Finite mixture partial least squares for segmentation and behavioral characterization of auction bidders

•We demonstrate how to segment, without a priori knowledge, online bidders using real time data.•Our model can capture and evaluate bidder behavior and personality.•FIMIX-PLS is capable of profiling and segmenting the bidders based on their individual characteristics.•Analysis confirms FIMIX-PLS' ability of segmenting bidders into statistically identifiable homogeneous groups.

The purpose of this study is to demonstrate how to empirically segment, without a priori knowledge, online auction bidders using experimental data and finite mixture models. The proposed method utilizes a finite mixture partial least squares (FIMIX-PLS) approach to examine bidder behaviors and personality characteristics, evaluate bidder differences, and then segment the bidders. The empirical experiment is conducted for two different auction mechanisms — English and Vickrey. Results from both auction mechanisms indicate that FIMIX-PLS is capable of profiling and segmenting the bidders based on their individual characteristics. The post hoc analysis confirms the segmentation scheme and the capability of FIMIX-PLS in segmenting bidders into statistically identifiable homogeneous groups without a priori information of group characteristics. Such advantage is practical for online businesses dealing with increasing amount of data about their customers on a real time basis.