2015 年 42 巻 1 号 p. 1-18
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.
