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Competing for Influencers in a Social Network

Author

Listed:
  • Zsolt Katona

    (Haas School of Business, UC Berkeley)

Abstract
This paper studies the competition between firms for influencers in a network. Firms spend effort to convince influencers to recommend their products. The analysis identifies the offensive and defensive roles of spending on influencers. The value of an influencer only depends on the in-degree distribution of the influence network. Influencers who exclusively cover a high number of consumers are more valuable to firms than those who mostly cover consumers also covered by other influencers. Firm profits are highest when there are many consumers with a very low or with very high in-degree. Consumers with an intermediate level of in-degree contribute negatively to profits and high in-degree consumers increase profits when market competition is not intense. Prices are generally lower when consumers are covered by many influencers, however, firms are not always worse off with lower prices. The nature of consumer response to recommendations makes an important difference. When first impressions dominate, firm profits for dense networks are higher, but when recommendations have a cumulative influence profits are reduced as the network becomes dense.

Suggested Citation

  • Zsolt Katona, 2013. "Competing for Influencers in a Social Network," Working Papers 13-06, NET Institute.
  • Handle: RePEc:net:wpaper:1306
    as

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    File URL: http://www.netinst.org/Katona_13-06.pdf
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    References listed on IDEAS

    as
    1. Raghuram Iyengar & Christophe Van den Bulte & Thomas W. Valente, 2011. "Opinion Leadership and Social Contagion in New Product Diffusion," Marketing Science, INFORMS, vol. 30(2), pages 195-212, 03-04.
    2. Varian, Hal R, 1980. "A Model of Sales," American Economic Review, American Economic Association, vol. 70(4), pages 651-659, September.
    3. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    4. Yuxin Chen & Ganesh Iyer, 2002. "Research Note Consumer Addressability and Customized Pricing," Marketing Science, INFORMS, vol. 21(2), pages 197-208, November.
    5. Tingting He & Dmitri Kuksov & Chakravarthi Narasimhan, 2012. "Intraconnectivity and Interconnectivity: When Value Creation May Reduce Profits," Marketing Science, INFORMS, vol. 31(4), pages 587-602, July.
    6. Yuxin Chen & Yogesh V. Joshi & Jagmohan S. Raju & Z. John Zhang, 2009. "A Theory of Combative Advertising," Marketing Science, INFORMS, vol. 28(1), pages 1-19, 01-02.
    7. Ganesh Iyer & David Soberman & J. Miguel Villas-Boas, 2005. "The Targeting of Advertising," Marketing Science, INFORMS, vol. 24(3), pages 461-476, May.
    8. Narasimhan, Chakravarthi, 1988. "Competitive Promotional Strategies," The Journal of Business, University of Chicago Press, vol. 61(4), pages 427-449, October.
    9. Peter Zubcsek & Miklos Sarvary, 2011. "Advertising to a social network," Quantitative Marketing and Economics (QME), Springer, vol. 9(1), pages 71-107, March.
    10. Nair, Harikesh S. & Manchanda, Puneet & Bhatia, Tulikaa, 2006. "Asymmetric Peer Effects in Physician Prescription Behavior: The Role of Opinion Leaders," Research Papers 1970, Stanford University, Graduate School of Business.
    11. Hema Yoganarasimhan, 2012. "Impact of social network structure on content propagation: A study using YouTube data," Quantitative Marketing and Economics (QME), Springer, vol. 10(1), pages 111-150, March.
    12. Juanjuan Zhang, 2011. "The Perils of Behavior-Based Personalization," Marketing Science, INFORMS, vol. 30(1), pages 170-186, 01-02.
    13. Yuxin Chen & Chakravarthi Narasimhan & Z. John Zhang, 2001. "Individual Marketing with Imperfect Targetability," Marketing Science, INFORMS, vol. 20(1), pages 23-41, November.
    14. Catherine Tucker, 2008. "Identifying Formal and Informal Influence in Technology Adoption with Network Externalities," Management Science, INFORMS, vol. 54(12), pages 2024-2038, December.
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    More about this item

    Keywords

    Social Networks; Influencers; Competition;
    All these keywords.

    JEL classification:

    • M31 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Marketing
    • C72 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Noncooperative Games
    • D44 - Microeconomics - - Market Structure, Pricing, and Design - - - Auctions

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