Estimating Individual Preferences in the Music CD Market

Abstract

This research proposes a model to analyze individual customer preferences using purchase records such as Point-of-Sales (POS) data. To some extent, we can identify the interests of customers from their demographics. Consumers are, however, essentially heterogeneous. It is difficult to determine individual customer behavior in detail through aggregate-level estimation. In this paper, we use a Markov chain Monte Carlo (MCMC) method to construct a hierarchical model for tackling this problem. The model encompasses both “commonality" and “heterogeneity." We apply this MCMC method to the music CD market, where customers have some commonalities although they are heterogeneous. This empirical analysis shows that a hierarchical Bayes (HB) model has a high predictive performance as compared to the naïve forecasting and aggregate-level models.