Quantitative and Spatially Explicit Clustering of Urban Grocery Shoppers in Montreal: Integrating Loyalty Data with Synthetic Population
This study integrates customer loyalty program data with a synthetic population to analyze grocery shopping behaviours in Montreal. Using clustering algorithms, we classify 295,631 loyalty program members into seven distinct consumer segments based on behavioural and sociodemographic attributes. The...
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MDPI AG
2025-04-01
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| Series: | ISPRS International Journal of Geo-Information |
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| author | Duo Zhang Laurette Dubé Antonia Gieschen Catherine Paquet Raja Sengupta |
| author_facet | Duo Zhang Laurette Dubé Antonia Gieschen Catherine Paquet Raja Sengupta |
| author_sort | Duo Zhang |
| collection | DOAJ |
| description | This study integrates customer loyalty program data with a synthetic population to analyze grocery shopping behaviours in Montreal. Using clustering algorithms, 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 methods. Overall, this study contributes to geographically explicit modelling, demonstrating the effectiveness of combining behavioural and synthetic demographic data in urban retail analysis. |
| format | Article |
| id | doaj-art-bbb22dbbc6d64e75b1b079cab4f5e162 |
| institution | OA Journals |
| issn | 2220-9964 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj-art-bbb22dbbc6d64e75b1b079cab4f5e1622025-08-20T02:28:14ZengMDPI AGISPRS International Journal of Geo-Information2220-99642025-04-0114415910.3390/ijgi14040159Quantitative and Spatially Explicit Clustering of Urban Grocery Shoppers in Montreal: Integrating Loyalty Data with Synthetic PopulationDuo Zhang0Laurette Dubé1Antonia Gieschen2Catherine Paquet3Raja Sengupta4Department of Geography, McGill University, Montreal, QC H3A 0G4, CanadaDesautels Faculty of Management, McGill University, Montreal, QC H3A 0G4, CanadaBusiness School, University of Edinburgh, Edinburgh EH8 9JS, UKDepartment of Marketing, Université Laval, Quebec City, QC G1V 0A6, CanadaDepartment of Geography, McGill University, Montreal, QC H3A 0G4, CanadaThis study integrates customer loyalty program data with a synthetic population to analyze grocery shopping behaviours in Montreal. Using clustering algorithms, 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 methods. Overall, this study contributes to geographically explicit modelling, demonstrating the effectiveness of combining behavioural and synthetic demographic data in urban retail analysis.https://www.mdpi.com/2220-9964/14/4/159customer shopping behaviourfood retailingspatial segmentationcustomer typologyagent-based model |
| spellingShingle | Duo Zhang Laurette Dubé Antonia Gieschen Catherine Paquet Raja Sengupta Quantitative and Spatially Explicit Clustering of Urban Grocery Shoppers in Montreal: Integrating Loyalty Data with Synthetic Population ISPRS International Journal of Geo-Information customer shopping behaviour food retailing spatial segmentation customer typology agent-based model |
| title | Quantitative and Spatially Explicit Clustering of Urban Grocery Shoppers in Montreal: Integrating Loyalty Data with Synthetic Population |
| title_full | Quantitative and Spatially Explicit Clustering of Urban Grocery Shoppers in Montreal: Integrating Loyalty Data with Synthetic Population |
| title_fullStr | Quantitative and Spatially Explicit Clustering of Urban Grocery Shoppers in Montreal: Integrating Loyalty Data with Synthetic Population |
| title_full_unstemmed | Quantitative and Spatially Explicit Clustering of Urban Grocery Shoppers in Montreal: Integrating Loyalty Data with Synthetic Population |
| title_short | Quantitative and Spatially Explicit Clustering of Urban Grocery Shoppers in Montreal: Integrating Loyalty Data with Synthetic Population |
| title_sort | quantitative and spatially explicit clustering of urban grocery shoppers in montreal integrating loyalty data with synthetic population |
| topic | customer shopping behaviour food retailing spatial segmentation customer typology agent-based model |
| url | https://www.mdpi.com/2220-9964/14/4/159 |
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