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Competitive Targeted Advertising Over Networks

Author

Listed:
  • Kostas Bimpikis

    (Graduate School of Business, Stanford University, Stanford, California 94305)

  • Asuman Ozdaglar

    (Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139)

  • Ercan Yildiz

    (Customer Research and Insights Team, Google, Menlo Park, California 94043)

Abstract

Recent advances in information technology have allowed firms to gather vast amounts of data regarding consumers’ preferences and the structure and intensity of their social interactions. This paper examines a game-theoretic model of competition between firms that can target their marketing budgets to individuals embedded in a social network. We provide a sharp characterization of the optimal targeted advertising strategies and highlight their dependence on the underlying social network structure. Furthermore, we provide conditions under which it is optimal for the firms to asymmetrically target a subset of the individuals and establish a lower bound on the ratio of their payoffs in these asymmetric equilibria. Finally, we find that at equilibrium firms invest inefficiently high in targeted advertising and the extent of the inefficiency is increasing in the centralities of the agents they target. Taken together, these findings shed light on the effect of the network structure on the outcome of marketing competition between the firms.

Suggested Citation

  • Kostas Bimpikis & Asuman Ozdaglar & Ercan Yildiz, 2016. "Competitive Targeted Advertising Over Networks," Operations Research, INFORMS, vol. 64(3), pages 705-720, June.
    • Handle: RePEc:inm:oropre:v:64:y:2016:i:3:p:705-720
    DOI: 10.1287/opre.2015.1430
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    References listed on IDEAS

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