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Tell Me Where You Are and I’ll Tell You What You Want: Using Location Data to Improve Marketing Decisions

Author

Listed:
  • Spann Martin

    (Professor of Electronic Commerce and Digital Markets, Ludwig-Maximilians-University of Munich, Germany)

  • Molitor Dominik

    (Assistant Professor, Gabelli School of Business, Fordham University, New York, United States of America)

  • Daurer Stephan

    (Professor of Business Information Systems at the Baden-Wuerttemberg Cooperative State University, Ravensburg, Germany)

Abstract

Location data has become more and more accessible. Smartphone applications such as location-based services collect location data on a large scale. Up to now, most approaches have relied on past data, but new developments in machine learning and artificial intelligence will soon enable more dynamic real-time use of location data. Companies that embrace these technologies will be able to create competitive advantages. Location data offers great potential to improve a variety of marketing decisions such as targeted pricing and advertising, store locations and in-store layout. Location based advertising is currently the most common application. It allows targeting all customers within a certain distance of a store. Besides advertising, location data can be used for dynamic pricing decisions. Customers close to competitor’s locations can be charged a lower price for particular products via discounts in order to reduce switching costs. Indoor tracking can help to optimize store design or the positioning of categories and brands. Granular location data about consumers’ movements hence further allows for minimizing potential offline transaction costs based on the distances to stores.

Suggested Citation

  • Spann Martin & Molitor Dominik & Daurer Stephan, 2016. "Tell Me Where You Are and I’ll Tell You What You Want: Using Location Data to Improve Marketing Decisions," NIM Marketing Intelligence Review, Sciendo, vol. 8(2), pages 30-37, November.
    • Handle: RePEc:vrs:gfkmir:v:8:y:2016:i:2:p:30-37:n:4
    DOI: 10.1515/gfkmir-2016-0013
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    Cited by:

    1. Evangelia Avraam & Andreas Veglis & Charalampos Dimoulas, 2021. "Publishing Patterns in Greek Media Websites," Social Sciences, MDPI, vol. 10(2), pages 1-15, February.

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