Evaluating the potential of agent-based modelling to capture consumer grocery retail store choice behaviours

Evolving consumer behaviours with regards to store and channel choice, shopping frequency, shopping mission and spending heighten the need for robust spatial modelling tools for use within retail analytics. In this paper, we report on collaboration with a major UK grocery retailer to assess the feasibility of modelling consumer store choice behaviours at the level of the individual consumer. We benefit from very rare access to our collaborating retailers’ customer data which we use to develop a proof-of-concept agent-based model (ABM). Utilising our collaborating retailers’ loyalty card database, we extract key consumer behaviours in relation to shopping frequency, mission, store choice and spending. We build these observed behaviours into our ABM, based on a simplified urban environment, calibrated and validated against observed consumer data. Our ABM is able to capture key spatiotemporal drivers of consumer store choice behaviour at the individual level. Our findings could afford new opportunities for spatial modelling within the retail sector, enabling the complexity of consumer behaviours to be captured and simulated within a novel modelling framework. We reflect on further model development required for use in a commercial context for location-based decision-making.