Predicting Influencer Actual Reach Using Linear Regression

Abstract: The influencer marketing industry has seen a tremendous growth in recent years, yet the effectiveness of this marketing form is still largely unexplored. This report aims to explore how various performance measures are linked to the reach of social media pages, utilizing the linear regression model. Three different data sets were collected manually, or using web scraping. By splitting these data sets to training- and test data we examined the degree to which the linear regression model can predict the actual reach, the page views and the weekly growth of an influencer. We concluded that there is a statistically significant correlation between multiple performance metrics of a social media page and the actual reach or the page views of that account. This study is however limited by its narrow data set and time frame, warranting future research in order to further establish the degree of this correlation. The results of this study can benefit companies in their process of selecting influencers to collaborate with, as well as determining the expected return on investment for that particular collaboration. This can in turn lead to a more efficient, authentic and transparent marketplace, and to consumers being less exposed to advertisement from misleading and malicious influencers.