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

82 Pages Posted: 18 Oct 2018 Last revised: 23 Mar 2020

See all articles by Rakesh Mallipeddi

Rakesh Mallipeddi

Tulane University

Subodha Kumar

Temple University - Department of Marketing and Supply Chain Management

Chelliah Sriskandarajah

Texas A&M University

Yunxia Zhu

College of Business, University of Nebraska-Lincoln

Date Written: March 14, 2020

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 the 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.

Keywords: Social Networks, Network Seeding, Influencer Marketing, Combinatorial Optimization, Scheduling

Suggested Citation

Mallipeddi, Rakesh and Kumar, Subodha and Sriskandarajah, Chelliah and Zhu, Yunxia, A Framework for Analyzing Influencer Marketing in Social Networks: Selection and Scheduling of Influencers (March 14, 2020). Available at SSRN: https://ssrn.com/abstract=3255198 or http://dx.doi.org/10.2139/ssrn.3255198

Rakesh Mallipeddi (Contact Author)

Tulane University ( email )

7 McAlister Drive
New Orleans, LA 70118
United States

Subodha Kumar

Temple University - Department of Marketing and Supply Chain Management ( email )

Philadelphia, PA 19122
United States

Chelliah Sriskandarajah

Texas A&M University ( email )

Langford Building A
798 Ross St.
77843-3137

Yunxia Zhu

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

HLH 511 D
P.O. Box 880491
Lincoln, NE 68588
United States

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