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

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Author Info

  • L. Elisa Celis

    ()
    (Department of Computer Science, University of Washington)

  • Gregory Lewis

    ()
    (Department of Economics, Harvard University)

  • Markus Mobius

    ()
    (Microsoft Research New England)

  • Hamid Nazerzadeh

    ()
    (Marshall School of Business, University of Southern California)

Abstract

Increasingly sophisticated tracking technology offers publishers the ability to offer 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.

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Bibliographic Info

Paper provided by NET Institute in its series Working Papers with number 11-21.

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Length: 30 pages
Date of creation: Sep 2011
Date of revision: Nov 2011
Handle: RePEc:net:wpaper:1121

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Web page: http://www.NETinst.org/

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Keywords: Advertising; Auctions; Mechanism Design;

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  1. Pascal Courty & Li Hao, 1997. "Sequential screening," Economics Working Papers 224, Department of Economics and Business, Universitat Pompeu Fabra.
  2. 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.
  3. Svaiter, Benar Fux & Monteiro, Paulo Klinger, 2008. "Optimal auction with a general distribution: virtual valuation without densities," Economics Working Papers (Ensaios Economicos da EPGE) 681, FGV/EPGE Escola Brasileira de Economia e Finan├žas, Getulio Vargas Foundation (Brazil).
  4. Ostrovsky, Michael & Schwarz, Michael, 2009. "Reserve Prices in Internet Advertising Auctions: A Field Experiment," Research Papers 2054, Stanford University, Graduate School of Business.
  5. Jean-Charles Rochet & Philippe Chone, 1998. "Ironing, Sweeping, and Multidimensional Screening," Econometrica, Econometric Society, vol. 66(4), pages 783-826, July.
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