Title: Using mixed integer optimisation to select variables for a store choice model

Authors: Toshiki Sato; Yuichi Takano; Takanobu Nakahara

Addresses: Graduate School of Systems and Information Engineering, University of Tsukuba, 1-1-1 Tennodai, Tsukuba-shi, Ibaraki 305-8573, Japan ' School of Network and Information, Senshu University, 2-1-1 Higashimita, Tama-ku, Kawasaki-shi, Kanagawa 214-8580, Japan ' School of Commerce, Senshu University, 2-1-1 Higashimita, Tama-ku, Kawasaki-shi, Kanagawa 214-8580, Japan

Abstract: This paper develops a store choice model to investigate consumers' store choice behaviour through the use of actual scanner panel data. For this purpose, we use the variable selection method proposed by Sato et al. (2015), which is based on mixed integer optimisation for logistic regression. Computational results demonstrated that when the Akaike information criterion is used as a goodness-of-fit measure, our approach yields a predictive performance better than that obtained using the common stepwise method of variable selection. Moreover, we clarified store choice factors by analysing the results of variable selection.

Keywords: store choice; logistic regression; variable selection; mixed integer optimisation; MIO; scanner panel data; Akaike information criterion; AIC; Bayesian information criterion; BIC; marketing strategy; choice modelling; store selection; consumer behaviour.

DOI: 10.1504/IJKESDP.2016.075980

International Journal of Knowledge Engineering and Soft Data Paradigms, 2016 Vol.5 No.2, pp.123 - 134

Received: 21 Jul 2015
Accepted: 24 Sep 2015

Published online: 20 Apr 2016 *

Full-text access for editors Access for subscribers Purchase this article Comment on this article