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Best of Both Worlds Ad Contracts: Guaranteed Allocation and Price with Programmatic Efficiency

Author

Listed:
  • Maxime C. Cohen

    (Desautels Faculty of Management, McGill University, Montreal, Quebec H3A 1G5, Canada)

  • Antoine Désir

    (Technology and Operations Management, INSEAD, 77305, Fontainebleau, France)

  • Nitish Korula

    (Google, New York 10011)

  • Balasubramanian Sivan

    (Google Research, New York 10011)

Abstract

Buying display ad impressions via real-time auctions comes with significant allocation and price uncertainties. We design and analyze a contract that mitigates this uncertainty risk by providing guaranteed allocation and prices while maintaining the efficiency of buying in an auction. We study how risk aversion affects the desire for guarantees and how to price a guaranteed allocation. We propose to augment the traditional auction with a programmatic purchase option (which we call a Market-Maker contract ) that removes allocation and price uncertainties. Instead of participating in the auction, advertisers can secure impressions in advance at a fixed premium price offered by the Market-Maker. It is then the responsibility of the Market-Maker to procure these impressions by bidding in the auction. We model buyers as risk-averse agents and analyze the equilibrium outcome when buyers face two purchase options (auction and Market-Maker contract). We derive analytical expressions for the Market-Maker price that reveal insightful relationships with uncertainties in the auction price and buyers’ risk levels. We also show the existence of a Market-Maker price that simultaneously improves the seller’s revenue and the sum of buyers’ utilities. As a building block to our analysis, we establish the truthfulness of the multiunit auction when buyers have nonquasilinear utilities because of risk aversion. Recently, the Google’s Display & Video 360 platform started offering a product akin to Market-Maker called “Guaranteed Packages,” which was inspired by this paper.

Suggested Citation

  • Maxime C. Cohen & Antoine Désir & Nitish Korula & Balasubramanian Sivan, 2023. "Best of Both Worlds Ad Contracts: Guaranteed Allocation and Price with Programmatic Efficiency," Management Science, INFORMS, vol. 69(7), pages 4027-4050, July.
    • Handle: RePEc:inm:ormnsc:v:69:y:2023:i:7:p:4027-4050
    DOI: 10.1287/mnsc.2022.4542
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    References listed on IDEAS

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    1. Stanley Reynolds & John Wooders, 2009. "Auctions with a buy price," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 38(1), pages 9-39, January.
    2. Victor F. Araman & Ioana Popescu, 2010. "Media Revenue Management with Audience Uncertainty: Balancing Upfront and Spot Market Sales," Manufacturing & Service Operations Management, INFORMS, vol. 12(2), pages 190-212, December.
    3. Amin Sayedi, 2018. "Real-Time Bidding in Online Display Advertising," Marketing Science, INFORMS, vol. 37(4), pages 553-568, August.
    4. Shunda, Nicholas, 2009. "Auctions with a buy price: The case of reference-dependent preferences," Games and Economic Behavior, Elsevier, vol. 67(2), pages 645-664, November.
    5. Nautz, D. & Wolfstetter, E., 1997. "Bid shading and risk aversion in multi-unit auctions with many bidders," Economics Letters, Elsevier, vol. 56(2), pages 195-200, October.
    6. Markowitz, Harry, 2014. "Mean–variance approximations to expected utility," European Journal of Operational Research, Elsevier, vol. 234(2), pages 346-355.
    7. Matthew Rabin, 2000. "Risk Aversion and Expected-Utility Theory: A Calibration Theorem," Econometrica, Econometric Society, vol. 68(5), pages 1281-1292, September.
    8. Ang, Andrew, 2014. "Asset Management: A Systematic Approach to Factor Investing," OUP Catalogue, Oxford University Press, number 9780199959327.
    9. Martin A. Lariviere, 2006. "A Note on Probability Distributions with Increasing Generalized Failure Rates," Operations Research, INFORMS, vol. 54(3), pages 602-604, June.
    10. John Turner, 2012. "The Planning of Guaranteed Targeted Display Advertising," Operations Research, INFORMS, vol. 60(1), pages 18-33, February.
    11. Devanur, Nikhil R. & Peres, Yuval & Sivan, Balasubramanian, 2019. "Perfect Bayesian Equilibria in repeated sales," Games and Economic Behavior, Elsevier, vol. 118(C), pages 570-588.
    12. Gérard P. Cachon, 2004. "The Allocation of Inventory Risk in a Supply Chain: Push, Pull, and Advance-Purchase Discount Contracts," Management Science, INFORMS, vol. 50(2), pages 222-238, February.
    13. René Caldentey & Gustavo Vulcano, 2007. "Online Auction and List Price Revenue Management," Management Science, INFORMS, vol. 53(5), pages 795-813, May.
    14. Maskin, Eric S & Riley, John G, 1984. "Optimal Auctions with Risk Averse Buyers," Econometrica, Econometric Society, vol. 52(6), pages 1473-1518, November.
    15. Milgrom,Paul, 2004. "Putting Auction Theory to Work," Cambridge Books, Cambridge University Press, number 9780521536721, October.
    16. Susan Athey & Emilio Calvano & Joshua Gans, 2013. "The Impact of the Internet on Advertising Markets for News Media," NBER Working Papers 19419, National Bureau of Economic Research, Inc.
    17. René Kirkegaard & Per Baltzer Overgaard, 2008. "Buy‐out prices in auctions: seller competition and multi‐unit demands," RAND Journal of Economics, RAND Corporation, vol. 39(3), pages 770-789, September.
    18. Soo-Haeng Cho & Christopher S. Tang, 2013. "Advance Selling in a Supply Chain Under Uncertain Supply and Demand," Manufacturing & Service Operations Management, INFORMS, vol. 15(2), pages 305-319, May.
    19. Levy, H & Markowtiz, H M, 1979. "Approximating Expected Utility by a Function of Mean and Variance," American Economic Review, American Economic Association, vol. 69(3), pages 308-317, June.
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