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

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. This paper was accepted by J. George Shanthikumar, big data analytics.

Celebrity selection in social media ecosystems: a flexible and interactive framework

“…Our study is also unique in a way that it provides a method to encapsulate the views of customers of a brand or a specific campaign in the selection of a celebrity. Further, our model can be use for influencer selection for brands, based on the premises of selection parameters and the group decision-making context (Mallipeddi et al , 2021; Schouten et al , 2020; Ye et al , 2020).…”

“…Moreover, the MGFEM and FITradeoff approaches can be applied to other areas of practice that use multi-criteria group decision-making, such as to assessing different criteria and designing a decision process for evaluating influencers in social media settings (Britt et al , 2020; Lee and Eastin, 2020) on dimensions of their popularity. The proposed approach and method can also be adapted to the context of social media influencer selection based on the interactive group decision-making context aligned with some key parameters, such as engagement, reach, sentiments, growth and interactive behavior (Mallipeddi et al , 2021; Schouten et al , 2020; Ye et al , 2020).…”

“…Brands have a bottom-line requirement to identify the suitability of these influencers for their products and services (Schouten et al , 2020). Some key elements that play a pivotal role in determining the index of influence for an influencer are engagement, reach, sentiments, growth and interactive behavior, among others (Aggrawal et al , 2018; Arora et al , 2019; Mallipeddi et al , 2021). Studies consider how the formulation of appropriate indices of influencer ranking affect the mindset of consumers, and how the implied loyalty evolves around the timescale and value generation (Jun and Yi, 2020; Pick, 2020).…”

“…The celebrity's role has changed from being a poster boy/girl of a brand to becoming an interactive personality representing the brand over different media types. The selection of the right candidate for endorsement has multi-faceted benefits, such as high visibility, high outreach, better connection with prospective customers, greater interaction and engagement with prospective customers and enhanced public reputation (Hollensen and Schimmelpfennig, 2013; Kim et al , 2017; Mallipeddi et al , 2021). The ecosystem of endorsement is flooded with celebrities, influencers and social media opinion leaders, so it is critical for brands to cut unwanted noise and identify the most suitable endorser for a given product, service, initiative, or business situation (Arora et al , 2019; Mallipeddi et al , 2021).…”

“…The selection of the right candidate for endorsement has multi-faceted benefits, such as high visibility, high outreach, better connection with prospective customers, greater interaction and engagement with prospective customers and enhanced public reputation (Hollensen and Schimmelpfennig, 2013; Kim et al , 2017; Mallipeddi et al , 2021). The ecosystem of endorsement is flooded with celebrities, influencers and social media opinion leaders, so it is critical for brands to cut unwanted noise and identify the most suitable endorser for a given product, service, initiative, or business situation (Arora et al , 2019; Mallipeddi et al , 2021). However, the selection mechanism is not straightforward, requiring assimilation of the thoughts of multiple stakeholders in the decision-making process to identify and prioritize the correct criteria in an objective manner so that a final index or rank order is obtained (Morone et al , 2016).…”