In-Store Journey Model with Purchasing Behavior Based on In-Store Journey Data and ID-POS Data

Abstract

ID-POS data have been analyzed in many retail stores for several decades, and the results have been used to support decision-making such as sales promotion and item arrangement in the stores. Such analysis affects various business performance like total sales. Although the data are so useful, it is difficult to grasp the extent of customer’s interest in items that were not purchased and to identify that it is a planned purchase or not. Therefore, we need to use in-store customer journey data to reveal that complementary. In this paper, we propose an in-store journey simulation model with purchasing behavior and carry out the agent-based simulation using actual in-store customer journey data acquired using the Bluetooth beacons and ID-POS data. From several computational experiments, we show that our model reproduces actual in-store customer journey and purchasing behavior, and we evaluate our model from the viewpoint of the difference between our results and the actual data. Finally, we predict the effect on a sales promotion using the proposed model and agent-based simulation.