Quantitative and Spatially Explicit Clustering of Urban Grocery Shoppers in Montreal: Integrating Loyalty Data with a Synthetic Population

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Abstract

This study integrates customer loyalty program data with a synthetic population to analyze grocery shopping behaviours in Montreal. Using clustering techniques, we classify 295,631 loyalty program members into seven distinct consumer segments based on behavioural and sociodemographic attributes. The findings reveal significant heterogeneity in consumer behaviour, emphasizing the impact of urban geography on shopping decisions. This segmentation also provides valuable insights for retailers optimizing store locations and marketing strategies, and for policymakers aiming to enhance urban accessibility. Additionally, our approach strengthens Agent-Based Model (ABM) simulations by incorporating demographic and behavioural diversity, leading to more realistic consumer representations. While integrating loyalty data with synthetic populations mitigates privacy concerns, challenges remain regarding data sparsity and demographic inconsistencies. Future research should explore multi-source data integration and advanced clustering techniques. Overall, this study contributes to geographically explicit modelling, demonstrating the effectiveness of combining behavioural and synthetic demographic data in urban retail analysis.