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Auctions for Online Display Advertising Exchanges: Approximations and Design

Author

Listed:
  • Santiago R. Balseiro

    () (Graduate School of Business, Columbia University)

  • Omar Besbes

    () (Graduate School of Business, Columbia University)

  • Gabriel Y. Weintraub

    () (Graduate School of Business, Columbia University)

Abstract

Ad Exchanges are emerging Internet markets where advertisers may purchase display ad placements, in real-time and based on specific viewer information, directly from publishers via a simple auction mechanism. Advertisers join these markets with a pre-specified budget and participate in multiple second-price auctions over the length of a campaign. This paper studies the competitive landscape that arises in Ad Exchanges and the implications for publishers' decisions. Our first main contribution is to introduce the novel notion of a Fluid Mean Field Equilibrium (FMFE) that is behaviorally appealing, computationally tractable, and in some important cases yields a closed-form characterization. Moreover, we show that a FMFE approximates well the rational behavior of advertisers in large markets. Our second main contribution is to use this framework to provide sharp prescriptions for key auction design decisions that publishers face in these markets, such as the reserve price, the allocation of impressions to the exchange versus an alternative channel, and the disclosure of viewers' information. Notably, we show that proper adjustment of the reserve price is key in (1) making profitable for the publisher to try selling all impressions in the exchange before utilizing the alternative channel; and (2) compensating for the thinner markets created by greater disclosure of viewers' information.

Suggested Citation

  • Santiago R. Balseiro & Omar Besbes & Gabriel Y. Weintraub, 2012. "Auctions for Online Display Advertising Exchanges: Approximations and Design," Working Papers 12-11, NET Institute.
  • Handle: RePEc:net:wpaper:1211
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    File URL: http://www.NETinst.org/Balseiro_12-11.pdf
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    References listed on IDEAS

    as
    1. Dirk Bergemann & Alessandro Bonatti, 2010. "Targeting in Advertising Markets: Implications for Offline vs. Online Media," Cowles Foundation Discussion Papers 1758, Cowles Foundation for Research in Economics, Yale University.
    2. Mireia Jofre-Bonet & Martin Pesendorfer, 2003. "Estimation of a Dynamic Auction Game," Econometrica, Econometric Society, vol. 71(5), pages 1443-1489, September.
    3. Marc Dudey, 1992. "Dynamic Edgeworth-Bertrand Competition," The Quarterly Journal of Economics, Oxford University Press, vol. 107(4), pages 1461-1477.
    4. Simon Board, 2009. "Revealing information in auctions: the allocation effect," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 38(1), pages 125-135, January.
    5. Mas-Colell, Andreu & Whinston, Michael D. & Green, Jerry R., 1995. "Microeconomic Theory," OUP Catalogue, Oxford University Press, number 9780195102680.
    6. Jonathan Levin & Paul Milgrom, 2010. "Online Advertising: Heterogeneity and Conflation in Market Design," American Economic Review, American Economic Association, vol. 100(2), pages 603-607, May.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. L. Elisa Celis & Gregory Lewis & Markus Mobius & Hamid Nazerzadeh, 2014. "Buy-It-Now or Take-a-Chance: Price Discrimination Through Randomized Auctions," Management Science, INFORMS, vol. 60(12), pages 2927-2948, December.
    2. repec:bla:jemstr:v:26:y:2017:i:3:p:557-570 is not listed on IDEAS
    3. Henk Kox & Bas Straathof & Gijsbert Zwart, 2017. "Targeted advertising, platform competition, and privacy," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 26(3), pages 557-570, September.

    More about this item

    Keywords

    auction design; revenue management; ad exchange; display advertising; internet; budget constraints; dynamic games; mean field; fl uid approximation;

    JEL classification:

    • C73 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Stochastic and Dynamic Games; Evolutionary Games
    • L86 - Industrial Organization - - Industry Studies: Services - - - Information and Internet Services; Computer Software

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