Audience response
Analyze likes-to-views patterns in YouTube content
Compare content signals with the share of viewers who chose to like each video
The problem
Likes-to-views is useful but blunt. It identifies videos that prompted a response without showing which creative passages may have contributed to that impression
What changes
Generate passage-level hypotheses from a video-level outcome
TubeCrisp is built for creators studying positive audience response. It keeps the original video connected to every score, quote, and conclusion
01
Weight content signals by their relationship with likes/view
02
Display an outcome-signal timeline inside each video
03
Treat the result as an informed estimate rather than observed click timing
Workflow
From a question to an exact passage
- Select likes/view as the dependent variable
- Calculate correlations across comparable videos
- Inspect passages with high and low outcome-signal scores
Start with the video already on your mind
Paste its URL and build a timeline around the signals you care about
Analyze a YouTube video