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Most researchers in marketing have typically relied on disaggregate data (e.g., consumer panels) 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. They illustrate the proposed methods using both simulated data and a real data set for which an extensive set of posterior predictive checks helps validate the aggregate-level estimation. In addition, the authors relate the empirical results to some of the findings in the literature about the coordination of coupon promotions and pricing and show how the methodology can be used to evaluate alternative coupon targeting policies.
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