General and Entertainment ranking performance
General
Global
United States
Entertainment
Global
United States
Total Videos
Content Library
Total Views
Lifetime Views
Subscribers
Community Size
Engagement Rate
Audience Interaction
Top performer by views
Wu-Tang Cat Ain’t Nothin’ To Fluff With 👐
2.3M
Views
100K
Likes
1.1K
Comments
Opportunity for growth
Murder Mittens (Official Lyric Video)
8.1K
Views
1.1K
Likes
116
Comments
Analysis of 26 long-form videos
Wu-Tang Cat Ain’t Nothin’ To Fluff With 👐
• Long videos make up 52% of total content
• Average performance: Lower than channel average
• Most common duration: Short Long (1-3 min)
Upload patterns and optimal timing insights
Content distribution across 7 main categories
Key performance indicators and trends
Entertainment dominates with 56% of content
Pet has highest avg views
Pop Music shows strongest growth
Most used tags across 271 tag instances
15 tags in category
Comprehensive tag metrics and ROI analysis
| Tag | Usage | Avg Views | Engagement | Growth | ROI Score |
|---|---|---|---|---|---|
| #dusty dubsTop 1 | 48 | 246.8K | 4.89% | -57% | 51/100 |
| #voiceoverTop 2 | 43 | 288.2K | 4.76% | -38% | 50/100 |
| #animal voiceoverTop 3 | 42 | 258K | 4.87% | -55% | 51/100 |
| #funny | 41 | 281.8K | 4.69% | -30% | 50/100 |
| #lol | 36 | 258.7K | 4.87% | -45% | 51/100 |
| #cat | 10 | 201K | 5.29% | +76% | 55/100 |
| #funny animals | 8 | 165.1K | 4.98% | -90% | 51/100 |
| #dog | 8 | 238.6K | 4.37% | -42% | 46/100 |
| #cats | 7 | 159.6K | 5.88% | +410% | 60/100 |
| #voiceovers | 6 | 361.6K | 4.48% | -45% | 48/100 |
| #dogs | 6 | 209K | 4.80% | +15% | 50/100 |
| #animal voiceovers | 4 | 267.2K | 4.53% | -96% | 48/100 |
| #animals | 4 | 52.4K | 6.43% | -76% | 65/100 |
| #hilarious | 4 | 135.6K | 7.68% | -74% | 78/100 |
| #lmao | 4 | 198.1K | 3.94% | -95% | 41/100 |
Strategic recommendations and performance highlights
Top 5 by view count





Top 5 by like count





Top 5 by comment count





Top 5 by engagement rate





Peak performance metrics across all categories
Top performers across multiple categories



Performance analysis and recommendations