Chain-wide and store-level analysis for cross-category management

When planning and implementing their price-promotions strategy, retail chain managers face the typical dilemma of “thinking globally, but acting locally.” In other words, they must plan their strategy, keeping in mind the global chain-level impact of their promotions, to deliver on the commitments made to manufacturers. At the same time, managers need to make sure that the implementation of such strategy takes into account the fact that each store caters to a different market with different needs and responses to marketing programs. Moreover, the retail chain manager must consider not only how the promotion of a brand affects competing brands and total category sales, but also how it could affect sales in other categories. Our proposed model addresses these two important aspects of chain-wide and store-level cross-category analysis. First, our proposed factor-regression model takes store differences and longitudinal market shifts into account, thereby providing the retail chain manager with unbiased global, chain-level estimates. It also provides stable local estimates of cross-category promotion effects at the store level. Second, while allowing this flexibility, our proposed model is parsimonious enough over existing alternatives, making it particularly useful for chain-wide and store-level cross-category analysis. We apply the proposed model to store-level data from one retail chain, comparing it with several competing approaches, and demonstrate that it provides the best balance between flexibility and parsimony. Most importantly, we show that the proposed model provides useful insights regarding cross-category effects at the chain-level, for individual stores, and their patterns across stores.