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Behavior-Based Advertising

Author

Listed:
  • Qiaowei Shen

    (Guanghua School of Management, Peking University, 100871 Be¼ing, P.R. China)

  • J. Miguel Villas-Boas

    (Haas School of Business, University of California, Berkeley, Berkeley, California 94720)

Abstract

This paper considers the effect of firms sending advertising messages to consumers based on their past purchase behavior. If past purchase behavior on a product category is positively correlated with a consumer having high preferences in another category, a firm may want to advertise more intensively to those consumers that purchased the former category, if possible. This paper finds that this can lead to lower prices in the initial category if the annoyance of receiving advertising is large, and to higher prices if the annoyance of receiving advertising is not too large, as consumers expect a possible additional surplus from the category affected by behavior-based advertising. If receiving advertising does not yield too much annoyance to consumers, firms end up better off due to both higher prices and the increased demand of better matching of advertising. Behavior-based advertising may also lead firms to sell less in the initial category than without behavior-based advertising, as a way to be able to better target the most valuable consumers. If the consumer annoyance of receiving advertising is large, firms may end up serving only a few consumers initially, as attracting more consumers requires prices that are too low, and the initial consumers are attracted because of the possibility of lower prices in the following period. The paper also investigates the effects of joint behavior-based pricing and advertising, and of different firms benefiting from the purchase information.

Suggested Citation

  • Qiaowei Shen & J. Miguel Villas-Boas, 2018. "Behavior-Based Advertising," Management Science, INFORMS, vol. 64(5), pages 2047-2064, May.
  • Handle: RePEc:inm:ormnsc:v:64:y:2018:i:5:p:2047-2064
    DOI: mnsc.2016.2719
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    References listed on IDEAS

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    2. Rodrigo Montes & Wilfried Sand-Zantman & Tommaso Valletti, 2019. "The Value of Personal Information in Online Markets with Endogenous Privacy," Management Science, INFORMS, vol. 65(3), pages 1342-1362, March.
    3. Pedro M. Gardete & Yakov Bart, 2018. "Tailored Cheap Talk: The Effects of Privacy Policy on Ad Content and Market Outcomes," Marketing Science, INFORMS, vol. 37(5), pages 733-752, September.
    4. Subodha Kumar & Yinliang (Ricky) Tan & Lai Wei, 2020. "When to Play Your Advertisement? Optimal Insertion Policy of Behavioral Advertisement," Information Systems Research, INFORMS, vol. 31(2), pages 589-606, June.
    5. Florian Hoffmann & Roman Inderst & Marco Ottaviani, 2020. "Persuasion Through Selective Disclosure: Implications for Marketing, Campaigning, and Privacy Regulation," Management Science, INFORMS, vol. 66(11), pages 4958-4979, November.
    6. Michel Tolksdorf, 2023. "On Point Predictions and Reference Dependence in Behavior-Based Pricing Experiments," Journal of Economics and Behavioral Studies, AMH International, vol. 15(1), pages 1-14.
    7. 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.
    8. Michael Arnold & Eric Schmidbauer & Lan Zhang, 2022. "Uniform and targeted informative advertising with asymmetric customer loyalty," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 31(1), pages 90-114, February.
    9. Yue Shen & Weijun Zhong & Shue Mei, 2023. "Advertising strategies of competing firms considering consumer privacy concerns," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 44(4), pages 2424-2437, June.
    10. J. Miguel Villas-Boas & Yunfei (Jesse) Yao, 2021. "A Dynamic Model of Optimal Retargeting," Marketing Science, INFORMS, vol. 40(3), pages 428-458, May.

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