Executive Summary
Most researchers in the marketing literature have typically relied on disaggregate data (e.g., consumer panel) to estimate the behavioral and managerial implications of coupon promotions. In this article, the authors propose the use of individual-level Bayesian methods for studying this problem when only aggregate data on consumer choices (market share) and coupon usage (number of distributed coupons and/or number of redeemed coupons) are available. The methodology is based on augmenting the aggregate data with unobserved (simulated) sequences of choices and coupon usage consistent with the aggregate data. The authors analyze various marketing scenarios that differ in terms of their assumptions about consumer choices, coupon availability, and coupon redemption.
Initially, the authors consider a situation in which the researcher observes aggregate market shares, marketing activity, the number of distributed coupons redeemed, and the number of coupon redemptions for each brand in each period. Then, they generalize the estimation procedure to assess more realistic situations. These generalizations include (1) the researcher observes the number of redeemed coupons in each period but not the total number of consumers that received a coupon, (2) consumers use coupons only when redemption enhances the utility of the chosen alternative, and (3) firms may coordinate their coupon distribution policy with other elements of the marketing mix.
The authors illustrate the proposed methods using both simulated data and a real data set for which an extensive set of posterior predictive checks are used to validate the aggregate-level estimation. In addition, they also relate their empirical results to some of the findings in the literature about the coordination of coupon promotions and pricing and show how their methodology can be used to answer relevant managerial questions typically reserved for panel data, such as the analysis and comparison of alternative coupon targeting policies.
Biography
Andrés Musalem is Assistant Professor of Marketing in the Fuqua School of Business of Duke University. He earned a BS in Industrial Engineering in 1999 and an MBA in 2001 from the University of Chile, an AM in Statistics in 2004, and a PhD in Marketing in 2006 from the Wharton School at the University of Pennsylvania. He joined the Fuqua School of Business faculty in 2006. His research interests are focused on the development of Bayesian and econometrics methods for the estimation of structural models of consumer and firm behavior from aggregate or limited data. Some areas of application of his research include retailing, sales promotion, category management, pricing, customer relationship management, and consumer response to out-of-stocks.
Eric T. Bradlow is K.P. Chao Professor, Professor of Marketing, Statistics, and Education, and Academic Director of the Wharton Small Business Development Center at the University of Pennsylvania. He earned a BS in Economics from the Wharton School in 1988 and an AM in Mathematical Statistics and a PhD in Mathematical Statistics from Harvard University in 1990 and 1994, respectively. He joined the Wharton faculty in 1996. Eric is editor-in-chief of Marketing Science, a Fellow of the American Statistical Association, a Senior Fellow of the Leonard Davis Institute for Health Economics, and a statistical Fellow of Bell Labs; he is past chair of the American Statistical Association Section on Statistics in Marketing and was previously named DuPont Corporation’s best young researcher. His research interests include Bayesian modeling; statistical computing; and developing new methodology for unique data structures with application to business problems, education, and psychometrics and health outcomes.
Jagmohan S. Raju is Joseph J. Aresty Professor, Professor of Marketing, and Executive Director Wharton Co-Sponsorship of Indian School of Business at the University of Pennsylvania. Professor Raju received his PhD, MA and MS from Stanford University, his MBA from the Indian Institute of Management, and his BTech from the Indian Institute of Technology, Delhi. He serves as the marketing editor of Management Science, is past president of the INFORMS Society for Marketing Science, and was secretary treasurer of the INFORMS College on Marketing. His research interests include pricing strategies, coupon programs, managing private labels, and sales force compensation.
J Marketing Research, Volume 45, Number 6, December 2008
View Table of Contents.