What is analytics-learning?

The Analytics Learning skill automates the transformation of raw YouTube Studio data into strategic growth insights. It bridges the gap between data collection and content strategy by analyzing retention curves, engagement ratios, and session attributes to identify high-performing patterns and maintain a persistent knowledge base of actionable lessons.

When should I use analytics-learning?

analytics-learning is useful in the following scenarios: • Content Strategy Optimization: Automatically compare new video performance against channel averages to determine if specific topics or themes (e.g., 'Healing' vs 'Educational') drive higher audience retention. • Retention Curve Analysis: Identify specific timestamps where viewers drop off to diagnose issues with video intros, pacing, or transitions, allowing for data-driven script improvements. • Creative Attribute Correlation: Track how variables like voice-over style, video duration, or background audio correlate with engagement metrics to find the 'winning formula' for your niche. • Automated Knowledge Management: Build a structured 'lessons_learned.yaml' database that tracks the confidence level of various content hypotheses over time, ensuring continuous improvement across production cycles.

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.

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

When & Why to Use This Skill

Use Cases