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Geomarketing models in supermarket location strategies

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  • Amparo Baviera-Puig
  • Juan Buitrago-Vera
  • Carmen Escriba-Perez

Abstract

Choosing where to open a new outlet is a critical decision for retail firms. Building on the multiplicative competitive interaction model from retail location theory, this paper develops a geomarketing model that can be used to devise supermarket location strategies. First, attributes that explain a supermarket’s pull on consumers were determined. These attributes included objective (taken from databases and empirical observation) and subjective (based on managerial judgements) variables relating to the supermarket and its trade area. Then, geographic information system tools were used to analyse real data at a highly detailed level (road section). From a geomarketing viewpoint, the model shows that sociodemographic characteristics of the supermarket’s trade area affect firms’ location strategies. The paper also discusses improvements for calibrating and validating this model. Adding the spatial organization of supermarkets to the model yields a different consumer behaviour pattern. This geomarketing model can help managers to design supermarket location strategies according to shop features, competitors and environment, whilst estimating supermarket sales.

Suggested Citation

  • Amparo Baviera-Puig & Juan Buitrago-Vera & Carmen Escriba-Perez, 2016. "Geomarketing models in supermarket location strategies," Journal of Business Economics and Management, Taylor & Francis Journals, vol. 17(6), pages 1205-1221, November.
    • Handle: RePEc:taf:jbemgt:v:17:y:2016:i:6:p:1205-1221
    DOI: 10.3846/16111699.2015.1113198
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    References listed on IDEAS

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    1. Thrall, Grant Ian, 2002. "Business Geography and New Real Estate Market Analysis," OUP Catalogue, Oxford University Press, number 9780195076363.
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    Cited by:

    1. Thomas Wieland, 2023. "Spatial shopping behavior during the Corona pandemic: insights from a micro-econometric store choice model for consumer electronics and furniture retailing in Germany," Journal of Geographical Systems, Springer, vol. 25(2), pages 291-326, April.
    2. Di Wu & Zhenning Xu & Seung Bach, 2023. "Using Google Trends to predict and forecast avocado sales," Journal of Marketing Analytics, Palgrave Macmillan, vol. 11(4), pages 629-641, December.
    3. Lu, Jialiang & Zheng, Xu & Nervino, Esterina & Li, Yanzhi & Xu, Zhihua & Xu, Yabo, 2024. "Retail store location screening: A machine learning-based approach," Journal of Retailing and Consumer Services, Elsevier, vol. 77(C).
    4. Ceren Erdin & Halil Emre Akbaş, 2019. "A Comparative Analysis of Fuzzy TOPSIS and Geographic Information Systems (GIS) for the Location Selection of Shopping Malls: A Case Study from Turkey," Sustainability, MDPI, vol. 11(14), pages 1-22, July.

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