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

Muhammad Akmal Hakim Ahmad Asmawi; Pradeep Isawasan; Savita K.S.; Lalitha Shamugam; Khairulliza Ahmad Salleh

doi:10.15575/join.v10i2.1633

Authors

Muhammad Akmal Hakim Ahmad Asmawi College of Computing, Informatics, and Mathematics, Universiti Teknologi MARA, Perak Branch, Tapah Campus, Malaysia https://orcid.org/0009-0003-8367-6398
Pradeep Isawasan College of Computing, Informatics and Mathematics, Universiti Teknologi MARA, Perak Branch, Malaysia https://orcid.org/0000-0002-4208-9547
Savita K.S. Department of Computing, Positive Computing Center, Universiti Teknologi PETRONAS, Malaysia https://orcid.org/0000-0002-6592-0890
Lalitha Shamugam Department of Business Analytics, Sunway Business School, Sunway University, Malaysia https://orcid.org/0000-0001-7833-6709
Khairulliza Ahmad Salleh College of Computing, Informatics, and Mathematics, Universiti Teknologi MARA, Perak Branch, Tapah Campus, Malaysia https://orcid.org/0000-0002-7362-2981

DOI:

https://doi.org/10.15575/join.v10i2.1633

Keywords:

Business Analytics, Engagement Metrics, E-Commerce, Social Media, TikTok

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.

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