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Targeted Advertising Strategies on Television

Author

Listed:
  • Esther Gal-Or

    (Katz School of Business, University of Pittsburgh, 368A Mervis Hall, Pittsburgh, Pennsylvania 15260)

  • Mordechai Gal-Or

    (A. J. Palumbo School of Business Administration, Duquesne University, 470 Rockwell Hall, Pittsburgh, Pennsylvania 15282)

  • Jerrold H. May

    (Katz School of Business, University of Pittsburgh, 214 Mervis Hall, Pittsburgh, Pennsylvania 15260)

  • William E. Spangler

    (A. J. Palumbo School of Business Administration, Duquesne University, 924 Rockwell Hall, Pittsburgh, Pennsylvania 15282)

Abstract

The personal video recorder (PVR) facilitates the use of targeted advertising by allowing companies to monitor television viewing behavior and to build demographic profiles of viewers from the data that are collected. Our research explores the extent to which an advertiser should allocate resources to increase the quality of its targeting. We present a game-theoretic model that extends the conventional measurement of targeting quality by exploring the trade-off between two measures: accuracy and recognition. Accuracy measures the likelihood that any target segment prediction is correct, while recognition conversely measures the likelihood that any member of the target segment is identified. We find that the relative resources allocated to improving accuracy and recognition depend upon the size of the population of viewers, the propensity of viewers to skip commercials, the overall cost of airing commercials, and the competitive environment. Furthermore, the incentives to improve accuracy are markedly different from those to improve recognition. Although improving accuracy does not affect the extent of price competition, improving recognition leads to intensified price competition and reduced profitability in the product market. Thus, when facing a competitor that pursues a strategy to improve its recognition of potential customers, an advertiser should choose to reduce its investment in recognition and increase its investment in accuracy.

Suggested Citation

  • Esther Gal-Or & Mordechai Gal-Or & Jerrold H. May & William E. Spangler, 2006. "Targeted Advertising Strategies on Television," Management Science, INFORMS, vol. 52(5), pages 713-725, May.
    • Handle: RePEc:inm:ormnsc:v:52:y:2006:i:5:p:713-725
    DOI: 10.1287/mnsc.1050.0489
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Karle, Heiko & Peitz, Martin, 2017. "De-targeting: Advertising an assortment of products to loss-averse consumers," European Economic Review, Elsevier, vol. 95(C), pages 103-124.
    2. Bellman, Steven & Murphy, Jamie & Treleaven-Hassard, Shiree & O'Farrell, James & Qiu, Lili & Varan, Duane, 2013. "Using Internet Behavior to Deliver Relevant Television Commercials," Journal of Interactive Marketing, Elsevier, vol. 27(2), pages 130-140.
    3. Ricarda Schauerte & Stéphanie Feiereisen & Alan J. Malter, 2021. "What does it take to survive in a digital world? Resource-based theory and strategic change in the TV industry," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 45(2), pages 263-293, June.
    4. Esther Gal-Or & Ronen Gal-Or & Nabita Penmetsa, 2018. "The Role of User Privacy Concerns in Shaping Competition Among Platforms," Information Systems Research, INFORMS, vol. 29(3), pages 698-722, September.
    5. Wang, Wei & Li, Gang & Fung, Richard Y.K. & Cheng, T.C.E., 2019. "Mobile Advertising and Traffic Conversion: The Effects of Front Traffic and Spatial Competition," Journal of Interactive Marketing, Elsevier, vol. 47(C), pages 84-101.
    6. Li, Sanxi & Sun, Hailin & Yu, Jun, 2023. "Competitive targeted online advertising," International Journal of Industrial Organization, Elsevier, vol. 87(C).
    7. Rosa Brana Esteves & Joana Resende, 2017. "Personalized Pricing with Targeted Advertising: Who are the Winners?," NIPE Working Papers 02/2017, NIPE - Universidade do Minho.
    8. Sumitro Banerjee & Alex P. Thevaranjan, 2019. "Targeting and salesforce compensation: When sales spill over to unprofitable customers," Quantitative Marketing and Economics (QME), Springer, vol. 17(1), pages 81-104, March.
    9. Yan Wang & Shue Mei & Weijun Zhong, 2022. "Advertising or recommender systems? A game‐theoretic analysis of online retailer platforms' decision‐making," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 43(6), pages 2119-2132, September.
    10. Zhao Jiang & Wu Dan & Liu Jie, 2020. "Distinct role of targeting precision of Internet-based targeted advertising in duopolistic e-business firms’ heterogeneous consumers market," Electronic Commerce Research, Springer, vol. 20(2), pages 453-474, June.
    11. Zhao Jiang & Dan Wu, 2022. "Targeting Precision in Imperfect Targeted Advertising: Implications for the Regulation of Market Structure and Efficiency," SAGE Open, , vol. 12(1), pages 21582440221, March.
    12. Mihai Banciu & Esther Gal-Or & Prakash Mirchandani, 2010. "Bundling Strategies When Products Are Vertically Differentiated and Capacities Are Limited," Management Science, INFORMS, vol. 56(12), pages 2207-2223, December.
    13. Lusi Li & Jianqing Chen & Srinivasan Raghunathan, 2018. "Recommender System Rethink: Implications for an Electronic Marketplace with Competing Manufacturers," Information Systems Research, INFORMS, vol. 29(4), pages 1003-1023, December.
    14. Chandra, Ambarish & Kaiser, Ulrich, 2010. "Targeted advertising in magazine markets," ZEW Discussion Papers 10-063, ZEW - Leibniz Centre for European Economic Research.
    15. Zhang, Jianqiang & He, Xiuli, 2019. "Targeted advertising by asymmetric firms," Omega, Elsevier, vol. 89(C), pages 136-150.
    16. Esteves, Rosa-Branca & Resende, Joana, 2019. "Personalized pricing and advertising: Who are the winners?," International Journal of Industrial Organization, Elsevier, vol. 63(C), pages 239-282.

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