Model-Based Segmentation Featuring Simultaneous Segment-Level Variable Selection

Journal of Marketing Research, Vol. 49, No. 5, pp. 725-736, 2012

Posted: 19 Jun 2016

See all articles by Sunghoon Kim

Sunghoon Kim

Pennsylvania State University

Duncan K. H. Fong

Pennsylvania State University

Wayne S. DeSarbo

Pennsylvania State University

Date Written: October 1, 2012

Abstract

The authors propose a new Bayesian latent structure regression model with variable selection to solve various commonly encountered marketing problems related to market segmentation and heterogeneity. The proposed procedure simultaneously performs segmentation and regression analysis within the derived segments, in addition to determining the optimal subset of independent variables per derived segment. The authors present comparative analyses contrasting the performance of the proposed methodology against standard latent class regression and traditional Bayesian finite mixture regression. They demonstrate that their proposed Bayesian model compares favorably with these traditional benchmark models. They then present an actual commercial customer satisfaction study performed for an electric utility company in the southeastern United States, in which they examine the heterogeneous drivers of perceived quality. Finally, they discuss limitations of the research and provide several directions for further research.

Keywords: Bayesian analysis, finite mixtures, perceived quality, multiple regression, customer satisfaction

Suggested Citation

Kim, Sunghoon and Fong, Duncan K. H. and DeSarbo, Wayne S., Model-Based Segmentation Featuring Simultaneous Segment-Level Variable Selection (October 1, 2012). Journal of Marketing Research, Vol. 49, No. 5, pp. 725-736, 2012, Available at SSRN: https://ssrn.com/abstract=2796748

Sunghoon Kim

Pennsylvania State University ( email )

University Park
State College, PA 16802
United States

Duncan K. H. Fong

Pennsylvania State University ( email )

308 armsby
university park, PA 16802
United States

Wayne S. DeSarbo (Contact Author)

Pennsylvania State University ( email )

University Park
State College, PA 16802
United States

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