V. Kumar

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.

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.

Expanding the Role of Marketing: From Customer Equity to Market Capitalization.

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 Impact of Customer Relationship Management Implementation on Cost and Profit Efficiencies: Evidence from the U.S. Commercial Banking Industry

The impact of customer relationship management (CRM) implementation on firm performance is an issue of considerable debate. This study examines the impact of CRM implementation on two metrics of firm performance—operational (cost) efficiency and the ability of firms to generate profits (profit efficiency)—using a large sample of U.S. commercial banks. The authors use stochastic frontier analysis to estimate cost and profit efficiencies and employ hierarchical linear modeling to assess the effect of CRM implementation on cost and profit efficiencies. They find that CRM implementation is associated with a decline in cost efficiency but an increase in profit efficiency. A firm-level factor, CRM commitment, reduces the negative effect of CRM implementation on cost efficiency. The authors also find that two adoption-related factors, time of adoption and time since adoption, influence the relationship between CRM implementation and cost and profit efficiencies. Early adopters benefit less from CRM implementation than late adopters. However, time since adoption improves the performance of firms that implement CRM. By demonstrating the different ways CRM implementation influences cost and profit measures, the study provides valuable insights to CRM researchers and managers.

Competitive advantage through engagement

The authors highlight the need for and develop a framework for engagement by reviewing the relevant literature and analyzing popular-press articles. They discuss the definitions of the focal constructs—customer engagement (CE) and employee engagement (EE)—in the engagement framework, capture these constructs’ multidimensionality, and develop and refine items for measuring CE and EE. They validate the proposed framework with data from 120 companies over two time periods, and they develop strategies to help firms raise their levels of CE and EE to improve performance. They also observe that the influence of EE on CE is moderated by employee empowerment, type of firm (business-to-business [B2B] vs. business-to-consumer [B2C]), and nature of industry (manufacturing vs. service); in particular, this effect is stronger for B2B (vs. B2C) firms and service (vs. manufacturing) firms. The authors find that although both CE and EE positively influence firm performance, the effect of CE on firm performance is stronger. Furthermore, the effect of CE and EE on performance is enhanced for B2B (vs. B2C) and for service (vs. manufacturing) firms.

Creating Enduring Customer Value

One of the most important tasks in marketing is to create and communicate value to customers to drive their satisfaction, loyalty, and profitability. In this study, the authors assume that customer value is a dual concept. First, in order to be successful, firms (and the marketing function) have to create perceived value for customers. Toward that end, marketers have to measure customer perceived value and have to provide customer perceptions of value through marketing-mix elements. Second, customers in return give value through multiple forms of engagement (customer lifetime value, in the widest sense) for the organization. Therefore, marketers need to measure and manage this value of the customer(s) to the firm and have to incorporate this aspect into real-time marketing decisions. The authors integrate and synthesize existing findings, show the best practices of implementation, and highlight future research avenues.

Perceived Risk, Product Returns, and Optimal Resource Allocation: Evidence from a Field Experiment

Relatively few retailers include metrics such as product returns in their customer selection and optimal resource allocation algorithms when measuring and maximizing customer value. Even when they do include this metric, increases in product return behavior are usually considered merely an economic cost that must be managed by decreasing the marketing resource allocations toward the customers making the returns. However, recent research has suggested that satisfactory product return experiences can actually benefit firms by lowering the customers perceived risk of current and future purchases. To better understand the role of this perceived risk in the firm–customer exchange process, the authors conduct a large-scale customer selection and optimal resource allocation field experiment with 26,000 customers from an online retailer over six months. They find that the firm is able to increase both its short-and long-term profits when accounting for the perceived risk related to product returns in addition to managing product return costs. Furthermore, the authors find that by including this risk, rather than simply implementing traditional customer lifetime value–based models generically, the firm can target more profitable customers.

Modeling customer opt-in and opt-out in a permission-based marketing context

The rise of new media is helping marketers evolve from digital to interactive marketing, which facilitates a two-way communication between marketers and customers without intruding on their privacy. However, while research has examined the drivers of customers’ opt-in and opt-out decisions, it has investigated neither the timing of the two decisions nor the influence of transactional activity on the length of time a customer stays with an e-mail program. In this study, the authors adopt a multivariate copula model using a pair-copula construction method to jointly model opt-in time (from a customers first purchase to the opt-in decision), opt-out time (from the opt-in decision to the opt-out decision), and average transaction amount. Through such multivariate dependences, this model significantly improves the predictive performance of the opt-out time in comparison with several benchmark models. The study offers several important findings: (1) marketing intensity affects opt-in and opt-out times, (2) customers with certain characteristics are more or less likely to opt in or opt out, and (3) firms can extend customer opt-out time and increase customer spending level by strategically allocating resources.

Unprofitable Cross-Buying: Evidence from Consumer and Business Markets

Conventional wisdom, marketing literature, and cross-selling practices to date are based on the notion that customer cross-buying is positively associated with customer profitability. However, this study finds that when certain customers with persistent adverse behavioral traits (e.g., limited spending, excessive revenue reversals, excessive service requests, promotion purchase behavior) engage in cross-buying, they exhibit a downward spiral of unprofitable relationship, with the losses increasing with higher levels of cross-buy. The authors analyze the customer databases of five firms and find that 10%–35% of the firms customers who cross-buy are unprofitable and account for a significant proportion (39%–88%) of the firms total loss from its customers. Consequently, the authors present a two-stage framework to enable managers to discern customers who are likely to engage in profitable versus unprofitable cross-buying. Overall, the findings refine the basic understanding of the cross-buy phenomenon and motivate managers to rethink their marketing practices and policies, which are typically designed to maximize cross-buy opportunities for every customer.

Defining, Measuring, and Managing Business Reference Value.

It is common for business-to-business firms to use references from client firms when trying to influence prospects to become new customers. In this study, the authors define the concept of business reference value (BRV) as the ability of a clients reference to influence prospects to purchase and the degree to which it does so. They develop a three-step method to compute BRV for a given client using a retrospective reported measure of reference value. Next, they use data from a financial services and a telecommunications firm to identify and empirically test the drivers of BRV. These drivers fall into four categories: (1) length of client relationship, (2) client firm size, (3) reference media format, and (4) reference congruency. Next, the authors empirically show that clients that have the highest BRV are not the same as the clients that have the highest customer lifetime value. They also show that an average client that is high on BRV has significantly different characteristics from the average client that is low on BRV. Finally, they derive implications for managing BRV.