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An Audience of One: Behaviorally Targeted Ads as Implied Social Labels

Author

Listed:
  • Christopher A. Summers
  • Robert W. Smith
  • Rebecca Walker Reczek

Abstract

“Behavioral targeting” is an Internet-based targeting strategy that delivers digital ads to individuals based on their online behavior (e.g., search, shopping). This research explores the unique ways in which consumers respond to ads using this type of targeting (vs. to ads that use more traditional forms of targeting), demonstrating that a behaviorally targeted ad can act as a social label even when it contains no explicit labeling information. Instead, when consumers recognize that the marketer has made an inference about their identity in order to serve them the ad, the ad itself functions as an implied social label. Across four studies, behaviorally targeted ads lead consumers to make adjustments to their self-perceptions to match the implied label; these self-perceptions then impact behavior including purchase intentions for the advertised product and other behaviors related to the implied label. Importantly, these effects only hold when the label is plausibly connected to consumers’ prior behavior (i.e., when the targeting is at least moderately accurate).

Suggested Citation

  • Christopher A. Summers & Robert W. Smith & Rebecca Walker Reczek, 2016. "An Audience of One: Behaviorally Targeted Ads as Implied Social Labels," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 43(1), pages 156-178.
  • Handle: RePEc:oup:jconrs:v:43:y:2016:i:1:p:156-178.
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    File URL: http://hdl.handle.net/10.1093/jcr/ucw012
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    Citations

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    Cited by:

    1. Felipe Thomaz & Carolina Salge & Elena Karahanna & John Hulland, 2020. "Learning from the Dark Web: leveraging conversational agents in the era of hyper-privacy to enhance marketing," Journal of the Academy of Marketing Science, Springer, vol. 48(1), pages 43-63, January.
    2. K. Sudhir & Seung Yoon Lee & Subroto Roy, 2021. "Lookalike Targeting on Others' Journeys: Brand Versus Performance Marketing," Cowles Foundation Discussion Papers 2302R, Cowles Foundation for Research in Economics, Yale University, revised Jun 2022.
    3. K. Sudhir & Seung Yoon Lee & Subroto Roy, 2021. "Lookalike Targeting on Others' Journeys: Brand Versus Performance Marketing," Cowles Foundation Discussion Papers 2302, Cowles Foundation for Research in Economics, Yale University.
    4. Kurt P. Munz & Minah H. Jung & Adam L. Alter, 2020. "Name Similarity Encourages Generosity: A Field Experiment in Email Personalization," Marketing Science, INFORMS, vol. 39(6), pages 1071-1091, November.
    5. Li Yan & Hean Tat Keh & Xiaoyu Wang, 2021. "Powering Sustainable Consumption: The Roles of Green Consumption Values and Power Distance Belief," Journal of Business Ethics, Springer, vol. 169(3), pages 499-516, March.
    6. Marine Agogué & Béatrice Parguel, 2020. "Nudging individuals’ creativity using social labeling," PLOS ONE, Public Library of Science, vol. 15(2), pages 1-18, February.
    7. Xiuxian Li & Pengwen Hou & Shuhua Zhang, 2023. "The optimal advertising strategy with differentiated targeted effect consumers," Annals of Operations Research, Springer, vol. 324(1), pages 1295-1336, May.
    8. Stylianos Despotakis & Jungju Yu, 2023. "Multidimensional Targeting and Consumer Response," Management Science, INFORMS, vol. 69(8), pages 4518-4540, August.
    9. Stefan F. Bernritter & Paul E. Ketelaar & Francesca Sotgiu, 2021. "Behaviorally targeted location-based mobile marketing," Journal of the Academy of Marketing Science, Springer, vol. 49(4), pages 677-702, July.
    10. Gupta, Shaphali & Leszkiewicz, Agata & Kumar, V. & Bijmolt, Tammo & Potapov, Dmitriy, 2020. "Digital Analytics: Modeling for Insights and New Methods," Journal of Interactive Marketing, Elsevier, vol. 51(C), pages 26-43.
    11. Nico Neumann & Catherine E. Tucker & Kumar Subramanyam & John Marshall, 2023. "Is first- or third-party audience data more effective for reaching the ‘right’ customers? The case of IT decision-makers," Quantitative Marketing and Economics (QME), Springer, vol. 21(4), pages 519-571, December.
    12. Rene Laub & Klaus M. Miller & Bernd Skiera, 2023. "The Economic Value of User Tracking for Publishers," Papers 2303.10906, arXiv.org.
    13. Spielmann, Nathalie & Mantonakis, Antonia, 2018. "In virtuo: How user-driven interactivity in virtual tours leads to attitude change," Journal of Business Research, Elsevier, vol. 88(C), pages 255-264.
    14. Jiwoong Shin & Jungju Yu, 2021. "Targeted Advertising and Consumer Inference," Marketing Science, INFORMS, vol. 40(5), pages 900-922, September.

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