Video Analytics Interpreter
by nicepkg
Interpret YouTube Analytics, TikTok Analytics, and video performance data. Identifies trends, explains metrics, and provides actionable recommendations for growth. Use when analyzing video performance, understanding metrics, or optimizing channel strategy.
Skill Details
Repository Files
1 file in this skill directory
name: video-analytics-interpreter description: Interpret YouTube Analytics, TikTok Analytics, and video performance data. Identifies trends, explains metrics, and provides actionable recommendations for growth. Use when analyzing video performance, understanding metrics, or optimizing channel strategy.
Video Analytics Interpreter
Transform raw video metrics into actionable growth strategies.
Key Metrics Explained
YouTube Analytics
π CORE METRICS:
VIEWS
- What: Total video plays (30+ seconds or full if shorter)
- Good: Trending upward week-over-week
- Warning: Sudden drops may indicate algorithm changes
WATCH TIME
- What: Total minutes watched
- Why it matters: #1 factor for YouTube algorithm
- Good: Higher than channel average
AVERAGE VIEW DURATION (AVD)
- What: Average time viewers watch
- Benchmark: 50%+ of video length is good
- Tip: Longer videos = lower % is acceptable
CLICK-THROUGH RATE (CTR)
- What: Impressions β Clicks percentage
- Good: 4-10% (varies by content type)
- Excellent: 10%+
- Warning: <2% needs thumbnail/title work
IMPRESSIONS
- What: Times thumbnail shown to users
- Note: Higher impressions = YouTube promoting you
- Tip: CTR Γ Impressions = Views potential
AUDIENCE RETENTION
- What: Graph showing when viewers leave
- Key: Look for drop-off points
- Goal: Flat line is ideal, gradual decline acceptable
ENGAGEMENT RATE
- What: (Likes + Comments) / Views
- Good: 4-8%
- Excellent: 8%+
TikTok Analytics
π TIKTOK METRICS:
VIDEO VIEWS
- Includes replays and loops
- Higher than YouTube due to autoplay
AVERAGE WATCH TIME
- Critical for algorithm
- Goal: Above 100% (indicates replays)
WATCH FULL VIDEO RATE
- % who watched entire video
- Good: 30%+ for 15-30 sec videos
- Excellent: 50%+
ENGAGEMENT RATE
- (Likes + Comments + Shares) / Views
- Good: 5-10%
- Viral potential: 15%+
SHARES
- Most important engagement type
- Strong shares = algorithm boost
- Indicates "save for later" or "send to friend"
PROFILE VIEWS
- Viewers who clicked your profile
- Indicates content sparked curiosity
- Goal: 1-3% of views
FOLLOWER CONVERSION
- New followers / Profile views
- Good: 10-20%
- Tip: Pin best content, optimize bio
Analytics Interpretation Framework
Step 1: Identify the Pattern
PERFORMANCE CATEGORIES:
π BREAKOUT SUCCESS (Top 10% of your content)
- Views: 3x+ your average
- CTR: Above your channel average
- Retention: Higher than similar videos
- Action: Double down, create more like this
β
SOLID PERFORMER (Above average)
- Views: 1.5-3x your average
- CTR: At or above average
- Retention: Consistent with similar content
- Action: Note what worked, iterate
π AVERAGE
- Views: Near your typical numbers
- CTR: Around channel average
- Retention: Normal patterns
- Action: Test new elements
β οΈ UNDERPERFORMER (Below average)
- Views: Below your average
- CTR: Lower than normal
- Retention: Early drop-offs
- Action: Analyze what went wrong
β FLOP (Bottom 10%)
- Views: Significantly below average
- CTR: Much lower than normal
- Retention: Severe early drop-off
- Action: Don't delete - learn from it
Step 2: Diagnose the Problem
LOW VIEWS DIAGNOSIS:
Q1: Is CTR low?
YES β Thumbnail/title problem
NO β Algorithm not promoting (impressions issue)
Q2: Is retention low?
YES β Content/hook problem
NO β Distribution/timing issue
Q3: Low impressions but good CTR/retention?
YES β Algorithm testing phase, be patient
β Or topic has limited search volume
DIAGNOSIS FLOWCHART:
βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
β Low Views? β
β β β
β Check CTR β Low? β Fix thumbnail/title β
β β β
β CTR OK? β Check Retention β Low? β Fix hook/content β
β β β
β Both OK? β Check Impressions β Low? β Topic/timing issueβ
β β β
β All OK but low views? β Give it time, algorithm testing β
βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
Step 3: Read the Retention Graph
RETENTION PATTERNS:
π EARLY DROP (0-30 seconds)
Problem: Weak hook, misleading thumbnail/title
Fix: Stronger hook, match expectations
π GRADUAL DECLINE (Steady downward slope)
Normal: Expected for most videos
Optimize: Add pattern breaks every 30-60 sec
π CLIFF DROP (Sudden sharp decline)
Problem: Boring section, promise unfulfilled
Fix: Cut/improve that section, better pacing
π SPIKE (Retention increases)
Meaning: Replay/rewind point
Opportunity: Create clip from this moment
βββ FLAT LINE (Stable)
Ideal: Viewers engaged throughout
Indicates: Strong content-hook match
Analytics Report Template
βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
VIDEO ANALYTICS REPORT
Video: [Title]
Published: [Date]
Analysis Period: [X days since publish]
βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
π PERFORMANCE SUMMARY:
βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
Views: [X] ([+/-X%] vs channel avg)
Watch Time: [X hours] ([+/-X%] vs channel avg)
CTR: [X%] ([+/-X%] vs channel avg)
Avg View Duration: [X:XX] ([X%] of video)
Engagement: [X%] (Likes: X, Comments: X)
Performance Tier: [π Breakout / β
Solid / π Average / β οΈ Under / β Flop]
π RETENTION ANALYSIS:
βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
Hook Effectiveness (0-30s): [Strong/Moderate/Weak]
- [X%] still watching at 30 seconds
Key Drop-off Points:
- [Timestamp]: [X%] dropped - Likely cause: [reason]
- [Timestamp]: [X%] dropped - Likely cause: [reason]
Rewatch Spikes:
- [Timestamp]: Viewers replayed - Content: [what happened]
Overall Shape: [Early drop / Gradual decline / Cliff / Flat]
π― TRAFFIC SOURCES:
βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
1. [Source]: [X%] - [Insight]
2. [Source]: [X%] - [Insight]
3. [Source]: [X%] - [Insight]
Best Performing Source: [Source] - Why: [explanation]
Underperforming Source: [Source] - Recommendation: [action]
π₯ AUDIENCE INSIGHTS:
βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
New vs Returning: [X% new / X% returning]
Geographic: Top countries [list]
Demographics: Primary age/gender [if available]
Device: [X% mobile / X% desktop / X% TV]
Subscriber Impact: [+X subscribers] ([X%] conversion)
π DIAGNOSIS:
βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
Primary Issue: [Identify main problem if any]
Root Cause: [Why this happened]
Secondary Issues: [Other concerns]
π‘ RECOMMENDATIONS:
βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
IMMEDIATE (This Video):
1. [Action] - Expected impact: [result]
2. [Action] - Expected impact: [result]
FUTURE VIDEOS:
1. [Lesson learned] - Apply to: [future content]
2. [Lesson learned] - Apply to: [future content]
A/B TEST SUGGESTION:
- Test: [Element to test]
- Hypothesis: [What you expect]
- Measure: [Metric to track]
π¬ CLIP OPPORTUNITY:
βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
High-engagement moment at [Timestamp]: "[Description]"
Recommended for: [TikTok/Shorts/Reels]
Hook angle: "[Suggested hook]"
π
NEXT STEPS:
βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
[ ] [Action item 1]
[ ] [Action item 2]
[ ] [Action item 3]
βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
How to Use
Full Analytics Review
Analyze this video's performance:
[Paste analytics data or describe metrics]
Comparisons:
- Channel average CTR: [X%]
- Channel average retention: [X%]
- Similar video performance: [description]
Quick Diagnosis
My video has [X views] but my CTR is [X%].
Why is it underperforming and how do I fix it?
Trend Analysis
Analyze these videos' performance trends:
Video 1: [Title] - [Views, CTR, Retention]
Video 2: [Title] - [Views, CTR, Retention]
Video 3: [Title] - [Views, CTR, Retention]
What patterns do you see?
Channel Health Check
Here are my last 10 videos' metrics:
[List videos with key metrics]
How is my channel performing overall?
What should I focus on?
Benchmarks by Content Type
CTR Retention Engagement
Educational 3-6% 40-60% 3-6%
Entertainment 5-10% 30-50% 5-10%
Gaming 4-8% 25-45% 4-8%
Vlog 3-7% 35-55% 4-8%
Product Review 4-8% 40-60% 3-6%
Tutorial 3-6% 45-65% 3-5%
Short-form 8-15% 70-100%+ 8-15%
Warning Signs to Watch
β οΈ IMMEDIATE ATTENTION:
- CTR dropped 50%+ from average
- Retention cliff in first 30 seconds
- Impressions declining week-over-week
- Subscriber loss instead of gain
π CONCERNING TRENDS:
- AVD decreasing over time
- Engagement rate declining
- More dislikes than usual
- Comments increasingly negative
Related Skills
Xlsx
Comprehensive spreadsheet creation, editing, and analysis with support for formulas, formatting, data analysis, and visualization. When Claude needs to work with spreadsheets (.xlsx, .xlsm, .csv, .tsv, etc) for: (1) Creating new spreadsheets with formulas and formatting, (2) Reading or analyzing data, (3) Modify existing spreadsheets while preserving formulas, (4) Data analysis and visualization in spreadsheets, or (5) Recalculating formulas
Clickhouse Io
ClickHouse database patterns, query optimization, analytics, and data engineering best practices for high-performance analytical workloads.
Clickhouse Io
ClickHouse database patterns, query optimization, analytics, and data engineering best practices for high-performance analytical workloads.
Analyzing Financial Statements
This skill calculates key financial ratios and metrics from financial statement data for investment analysis
Data Storytelling
Transform data into compelling narratives using visualization, context, and persuasive structure. Use when presenting analytics to stakeholders, creating data reports, or building executive presentations.
Kpi Dashboard Design
Design effective KPI dashboards with metrics selection, visualization best practices, and real-time monitoring patterns. Use when building business dashboards, selecting metrics, or designing data visualization layouts.
Dbt Transformation Patterns
Master dbt (data build tool) for analytics engineering with model organization, testing, documentation, and incremental strategies. Use when building data transformations, creating data models, or implementing analytics engineering best practices.
Sql Optimization Patterns
Master SQL query optimization, indexing strategies, and EXPLAIN analysis to dramatically improve database performance and eliminate slow queries. Use when debugging slow queries, designing database schemas, or optimizing application performance.
Anndata
This skill should be used when working with annotated data matrices in Python, particularly for single-cell genomics analysis, managing experimental measurements with metadata, or handling large-scale biological datasets. Use when tasks involve AnnData objects, h5ad files, single-cell RNA-seq data, or integration with scanpy/scverse tools.
Xlsx
Spreadsheet toolkit (.xlsx/.csv). Create/edit with formulas/formatting, analyze data, visualization, recalculate formulas, for spreadsheet processing and analysis.
