JSM 2005 Online Program

Abstract #303315

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Activity Number:	396
Type:	Contributed
Date/Time:	Wednesday, August 10, 2005 : 10:30 AM to 12:20 PM
Sponsor:	Section on Bayesian Statistical Science
Abstract - #303315
Title:	Market Segmentation Using Bayesian Model-based Clustering
Author(s):	Pascal van Hattum*+
Companies:	University Utrecht
Address:	Marimbastraat 15, Amersfoort, 3822DD, Netherlands
Keywords:	Latent Class Analysis ; Log-Linear Modeling ; Market Segmentation ; MCMC methods ; Missing Data
Abstract:	Marketing companies often have data matrices with more than 2500 records and more than 100 items. In such datasets, records often represent persons, and items are often answers to questions obtained via market research. One multivariate analysis that can be performed on such a dataset is a cluster analysis. The main goal of cluster analysis from a market segmentation perspective is to find groups (=clusters) of persons who give the same answers to the items. A simple cluster model assuming within cluster independence of the item responses often is not sufficient for such marketing datasets. Some items have strong within-cluster dependencies. This paper will present a model-based clustering approach for the analysis of large datasets, in particular from marketing companies. The approach is a latent mixture of log-linear models that contain main-effects and specific sets of two-way interactions (to account for within-cluster dependencies) and deals with data missing by design. In the application, a MCMC algorithm is used to group persons according to their item responses.

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