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Segmentation, Incentives, and Privacy

Author

Listed:
  • Kobbi Nissim

    (Department of Computer Science, Georgetown University, Washington, DC 20057)

  • Rann Smorodinsky

    (Faculty of Industrial Engineering and Management, Technion–Israel Institute of Technology, Haifa 3200004, Israel)

  • Moshe Tennenholtz

    (Faculty of Industrial Engineering and Management, Technion–Israel Institute of Technology, Haifa 3200004, Israel)

Abstract

Data-driven segmentation is the powerhouse behind the success of online advertising. Various underlying challenges for successful segmentation have been studied by the academic community, with one notable exception—consumers’ incentives have been typically ignored. This lacuna is troubling, as consumers have much control over the data being collected. Missing or manipulated data could lead to inferior segmentation. The current work proposes a model of prior-free segmentation, inspired by models of facility location and, to the best of our knowledge, provides the first segmentation mechanism that addresses incentive compatibility, efficient market segmentation, and privacy in the absence of a common prior.

Suggested Citation

  • Kobbi Nissim & Rann Smorodinsky & Moshe Tennenholtz, 2018. "Segmentation, Incentives, and Privacy," Mathematics of Operations Research, INFORMS, vol. 43(4), pages 1252-1268, November.
    • Handle: RePEc:inm:ormoor:v:43:y:2018:i:4:p:1252-1268
    DOI: 10.1287/moor.2017.0903
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    References listed on IDEAS

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