analytics-learning

name	Analytics Learning
tier	4
load_policy	conditional
description	Process YouTube analytics to extract actionable insights
version	1.0.0
parent_skill	growth-learning

Analytics Learning Skill

Data-Driven Improvement

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

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Purpose

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

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Command

/learn-analytics session-name

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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

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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

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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%+

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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

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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

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Confidence Levels

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

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Output

After analysis:

1. Summary Report: Key findings with evidence
2. Knowledge Update: New entries in lessons_learned.yaml
3. Recommendations: Actions for next sessions
4. Questions: Areas needing more data

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Related Resources

• Skill: tier4-growth/feedback-integration/ (comment analysis)
• Knowledge: knowledge/lessons_learned.yaml
• Knowledge: knowledge/analytics_history/