Market Segment Derivation and Profiling Via a Finite Mixture Model Framework

Marketing Letters, Volume 13, Issue 1, pp 17-25, 2002

Posted: 13 Jun 2016

See all articles by Michel Wedel

Michel Wedel

University of Maryland - Robert H. Smith School of Business, Marketing Department; University of Groningen - Faculty of Economics and Business

Wayne S. DeSarbo

Pennsylvania State University

Date Written: February 2002

Abstract

The Marketing literature has shown how difficult it is to profile market segments derived with finite mixture models, especially using traditional descriptor variables (e.g., demographics). Such profiling is critical for the proper implementation of segmentation strategy. We propose a new finite mixture modelling approach that provides a variety of model specifications to address this segmentation dilemma. Our proposed approach allows for a large number of nested models (special cases) and associated tests of (local) independence to distinguish amongst them. A commercial application to customer satisfaction is provided where a variety of different model specifications are tested and compared.

Keywords: finite mixture models, market segmentation, concomitant variables, customer satisfaction

Suggested Citation

Wedel, Michel and DeSarbo, Wayne S., Market Segment Derivation and Profiling Via a Finite Mixture Model Framework (February 2002). Marketing Letters, Volume 13, Issue 1, pp 17-25, 2002. Available at SSRN: https://ssrn.com/abstract=2792366

Michel Wedel

University of Maryland - Robert H. Smith School of Business, Marketing Department ( email )

College Park, MD 20742
United States
301.405.2162 (Phone)
301.405.0146 (Fax)

HOME PAGE: http://www.rhsmith.umd.edu/marketing/faculty/wedel.html

University of Groningen - Faculty of Economics and Business ( email )

Postbus 72
9700 AB Groningen
Netherlands

Wayne S. DeSarbo (Contact Author)

Pennsylvania State University ( email )

University Park
State College, PA 16802
United States

Register to save articles to
your library

Register

Paper statistics

Abstract Views
109
PlumX Metrics