EXAMINING BRAND-SWITCHING BEHAVIOR USING LATENT CLASS DYNAMIC MULTINOMIAL PROBIT MODELS WITH RANDOM EFFECTS

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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.

Journal

  • Behaviormetrika

    Behaviormetrika 42 (1), 1-18, 2015

    The Behaviormetric Society

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