Inference for Product Competition and Separable Demand
Marketing Science, Vol. 38, No. 4, pp. 690–710, 2019
59 Pages Posted: 1 Nov 2016 Last revised: 23 Jun 2020
Date Written: November 29, 2018
This paper presents a methodology for identifying groups of products that exhibit similar patterns in demand and responsiveness to changes in price using store-level sales data. We use the concept of economic separability as the basis for establishing similarity between products, and build a weakly separable model of aggregate demand. A common issue with separable demand models is that the partition of products into separable groups must be known a priori, which severely shrinks the set of admissible substitution patterns. We develop a methodology which allows the partition to be an estimated model parameter. In particular, we specify a log-linear demand system in which weak separability induces equality restrictions on a subset of cross-price elasticity parameters. An advantage of our approach is that we are able to find groups of separable products rather than just test whether a given set of groups is separable. Our method is applied to two aggregate, store-level data sets. We find evidence that the separable structure of demand can be inconsistent with category labels, which has implications for optimal category marketing strategies.
Keywords: price elasticities, category demand, price promotions, random partition models, Bayesian inference
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