions are predictive of sales, they have only examined the absolute accuracy of intentions, not their accuracy relative to other forecasting methods. For example, no research has been bale to demonstrate that intentions-based forecasts can improve upon a simple extrapolation of past sales trends. We examined the relative accuracy of four methods that forecast sales from intentions. We tested these methods using four data sets involving different products and time horizons; one of French automobile sales, two of US automobile sales, and one of US wireless services. For all four products and time horizons, each of the four intentions-based forecasting methods was more accurate than an extrapolation of past sales. Combinations of these forecasting methods using equal weights lead to even greater accuracy, with error rates about one-third lower than extrapolations of past sales. Thus, it appears that purchase intentions can provide better forecasts than a study, the consistence of the results in our study suggest that intentions are a valuable input to sales forecasts.

This study tests and contrasts the ability of multidimensional scaling (MDS) and nonlinear mapping (NLM) in recovering complex data structures in attribute space, and aiding researchers and practitioners in making neighborhood interpretations. The relative merits of both MDS and NLM for product positioning are explored and discussed. A formal comparison of the performance of NLM versus MDS is presented using both simulated and actual data. The results of this study provid3e direction as to the conditions under which a nonlinear mapping algorithm is preferable over MDS.

This paper describes a decision support system developed to prioritize oil and gas exploration activities. During each budgeting cycle, the exploration division of a major oil company must review a number of potential oil and gas plays, and assign personnel to the most promising ones. These personnel are teams of geologists and geophysicists who are responsible for the exploration activities on the site. The number of interesting plays always exceeded the available exploration budget and manpower, so management was concerned about the development of a consistent ranking system of oil and gas plays that could be used to guide the budgeting and manpower decision. The development of the ranking system was based on familiar concepts from decision theory, specifically, multiattribute models and the value of information. Linear approximations to more complex mathematical functions were developed to simplify the model. However, this system was designed to be used on a routine basis by personnel unfamiliar with these technical issues, so they were masked from the user. The successful implementation of the proposed decision support system is described.

This study attempts to build a decision model to evaluate job alternatives for doctorial students. The decision model is based on the Von Neumann-Morgenstern utility function. In this case of uncertainty, the utility function can order only preferences. In the case of certainty, the measurable value function can order both preferences as well as preference differences. The utility function is tested for the presence of the strength of preference concept using the theorem of "Substitution of Equal Exchange." A methodology has also been proposed to obtain responses to the utility function and then transforming that function to a measurable value function. The importance of the concept of ordering preference differences and incorporating risk measure is revealed by the superiority of the measurable value function over the utility function in terms of predictive validity.

The literature on cross national diffusion models is gaining increased importance today due to the needs of present day managers. New product sales growth in a given nation or society is affected by many factors (Rogers 1995), and of these, sociocontagion (word of mouth) has been found to be the most important factor that characterizes the diffusion process (Bass 1969, Moore 1995). Hence, it is interesting and perhaps challenging to analyze what would happen if a new product diffuses in parallel in two neighboring but culturally different countries. Not only will we expect the diffusion process in the two countries to be different, but we will also expect some interaction among them, especially if the two societies mingle with each other. There are two streams of research in cross national diffusion. The first type focuses on exploring the differences between diffusion processes in two countries and finding out whether those differences can be attributed to social and cultural difference between the countries involved. Examples of this type of research are found in Takada and Jain (1991), Gatigon et al. (1989), Helsen et al. (1993), and Kumar et al. (1998). These studies did find some relationship between the cultural differences of the countries studies and the differences in the diffusion process. The second stream of research focuses on modeling explicitly the interaction between the diffusion processes in two countries. The interaction is typically captured through lead-lag effect (Eliashberg and Helsen 1996, Kalish et al. 1995), where the sales process in the lead country (i.e., the country where the product was first introduced) is modeled to affect the sales process in the lag country (i.e., the country where the product was introduced a few years later. Another method to study the interaction among the diffusion processes in two countries was suggested by Putsis et al. (1997), who used a "mixing model" to empirically explore the existence of such interactions. These studies basically observed that, when a new product is introduced early in one country and with a time lag in subsequent countries, the consumers in the lag countries learn about the product from the lead country adopters, resulting in a faster diffusion rate in the lag countries. Ganesh and Kumar (1996) formulized this effect as the learning effect to be influenced by country-specific factors (cultural similarity, economic similarity, and time lag elapsed between the lead and the lag countries) and product specific factors (continuous vs. discontinuous innovation and the presence or absence of a standardized technology). A careful analysis of the extant literature on the second stream of research would reveal that neither the learning effect model nor the mixing model can be modified to accommodate the other model. Our contribution to the literature exactly addresses this point. In this paper, an alternative framework is proposed that has two unique features. First the framework is flexible enough to not only account for the lead country affecting the lag countries and vice versa, but also to accommodate the simultaneous interaction among countries in explaining the diffusion processes in the countries concerned. Using multiple product categories and a variety of new product introduction situations, we empirically demonstrate the flexibility and efficiency of our proposed framework. We found strong evidence of all types of interaction, namely, lead lag, lag lead, and simultaneous, which evidence suggests that one cannot afford to omit any of the interactions. The second unique feature of our paper is the estimation procedure that we used. Because statistical estimation of a dynamic process that includes lead-lag, lag-lead, and simultaneous types of causality within a single framework is not straightforward, we suggest and iterative estimation procedure for the estimation. This new procedure not only proved to be flexible in accommodating difference types of interaction, but also converged rather quickly in all of the cases that we empirically tested. Noting that the statistical properties of these estimators are not generally available, we carried out a simulation exercise that clearly revealed the efficiency of the proposed estimation procedure. After analyzing the interaction, we went further and showed that the magnitude of the cross-national influences is affected by certain country-specific and product specific factors. The flexibility of the proposed method over the existing methods is demonstrated through obtaining superior forecasts with the proposed method. Several interesting insights for managers concerned with formulating international marketing strategies are offered.

The primary purpose of this article is to shed more light on the more complex challenges of global market segmentation (GMS). To provide a complete understanding, we discuss some of the well-known issues in segmenting foreign markets and move on to state the various properties of global target markets. We conclude that companies can implement GMS most effectively by first gaining a full understanding of both local and global concerns.

To compete effectively in the global marketplace, marketing managers require insight into how a product gets adopted in different countries. For example, can international marketers identify specific cultural traits that may help them to forecast how quickly a new product will be adopted in a particular country or in a group of somehow related countries? Similarly, can they identify factors that suggest why the adoption process differs among countries? Although these diffusion-related questions address critical issues for international marketing managers, only a few studies have explored cross-national diffusion. To help fill this gap, V. Kumar, Jaishankar Ganesh, and Raj Echambadi present the results of a study that replicates and extends the findings of three previously published studies of cross-national diffusion. Their research aims to replicate four findings from the previous studies: the role of country-specific effects in explaining differences in diffusion parameters, the presence of a lead-lag effect, the use of cultural variables to explain systematically the diffusion patterns across countries, and the merit of country segmentation schemes based on diffusion parameters. They extend the previous research by integrating cross-sectional and time lag variables into a single framework, and they demonstrate how managers can apply this integrated framework for forecasting the diffusion of new products. They replicate the findings from the previous studies by using annual sales data for five product categories (VCRs, microwave ovens, cellular phones, home computers, and CD players) in the following countries: Austria, Belgium, Denmark, Finland, France, Germany, Italy, The Netherlands, Norway, Portugal, Spain, Sweden, Switzerland, and the UK. The product categories and time periods covered differ from the ones in the previous studies; some overlap exists among the countries in this study and the ones in the previous studies. The findings in this study suggest that country-specific characteristics (for example, cosmopolitanism, mobility, percentage of women in the labor force) are useful for identifying the differences in diffusion patterns across countries and innovations. This study also suggests that the lead-lag effect helps to explain differences in diffusion across countries. Factors that this study identifies as possibly influencing the clustering of countries with similar diffusion patterns include timing of entry, geographical proximity, and cultural or economic similarity.

The extant literature on the choice of entry modes into international markets has assumed that the mode of entry is a single stage rational analytic decision by the manager. A contingency framework for the mode of entry decision which accommodates alternative decision strategies is presented. As a part of the contingency framework, a strategy-rational analytic-used by past researchers and an alternative strategy of decision making-cybernetic-are discussed. Each decision strategy has its own merits and disadvantages. An outcome of the cybernetic strategy is a hierarchical model for the mode of entry decision. Implications of the alternative strategies for the mode of entry decision are also provided.

Recent studies on cross-national diffusion have observed that when a new product innovation is introduced early in one country (the lead country) and with a time subsequent countries (the lag countries); the consumers are the lag countries learn about the product from the lead country adopters, resulting in a faster diffusion rate in lag countries. This study attempts to examine the relationship between lead and lag countries and to systematically capture the learning effect that takes place between the two social systems. In particular, this research examines the diffusion of retail point-of-sale scanners in countries that belong to the European Union, the Unites States, and Japan. It offers interesting insights for formulating efficient international marketing strategies.

This study develops a logit choice model of factors that can contribute to developing a competitive advantage for exporting by a small business. The data used in the study are from 142 small businesses in the gulf coast region of the United States. Among various factors, international training, level of preparation to enter foreign markets and as active promotional strategy are found to be significant predictors of obtaining a competitive advantage for exporting. Within the active promotional strategy, additional analysis revealed the use of letter campaigns and international salesman to be the most effective strategies for consistent exporting by small businesses. The predictive validity, evaluated by the U-method, of the proposed framework was high and managerial inferences are drawn for small businesses to obtain a competitive advantage for exporting to foreign markets.

The authors address the issue of portfolio management in the context of an international marketing problem. Screening, identification, and selection characterize the three stages in the evaluation of potential foreign markets. The proposed methodology is used for identifying potential foreign markets. This article contributes to the existing literature in four ways: (a) it integrates the past research on international market evaluation into a comprehensive framework, (b) it fills a gap in this area, by offering a flexible, cost efficient methodology that is easy to comprehend and adopt, (c) it simultaneously considers the objectives of the firm, its resource constraints, and expansion strategies while identifying potential foreign markets, and (d) it introduces a multicriteria methodology for solving problems of multiobjective decision models, to the international marketing managers.

Understanding the effectiveness of various marketing efforts in the international marketplace is a growing need for United States businesses. Unfortunately, many businesses, especially small ones are not consistently successful in exporting their products. Although many factors can contribute to exporting on a consistent basis, this study analyzes the various international marketing methods utilized by small businesses and determines which methods, especially direct marketing, can significantly contribute to consistent exporting of products. The data used in the study are provided by small businesses in the United States., and a logit choice model is formulated. Among various marketing methods, direct marketing is found to be a significant predictor of exporting on a consistent basis, in addition to such factors as prior experience and extent of preparation to enter foreign markets. Further analysis revealed that among the direct marketing approaches, both the letter and salesman campaigns were found to be more effective than the phone campaign. Therefore, direct marketing efforts may be targeted toward more efficient approaches, such as developing contracts through mail and salesmen.

The phenomenon of cross-buying by consumers enables retailers to cross-sell their products and increase revenue contribution from existing customers. The effectiveness of cross-selling can be greatly improved by identifying the drivers of cross-buy and using them to target the right customers. In this study we identify exchange characteristics such as average interpurchase time, ratio of product returns, and focused buying, and product characteristics such as category of first purchase, as important drivers of cross-buy. The impact of marketing efforts of the firm on cross-buy is also identified. The results of the study have important implications for academicians in understanding what drives cross-buying as well as practitioners to help design more effective cross-selling strategies.