IDEAS home Printed from https://ideas.repec.org/p/net/wpaper/2208.html
   My bibliography  Save this paper

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

Author

Listed:
  • Ron Berman

    (The Wharton School, University of Pennsylvania, 3730 Walnut Street, Philadelphia, PA 19104)

  • Aniko Oery

    (Yale School of Management, 165 Whitney Avenue, New Haven, CT 06511)

  • Xudong Zheng

    (Department of Economics, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD 21218)

Abstract

We analyze influencer marketing and advertising campaigns that can facilitate learning about uncertain qualities of products, while making consumers aware of them. We establish conditions for when influencer marketing, which lets consumers learn from other followers, is preferred to advertising, that does not enable such information sharing. Influencers facilitate learning when they are consistent in their post quality and have homogeneous followers relative to the degree of targeting of advertising campaigns. Whether efficient learning increases profits depends on the quality uncertainty of the product, e.g., whether the brand is established or unknown. For established brands, we find that many micro-influencers are more profitable than a targeted ad campaign, while for unknown brands, either macro-influencers with many followers or micro-influencers are more profitable. We also show that influencer campaigns tend to either "go viral" or "go bust", highlighting the value of ex-post promotional coupons. Additionally, for niche products we find that the heterogeneity of the follower base affects learning efficiency the most, while for mass products, the creativity of the influencer is the more important factor.

Suggested Citation

  • Ron Berman & Aniko Oery & Xudong Zheng, 2022. "Influence or Advertise: The Role of Social Learning in Influencer Marketing," Working Papers 22-08, NET Institute.
  • Handle: RePEc:net:wpaper:2208
    as

    Download full text from publisher

    File URL: http://www.netinst.org/Berman_22-08.pdf
    Download Restriction: no
    ---><---

    More about this item

    Keywords

    Influencer marketing; social learning; online advertising; word of mouth;
    All these keywords.

    JEL classification:

    • M31 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Marketing
    • M37 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Advertising
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness

    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:net:wpaper:2208. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Nicholas Economides (email available below). General contact details of provider: http://www.NETinst.org/ .

    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.