Clustering grocery shopping paths of customers by using optimization-based models

Abstract

This study presents a preliminary investigation of shopping behavior of customers in a grocery store. Using each customer's in-store shopping path information, gathered by a wireless video camera that is affixed to the shopping cart, we classify customers into a predetermined number of clusters, and create a shopping path-based segmentation of customers. For this purpose a number of optimization models are developed. The results are presented in this paper. The next step is to analyze this collected data from different perspectives and developing different optimization models to achieve a better solution to the above clustering problem.