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Buy-it-now or Take-a-chance: A New Pricing Mechanism for Online Advertising

Author

Listed:
  • Markus Mobius

    (Microsoft Research)

  • Hamid Nazerzadeh

    (University of Southern California)

  • Gregory Lewis

    (Harvard University)

  • Elisa Celis

    (University of Washington)

Abstract

Increasingly sophisticated tracking technology oers publishers the ability to oer targeted advertisements to advertisers. Such targeting enhances advertising efficiency by improving the match quality between advertisers and users, but also thins the market of interested advertisers. Using bidding data from Microsoft's Ad Exchange (AdECN) platform, we show that there is often a substantial gap between the highest and second highest willingness to pay. This motivates our new BIN-TAC mechanism, which is effective in extracting revenue when such a gap exists. Bidders can "buy-it-now", or alternatively take-a-chance" in an auction, where the top d > 1 bidders are equally likely to win. The randomized take-a-chance allocation incentivizes high valuation bidders to buy-it-now. We show that for a large class of distributions, this mechanism achieves similar allocations and revenues as Myerson's optimal mechanism, and outperforms the second-price auction with reserve. For the AdECN data, we use structural methods to estimate counterfactual revenues, and find that our BIN-TAC mechanism improves revenue by 11% relative to an optimal second-price auction.

Suggested Citation

  • Markus Mobius & Hamid Nazerzadeh & Gregory Lewis & Elisa Celis, 2012. "Buy-it-now or Take-a-chance: A New Pricing Mechanism for Online Advertising," 2012 Meeting Papers 443, Society for Economic Dynamics.
  • Handle: RePEc:red:sed012:443
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    File URL: https://economicdynamics.org/meetpapers/2012/paper_443.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. Ostrovsky, Michael & Schwarz, Michael, 2009. "Reserve Prices in Internet Advertising Auctions: A Field Experiment," Research Papers 2054, Stanford University, Graduate School of Business.
    3. Monteiro, Paulo Klinger & Svaiter, Benar Fux, 2010. "Optimal auction with a general distribution: Virtual valuation without densities," Journal of Mathematical Economics, Elsevier, vol. 46(1), pages 21-31, January.
    4. Pascal Courty & Li Hao, 2000. "Sequential Screening," Review of Economic Studies, Oxford University Press, vol. 67(4), pages 697-717.
    5. Jean-Charles Rochet & Philippe Chone, 1998. "Ironing, Sweeping, and Multidimensional Screening," Econometrica, Econometric Society, vol. 66(4), pages 783-826, July.
    6. Roger B. Myerson, 1981. "Optimal Auction Design," Mathematics of Operations Research, INFORMS, vol. 6(1), pages 58-73, February.
    Full references (including those not matched with items on IDEAS)

    More about this item

    JEL classification:

    • D44 - Microeconomics - - Market Structure, Pricing, and Design - - - Auctions
    • L86 - Industrial Organization - - Industry Studies: Services - - - Information and Internet Services; Computer Software

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