Segmentation, Incentives, and Privacy

Kobbi Nissim (), Rann Smorodinsky () and Moshe Tennenholtz ()
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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

Mathematics of Operations Research, 2018, vol. 43, issue 4, 1252-1268

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.

Keywords: noncooperative games; marketing segmentation; facility location (search for similar items in EconPapers)
Date: 2018
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