TikTok’s Influence on Spotify Chart Performance

Data analysis project examining how TikTok virality influences Spotify chart performance using streaming trends and ranking data.

Graphs displaying two songs that show recurring seasonal popularity on Spotify..
Graphs illustrating two songs that gained popularity on TikTok before trending elsewhere.
Graphs illustrating two songs that became popular on Spotify before trending on TikTok.
Pie chart showing the percentage of songs that trended first on TikTok, Spotify, or both simultaneously.

TikTok’s Influence on Spotify Chart Performance Project Details

This project explores the relationship between TikTok virality and Spotify chart performance to understand which platform influences music popularity first. Using streaming and ranking data from Spotify alongside trending TikTok songs, we analyzed how songs gain traction across platforms over time. Our initial hypothesis assumed TikTok consistently drives Spotify success, reflecting the platform’s viral nature. To test this, we tracked when songs peaked on each platform and compared their performance trends.

Using Python, Pandas, and Excel, we cleaned and filtered U.S.-based Spotify chart data, focusing on streaming counts, rankings, and date-based trends. Time-series analysis was used to identify peak popularity periods and calculate average rankings before and after TikTok virality. Visualizations created with Matplotlib revealed that Spotify trends are generally more stable, while TikTok trends fluctuate rapidly due to short-form content cycles. This analysis highlighted how platform algorithms, user behavior, and content format affect music discovery.

TikTok sometimes influences Spotify’s charts, but for the most part, both tend to trend at the same time.

The results showed that while TikTok often accelerates exposure, it does not always lead Spotify trends. Approximately 66% of songs trended on TikTok first, while 33% gained popularity on Spotify earlier, with many trending simultaneously.

Project information

  • Category Data Science
  • Technologies Python, Pandas, Matplotlib, Excel, Google Colab, CSV datasets/Kaggle
  • Skills Data cleaning and preprocessing, Exploratory data analysis (EDA), Time-series analysis, Cross-platform trend analysis
  • Project date April 2023 - May 2023
  • Final Presentation Final Presentation Download
  • Project Analysis Report Analysis Report Download