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Finding Similar Mobile Consumers with a Privacy-Friendly Geosocial Design

Author

Listed:
  • Foster Provost

    (Department of Information, Operations and Management Sciences, Leonard N. Stern School of Business, New York University, New York, New York 10012)

  • David Martens

    (Department of Engineering Management, Faculty of Applied Economics, University of Antwerp, B-2000 Antwerp, Belgium)

  • Alan Murray

    (Coriolis Labs, New York, New York 10021)

Abstract

This paper focuses on finding the same and similar users based on location-visitation data in a mobile environment. We propose a new design that uses consumer-location data from mobile devices (smartphones, smart pads, laptops, etc.) to build a “geosimilarity network” among users. The geosimilarity network (GSN) could be used for a variety of analytics-driven applications, such as targeting advertisements to the same user on different devices or to users with similar tastes, and to improve online interactions by selecting users with similar tastes. The basic idea is that two devices are similar, and thereby connected in the GSN, when they share at least one visited location. They are more similar as they visit more shared locations and as the locations they share are visited by fewer people. This paper first introduces the main ideas and ties them to theory and related work. It next introduces a specific design for selecting entities with similar location distributions, the results of which are shown using real mobile location data across seven ad exchanges. We focus on two high-level questions: (1) Does geosimilarity allow us to find different entities corresponding to the same individual, for example, as seen through different bidding systems? And (2) do entities linked by similarities in local mobile behavior show similar interests, as measured by visits to particular publishers? The results show positive results for both. Specifically, for (1), even with the data sample’s limited observability, 70%–80% of the time the same individual is connected to herself in the GSN. For (2), the GSN neighbors of visitors to a wide variety of publishers are substantially more likely also to visit those same publishers. Highly similar GSN neighbors show very substantial lift.

Suggested Citation

  • Foster Provost & David Martens & Alan Murray, 2015. "Finding Similar Mobile Consumers with a Privacy-Friendly Geosocial Design," Information Systems Research, INFORMS, vol. 26(2), pages 243-265, June.
  • Handle: RePEc:inm:orisre:v:26:y:2015:i:2:p:243-265
    DOI: 10.1287/isre.2015.0576
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    References listed on IDEAS

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    1. Ritu Agarwal & Anil K. Gupta & Robert Kraut, 2008. "Editorial Overview ---The Interplay Between Digital and Social Networks," Information Systems Research, INFORMS, vol. 19(3), pages 243-252, September.
    2. Mauro Bampo & Michael T. Ewing & Dineli R. Mather & David Stewart & Mark Wallace, 2008. "The Effects of the Social Structure of Digital Networks on Viral Marketing Performance," Information Systems Research, INFORMS, vol. 19(3), pages 273-290, September.
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    Citations

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    Cited by:

    1. Chang-Yi Kao & Hao-En Chueh, 2022. "A Real-Time Bidding Gamification Service of Retailer Digital Transformation," SAGE Open, , vol. 12(2), pages 21582440221, April.
    2. Nagle, Tadhg & Doyle, Cathal & Alhassan, Ibrahim & Sammon, David, 2020. "The Research Method we Need or Deserve? A Literature Review of the Design Science Research Landscape," OSF Preprints yjbd7_v1, Center for Open Science.
    3. Mathias Eggert & Jens Alberts, 2020. "Frontiers of business intelligence and analytics 3.0: a taxonomy-based literature review and research agenda," Business Research, Springer;German Academic Association for Business Research, vol. 13(2), pages 685-739, July.
    4. Hana Choi & Carl F. Mela & Santiago R. Balseiro & Adam Leary, 2020. "Online Display Advertising Markets: A Literature Review and Future Directions," Information Systems Research, INFORMS, vol. 31(2), pages 556-575, June.
    5. Darvasi, Gábor & Spann, Martin & Zubcsek, Peter Pal, 2024. "How observation of other shoppers increases the in-store use of mobile technology," Journal of Retailing and Consumer Services, Elsevier, vol. 77(C).
    6. TOBBACK, Ellen & MARTENS, David, 2017. "Retail credit scoring using fine-grained payment data," Working Papers 2017011, University of Antwerp, Faculty of Business and Economics.
    7. PRAET, Stiene & VAN AELST, Peter & MARTENS, David, 2018. "I like, therefore I am. Predictive modeling to gain insights in political preference in a multi-party system," Working Papers 2018014, University of Antwerp, Faculty of Business and Economics.
    8. Siliang Tong & Xueming Luo & Bo Xu, 2020. "Personalized mobile marketing strategies," Journal of the Academy of Marketing Science, Springer, vol. 48(1), pages 64-78, January.
    9. MOEYERSOMS, Julie & D'ALESSANDRO, Brian & PROVOST, Foster & MARTENS, David, 2017. "Attributing value in a data pooling setting for predictive modeling," Working Papers 2017009, University of Antwerp, Faculty of Business and Economics.

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