Rajkumar Venkatesan, V. Kumar, & Nalini Ravishanker
Executive Summary
Retailers are employing various strategies that are designed to encourage customers to shop in multiple channels. These strategies are based on the belief that multichannel customers have a higher annual purchase volume than single-channel customers. Evidence from the cross-sectional analysis of the profitability of multichannel customers conducted to date precludes researchers from inferring whether profitable customers tend to shop in multiple channels in general or whether shopping in multiple channels can lead to higher customer profits. The authors use longitudinal information on customer transactions to explore whether shopping in multiple channels leads to higher customer profits. Predicting the time a customer takes to adopt an additional channel (i.e., channel adoption duration) would help multichannel retailers in various resource allocation decisions by prioritizing channel adoption campaigns. A theoretical basis for the identification of the customer–firm interaction factors is also preferred so that the proposed framework is sufficiently generalizable across various contexts.
Therefore, the authors explore the influence of multichannel shopping on customer profitability. Using the social exchange theory, they also propose a conceptual framework for the impact of customer–firm interaction characteristics on customer channel adoption duration. The interaction characteristics are classified into purchase-related, frequency-related, and channel-related attributes. Finally, they develop a shared frailty hazard model framework for testing their conceptual framework and predicting channel adoption duration.
The authors use the customer database of a large apparel manufacturer that provides three channels for transactions—full-price bricks-and-mortar stores, discount bricks-and-mortar stores, and a Web site.
The longitudinal analysis indicates that managers have the opportunity to increase customer profits by encouraging customers to shop in multiple channels. One rationale for the higher profitability of multichannel customers is that firms can provide several add-on services to customers through their multiple channels (e.g., order online and pick up offline). Customers who shop in multiple channels (regardless of their purchase activity) are exposed to these services the firm provides and therefore are expected to be more satisfied with the firm, which in turn leads to higher profits.
The authors find strong support for using the social exchange theory to understand customer channel adoption duration. Frequency-related interaction characteristics (purchase frequency and frequency of marketing communications) have the highest influence on second-channel adoption duration. In contrast, proportion of returns has the highest influence on third-channel adoption duration, followed by the frequency-related attributes. The analyses urge managers to be aware that there is an optimal frequency of communication for each customer, and overcommunicating to customers can have dysfunctional consequences, such as longer channel adoption durations. Managers can expect better responses by directing a higher level of discounts to single-channel customers than to two-channel customers. Single-channel customers who shop frequently, have moderate basket sizes, and purchase across different categories are likely to adopt a second channel faster. Finally, the authors illustrate how their model framework can be adopted in a phased manner (that includes several pilot field studies) for managing multichannel marketing resources.
Biography
Rajkumar Venkatesan is Associate Professor of Business Administration in the Darden Graduate School of Business Administration at the University of Virginia. Raj has been on the faculty at the University of Connecticut and received his PhD from the University of Houston. Raj’s research focus is on designing marketing strategies that maximize customer profitability, understanding the pricing strategies of online retailers, and developing models for forecasting sales of new products. His research has appeared in several journals, including Journal of Marketing, Journal of Marketing Research, Marketing Science, and Harvard Business Review. Raj’s research has been recognized with awards such as the Don Lehmann Award for the Best Dissertation based an article published in Journal of Marketing and Journal of Marketing Research, the Marketing Science Institute Alden G. Clayton Award for the best marketing dissertation proposal, and the Institute for the Study of Business Markets outstanding dissertation proposal award.
V. Kumar is ING Chair Professor of Marketing and Executive Director of the ING Center for Financial Services in the School of Business at the University of Connecticut. V. Kumar has been recognized with more than 15 teaching and research excellence awards, including the Paul H. Root Award (two times) for the article published in Journal of Marketing that best contributed to the practice of marketing, and the Don Lehmann Award (two times) for the best paper published in the Journal of Marketing/Journal of Marketing Research over a two-year period. Recently, one of his articles in Forecasting won the Outstanding Paper Award from the International Institute of Forecasters. He has published more than 75 articles in many scholarly journals in marketing, including Harvard Business Review, Journal of Marketing, Journal of Marketing Research, Marketing Science, and Operations Research. He has coauthored multiple textbooks on Marketing Research. He has authored a book titled International Marketing Research, which is based on his marketing research experience across the globe, and his book Customer Relationship Management: A Databased Approach was recently released. His current research focuses on multichannel shopping behavior, international diffusion models, customer relationship management, customer lifetime value analysis, sales and market share forecasting, international marketing research and strategy, coupon promotions, and market orientation. He has taught in several universities and organizations worldwide. He was recently listed as one of the top-five-ranked scholars in marketing worldwide. He has consulted for many global Fortune 500 firms. He received his PhD from the University of Texas at Austin.
Nalini Ravishanker is a professor and Undergraduate Program Director in the Department of Statistics at the University of Connecticut, Storrs. She received a PhD in Statistics from New York University in 1987. She is a fellow of the American Statistical Association and serves as associate editor for The American Statistician, Journal of Forecasting, and International Journal of Statistics and Systems. Her research interests include time-series and spatial modeling, times-to-events analysis, inference for stable processes, signal processing, simultaneous inference procedures, and statistical applications in actuarial science, marketing, and transportation engineering.
Journal of Marketing, Vol. 71, No. 2, April 2007
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