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Correcting Audience Externalities in Television Advertising

Author

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  • Kenneth C. Wilbur

    (Rady School of Management, University of California, San Diego, California 92093)

  • Linli Xu

    (Carlson School of Management, University of Minnesota, Minneapolis, Minnesota 55455)

  • David Kempe

    (Department of Computer Science, University of Southern California, Los Angeles, California 90033)

Abstract

When a television advertisement causes viewers to switch channels, it reduces the audience available to subsequent advertisers. This audience loss is not reflected in the advertisement price, resulting in an audience externality. The present article analyzes the television network's problem of how to select, order, and price advertisements in a break of endogenous length in order to correct audience externalities. It proposes the Audience Value Maximization Algorithm (AVMA), which considers many possible advertisement orderings within a dynamic programming framework with a strategy-proof pricing mechanism. Two data sets are used to estimate heterogeneity in viewer-switching probabilities and advertiser willingness-to-pay parameters in order to evaluate the algorithm's performance. A series of simulations shows that AVMA typically maximizes audience value to advertisers, increases network revenue relative to several alternatives, and runs quickly enough to implement.

Suggested Citation

  • Kenneth C. Wilbur & Linli Xu & David Kempe, 2013. "Correcting Audience Externalities in Television Advertising," Marketing Science, INFORMS, vol. 32(6), pages 892-912, November.
  • Handle: RePEc:inm:ormksc:v:32:y:2013:i:6:p:892-912
    DOI: 10.1287/mksc.2013.0807
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    References listed on IDEAS

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    3. Omid Rafieian, 2023. "Optimizing User Engagement Through Adaptive Ad Sequencing," Marketing Science, INFORMS, vol. 42(5), pages 910-933, September.
    4. Mengdi Wang & Dong Li, 2020. "What motivates audience comments on live streaming platforms?," PLOS ONE, Public Library of Science, vol. 15(4), pages 1-12, April.
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    6. Trivedi, Minakshi & Sridhar, Karthik & Kumar, Ashish, 2016. "Impact of Healthy Alternatives on Consumer Choice: A Balancing Act," Journal of Retailing, Elsevier, vol. 92(1), pages 65-82.
    7. Beth L. Fossen & Alexander Bleier, 2021. "Online program engagement and audience size during television ads," Journal of the Academy of Marketing Science, Springer, vol. 49(4), pages 743-761, July.
    8. Jūra Liaukonytė & Alminas Žaldokas, 2022. "Background Noise? TV Advertising Affects Real-Time Investor Behavior," Management Science, INFORMS, vol. 68(4), pages 2465-2484, April.
    9. Beth L. Fossen & David A. Schweidel, 2017. "Television Advertising and Online Word-of-Mouth: An Empirical Investigation of Social TV Activity," Marketing Science, INFORMS, vol. 36(1), pages 105-123, January.
    10. Shi, Yang & Zhao, Ying, 2019. "Modeling Advertisers' Willingness to Pay in TV Commercial Slot Auctions," Journal of Interactive Marketing, Elsevier, vol. 48(C), pages 120-133.
    11. Sylvia Hristakeva & Julie Holland Mortimer, 2023. "Price Dispersion and Legacy Discounts in the National Television Advertising Market," Marketing Science, INFORMS, vol. 42(6), pages 1162-1183, November.
    12. Beth L. Fossen & David A. Schweidel, 2019. "Social TV, Advertising, and Sales: Are Social Shows Good for Advertisers?," Marketing Science, INFORMS, vol. 38(2), pages 274-295, March.
    13. Matthew McGranaghan & Jura Liaukonyte & Kenneth C. Wilbur, 2022. "How Viewer Tuning, Presence, and Attention Respond to Ad Content and Predict Brand Search Lift," Marketing Science, INFORMS, vol. 41(5), pages 873-895, September.
    14. Anthony Dukes & Qihong Liu & Jie Shuai, 2022. "Skippable Ads: Interactive Advertising on Digital Media Platforms," Marketing Science, INFORMS, vol. 41(3), pages 528-547, May.
    15. Anna E. Tuchman & Harikesh S. Nair & Pedro M. Gardete, 2018. "Television ad-skipping, consumption complementarities and the consumer demand for advertising," Quantitative Marketing and Economics (QME), Springer, vol. 16(2), pages 111-174, June.
    16. David A. Schweidel & Natasha Zhang Foutz & Robin J. Tanner, 2014. "Synergy or Interference: The Effect of Product Placement on Commercial Break Audience Decline," Marketing Science, INFORMS, vol. 33(6), pages 763-780, November.
    17. Beth L. Fossen & Girish Mallapragada & Anwesha De, 2021. "Impact of Political Television Advertisements on Viewers’ Response to Subsequent Advertisements," Marketing Science, INFORMS, vol. 40(2), pages 305-324, March.
    18. He, Chen, 2018. "Essays on the role and effects of advertising," Other publications TiSEM 47a3272a-54f1-4a90-9714-c, Tilburg University, School of Economics and Management.
    19. Jura Liaukonyte & Thales Teixeira & Kenneth C. Wilbur, 2015. "Television Advertising and Online Shopping," Marketing Science, INFORMS, vol. 34(3), pages 311-330, May.
    20. Khim-Yong Goh & Kai-Lung Hui & Ivan P. L. Png, 2015. "Privacy and Marketing Externalities: Evidence from Do Not Call," Management Science, INFORMS, vol. 61(12), pages 2982-3000, December.
    21. Shunyao Yan & Klaus M. Miller & Bernd Skiera, 2020. "How Does the Adoption of Ad Blockers Affect News Consumption?," Papers 2005.06840, arXiv.org, revised Aug 2021.
    22. Jordi McKenzie, 2023. "The economics of movies (revisited): A survey of recent literature," Journal of Economic Surveys, Wiley Blackwell, vol. 37(2), pages 480-525, April.
    23. Stourm, Valeria & Bax, Eric, 2017. "Incorporating hidden costs of annoying ads in display auctions," International Journal of Research in Marketing, Elsevier, vol. 34(3), pages 622-640.
    24. Kenneth C. Wilbur, 2016. "Advertising Content and Television Advertising Avoidance," Journal of Media Economics, Taylor & Francis Journals, vol. 29(2), pages 51-72, April.
    25. Yuxin Chen & Qihong Liu, 2022. "Signaling Through Advertising When an Ad Can Be Blocked," Marketing Science, INFORMS, vol. 41(1), pages 166-187, January.

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