A Data Science Approach to Exploring the Relationship Between TikTok Engagement and Revenue in Malaysia: A Case Study of the Beauty and Personal Care Sector

Abstract

TikTok has reshaped digital marketing in the beauty and personal care sector, yet the relationship between engagement metrics and revenue outcomes remains unclear. This study aims to examine how public engagement metrics (likes, comments, shares, and live interactions) relate to revenue performance among TikTok influencers. Using the Data Science Trajectories (DST) framework, data from 17 Malaysian influencers across Celebrity, Macro, Meso, and Micro categories were analyzed through descriptive statistics and machine learning models implemented in Python. The findings reveal that high engagement does not consistently lead to higher revenue. Live sessions were more effective than standard videos in driving sales due to real-time interaction. While Celebrity influencers led in revenue, Meso influencers recorded the highest engagement rates. A Random Forest regression model showed strong predictive power (R² = 0.94), demonstrating that public-facing metrics can be used to estimate revenue. The study also introduces category-based engagement rate benchmarks and highlights the unique value of live content in converting engagement into sales. This research contributes to the growing body of work on TikTok marketing by combining statistical and predictive techniques to link engagement behavior with commercial outcomes, offering actionable insights for both practitioners and scholars.