Examining Brand-Switching Behavior Using Latent Class Dynamic Multinomial Probit Models with Random Effects

Abstract

Recent studies that analyze scanner panel data often use hierarchical Bayes modeling with dynamic structures and random effects to model consumers' heterogeneity. In this study, we propose a hybrid version of a hierarchical Bayes model with dynamic structures in which both latent classes and random effects are assumed. The proposed model explains consumer heterogeneity as it relates to brand-switching behavior by using latent classes and random effects. This makes it possible to estimate brand-switching behavior accurately by explaining within-class heterogeneity in coefficients with random effects. The proposed method is then applied to an Information Resources Inc. marketing data set with noteworthy results.