IDEAS home Printed from https://ideas.repec.org/p/cwl/cwldpp/2358.html
   My bibliography  Save this paper

Influence or Advertise: The Role of Social Learning in Influencer Marketing

Author

Listed:

Abstract

We compare influencer marketing to targeted advertising from information aggregation and product awareness perspectives. Influencer marketing leverages network effects by allowing consumers to socially learn from each other about their experienced content utility, but consumers may not know whether to attribute promotional post popularity to high content or high product quality. If the quality of a product is uncertain (e.g., it belongs to an unknown brand), then a mega influencer with consistent content quality fosters more information aggregation than a targeted ad and thereby yields higher profits. When we compare influencer marketing to untargeted ad campaigns or if the product has low quality uncertainty (e.g., belongs to an established brand), then many micro influencers with inconsistent content quality create more consumer awareness and yield higher profits. For products with low quality uncertainty, the firm wants to avoid information aggregation as it disperses posterior beliefs of consumers and leads to fewer purchases at the optimal price. Our model can also explain why influencer campaigns either "go viral" or "go bust," and how for niche products, micro-influencers with consistent content quality can be a valuable marketing tool.

Suggested Citation

  • Ron Berman & Aniko Oery & Xudong Zheng, 2023. "Influence or Advertise: The Role of Social Learning in Influencer Marketing," Cowles Foundation Discussion Papers 2358, Cowles Foundation for Research in Economics, Yale University.
  • Handle: RePEc:cwl:cwldpp:2358
    as

    Download full text from publisher

    File URL: https://cowles.yale.edu/sites/default/files/2023-04/d2358.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Matthew Mitchell, 2021. "Free ad(vice): internet influencers and disclosure regulation," RAND Journal of Economics, RAND Corporation, vol. 52(1), pages 3-21, March.
    2. Arthur Campbell, 2013. "Word-of-Mouth Communication and Percolation in Social Networks," American Economic Review, American Economic Association, vol. 103(6), pages 2466-2498, October.
    3. Bikhchandani, Sushil & Hirshleifer, David & Welch, Ivo, 1992. "A Theory of Fads, Fashion, Custom, and Cultural Change in Informational Cascades," Journal of Political Economy, University of Chicago Press, vol. 100(5), pages 992-1026, October.
    4. Davide Crapis & Bar Ifrach & Costis Maglaras & Marco Scarsini, 2017. "Monopoly Pricing in the Presence of Social Learning," Management Science, INFORMS, vol. 63(11), pages 3586-3608, November.
    5. Dmitri Kuksov & Chenxi Liao, 2019. "Opinion Leaders and Product Variety," Marketing Science, INFORMS, vol. 38(5), pages 812-834, September.
    6. Ron Berman & Zsolt Katona, 2020. "Curation Algorithms and Filter Bubbles in Social Networks," Marketing Science, INFORMS, vol. 39(2), pages 296-316, March.
    7. Itay P. Fainmesser & Andrea Galeotti, 2021. "The Market for Online Influence," American Economic Journal: Microeconomics, American Economic Association, vol. 13(4), pages 332-372, November.
    8. Yuichiro Kamada & Aniko Öry, 2020. "Contracting with Word-of-Mouth Management," Management Science, INFORMS, vol. 66(11), pages 5094-5107, November.
    9. Bar Ifrach & Costis Maglaras & Marco Scarsini & Anna Zseleva, 2019. "Bayesian Social Learning from Consumer Reviews," Operations Research, INFORMS, vol. 67(5), pages 1209-1221, September.
    10. Abhijit V. Banerjee, 1992. "A Simple Model of Herd Behavior," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 107(3), pages 797-817.
    11. Galeotti, Andrea & Ghiglino, Christian & Squintani, Francesco, 2013. "Strategic information transmission networks," Journal of Economic Theory, Elsevier, vol. 148(5), pages 1751-1769.
    12. Juanjuan Zhang, 2010. "The Sound of Silence: Observational Learning in the U.S. Kidney Market," Marketing Science, INFORMS, vol. 29(2), pages 315-335, 03-04.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Li, Feng & Du, Timon C. & Wei, Ying, 2023. "This is what’s in store for you: How online social learning affects product positioning," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 179(C).
    2. Davide Crapis & Bar Ifrach & Costis Maglaras & Marco Scarsini, 2017. "Monopoly Pricing in the Presence of Social Learning," Management Science, INFORMS, vol. 63(11), pages 3586-3608, November.
    3. Sgrignoli, P. & Agliari, E. & Burioni, R. & Schianchi, A., 2015. "Instability and network effects in innovative markets," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 108(C), pages 260-271.
    4. Stephanie De Mel & Kaivan Munshi & Soenje Reiche & Hamid Sabourian, 2021. "Herding with Heterogeneous Ability: An Application to Organ Transplantation," Cowles Foundation Discussion Papers 2308, Cowles Foundation for Research in Economics, Yale University.
    5. Anna K. Edenbrandt & Christian Gamborg & Bo Jellesmark Thorsen, 2020. "Observational learning in food choices: The effect of product familiarity and closeness of peers," Agribusiness, John Wiley & Sons, Ltd., vol. 36(3), pages 482-498, June.
    6. Liangfei Qiu & Arunima Chhikara & Asoo Vakharia, 2021. "Multidimensional Observational Learning in Social Networks: Theory and Experimental Evidence," Information Systems Research, INFORMS, vol. 32(3), pages 876-894, September.
    7. Yiangos Papanastasiou, 2020. "Fake News Propagation and Detection: A Sequential Model," Management Science, INFORMS, vol. 66(5), pages 1826-1846, May.
    8. Daron Acemoglu & Asuman Ozdaglar, 2011. "Opinion Dynamics and Learning in Social Networks," Dynamic Games and Applications, Springer, vol. 1(1), pages 3-49, March.
    9. Catherine Tucker & Juanjuan Zhang, 2011. "How Does Popularity Information Affect Choices? A Field Experiment," Management Science, INFORMS, vol. 57(5), pages 828-842, May.
    10. Jurui Zhang & Yong Liu & Yubo Chen, 2015. "Social Learning in Networks of Friends versus Strangers," Marketing Science, INFORMS, vol. 34(4), pages 573-589, July.
    11. Shijie Lu & Dai Yao & Xingyu Chen & Rajdeep Grewal, 2021. "Do Larger Audiences Generate Greater Revenues Under Pay What You Want? Evidence from a Live Streaming Platform," Marketing Science, INFORMS, vol. 40(5), pages 964-984, September.
    12. Li Chen & Yiangos Papanastasiou, 2021. "Seeding the Herd: Pricing and Welfare Effects of Social Learning Manipulation," Management Science, INFORMS, vol. 67(11), pages 6734-6750, November.
    13. Yuetao Gao, 2018. "On the Use of Overt Anti-Counterfeiting Technologies," Marketing Science, INFORMS, vol. 37(3), pages 403-424, May.
    14. Jin Huang, 2017. "To Glance or to Peruse: Observational and Active Learning from Peer Consumers," Working Papers wp2018_1716, CEMFI.
    15. Cen, Ling & Chang, Yuk Ying & Dasgupta, Sudipto, 2022. "Do Analysts Learn from Each Other? Evidence from Analysts’ Location Diversity," CEPR Discussion Papers 15057, C.E.P.R. Discussion Papers.
    16. Catherine Tucker & Juanjuan Zhang & Ting Zhu, 2013. "Days on market and home sales," RAND Journal of Economics, RAND Corporation, vol. 44(2), pages 337-360, June.
    17. Amy Wenxuan Ding & Shibo Li, 2019. "Herding in the consumption and purchase of digital goods and moderators of the herding bias," Journal of the Academy of Marketing Science, Springer, vol. 47(3), pages 460-478, May.
    18. Liangfei Qiu & Asoo Vakharia & Arunima Chhikara, 2019. "Multi-Dimensional Observational Learning in Social Networks: Theory and Experimental Evidence," Working Papers 19-01, NET Institute.
    19. Jin Huang, 2017. "To Glance or to Peruse: Observational and Active Learning from Peer Consumers," Working Papers wp2017_1716, CEMFI.
    20. Daniel Garcia & Sandro Shelegia, 2018. "Consumer search with observational learning," RAND Journal of Economics, RAND Corporation, vol. 49(1), pages 224-253, March.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:cwl:cwldpp:2358. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Brittany Ladd (email available below). General contact details of provider: https://edirc.repec.org/data/cowleus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.