AcknowledgementThis research is supported by an ESRC CASE Award (2010)(2011)(2012)(2013) as part of RIBEN. Data have been provided by a collaborating retailer who guided the initial selection of study stores. AbstractThe Spatial Interaction Model (SIM) is an important tool for retail location analysis and store revenue estimation, particularly within the grocery sector. However, there are few examples of SIM development within the literature that capture the complexities of consumer behaviour or discuss model developments and extensions necessary to produce models which can predict store revenues to a high degree of accuracy. This paper reports a new disaggregated model with more sophisticated demand terms which reflect different types of retail consumer (by income or social class), with different shopping behaviours in terms of brand choice. We also incorporate seasonal fluctuations in demand driven by tourism, a -2 -major source of non-residential demand, allowing us to calibrate revenue predictions against seasonal sales fluctuations experienced at individual stores. We demonstrate that such disaggregated models need empirical data for calibration purposes, without which model extensions are likely to remain theoretical only. Using data provided by a major grocery retailer, we demonstrate that statistically, spatially and in terms of revenue estimation, models can be shown to produce extremely good forecasts and predictions concerning store patronage and store revenues, including much more realistic behaviour regarding store selection. We also show that it is possible to add a tourist demand layer which can make considerable forecasting improvements relative to models built only with residential demand.

Purpose -The purpose of this paper is to understand the contribution of visitor demand to the seasonal sales variations experienced at grocery retailers in Cornwall, South West England. Design/methodology/approach -Working collaboratively with a major UK retailer provides access to store trading information and customer data from a popular loyalty card scheme. The authors use spatial analysis to identify revenue originating from outside the store catchment, and explore the spatial and temporal nature of the visitor demand recorded in-store. Findings -The paper demonstrates the significant degree of seasonality experienced around stores in terms of their revenue generated from out-of-catchment visitors, and highlights implications for store location planning. Most notably, visitor expenditure tends to demonstrate far more spatial and temporal clustering than residential demand. The authors argue that it is essential for retailers to ensure that their location planning makes full use of all available consumer data to understand the local nature of demand, including the impact of visitor expenditure.Research limitations/implications -The authors aim to use this insight to develop a spatial decision support system (SDSS) for use within site location planning in the retail sector. This would incorporate a spatial interaction model to estimate and account for variation in local demand generated by seasonal tourist visits. Originality/value -Customer level loyalty card data are rarely available for academic investigations and the authors are able to provide a unique insight into customer expenditure in tourist locations. There has been little exploration of seasonal tourist demand in store location planning, and this study addresses an identified academic and commercial need.

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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. The UK groceries retail sector has traditionally been at the forefront of applied retail modelling through sustained research and innovation, in-part via collaboration with academia. In this paper we report on collaboration with a major UK grocery retailer in order 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. This could offer considerable enhancement to traditionally-applied spatial interaction models (SIMs) which, even after considerable disaggregation, cannot fully capture the complex and individualised spatiotemporal drivers of shopping mission and store choice. 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 for store revenue estimation and impact assessment. We strongly assert that changing consumer behaviours, coupled with the growing availability of individual-level consumer data, creates a unique opportunity for a