Market structure across stores: An application of a random coefficients logit model with store level data

Market structure analysis continues to be a topic of considerable interest to marketing researchers. One of the most common representations of the manner in which brands compete in a market is via market maps that show the relative locations of brands in multi-attribute space. In this paper, we use logit brand choice models to estimate a heterogeneous demand system capable of identifying such brand maps. Unlike the previous literature, we use only aggregate store-level data to obtain these maps. Additionally, by recognizing that there exists heterogeneity in consumer preferences both within a store's market area as well as across store market areas, the approach allows us to identify store-specific brand maps. The methodology also accounts for endogeneity in prices due to possible correlation between unobserved factors, such as shelf space and shelf location that affect brand sales, and prices. We provide an empirical application of our methodology to store level data from a retail chain for the laundry detergents product category. From a manager's perspective, our model enables micromarketing strategies in which promotions are targeted to those stores in which a brand has the most favorable, differentiated, position.