The rapid growth of e-commerce, particularly on platforms that merge socializing and shopping functions like TikTok, has led to the rise of the influencer economy, where influencers drive significant sales through their online presence. This paper aims to forecast the prominent factors influencing influencer sales on TikTok, motivated by the need to better understand this evolving economic model. We establish an original dataset containing data from 100 influencers over a three-month period on TikTok's e-commerce platform. A comprehensive descriptive analysis is conducted to identify variations among influencers, followed by a data mining process to extract key characteristics based on their behavior across different levels, including daily activities, marketing strategies, and basic demographic information. A predictive model is developed to assess influencers’ sales levels, revealing that the root mean square error (RMSE) of the model is close to 13. In particular, the study identifies the top 10 most influential sales features. These findings contribute to a deeper understanding of the factors driving influencer sales, offering valuable insights for both influencers and marketers in optimizing their strategies for success on TikTok.