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
Globally, geospatial concepts are becoming increasingly important in epidemiological and public health research. Individual level linked population-based data afford researchers with opportunities to undertake complex analyses unrivalled by other sources. However, there are significant challenges associated with using such data for impactful geohealth research. Issues range from extracting, linking and anonymising data, to the translation of findings into policy whilst working to often conflicting agendas of government and academia. Innovative organisational partnerships are therefore central to effective data use. To extend and develop existing collaborations between the institutions, in June 2019, authors from the Leeds Institute for Data Analytics and the Alan Turing Institute, London, visited the Geohealth Laboratory based at the University of Canterbury, New Zealand. This paper provides an overview of insight shared during a two-day workshop considering aspects of linked population-based data for impactful geohealth research. Specifically, we discuss both the collaborative partnership between New Zealand’s Ministry of Health (MoH) and the University of Canterbury’s GeoHealth Lab and novel infrastructure, and commercial partnerships enabled through the Leeds Institute for Data Analytics and the Alan Turing Institute in the UK. We consider the New Zealand Integrated Data Infrastructure as a case study approach to population-based linked health data and compare similar approaches taken by the UK towards integrated data infrastructures, including the ESRC Big Data Network centres, the UK Biobank, and longitudinal cohorts. We reflect on and compare the geohealth landscapes in New Zealand and the UK to set out recommendations and considerations for this rapidly evolving discipline.
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