A generalized ordinal finite mixture regression model for market segmentation
Department
Economics, Finance and Quantitative Analysis
Document Type
Article
Publication Date
12-1-2021
Abstract
Model-based market segmentation analyses often involve an ordinal dependent variable as ordinal responses are frequently collected in marketing research. In the Bayesian segmentation literature, there are models for an interval- or ratio-scaled dependent variable but there is not any general model for an ordinal dependent variable. In this manuscript, the authors propose a new Bayesian procedure to simultaneously perform segmentation and ordinal regression with variable selection within each derived segment. The procedure is robust to outliers and it also provides an option to include concomitant variables that allows the simultaneous profiling of the derived segments. The authors demonstrate that the practice of treating ordinal responses as interval- or ratio-scales to apply existing Bayesian segmentation procedures can lead to very misleading results and conclusions. Through simulation studies, the authors show that the proposed procedure outperforms several benchmark Bayesian segmentation models in parameter recovery, segment retention, and segment membership prediction for such data. Finally, they provide a commercial business customer satisfaction empirical application to illustrate the usefulness of the proposed model.
Journal Title
International Journal of Research in Marketing
Journal ISSN
01678116
Volume
38
Issue
4
First Page
1055
Last Page
1072
Digital Object Identifier (DOI)
10.1016/j.ijresmar.2021.02.002