Location-based tracking data and customer movement pattern analysis for sustainable fashion business

Abstract

Retailers need accuratemovement pattern analysis of human-tracking data tomaximize the space performance of their stores and to improve the sustainability of their business. However, researchers struggle to precisely measure customers' movement patterns and their relationships with sales. In this research, we adopt indoor positioning technology, including wireless sensor devices and fingerprinting techniques, to track customers' movement patterns in a fashion retail store over four months. Specifically, we conducted three field experiments in three different timeframes. In each experiment, we rearranged one element of the visual merchandising display (VMD) to track and compare customer movement patterns before and after the rearrangement. For the analysis, we connected customers' discrete location data to identify meaningful patterns in customers' movements. We also used customers' location and time information tomatch identifiedmovement pattern datawith sales data. After classifying individuals' movements by time and sequences, we found that stay time in a particular zone had a greater impact on sales than the total stay time in the store. These results challenge previous findings in the literature that suggest that the longer customers stayed in a store, the more they purchase. Further, the results confirmed that effective store rearrangement could change not only customer movement patterns but also overall sales of store zones. This research can be a foundation for various practical applications of tracking data technologies.