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The Value of Consumer Data in Online Advertising

Author

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  • Marc Bourreau

    (SES - Département Sciences Economiques et Sociales - Télécom ParisTech, ECOGE - Economie Gestion - I3 SES - Institut interdisciplinaire de l’innovation de Telecom Paris - Télécom ParisTech - I3 - Institut interdisciplinaire de l’innovation - CNRS - Centre National de la Recherche Scientifique)

  • Bernard Caillaud

    (PSE - Paris-Jourdan Sciences Economiques - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris sciences et lettres - INRA - Institut National de la Recherche Agronomique - EHESS - École des hautes études en sciences sociales - ENPC - École des Ponts ParisTech - CNRS - Centre National de la Recherche Scientifique)

  • Romain de Nijs

    (X - École polytechnique)

Abstract

In this paper we propose a model where consumer personal data have multidimensional characteristics, and are used by platforms to offer ad slots with better targeting possibilities to a market of differentiated advertisers through real-time auctions. A platform controls the amount of information about consumers that it discloses to advertisers, thereby affecting the dispersion of advertisers’ valuations for the slot. We first show by way of simulations that the amount of consumer-specific information that is optimally revealed to advertisers increases with the degree of competition on the advertising market and decreases with the cost of information disclosure for a monopolistic platform, competing platforms or a welfare-maximizing platform, provided the advertising market is not highly concentrated. Second, we exhibit different properties between the welfare-maximizing situation and the imperfectly competitive market situations with respect to how the incremental value of information varies: there are decreasing social returns to consumers’ data, while private returns may be increasing or decreasing locally.
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Suggested Citation

  • Marc Bourreau & Bernard Caillaud & Romain de Nijs, 2017. "The Value of Consumer Data in Online Advertising," Post-Print hal-02085279, HAL.
    • Handle: RePEc:hal:journl:hal-02085279
    DOI: 10.1515/rne-2017-0066
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    Cited by:

    1. Dirk Bergemann & Benjamin Brooks & Stephen Morris, 2021. "Optimal Information Disclosure in Auctions," Cowles Foundation Discussion Papers 2318, Cowles Foundation for Research in Economics, Yale University.
    2. Shuilin Liu & Xudong Lin & Xiaoli Huang & Hanyang Luo & Sumin Yu, 2023. "Research on Service-Driven Benign Market with Platform Subsidy Strategy," Mathematics, MDPI, vol. 11(2), pages 1-21, January.
    3. Anna D’Annunzio & Antonio Russo, 2024. "Intermediaries in the Online Advertising Market," Marketing Science, INFORMS, vol. 43(1), pages 33-53, January.

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