  Influencing a target audience through social media content has become a new
focus of interest for marketing leaders. While a large amount of heterogeneous
data is produced by influencers on a daily basis, professionals in the
influ-encer marketing (IM) field still rely on simple quantitative metrics such
as community size or engagement rate to estimate the value of an influencer. As
few research papers have proposed a framework to quantify the performance of an
influencer by using quantitative influencer data (number of followers,
engagement,...), qualitative information (age, country, city, etc...), natural
text (profile and post descriptions) and visual information (images and
videos), we decided to explore these variables and quantitatively evaluate
their dependencies. By analyzing 713,824 influencers on 5 social media
platforms over a period of one year, we identified relationships between value
proposition data (engagement, reach, audience), demographics, natural text
patterns and visual information. The main goal of this paper is to provide IM
professionals with a modern methodology to better understand the value of their
influencers and to feed machine learning algorithms for clustering, scoring or
recommendation.
