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Decomposing the Sales Promotion Bump with Store Data

Faculty of Economics and Business Administration, Tilburg University, P.O. Box 90153, 5000 LE Tilburg, The Netherlands
Faculty of Economics, Department of Marketing and Marketing Research, University of Groningen, P.O. Box 800, 9700 AV, Groningen, The Netherlands
Yale School of Management, Yale University, P.O. Box 208200, New Haven, Connecticut 06520-8200, and Faculty of Economics, University of Groningen, Groningen, The Netherlands
heerde{at}uvt.nl
p.s.h.leeflang{at}eco.rug.nl
dick.wittink{at}yale.edu

Sales promotions generate substantial short-term sales increases. To determine whether the sales promotion bump is truly beneficial from a managerial perspective, we propose a system of store-level regression models that decomposes the sales promotion bump into three parts: cross-brand effects (secondary demand), cross-period effects (primary demand borrowed from other time periods), and category-expansion effects (remaining primary demand). Across four store-level scanner datasets, we find that each of these three parts contribute about one third on average. One extension we propose is the separation of the category-expansion effect into cross-store and market-expansion effects. Another one is to split the cross-item effect (total across all other items) into cannibalization and between-brand effects. We also allow for a flexible decomposition by allowing all effects to depend on the feature/display support condition and on the magnitude of the price discount. The latter dependence is achieved by local polynomial regression. We find that feature-supported price discounts are strongly associated with cross-period effects while display-only supported price discounts have especially strong category-expansion effects. While the role of the category-expansion effect tends to increase with higher price discounts, the roles of cross-brand and cross-period effects both tend to decrease.

Key Words: econometric models; market response models; sales promotion; regression and other statistical techniques
History: Received: December 22, 2000;

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