Turning TikTok Data Into a Repeatable Growth System

Brian wanted a way to stop guessing what works on TikTok and start making decisions from real data. The goal was simple: find high-performing content patterns, break them down, and reuse what’s already working.

My approach was to build an automated workflow that continuously pulls TikTok videos, filters them, and ranks them by performance. I structured it to loop through creators, scrape recent posts, check validity, and store only useful data. From there, videos are sorted by views and trimmed to top performers.

I integrated Airtable for storage, Apify for scraping, and Gemini for analysis and file handling. Each video gets processed, analyzed, and turned into structured insights that can guide content decisions.

The result is a system that runs on its own, surfaces winning content, and removes the manual effort. Instead of scrolling for hours, Brian now has a clear, data-backed direction on what to create next.

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Turning TikTok Data Into a Repeatable Growth System