Accounting for temporal demand variations in retail location models

This article develops and calibrates a spatial interaction model (SIM) incorporating additional temporal characteristics of consumer demand for the U.K. grocery market. SIMs have been routinely used by the retail sector for location modeling and revenue prediction and have a good record of success, especially in the supermarket/hypermarket sector. However, greater planning controls and a more competitive trading environment in recent years has forced retailers to look to new markets. This has meant a greater focus on the convenience market which creates new challenges for retail location models. In this article, we present a custom built SIM for the grocery market in West Yorkshire incorporating trading and consumer data provided by a major U.K. retailer. We show that this model works well for supermarkets and hypermarkets but poorly for convenience stores. We then build a series of new demand layers taking into account the spatial distributions of demand at the time of day that consumers are likely to use grocery stores. These new demand layers include workplace populations, university student populations and secondary school children. When these demand layers are added to the models, we see a very promising increase in the accuracy of the revenue forecasts.