An Audience Threshold in the Millions: Exploring Influencer Marketing Activations Online

Influencer marketing campaigns often struggle to predict audience activations and their prospective financial returns. This is further hindered by a lack of statistically relevant approaches to model expected outcomes. The current analysis offers a guided approach and recommendations for estimating and predicting social media engagements. By leveraging graph theory, regression analysis, and projection methods, the study gains insights from social media data to set feasible expectations for influencer marketing campaigns. The study demonstrates that network analysis, particularly degree-centrality, works well for choosing an initial set, provided analysts iterate over the degree-centrality range choices via subsequent regression analyses. X platform data (herein referred to by its former name, Twitter) from the Higgs Boson Graph Dataset is utilized to operationalize the investigation’s approach.