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Understanding the customer behaviours behind transactional data has highcommercial value in the grocery retail industry. Customers generate millions oftransactions every day, choosing and buying products to satisfy specificshopping needs. Product availability may vary geographically due to localdemand and local supply, thus driving the importance of analysing transactionswithin their corresponding store and regional context. Topic models provide apowerful tool in the analysis of transactional data, identifying topics thatdisplay frequently-bought-together products and summarising transactions asmixtures of topics. We use the Segmented Topic Model (STM) to capture customerbehaviours that are nested within stores. STM not only provides topics andtransaction summaries but also topical summaries at the store level that can beused to identify regional topics. We summarised the posterior distribution ofSTM by post-processing multiple posterior samples and selecting semantic modesrepresented as recurrent topics. We use linear Gaussian process regression tomodel topic prevalence across British territory while accounting for spatialautocorrelation. We implement our methods on a dataset of transactional datafrom a major UK grocery retailer and demonstrate that shopping behaviours mayvary regionally and nearby stores tend to exhibit similar regional demand.

Regional Topics in British Grocery Retail Transactions