Determinants of Store-Level Price Elasticity
Using weekly scanner data representing 18 product categories, we estimate store-specific price elasticities for a chain of 83 supermarkets. The elasticities prove to be robust to model specification and display remarkable commonality across the diverse set of product categories. We then relate these price sensitivities to a comprehensive set of demographic and competitor variables that describe the trading areas of each of the stores. Despite the inability of previous research to find much of a relationship between consumer characteristics and price sensitivity, 11 demographic and competitive variables explain on average over 60% of the variation in price response. Moreover, we find that the consumer variables are much more influential than competitive variables. We also implement a Bayesian random coefficient pooling model to accommodate the heterogeneity that does exist in the data.
This is the pre-peer-reviewed version of the following article:
Hoch, Stephen J; Kim, Byung-Do; Montgomery, Alan L; Rossi, Peter E. (Feb 1995), "The Implementation Challenge of Pricing Decision Support Systems for Retail Managers", Journal of Marketing Research 32, 1; ABI/INFORM Complete. pg. 17, which has been published in final form at Journal of Marketing Research.