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A Framework for Analyzing Influencer Marketing in Social Networks: Selection and Scheduling of Influencers

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  • Rakesh R. Mallipeddi

    (A. B. Freeman School of Business, Tulane University, New Orleans, Louisiana 70118)

  • Subodha Kumar

    (Fox School of Business, Temple University, Philadelphia, Pennsylvania 19122)

  • Chelliah Sriskandarajah

    (Mays Business School, Texas A&M University, College Station, Texas 77843)

  • Yunxia Zhu

    (College of Business, University of Nebraska-Lincoln, Lincoln, Nebraska 68588)

Abstract

Explosive growth in the number of users on various social media platforms has transformed the way firms strategize their marketing activities. To take advantage of the vast size of social networks, firms have now turned their attention to influencer marketing wherein they employ independent influencers to promote their products on social media platforms. Despite the recent growth in influencer marketing, the problem of network seeding (i.e., identification of influencers to optimally post a firm’s message or advertisement) neither has been rigorously studied in the academic literature nor has been carefully addressed in practice. We develop a data-driven optimization framework to help a firm successfully conduct (i) short-horizon and (ii) long-horizon influencer marketing campaigns, for which two models are developed, respectively, to maximize the firm’s benefit. The models are based on the interactions with marketers, observation of firms’ message placements on social media, and model parameters estimated via empirical analysis performed on data from Twitter. Our empirical analysis discovers the effects of collective influence of multiple influencers and finds two important parameters to be included in the models, namely, multiple exposure effect and forgetting effect. For the short-horizon campaign, we develop an optimization model to select influencers and present structural properties for the model. Using these properties, we develop a mathematical programming based polynomial time procedure to provide near-optimal solutions. For the long-horizon problem, we develop an efficient solution procedure to simultaneously select influencers and schedule their message postings over a planning horizon. We demonstrate the superiority of our solution strategies for both short- and long-horizon problems against multiple benchmark methods used in practice. Finally, we present several managerially relevant insights for firms in the influencer marketing context.

Suggested Citation

  • Rakesh R. Mallipeddi & Subodha Kumar & Chelliah Sriskandarajah & Yunxia Zhu, 2022. "A Framework for Analyzing Influencer Marketing in Social Networks: Selection and Scheduling of Influencers," Management Science, INFORMS, vol. 68(1), pages 75-104, January.
  • Handle: RePEc:inm:ormnsc:v:68:y:2022:i:1:p:75-104
    DOI: 10.1287/mnsc.2020.3899
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    References listed on IDEAS

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    1. Naveen Kumar & Liangfei Qiu & Subodha Kumar, 2022. "A Hashtag Is Worth a Thousand Words: An Empirical Investigation of Social Media Strategies in Trademarking Hashtags," Information Systems Research, INFORMS, vol. 33(4), pages 1403-1427, December.

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