Model-Based Segmentation Featuring Simultaneous Segment-Level Variable Selection

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.

• 저널Journal of Marketing Research · 49(5) · 725–736
• 토픽Bayesian Methods and Mixture Models · Artificial Intelligence
• DOI10.1509/jmr.10.0395
• 저자Sung‐Hoon Kim, Duncan Κ. H. Fong, Wayne S. DeSarbo