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The Planning of Guaranteed Targeted Display Advertising

Author

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  • John Turner

    (The Paul Merage School of Business, University of California at Irvine, Irvine, California 92697)

Abstract

As targeted advertising becomes prevalent in a wide variety of media vehicles, planning models become increasingly important to ad networks that need to match ads to appropriate audience segments, provide a high quality of service (meet advertisers' goals), and ensure that ad serving opportunities are not wasted. We define Guaranteed Targeted Display Advertising (GTDA) as a class of media vehicles that include webpage banner ads, video games, electronic outdoor billboards, and the next generation of digital TV, and formulate the GTDA planning problem as a transportation problem with quadratic objective. By modeling audience uncertainty, forecast errors, and the ad server's execution of the plan, we derive sufficient conditions that state when our quadratic objective is a good surrogate for several ad delivery performance metrics. Moreover, our quadratic objective allows us to construct duality-based bounds for evaluating aggregations of the audience space, leading to two efficient algorithms for solving large problems: the first intelligently refines the audience space into successively smaller blocks, and the second uses scaling to find a feasible solution given a fixed audience space partition. Near-optimal schedules can often be produced despite significant aggregation.

Suggested Citation

  • John Turner, 2012. "The Planning of Guaranteed Targeted Display Advertising," Operations Research, INFORMS, vol. 60(1), pages 18-33, February.
    • Handle: RePEc:inm:oropre:v:60:y:2012:i:1:p:18-33
    DOI: 10.1287/opre.1110.0996
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    References listed on IDEAS

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    Cited by:

    1. Shen, Yuelin, 2018. "Pricing contracts and planning stochastic resources in brand display advertising," Omega, Elsevier, vol. 81(C), pages 183-194.
    2. Miguel A. Lejeune & John Turner, 2019. "Planning Online Advertising Using Gini Indices," Operations Research, INFORMS, vol. 67(5), pages 1222-1245, September.
    3. 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.
    4. Veronica Marotta & Yue Wu & Kaifu Zhang & Alessandro Acquisti, 2022. "The Welfare Impact of Targeted Advertising Technologies," Information Systems Research, INFORMS, vol. 33(1), pages 131-151, March.
    5. Santiago R. Balseiro & Omar Besbes & Gabriel Y. Weintraub, 2015. "Repeated Auctions with Budgets in Ad Exchanges: Approximations and Design," Management Science, INFORMS, vol. 61(4), pages 864-884, April.
    6. Radha Mookerjee & Subodha Kumar & Vijay S. Mookerjee, 2017. "Optimizing Performance-Based Internet Advertisement Campaigns," Operations Research, INFORMS, vol. 65(1), pages 38-54, February.
    7. Ying-Ju Chen, 2017. "Optimal Dynamic Auctions for Display Advertising," Operations Research, INFORMS, vol. 65(4), pages 897-913, August.
    8. Radha Mookerjee & Subodha Kumar & Vijay S. Mookerjee, 2017. "Optimizing Performance-Based Internet Advertisement Campaigns," Operations Research, INFORMS, vol. 65(1), pages 38-54, February.
    9. Deza, Antoine & Huang, Kai & Metel, Michael R., 2015. "Chance constrained optimization for targeted Internet advertising," Omega, Elsevier, vol. 53(C), pages 90-96.
    10. Zhen Sun & Milind Dawande & Ganesh Janakiraman & Vijay Mookerjee, 2017. "Not Just a Fad: Optimal Sequencing in Mobile In-App Advertising," Information Systems Research, INFORMS, vol. 28(3), pages 511-528, September.
    11. Huaxiao Shen & Yanzhi Li & Jingjing Guan & Geoffrey K.F. Tso, 2021. "A Planning Approach to Revenue Management for Non‐Guaranteed Targeted Display Advertising," Production and Operations Management, Production and Operations Management Society, vol. 30(6), pages 1583-1602, June.
    12. Vahideh Sadat Abedi, 2017. "Allocation of advertising budget between multiple channels to support sales in multiple markets," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(2), pages 134-146, February.
    13. Manmohan Aseri & Milind Dawande & Ganesh Janakiraman & Vijay Mookerjee, 2018. "Procurement Policies for Mobile-Promotion Platforms," Management Science, INFORMS, vol. 64(10), pages 4590-4607, October.
    14. Abedi, Vahideh Sadat, 2019. "Compartmental diffusion modeling: Describing customer heterogeneity & communication network to support decisions for new product introductions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 536(C).
    15. Bert De Reyck & Ioannis Fragkos & Yael Grushka-Cockayne & Casey Lichtendahl & Hammond Guerin & Andrew Kritzer, 2017. "Vungle Inc. Improves Monetization Using Big Data Analytics," Interfaces, INFORMS, vol. 47(5), pages 454-466, October.
    16. 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.
    17. 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.
    18. Shinjini Pandey & Goutam Dutta & Harit Joshi, 2017. "Survey on Revenue Management in Media and Broadcasting," Interfaces, INFORMS, vol. 47(3), pages 195-213, June.
    19. Ali Hojjat & John Turner & Suleyman Cetintas & Jian Yang, 2017. "A Unified Framework for the Scheduling of Guaranteed Targeted Display Advertising Under Reach and Frequency Requirements," Operations Research, INFORMS, vol. 65(2), pages 289-313, April.
    20. Zhang, Jianqiang & He, Xiuli, 2019. "Targeted advertising by asymmetric firms," Omega, Elsevier, vol. 89(C), pages 136-150.

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