Mega or Micro? Influencer Selection Using Follower Elasticity
63 Pages Posted:
Date Written: July 26, 2022
Despite the explosive growth of influencer marketing, wherein companies sponsor social media personalities to promote their brands, there is little research to guide companies’ selection of influencer partners. One common criterion is popularity: while some firms sponsor “mega” influencers with millions of followers, other firms partner with “micro” influencers, who may only have several thousands of followers, but may also cost less to sponsor. To quantify this trade-off between reach and cost, we develop a framework for estimating the follower elasticity of impressions, or FEI, which measures a video’s percentage gain in impressions corresponding to a percentage increase in the follower size of its creator. Computing FEI involves estimating the causal effect of an influencer’s popularity on the view counts of their videos, which we achieve through a combination of a unique dataset collected from TikTok, a representation learning model for quantifying video content, and a machine learning-based causal inference method. We find that FEI is always positive, but often nonlinearly related to follower size, suggesting different optimal sponsorship strategies than those observed in practice. We examine the factors that predict variation in these FEI curves, and show how firms can use these results to better determine influencer partnerships.
Keywords: influencer marketing, causal inference, deep learning, representation learning, heterogeneous treatment effects, video data
Suggested Citation: Suggested Citation