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.

基于模型的市场细分分析通常涉及有序因变量，因为在市场研究中经常收集有序响应。在贝叶斯分割文献中，有区间或比率尺度因变量的模型，但没有序数因变量的任何通用模型。在这份手稿中，作者提出了一种新的贝叶斯程序，以在每个派生的片段中通过变量选择同时执行分段和序数回归。该过程对异常值具有鲁棒性，并且还提供了包括伴随变量的选项，从而允许对派生段进行同步分析。作者证明，将序数响应视为区间或比率尺度以应用现有贝叶斯分割程序的做法可能会导致非常具有误导性的结果和结论。通过模拟研究，作者表明所提出的程序在参数恢复、段保留和对此类数据的段成员资格预测方面优于几个基准贝叶斯分割模型。最后，他们提供了一个商业客户满意度实证应用来说明所提出模型的有用性。