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

Authors:
Ruben Mancha;Mark T. Leung;Jan Clark;Minghe Sun
Affiliations:
Department of Finance and Decision Sciences, School of Business, Trinity University, United States;Department of Management Science and Statistics, College of Business, University of Texas at San Antonio, United States;Department of Information Systems and Cyber Security, College of Business, University of Texas at San Antonio, United States;Department of Management Science and Statistics, College of Business, University of Texas at San Antonio, United States
Venue:
Decision Support Systems
Year:
2014

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Abstract

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.