Rajkumar Venkatesan

A Customer Lifetime Value Framework for Customer Selection and Resource Allocation Strategy

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 authors’ framework to allocate marketing resources efficiently across customers and channels of communication.

Undervalued or Overvalued Customers: Capturing Total Customer Engagement Value

Customers can interact with and create value for firms in a variety of ways. This article proposes that assessing the value of customers based solely upon their transactions with a firm may not be sufficient, and valuing this engagement correctly is crucial in avoiding undervaluation and overvaluation of customers. We propose four components of a customer’s engagement value (CEV) with a firm. The first component is customer lifetime value (the customer’s purchase behavior), the second is customer referral value (as it relates to incentivized referral of new customers), the third is customer influencer value (which includes the customer’s behavior to influence other customers, that is increasing acquisition, retention, and share of wallet through word of mouth of existing customers as well as prospects), and the fourth is customer knowledge value (the value added to the firm by feedback from the customer). CEV provides a comprehensive framework that can ultimately lead to more efficient marketing strategies that enable higher long-term contribution from the customer. Metrics to measure CEV, future research propositions regarding relationships between the four components of CEV are proposed and marketing strategies that can leverage these relationships suggested.

Multichannel Shopping: Causes and Consequences

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.

Practice Prize Report---The Power of CLV: Managing Customer Lifetime Value at IBM

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

Optimal Customer Relationship Management Using Bayesian Decision Theory: An Application for Customer Selection

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.

Empirical Analysis of the Impact of Recommender Systems on Sales

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.

From Customer Lifetime Value to Shareholder Value Theory, Empirical Evidence, and Issues for Future Research

Online retailers are increasingly using information technologies to provide value-added services to customers. Prominent examples of these services are online recommender systems and consumer feedback mechanisms, both of which serve to reduce consumer search costs and uncertainty associated with the purchase of unfamiliar products. The central question we address is how recommender systems affect sales. We take into consideration the interaction among recommendations, sales, and price. We then develop a robust empirical model that incorporates the indirect effect of recommendations on sales through retailer pricing, potential simultaneity between sales and recommendations, and a comprehensive measure of the strength of recommendations. Applying the model to a panel data set collected from two online retailers, we found that the strength of recommendations has a positive effect on sales. Moreover, this effect is moderated by the recency effect, where more recently released recommended items positively affect the cross-selling efforts of sellers. We also show that recommender systems help to reinforce the long-tail phenomenon of electronic commerce, and obscure recommendations positively affect cross-selling. We also found a positive effect of recommendations on prices. These results suggest that recommendations not only improve sales but they also provide added flexibility to retailers to adjust their prices. A comparative analysis reveals that recommendations have a higher effect on sales than does consumer feedback. Our empirical results show that providing value-added services, such as digital word of mouth and recommendations, allows retailers to charge higher prices while at the same time increasing demand by providing more information regarding the quality and match of products.

A genetic algorithms approach to growth phase forecasting of wireless subscribers

The authors propose a chain of effects framework for understanding how customer lifetime value (CLV) affects shareholder value (SHV). In the chain of effects framework, the authors propose that CLV serves as an intermediary in the relationship between firm actions and SHV. They also introduce the notion of the “prescient” value of CLV called CLV-P, which captures the impact on CLV from future modifications to a firm’s business model as well as competitive reactions. Finally, they identify econometric and data-related challenges in establishing the link, which suggest directions for future research.

Measuring and Managing Returns from Retailer-Customized Coupon Campaigns

Abstract 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 seven Western European Countries is used in this study to illustrate the benefits of using the new technique. The parameter estimates obtained from GAs exhibit good consistency comparable to NLS, OLS, and a naive 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.

Forecasting category sales and market share for wireless telephone subscribers: a combined approach

The authors assess how and why retailer-customized coupon campaigns affect customer purchases. The conceptual model proposes effects on trip incidence and revenues through the mere exposure to campaigns (exposure effect) and the redemption of coupons (redemption effect). The authors propose monetary savings of the coupons, regularity of the campaigns, and coupon fit with customer preferences as moderators. Analysis of data from a group of regional grocery chains that were part of a quasi experiment demonstrates that retailer-customized coupon campaigns have a positive exposure and redemption effect on customer purchases. Mere exposure to customized coupon campaigns contributes more than coupon redemption to campaign returns. Consistent with theoretical expectations, customized coupon campaigns are more effective if they provide more discounts, are unexpected, and are positioned as specially selected for and customized to consumer preferences. The substantial exposure effects suggest that managers should look beyond redemption rates and also consider sales lift from nonredeemers when measuring the effectiveness of customized coupon campaigns.