Who’s got the coupon? Estimating Consumer Preferences and Coupon Usage from Aggregate Information

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, we 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. Different marketing scenarios are analyzed, which differ in terms of their assumptions about consumer choices, coupon availability and coupon redemption. Initially, we consider a situation where 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, we generalize the estimation procedure to handle more realistic situations. These generalizations include: i) the researcher observes the number of redeemed coupons in each period, but not the total number of consumers that received a coupon, ii) consumers use coupons only when its redemption enhances the utility of the chosen alternative and iii) firms may coordinate their coupon distribution policy with other elements of the marketing mix. The proposed methods are illustrated 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, we also relate our empirical results to some of the findings in the literature about the coordination of coupon promotions and pricing and we show how our methodology can be used to answer relevant managerial questions normally reserved for panel data, such as the analysis and comparison of alternative coupon targeting policies.