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A Unified Framework for the Scheduling of Guaranteed Targeted Display Advertising Under Reach and Frequency Requirements

Author

Listed:
  • Ali Hojjat

    (Peter T. Paul College of Business and Economics, University of New Hampshire, Durham, New Hampshire 03824)

  • John Turner

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

  • Suleyman Cetintas

    (Yahoo Research, Sunnyvale, California 94089)

  • Jian Yang

    (Yahoo Research, Sunnyvale, California 94089)

Abstract

Motivated by recent trends in online advertising and advancements made by online publishers, we consider a new form of contract that allows advertisers to specify the number of unique individuals that should see their ad ( reach ) and the minimum number of times each individual should be exposed ( frequency ). We develop an optimization framework that aims for minimal under-delivery and proper spread of each campaign over its targeted demographics. As well, we introduce a pattern -based delivery mechanism that allows us to integrate a variety of interesting features into a website’s ad allocation optimization problem that have not been possible before. For example, our approach allows publishers to implement any desired pacing of ads over time at the user level or control the number of competing brands seen by each individual. We develop a two-phase algorithm that employs column generation in a hierarchical scheme with three parallelizable components. Numerical tests with real industry data show that our algorithm produces high-quality solutions and has promising run-time and scalability. Several extensions of the model are presented, e.g., to account for multiple ad positions on the webpage or randomness in the website visitors’ arrival process.

Suggested Citation

  • 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.
    • Handle: RePEc:inm:oropre:v:65:y:2017:i:2:p:289-313
    DOI: 10.1287/opre.2016.1567
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    References listed on IDEAS

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    Cited by:

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    2. Zikun Ye & Dennis J. Zhang & Heng Zhang & Renyu Zhang & Xin Chen & Zhiwei Xu, 2023. "Cold Start to Improve Market Thickness on Online Advertising Platforms: Data-Driven Algorithms and Field Experiments," Management Science, INFORMS, vol. 69(7), pages 3838-3860, July.
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    4. Miguel A. Lejeune & John Turner, 2019. "Planning Online Advertising Using Gini Indices," Operations Research, INFORMS, vol. 67(5), pages 1222-1245, September.
    5. Selvaprabu Nadarajah & Andre A. Cire, 2020. "Network-Based Approximate Linear Programming for Discrete Optimization," Operations Research, INFORMS, vol. 68(6), pages 1767-1786, November.
    6. 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.
    7. Raghav Singal & Omar Besbes & Antoine Desir & Vineet Goyal & Garud Iyengar, 2022. "Shapley Meets Uniform: An Axiomatic Framework for Attribution in Online Advertising," Management Science, INFORMS, vol. 68(10), pages 7457-7479, October.

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