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

Rakesh R. Mallipeddi (), Subodha Kumar (), Chelliah Sriskandarajah () and Yunxia Zhu ()
Additional contact information
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

Management Science, 2022, vol. 68, issue 1, 75-104

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.

Keywords: social networks; seeding strategies; influencer marketing; combinatorial optimization; scheduling; genetic algorithm (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

Downloads: (external link)
http://dx.doi.org/10.1287/mnsc.2020.3899 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:ormnsc:v:68:y:2022:i:1:p:75-104

Access Statistics for this article

More articles in Management Science from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().