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Exclusive Placement in Online Advertising

Author

Listed:
  • Amin Sayedi

    (University of Washington, Seattle, Washington 98195)

  • Kinshuk Jerath

    (Columbia Business School, Columbia University, New York, New York 10027)

  • Marjan Baghaie

    (Uber Techonologies, San Francisco, California 94103)

Abstract

A recent development in online advertising has been the ability of advertisers to have their ads displayed exclusively on (a part of) a web page. We study this phenomenon in the context of both sponsored search advertising and display advertising. Ads are sold through auctions, and when exclusivity is allowed, the seller accepts two bids from advertisers, where one bid is for the standard display format in which multiple advertisers are displayed, and the other bid is for being shown exclusively (therefore they are called two-dimensional, or 2D, auctions). We identify two opposing forces at play in an auction that provides the exclusive placement option—allowing more flexible expression of preferences through bidding for exclusivity increases competition among advertisers, leading to higher bids, which increases the seller’s revenue (between-advertiser competition effect), but it also gives advertisers the incentive to shade their bids for their nonpreferred outcomes, which decreases the seller’s revenue (within-advertiser competition effect). Depending on which effect is stronger, the revenue may increase or decrease. We find that the 2D generalized second price (GSP 2D ) auction, which is an extension of the widely used generalized second price (GSP) auction and on which currently used auctions for exclusive placement are based, may lead to higher or lower revenue under different parametric conditions. Paradoxically, the revenue from allowing exclusive placement decreases as bidders have higher valuations for exclusive placement. We verify several key implications from our analysis of GSP 2D using data from Bing for over 100,000 auctions. As a possible solution (applicable to both sponsored search and display advertising), we show that using VCG 2D , which is the adaptation of the Vickrey–Clarke–Groves (VCG) auction for the 2D setting, guarantees weakly higher revenue when exclusive display is allowed. This is because it induces truthful bidding, which alleviates the problem of bid shading due to the within-advertiser competition effect.

Suggested Citation

  • Amin Sayedi & Kinshuk Jerath & Marjan Baghaie, 2018. "Exclusive Placement in Online Advertising," Marketing Science, INFORMS, vol. 37(6), pages 970-986, November.
  • Handle: RePEc:inm:ormksc:v:37:y:2018:i:6:p:970-986
    DOI: 10.1287/mksc.2018.1098
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    References listed on IDEAS

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    1. Susan Athey & Glenn Ellison, 2011. "Position Auctions with Consumer Search," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 126(3), pages 1213-1270.
    2. Wilfred Amaldoss & Preyas S. Desai & Woochoel Shin, 2015. "Keyword Search Advertising and First-Page Bid Estimates: A Strategic Analysis," Management Science, INFORMS, vol. 61(3), pages 507-519, March.
    3. Woochoel Shin, 2015. "Keyword Search Advertising and Limited Budgets," Marketing Science, INFORMS, vol. 34(6), pages 882-896, November.
    4. Benjamin Edelman & Michael Ostrovsky & Michael Schwarz, 2007. "Internet Advertising and the Generalized Second-Price Auction: Selling Billions of Dollars Worth of Keywords," American Economic Review, American Economic Association, vol. 97(1), pages 242-259, March.
    5. Matthew Cary & Aparna Das & Benjamin Edelman & Ioannis Giotis & Kurtis Heimerl & Anna R. Karlin & Claire Mathieu & Michael Schwarz, 2008. "On Best-Response Bidding in GSP Auctions," NBER Working Papers 13788, National Bureau of Economic Research, Inc.
    6. Yeon-Koo Che, 1993. "Design Competition through Multidimensional Auctions," RAND Journal of Economics, The RAND Corporation, vol. 24(4), pages 668-680, Winter.
    7. Anthony Dukes & Esther Gal–Or, 2003. "Negotiations and Exclusivity Contracts for Advertising," Marketing Science, INFORMS, vol. 22(2), pages 222-245, November.
    8. Fernando Branco, 1997. "The Design of Multidimensional Auctions," RAND Journal of Economics, The RAND Corporation, vol. 28(1), pages 63-81, Spring.
    9. Preyas S. Desai & Woochoel Shin & Richard Staelin, 2014. "The Company That You Keep: When to Buy a Competitor's Keyword," Marketing Science, INFORMS, vol. 33(4), pages 485-508, July.
    10. Francesco Decarolis & Maris Goldmanis & Antonio Penta, 2020. "Marketing Agencies and Collusive Bidding in Online Ad Auctions," Management Science, INFORMS, vol. 66(10), pages 4433-4454, October.
    11. Yi Zhu & Kenneth C. Wilbur, 2011. "Hybrid Advertising Auctions," Marketing Science, INFORMS, vol. 30(2), pages 249-273, 03-04.
    12. William Vickrey, 1961. "Counterspeculation, Auctions, And Competitive Sealed Tenders," Journal of Finance, American Finance Association, vol. 16(1), pages 8-37, March.
    13. Gomes, Renato & Sweeney, Kane, 2014. "Bayes–Nash equilibria of the generalized second-price auction," Games and Economic Behavior, Elsevier, vol. 86(C), pages 421-437.
    14. Hal R. Varian & Christopher Harris, 2014. "The VCG Auction in Theory and Practice," American Economic Review, American Economic Association, vol. 104(5), pages 442-445, May.
    15. Thiel, Stuart E., 1988. "Multidimensional auctions," Economics Letters, Elsevier, vol. 28(1), pages 37-40.
    16. Przemyslaw Jeziorski & Ilya Segal, 2015. "What Makes Them Click: Empirical Analysis of Consumer Demand for Search Advertising," American Economic Journal: Microeconomics, American Economic Association, vol. 7(3), pages 24-53, August.
    17. Amin Sayedi & Kinshuk Jerath & Kannan Srinivasan, 2014. "Competitive Poaching in Sponsored Search Advertising and Its Strategic Impact on Traditional Advertising," Marketing Science, INFORMS, vol. 33(4), pages 586-608, July.
    18. Kinshuk Jerath & Liye Ma & Young-Hoon Park & Kannan Srinivasan, 2011. "A "Position Paradox" in Sponsored Search Auctions," Marketing Science, INFORMS, vol. 30(4), pages 612-627, July.
    19. Shijie Lu & Yi Zhu & Anthony Dukes, 2015. "Position Auctions with Budget Constraints: Implications for Advertisers and Publishers," Marketing Science, INFORMS, vol. 34(6), pages 897-905, November.
    20. Edward Clarke, 1971. "Multipart pricing of public goods," Public Choice, Springer, vol. 11(1), pages 17-33, September.
    21. Zsolt Katona & Miklos Sarvary, 2010. "The Race for Sponsored Links: Bidding Patterns for Search Advertising," Marketing Science, INFORMS, vol. 29(2), pages 199-215, 03-04.
    22. Groves, Theodore, 1973. "Incentives in Teams," Econometrica, Econometric Society, vol. 41(4), pages 617-631, July.
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    Cited by:

    1. Jiwoong Shin & Woochoel Shin, 2023. "A Theory of Irrelevant Advertising: An Agency-Induced Targeting Inefficiency," Management Science, INFORMS, vol. 69(8), pages 4481-4497, August.
    2. Hana Choi & Carl F. Mela & Santiago R. Balseiro & Adam Leary, 2020. "Online Display Advertising Markets: A Literature Review and Future Directions," Information Systems Research, INFORMS, vol. 31(2), pages 556-575, June.
    3. Amin Sayedi, 2018. "Real-Time Bidding in Online Display Advertising," Marketing Science, INFORMS, vol. 37(4), pages 553-568, August.
    4. W. Jason Choi & Amin Sayedi, 2019. "Learning in Online Advertising," Marketing Science, INFORMS, vol. 38(4), pages 584-608, July.
    5. Hemant K. Bhargava & Gergely Csapó & Rudolf Müller, 2020. "On Optimal Auctions for Mixing Exclusive and Shared Matching in Platforms," Management Science, INFORMS, vol. 66(6), pages 2653-2676, June.
    6. Alison Watts, 2021. "Fairness and Efficiency in Online Advertising Mechanisms," Games, MDPI, vol. 12(2), pages 1-11, April.
    7. Fei Long & Kinshuk Jerath & Miklos Sarvary, 2022. "Designing an Online Retail Marketplace: Leveraging Information from Sponsored Advertising," Marketing Science, INFORMS, vol. 41(1), pages 115-138, January.
    8. Pallavi Pal, 2023. "Sponsored Search Auction and the Revenue- Maximizing Number of Ads per Page," CESifo Working Paper Series 10299, CESifo.
    9. W. Jason Choi & Amin Sayedi, 2023. "Open and Private Exchanges in Display Advertising," Marketing Science, INFORMS, vol. 42(3), pages 451-475, May.

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