Model-Based Segmentation Featuring Simultaneous Segment-Level Variable Selection

• Activity Number: 398
• Type: Topic Contributed
• Date/Time: Tuesday, July 31, 2012 : 2:00 PM to 3:50 PM
• Sponsor: Section on Statistics in Marketing
• Author(s): Sunghoon Kim*+ and Duncan Fong and Wayne S. DeSarbo
• Companies: Penn State University and Penn State University and Penn State University
• Keywords: Bayesian Analysis ; Finite Mixtures ; Perceived Quality ; Multiple Regression ; Customer Satisfaction

Abstract

We 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, as well as determining the optimal subset of independent variables per derived segment. Comparative analyses are presented contrasting the performance of the proposed methodology against standard latent class regression and traditional Bayesian finite mixture regression. Our proposed Bayesian model is shown to compare favorably against these traditional benchmark models. We then present an actual commercial customer satisfaction study performed for an electric utility company in the Southeastern part of the US where we examine the heterogeneous drivers of perceived quality. Finally, we discuss limitations of the research and provide several directions for future research.