Consumer Clustering ModelBased on the Time of New Product Adoption Using ID-POS Data

Abstract

In marketing literature, consumer behavior that is selecting one brand from various choices is called “brand choice”. For consumer heterogeneity, in general, brand choice behavior is modeled by hierarchical Bayes discrete choice model like a logit or probit which has consumer's individual-level parameters. This study proposes a brand choice model for consumer clustering in terms of a new product adoption. In particular, the model is constructed by hierarchical bayes probit model having a Dirichlet process (DP) prior with time ordering clustering constraint. Features of this model is that (1) the model enables the estimation of the number of clusters, and then it's not necessary to set that before analysis. (2) Time ordering clustering leads to estimation of breakpoints among consumer clusters.Consumer is categorized into an adequate time ordering cluster based on the similarity of market response.The model estimates provide useful information corresponding to the marketing concepts containing time ordering clusters like Rogers's innovation adoption curve, product life cycle management (PLC). The model is estimated by Markov Chain Monte Carlo sampling method, especially for the DP prior Neal (2000)'s Metropolis-Hastings based algorithm modified to fulfill the constraint is used.