Cost-Effective Social Media Influencer Marketing

Xiao Han (), Leye Wang () and Weiguo Fan ()
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Xiao Han: School of Information Management and Engineering, Shanghai University of Finance and Economics, Shanghai 200433, China
Leye Wang: School of Computer Science, Peking University, Beijing 100871, China; Key Laboratory of High Confidence Software Technologies (Peking University), Ministry of Education, Beijing 100871, China
Weiguo Fan: Department of Business Analytics, Tippie College of Business, University of Iowa, Iowa City, Iowa 52242

INFORMS Journal on Computing, 2023, vol. 35, issue 1, 138-157

Abstract: It is becoming more and more promising that marketers hire influencers to launch campaigns for spreading items (e.g., articles or videos about products) over social media platforms. Such social media influencer marketing may generate tremendous utility if the influencers persuade their followers to adopt the recommended items. This could further spur extensive spontaneous item propagation on social media. Although prior studies mainly focus on influencer-selection strategies by the influencers’ traits, marketers with a number of items are often requested to determine both influencers and marketing items. The appropriateness between influencers and items is critical, but rarely considered in prior influencer-identification methods. We thus formulate and solve a novel cost-effective social media influencer marketing problem to maximize marketers’ utility by selecting appropriate pairwise combinations of influencers and items (i.e., item-influencer pairs). In particular, we first model utility functions and propose a simulation-based method to estimate the appropriateness of arbitrarily given item-influencer pairs by their potential utility. With the estimated utility, we devise an algorithm to iteratively select appropriate item-influencer pairs under various realistic conditions, including marketers’ budget, influencers’ payments, item-user fitness, social propagation, and influencers’ marketing slots. We theoretically prove that the marketing utility achieved by our method is near-optimal. We also conduct extensive empirical experiments with three real-world data sets to verify the superiority of our method in terms of cost-effectiveness and computational efficiency. Lastly, we discuss insightful theoretical and practical implications.

Keywords: influencer marketing; social influence; submodular optimization (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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