A Bayesian Technique to Discriminate between Stochastic Models of Brand Choice

This paper describes a Bayesian model-discrimination procedure which determines for each consumer the stochastic model of brand choice which is best supported by his purchasing behavior. The Bayesian technique is illustrated by means of two Markov models and two Bernoulli models. We first discuss in some detail how we set priors for the four models. Then we use simulated consumer panel data to demonstrate that the Bayesian technique is a good discriminator among the four models. The technique is then applied to some actual aluminum foil purchase data. We provide estimates of the proportion of aluminum foil consumers in each of the four model segments and the degree to which the size of each segment changes over time. We also show that model discrimination at the individual consumer level has important implications for market segmentation, pricing, and promotion.