Using Multimarket Data to Predict Brand Performance in Markets for Which No or Poor Data Exist

Abstract

The authors show how multimarket data can be used to make predictions about brand performance in markets for which no or poor data exist. To obtain these predictions, the authors propose a model for market similarity that incorporates the structure of the U.S. retailing industry and the geographic location of markets. The model makes use of the idea that if two markets have the same retailers or are located close to each other, then branded goods in these markets should have similar sales performance (other factors being held constant). In holdout samples, the proposed spatial prediction method improves greatly on naive predictors such as global-market averages, nearest neighbor predictors, or local averages. In addition, the authors show that the spatial model gives more plausible estimates of price elasticities. It does so for two reasons. First, the spatial model helps solve an omitted variables problem by allowing for unobserved factors with a cross-market structure. An example of such unobserved factors is the shelf-space allocations made at the retail-chain level. Second, the model deals with uninformative estimates of price elasticities by drawing them toward their local averages. The authors discuss other substantive issues as well as future research.