YouTube Competitive Analysis
Outlier detection and packaging pattern extraction for YouTube channels.
When to Use
- User asks for YouTube competitive analysis
- User wants to find viral video patterns
- User wants packaging/title inspiration from specific creators
- User wants to track competitor YouTube performance
Prerequisites
- YouTube Data API v3 key set as
$YOUTUBE_API_KEY
Usage
# Analyze specific channels
python3 analyze.py "$YOUTUBE_API_KEY" --channels "@handle1,@handle2" --days 30
# Use predefined sets
python3 analyze.py "$YOUTUBE_API_KEY" --set ai
python3 analyze.py "$YOUTUBE_API_KEY" --set business
python3 analyze.py "$YOUTUBE_API_KEY" --set both
# Export formats
python3 analyze.py "$YOUTUBE_API_KEY" --set both --output json
python3 analyze.py "$YOUTUBE_API_KEY" --set both --output consolePredefined Channel Sets
AI Creators: Jeff Su, Alex Finn, Riley Brown, Dan Martell, Matt Wolfe, Nate Herk, Grace Leung, Matt Berman
Business Creators: Alex Hormozi, Gary Vaynerchuk, Patrick Bet-David, Codie Sanchez, Leila Hormozi, Iman Gadzhi, My First Million
Output Interpretation
- Multiplier: Times above channel average (2.0x = double normal)
- Outlier threshold: 2x average. Study anything above this.
- Title patterns: Common words in outlier titles indicate proven formats
- Cadence: Videos per week. Higher cadence creators may have lower per-video averages.
Packaging Skeletons (Proven Formats)
Long-form:
- "X, Clearly Explained"
- "X hours of Y in Z minutes"
- "The Laziest Way to X"
- "Give me X minutes and I'll Y"
- "X INSANE Use Cases for Y"
Shorts:
- "2024 vs 2025 X" (year comparison)
- "Bad Good Great X" (tier ranking)
- "Stop doing X, do Y instead" (contrarian)