MSI Analytics Conference Rebroadcast: Mega or Micro? Influencer Selection Using Follower Elasticity
This session is a rebroadcast from MSI’s 2023 Analytics Conference.
In the quickly growing space of influencer marketing, one common criterion companies use to select influencer partners is popularity: while some companies sponsor “mega” influencers with millions of followers, others partner with “micro” influencers, who may only have several thousands of followers, but may also cost less to sponsor. In this research, Ryan Dew and his coauthors develop a framework for navigating this trade-off, and quantifying the returns to influencer popularity. They use this methodology to develop guidelines as to which companies and campaigns may benefit most from mega versus micro influencers.
Ryan Dew is an Assistant Professor of Marketing at the Wharton School of the University of Pennsylvania. His research explores how machine learning and Bayesian statistical methodologies can solve real world marketing problems, and enhance the capacity of marketing managers to make data-driven decisions. Methodologically, he uses techniques from machine learning, Bayesian nonparametrics, and Bayesian econometrics.