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Yield Optimization of Display Advertising with Ad Exchange

Author

Listed:
  • Santiago R. Balseiro

    (Fuqua School of Business, Duke University, Durham, North Carolina 27708)

  • Jon Feldman

    (Google Research, New York, New York 10011)

  • Vahab Mirrokni

    (Google Research, New York, New York 10011)

  • S. Muthukrishnan

    (Google Research, New York, New York 10011)

Abstract

It is clear from the growing role of ad exchanges in the real-time sale of advertising slots that Web publishers are considering a new alternative to their more traditional reservation-based ad contracts. To make this choice, the publisher must trade off, in real-time, the short-term revenue from ad exchange with the long-term benefits of delivering good spots to the reservation ads. In this paper we formalize this combined optimization problem as a multiobjective stochastic control problem and derive an efficient policy for online ad allocation in settings with general joint distribution over placement quality and exchange prices. We prove the asymptotic optimality of this policy in terms of any arbitrary trade-off between the quality of delivered reservation ads and revenue from the exchange, and we show that our policy approximates any Pareto-optimal point on the quality-versus-revenue curve. Experimental results on data derived from real publisher inventory confirm that there are significant benefits for publishers if they jointly optimize over both channels.Data, as supplemental material, are available at http://dx.doi.org/10.1287/mnsc.2014.2017 . This paper was accepted by Dimitris Bertsimas, optimization.

Suggested Citation

  • Santiago R. Balseiro & Jon Feldman & Vahab Mirrokni & S. Muthukrishnan, 2014. "Yield Optimization of Display Advertising with Ad Exchange," Management Science, INFORMS, vol. 60(12), pages 2886-2907, December.
    • Handle: RePEc:inm:ormnsc:v:60:y:2014:i:12:p:2886-2907
    DOI: 10.1287/mnsc.2014.2017
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    References listed on IDEAS

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    1. 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.
    2. Guillermo Gallego & Garrett van Ryzin, 1994. "Optimal Dynamic Pricing of Inventories with Stochastic Demand over Finite Horizons," Management Science, INFORMS, vol. 40(8), pages 999-1020, August.
    3. Milgrom, Paul R & Weber, Robert J, 1982. "A Theory of Auctions and Competitive Bidding," Econometrica, Econometric Society, vol. 50(5), pages 1089-1122, September.
    4. Yuri Levin & Jeff McGill & Mikhail Nediak, 2008. "Risk in Revenue Management and Dynamic Pricing," Operations Research, INFORMS, vol. 56(2), pages 326-343, April.
    5. Kalyan Talluri & Garrett van Ryzin, 1998. "An Analysis of Bid-Price Controls for Network Revenue Management," Management Science, INFORMS, vol. 44(11-Part-1), pages 1577-1593, November.
    6. Retsef Levi & Ana Radovanović, 2010. "Provably Near-Optimal LP-Based Policies for Revenue Management in Systems with Reusable Resources," Operations Research, INFORMS, vol. 58(2), pages 503-507, April.
    7. Shipra Agrawal & Zizhuo Wang & Yinyu Ye, 2014. "A Dynamic Near-Optimal Algorithm for Online Linear Programming," Operations Research, INFORMS, vol. 62(4), pages 876-890, August.
    8. Martin A. Lariviere, 2006. "A Note on Probability Distributions with Increasing Generalized Failure Rates," Operations Research, INFORMS, vol. 54(3), pages 602-604, June.
    9. John Turner, 2012. "The Planning of Guaranteed Targeted Display Advertising," Operations Research, INFORMS, vol. 60(1), pages 18-33, February.
    10. Stefanus Jasin & Sunil Kumar, 2012. "A Re-Solving Heuristic with Bounded Revenue Loss for Network Revenue Management with Customer Choice," Mathematics of Operations Research, INFORMS, vol. 37(2), pages 313-345, May.
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