IDEAS home Printed from https://ideas.repec.org/p/cpr/ceprdp/18087.html
   My bibliography  Save this paper

Managed Campaigns and Data-Augmented Auctions for Digital Advertising

Author

Listed:
  • Bergemann, Dirk
  • Bonatti, Alessandro
  • Wu, Nick

Abstract

We develop an auction model for digital advertising. A monopoly platform has access to data on the value of the match between advertisers and consumers. The platform support bidding with additional information and increase the feasible surplus for on-platform matches. Advertisers jointly determine their pricing strategy both on and off the platform, as well as their bidding for digital advertising on the platform. We compare a data-augmented second-price auction and a managed campaign mechanism. In the data-augmented auction, the bids by the advertisers are informed by the data of the platform regarding the value of the match. This results in a socially efficient allocation on the platform, but the advertisers increase their product prices off the platform to be more competitive on the platform. In consequence, the allocation off the platform is inefficient due to excessively high product prices. The managed campaign mechanism allows advertisers to submit budgets that are then transformed into matches and prices through an autobidding algorithm. Compared to the data-augmented second-price auction, the optimal managed campaign mechanism increases the revenue of the digital platform. The product prices off the platform increase and the consumer surplus decreases.

Suggested Citation

  • Bergemann, Dirk & Bonatti, Alessandro & Wu, Nick, 2023. "Managed Campaigns and Data-Augmented Auctions for Digital Advertising," CEPR Discussion Papers 18087, C.E.P.R. Discussion Papers.
  • Handle: RePEc:cpr:ceprdp:18087
    as

    Download full text from publisher

    File URL: https://cepr.org/publications/DP18087
    Download Restriction: CEPR Discussion Papers are free to download for our researchers, subscribers and members. If you fall into one of these categories but have trouble downloading our papers, please contact us at subscribers@cepr.org
    ---><---

    As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Heski Bar-Isaac & Sandro Shelegia, 2023. "Search, Showrooming, and Retailer Variety," Marketing Science, INFORMS, vol. 42(2), pages 251-270, March.
    2. Chengsi Wang & Julian Wright, 2020. "Search platforms: showrooming and price parity clauses," RAND Journal of Economics, RAND Corporation, vol. 51(1), pages 32-58, March.
    3. Fuchs, William & Skrzypacz, Andrzej, 2015. "Government interventions in a dynamic market with adverse selection," Journal of Economic Theory, Elsevier, vol. 158(PA), pages 371-406.
    4. Dirk Bergemann & Alessandro Bonatti, 2022. "Data, Competition, and Digital Platforms," Cowles Foundation Discussion Papers 2343, Cowles Foundation for Research in Economics, Yale University.
    5. Jean Tirole, 2012. "Overcoming Adverse Selection: How Public Intervention Can Restore Market Functioning," American Economic Review, American Economic Association, vol. 102(1), pages 29-59, February.
    6. Santiago R. Balseiro & Yonatan Gur, 2019. "Learning in Repeated Auctions with Budgets: Regret Minimization and Equilibrium," Management Science, INFORMS, vol. 65(9), pages 3952-3968, September.
    7. Thomas Philippon & Vasiliki Skreta, 2012. "Optimal Interventions in Markets with Adverse Selection," American Economic Review, American Economic Association, vol. 102(1), pages 1-28, February.
    8. Yash Kanoria & Hamid Nazerzadeh, 2020. "Dynamic Reserve Prices for Repeated Auctions: Learning from Bids," Papers 2002.07331, arXiv.org.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Bonatti, Alessandro & Bergemann, Dirk, 2022. "Data, Competition, and Digital Platforms," CEPR Discussion Papers 17544, C.E.P.R. Discussion Papers.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Dirk Bergemann & Alessandro Bonatti & Nicholas Wu, 2023. "How Do Digital Advertising Auctions Impact Product Prices?," Papers 2304.08432, arXiv.org, revised Jul 2023.
    2. Bonatti, Alessandro & Bergemann, Dirk, 2022. "Data, Competition, and Digital Platforms," CEPR Discussion Papers 17544, C.E.P.R. Discussion Papers.
    3. Zhang, Hanzhe & Hu, Yunzhi, 2020. "Overcoming Borrowing Stigma: The Design of Lending-of-Last-Resort Policies," Working Papers 2020-7, Michigan State University, Department of Economics.
    4. William Fuchs & Andrzej Skrzypacz, 2019. "Costs and benefits of dynamic trading in a lemons market," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 33, pages 105-127, July.
    5. Huberto M. Ennis, 2019. "Interventions in Markets with Adverse Selection: Implications for Discount Window Stigma," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 51(7), pages 1737-1764, October.
    6. Vincent Maurin, 2016. "Liquidity Fluctuations in Over the Counter Markets," 2016 Meeting Papers 218, Society for Economic Dynamics.
    7. Madison, Florian, 2019. "Frictional asset reallocation under adverse selection," Journal of Economic Dynamics and Control, Elsevier, vol. 100(C), pages 115-130.
    8. Davoodalhosseini, Seyed Mohammadreza, 2019. "Constrained efficiency with adverse selection and directed search," Journal of Economic Theory, Elsevier, vol. 183(C), pages 568-593.
    9. Taneli Mäkinen & Francesco Palazzo, 2017. "The double bind of asymmetric information in over-the-counter markets," Temi di discussione (Economic working papers) 1128, Bank of Italy, Economic Research and International Relations Area.
    10. Carré, Sylvain & Collin-Dufresne, Pierre & Gabriel, Franck, 2022. "Insider trading with penalties," Journal of Economic Theory, Elsevier, vol. 203(C).
    11. Lee, Michael Junho & Neuhann, Daniel, 2023. "Collateral quality and intervention traps," Journal of Financial Economics, Elsevier, vol. 147(1), pages 159-171.
    12. Vincent Maurin, 2022. "Liquidity Fluctuations in Over‐the‐Counter Markets," Journal of Finance, American Finance Association, vol. 77(2), pages 1325-1369, April.
    13. Braz Camargo & Kyungmin Kim & Benjamin Lester, 2016. "Information Spillovers, Gains from Trade, and Interventions in Frozen Markets," The Review of Financial Studies, Society for Financial Studies, vol. 29(5), pages 1291-1329.
    14. Palazzo, Francesco, 2017. "Search costs and the severity of adverse selection," Research in Economics, Elsevier, vol. 71(1), pages 171-197.
    15. Davoodalhosseini, Seyed Mohammadreza, 2022. "Optimal taxation in asset markets with adverse selection," European Economic Review, Elsevier, vol. 147(C).
    16. Andrea Attar & Thomas Mariotti & François Salanié, 2021. "Entry-Proofness and Discriminatory Pricing under Adverse Selection," American Economic Review, American Economic Association, vol. 111(8), pages 2623-2659, August.
    17. Andrea Attar & Thomas Mariotti & François Salanié, 2020. "The Social Costs of Side Trading," The Economic Journal, Royal Economic Society, vol. 130(630), pages 1608-1622.
    18. Heinsalu, Sander, 2020. "Investing to access an adverse selection market," International Journal of Industrial Organization, Elsevier, vol. 72(C).
    19. Toni Ahnert & Martin Kuncl, 2019. "Loan Insurance, Market Liquidity, and Lending Standards," Staff Working Papers 19-47, Bank of Canada.
    20. Gorkem Celik & Michael Peters, 2016. "Reciprocal relationships and mechanism design," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 49(1), pages 374-411, February.

    More about this item

    Keywords

    Matching; Competition; Advertising; Auctions; Data;
    All these keywords.

    JEL classification:

    • D44 - Microeconomics - - Market Structure, Pricing, and Design - - - Auctions
    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:cpr:ceprdp:18087. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: the person in charge (email available below). General contact details of provider: https://www.cepr.org .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.