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The Welfare Impact of Targeted Advertising Technologies

Author

Listed:
  • Veronica Marotta

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

  • Yue Wu

    (Katz Graduate School of Business, University of Pittsburgh, Pittsburgh, Pennsylvania 15260)

  • Kaifu Zhang

    (Taobao Marketplace, Alibaba Group, Yu Hang District, Hangzhou 311121, Zhejiang Province, China)

  • Alessandro Acquisti

    (Heinz College, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213)

Abstract

We analyze the welfare implications of consumer data sharing, and restrictions to that sharing, in the context of online targeted advertising. Targeting technologies offer firms the ability to reach desired audiences through intermediary platforms. The platforms run auctions in real time to display ads on internet sites, leveraging consumers’ personal information collected online to personalize the ads. The online advertising industry posits that targeted advertising benefits advertising firms (that is, merchants who want to target ads to the desired consumers), consumers who see ads for preferred products, and the intermediary platforms that match consumers with firms. However, the claims that targeted advertising benefits all players involved have not been fully vetted in the literature. We develop an analytical model to analyze the economic and welfare implications of targeting technologies for those three players under alternative consumer information regimes. The regimes differ in the type and amount of consumer data available to the intermediary and to the advertising firms, and reflect the presence or absence of technological or regulatory restrictions to personal information flows. We find evidence of incentive misalignment among the players, as the intermediary prefers to share only a subset of consumer information with firms, whereas advertising firms prefer having complete information about the consumers. As such, a strategic intermediary with the ability to control which information is shared during the auction can have an incentive to use only the information that maximizes its payoff, overlooking the interests of both advertising firms and consumers. The information regimes that maximize consumer welfare vastly differ depending on consumers’ heterogeneity along two dimensions: a horizontal dimension, capturing consumer’s heterogeneity in product preferences; and a vertical dimension, capturing consumers’ heterogeneity in purchase power. Consumers prefer none of their personal information to be used for targeting only in limited circumstances. Otherwise, consumers are either indifferent or prefer only specific types of information to be used for targeting.

Suggested Citation

  • 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.
  • Handle: RePEc:inm:orisre:v:33:y:2022:i:1:p:131-151
    DOI: 10.1287/isre.2021.1024
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    References listed on IDEAS

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    1. Xiaoquan (Michael) Zhang & Juan Feng, 2011. "Cyclical Bid Adjustments in Search-Engine Advertising," Management Science, INFORMS, vol. 57(9), pages 1703-1719, February.
    2. De Liu & Jianqing Chen & Andrew B. Whinston, 2010. "Ex Ante Information and the Design of Keyword Auctions," Information Systems Research, INFORMS, vol. 21(1), pages 133-153, March.
    3. Gene M. Grossman & Carl Shapiro, 1984. "Informative Advertising with Differentiated Products," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 51(1), pages 63-81.
    4. Avi Goldfarb & Catherine E. Tucker, 2011. "Privacy Regulation and Online Advertising," Management Science, INFORMS, vol. 57(1), pages 57-71, January.
    5. Avi Goldfarb, 2014. "What is Different About Online Advertising?," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 44(2), pages 115-129, March.
    6. Ganesh Iyer & David Soberman & J. Miguel Villas-Boas, 2005. "The Targeting of Advertising," Marketing Science, INFORMS, vol. 24(3), pages 461-476, May.
    7. 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.
    8. David A. Soberman, 2004. "Research Note: Additional Learning and Implications on the Role of Informative Advertising," Management Science, INFORMS, vol. 50(12), pages 1744-1750, December.
    9. Santiago R. Balseiro & Yonatan Gur, 2019. "Learning in Repeated Auctions with Budgets: Regret Minimization and Equilibrium," Management Science, INFORMS, vol. 65(9), pages 3952-3968, September.
    10. 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.
    11. Alessandro Acquisti & Curtis Taylor & Liad Wagman, 2016. "The Economics of Privacy," Journal of Economic Literature, American Economic Association, vol. 54(2), pages 442-492, June.
    12. Curtis R. Taylor, 2004. "Consumer Privacy and the Market for Customer Information," RAND Journal of Economics, The RAND Corporation, vol. 35(4), pages 631-650, Winter.
    13. Kaifu Zhang & Zsolt Katona, 2012. "Contextual Advertising," Marketing Science, INFORMS, vol. 31(6), pages 980-994, November.
    14. Yu (Jeffrey) Hu & Jiwoong Shin & Zhulei Tang, 2016. "Incentive Problems in Performance-Based Online Advertising Pricing: Cost per Click vs. Cost per Action," Management Science, INFORMS, vol. 62(7), pages 2022-2038, July.
    15. Abraham, Ittai & Athey, Susan & Babaioff, Moshe & Grubb, Michael D., 2020. "Peaches, lemons, and cookies: Designing auction markets with dispersed information," Games and Economic Behavior, Elsevier, vol. 124(C), pages 454-477.
    16. Justin P. Johnson, 2013. "Targeted advertising and advertising avoidance," RAND Journal of Economics, RAND Corporation, vol. 44(1), pages 128-144, March.
    17. William Vickrey, 1961. "Counterspeculation, Auctions, And Competitive Sealed Tenders," Journal of Finance, American Finance Association, vol. 16(1), pages 8-37, March.
    18. Krishnamurthy Iyer & Ramesh Johari & Mukund Sundararajan, 2014. "Mean Field Equilibria of Dynamic Auctions with Learning," Management Science, INFORMS, vol. 60(12), pages 2949-2970, December.
    19. Alexandre de Cornière & Romain de Nijs, 2016. "Online advertising and privacy," RAND Journal of Economics, RAND Corporation, vol. 47(1), pages 48-72, February.
    20. Jonathan Levin & Paul Milgrom, 2010. "Online Advertising: Heterogeneity and Conflation in Market Design," American Economic Review, American Economic Association, vol. 100(2), pages 603-607, May.
    21. John Turner, 2012. "The Planning of Guaranteed Targeted Display Advertising," Operations Research, INFORMS, vol. 60(1), pages 18-33, February.
    22. Andrei Hagiu & Bruno Jullien, 2011. "Why do intermediaries divert search?," RAND Journal of Economics, RAND Corporation, vol. 42(2), pages 337-362, June.
    23. Vincent Conitzer & Curtis R. Taylor & Liad Wagman, 2012. "Hide and Seek: Costly Consumer Privacy in a Market with Repeat Purchases," Marketing Science, INFORMS, vol. 31(2), pages 277-292, March.
    24. Anindya Ghose & Sha Yang, 2009. "An Empirical Analysis of Search Engine Advertising: Sponsored Search in Electronic Markets," Management Science, INFORMS, vol. 55(10), pages 1605-1622, October.
    25. Sha Yang & Anindya Ghose, 2010. "Analyzing the Relationship Between Organic and Sponsored Search Advertising: Positive, Negative, or Zero Interdependence?," Marketing Science, INFORMS, vol. 29(4), pages 602-623, 07-08.
    26. Jin-Hyuk Kim & Liad Wagman, 2015. "Screening incentives and privacy protection in financial markets: a theoretical and empirical analysis," RAND Journal of Economics, RAND Corporation, vol. 46(1), pages 1-22, March.
    27. Tucker, Catherine E., 2012. "The economics of advertising and privacy," International Journal of Industrial Organization, Elsevier, vol. 30(3), pages 326-329.
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