Webinars
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.
speaker
Ryan Dew is an Assistant Professor of Marketing at the Wharton School of the University of Pennsylvania, where he is affiliated with the Wharton AI and Analytics Initiative. He is an applied methodologist, broadly interested in the intersection of marketing, machine learning, and Bayesian statistics. In his research, he focuses on problems in marketing measurement, customer analytics, and data-driven design, with an eye to developing and applying flexible, interpretable, computational tools, often using unstructured data like text and images. At Wharton, he teaches data analysis for marketing classes to undergraduate, MBA, and doctoral students. He has received several awards for both teaching and research, including the 2022 Frank M. Bass Award, the 2018 INFORMS Society for Marketing Science Doctoral Dissertation Award, and the Wharton Teaching Excellence Award. In recognition of his research, he was named a 2023 MSI Young Scholar. He received his Ph.D. in Marketing from Columbia University, and his B.A. in Mathematics from the University of Pennsylvania. For more about Ryan, please visit: http://www.rtdew.com