Analytics Learning Skill

Data-Driven Improvement

This skill processes YouTube Studio analytics to understand what works and improve future sessions.

Purpose

Extract actionable insights from performance data and update the knowledge base.

Command

/learn-analytics session-name

Input Data

User provides from YouTube Studio:

| Metric | Description | |--------|-------------| | Views | Total view count | | Watch Time | Total hours watched | | Average View Duration | Mean watch time | | Retention % | % of video watched | | Likes / Dislikes | Engagement signals | | Comments | Comment count | | Shares | Social shares | | Subscribers Gained | New subscriptions | | Impressions | How often shown | | CTR | Click-through rate |

Analysis Process

1. Benchmark Comparison

Compare session metrics to portfolio averages:

| Metric | This Session | Average | Verdict | |--------|--------------|---------|---------| | Retention | 48% | 42% | Above average | | Like Ratio | 6.2% | 5.8% | Slightly above | | Comments | 24 | 18 | Above average |

2. Pattern Identification

Correlate session attributes with performance:

| Attribute | Correlation | |-----------|-------------| | Topic: Healing | +15% retention | | Duration: 25 min | Optimal | | Voice: Neural2-H | Consistent | | Binaural: Theta | +8% engagement |

3. Insight Extraction

Generate specific, actionable findings:

- finding: "Healing topics achieve higher retention"
  evidence: "62% vs 45% average across 5 sessions"
  action: "Prioritize healing themes"
  confidence: high
  timestamp: "2025-01-15"

4. Knowledge Update

Store in knowledge/lessons_learned.yaml:

lessons:
  - id: "LESSON-2025-001"
    category: "content"
    finding: "Healing topics achieve higher retention"
    evidence: "62% vs 45% average across 5 sessions"
    action: "Prioritize healing themes"
    confidence: high
    sessions_analyzed:
      - "inner-child-healing"
      - "heart-chakra-restore"
      - "grief-release-theta"
    date_discovered: "2025-01-15"
    date_validated: null

Retention Analysis

Retention Curve Patterns

| Pattern | Meaning | Action | |---------|---------|--------| | Steep initial drop | Poor hook/intro | Improve pre-talk | | Drop at 5-7 min | Induction too slow | Tighten pacing | | Steady through journey | Good engagement | Maintain approach | | Drop at integration | Exit feels abrupt | Smooth emergence |

Target Retention by Section

| Section | Target Retention | |---------|------------------| | Pre-Talk (0-3 min) | 90%+ | | Induction (3-8 min) | 75%+ | | Journey (8-22 min) | 55%+ | | Integration (22-28 min) | 45%+ | | Close (28-30 min) | 40%+ |

Engagement Analysis

Like Ratio Interpretation

| Like Ratio | Interpretation | |------------|----------------| | >10% | Exceptional resonance | | 6-10% | Strong positive response | | 4-6% | Normal engagement | | <4% | Review content quality |

Comment Analysis Signals

| Signal | Meaning | |--------|---------| | Emotional sharing | Deep impact | | Questions | Interest but confusion | | Requests | Unmet needs | | Criticism | Quality issues |

Session Attribute Tracking

For each session, track:

session_attributes:
  topic: "healing"
  sub_topic: "inner_child"
  duration: 25
  depth_level: "Layer2"
  voice_id: "en-US-Neural2-H"
  binaural_target: "theta"
  archetypes:
    - "Guide"
    - "Healer"
  imagery_style: "eden_garden"

metrics:
  views: 1250
  watch_time_hours: 312
  avg_view_duration: "14:58"
  retention_percent: 48
  likes: 78
  dislikes: 2
  comments: 24
  shares: 12
  subs_gained: 15
  impressions: 8500
  ctr: 14.7

Confidence Levels

| Level | Definition | |-------|------------| | high | 5+ sessions, consistent pattern | | medium | 3-4 sessions, emerging pattern | | low | 1-2 sessions, hypothesis only |

Analytics Learning