# Creator Analytics Flywheel

Comprehensive analytics system for content creators built on the quiz architecture to maximize creator value through progressive insights

<Info>
We'll show you how learners interact with your content through our proven 12-cell mastery system, guide you on what to build next based on real coverage gaps, and gradually connect your content to downstream trading improvements—all while keeping the system fair and explainable.
</Info>

## The Analytics Loop

### Core Infrastructure

<CardGroup cols={2}>
  <Card title="12-Cell Framework" icon="grid">
    4 domains (ANALYSIS, STRATEGY, RISK, EXECUTION) × 3 Bloom levels (Apply, Analyze, Evaluate)
  </Card>
  <Card title="Item Health System" icon="heart-pulse">
    Success rate, discrimination index, abandon rate, time Z-scores with automatic status progression
  </Card>
  <Card title="Mastery Tracking" icon="chart-line">
    Beta model per cell: E[θ] = α/(α+β) with spaced review triggers
  </Card>
  <Card title="Creator Analytics" icon="chart-area">
    Exposure-weighted impact, difference-in-differences uplift, coverage gap identification
  </Card>
</CardGroup>

## Progressive Dashboard Views

### View 1: Getting Started (Days 1-30)

<Note>
For new creators with < 100 learners or in their first 30 days
</Note>

<Frame caption="Getting Started Dashboard">
```
┌─────────────────────────────────────┐
│ Your Content Health Score: 72/100   │
│ Active Learners (7d): 47            │
│ Items Published: 23                  │
│ Best Performing Cell: RISK-Apply    │
└─────────────────────────────────────┘

Coverage Grid (Your 12 Cells):
┌─────────┬────────┬─────────┬──────────┐
│         │ Apply  │ Analyze │ Evaluate │
├─────────┼────────┼─────────┼──────────┤
│ANALYSIS │   ✓3   │   ✓2    │    ⚠️1    │
│STRATEGY │   ✓4   │   📍0    │    📍0    │
│RISK     │   ✓5   │   ✓3    │    ✓2    │
│EXECUTION│   📍0   │   📍0    │    📍0    │
└─────────┴────────┴─────────┴──────────┘
✓ = Healthy  ⚠️ = Needs revision  📍 = Gap

Top Priority: Add EXECUTION content (67% need this)
```
</Frame>

#### Health Score Calculation

<Steps>
  <Step title="Item Quality (40 pts)">
    Average discrimination × (1 - abandon_rate)
  </Step>
  <Step title="Coverage (30 pts)">
    Cells with ≥2 healthy items / 12
  </Step>
  <Step title="Engagement (30 pts)">
    Completion rate × median session length factor
  </Step>
</Steps>

### View 2: Growing (100-500 learners)

<Note>
Unlocked after 100+ learners OR 30+ days of activity
</Note>

#### Mastery Flow Visualization

<Frame caption="Learner Journey Analysis">
```
ANALYSIS-Apply (87%) → STRATEGY-Apply (73%) → RISK-Apply (68%)
                                ↓
                    [Struggling Here - Suggest Content]
                                ↓
                        RISK-Analyze (42%)

Your Impact: +8% mastery gain vs baseline
(confidence: medium, n=127)
```
</Frame>

#### Item Performance Table

| Item Title | Health Status | Success Rate | Discrimination | Action |
|------------|--------------|--------------|----------------|--------|
| Stop Loss Basics | stable | 72% | 0.31 | — |
| Position Sizing | needs_edit | 45% | 0.08 | [Revise] |
| Risk/Reward | stable | 68% | 0.28 | — |

### View 3: Established (500+ learners)

<Note>
Full analytics suite with behavioral bridge metrics
</Note>

<Frame caption="Competition Attribution Dashboard">
```
┌──────────────────────────────────────────┐
│ Your RISK-Apply Modules Improved:        │
├──────────────────────────────────────────┤
│ • Rules Compliance: +31% (89 competitors)│
│ • Loss Exit Discipline: +23% (147 comp.) │
│ • Position Size Consistency: +18% (112)  │
│                                          │
│ Your RISK-Analyze Modules Reduced:       │
│ • Revenge Trading: -42% (203 competitors)│
│ • Pressure Performance Delta: -15% (156) │
│                                          │
│ Sample: 12 competitions, 2,847 trades    │
│ Confidence: HIGH (structured constraints) │
└──────────────────────────────────────────┘
```
</Frame>

<Tip>
Pressure Performance Delta measures behavioral consistency between practice and competition trading. A -15% improvement means 15% less deviation under competitive pressure.
</Tip>

## Content Creation Flow

### Spec-First Generation

<Frame caption="AI Pipeline Integration">
```json
{
  "domain": "RISK",
  "bloom": "Analyze",
  "constraints": ["policy-level only"],
  "grounding": ["doc://your_material#section3"],
  "expected_impact": {
    "addresses_gap": true,
    "learner_need": "68% struggle here after RISK-Apply",
    "competition_relevance": "reduces oversizing violations"
  }
}
```
</Frame>

### Publishing Timeline

<Steps>
  <Step title="Immediate (Same Day)">
    View attempt rate, completion rate, cell coverage
  </Step>
  <Step title="After 50 Attempts (3-7 days)">
    Initial health metrics, discrimination index, time normalization
  </Step>
  <Step title="After 200 Attempts (2-3 weeks)">
    Full health status, revision suggestions, impact preview
  </Step>
</Steps>

## The Quality Flywheel

<CardGroup cols={2}>
  <Card title="Stage 1: Measure" icon="chart-bar">
    Automatic tracking of attempts, mastery updates, gap identification
  </Card>
  <Card title="Stage 2: Analyze" icon="microscope">
    Weekly confusion reports, health monitoring, coverage analytics
  </Card>
  <Card title="Stage 3: Recommend" icon="lightbulb">
    Real-time suggestions based on gaps, audience progression, behaviors
  </Card>
  <Card title="Stage 4: Create/Revise" icon="pen">
    One-click templates, revision workflow, A/B testing
  </Card>
  <Card title="Stage 5: Validate" icon="check">
    Performance comparison, mastery improvements, behavioral tracking
  </Card>
</CardGroup>

## Implementation Phases

### Phase A: Foundation (Months 1-2)

<Info>
Focus on basic metrics and coverage
</Info>

- Impact MVP with sample gates
- Coverage bounties for underserved cells
- Basic creator following
- Rules-based struggle detection

### Phase B: Behavioral Bridge (Months 3-6)

<Info>
Connect content to trading behaviors
</Info>

- Revenue share pilot with quality multiplier
- Impact dashboards
- Asynchronous cohorts
- Mastery flow visualization

### Phase C: Full Attribution (Months 6-12)

<Info>
Complete impact measurement and prediction
</Info>

- Collaborative filtering
- Uplift modeling
- Performance forecasting
- Competition impact attribution

## What Remains Unchanged

<Warning>
These core systems stay separate to maintain fairness
</Warning>

### XP System
- XP rewards engagement only
- Never tied to difficulty or creator
- No "bonus XP" items

### Privacy & Fairness
- All views are aggregated
- Minimum sample requirements
- Market condition normalization
- No individual learner data

### Core Classification
- 12-cell matrix unchanged
- Hybrid classifier consistency
- Precedence rules prevent drift

## Success Metrics

### Platform Health (Monthly)

<Check>
- Classification reliability: κ & AC1 ≥ 0.75
- Item health: < 5% in quarantine
- Coverage: All cells have ≥10 healthy items
</Check>

### Creator Success (Quarterly)

<Check>
- 80% of creators check dashboard weekly
- 60% of flagged items revised within 14 days
- 40% improvement in underserved cells
</Check>

### Learner Outcomes

<Check>
- D7 retention ≥ 40%, D30 ≥ 20%
- +8-12% mastery gain in targeted cells
- < 5% recommendations to unhealthy items
</Check>

## Communication Framework

### Always Tell Creators

<CardGroup cols={2}>
  <Card title="About Their Content" icon="file-alt">
    - Exact health status and why
    - Sample sizes behind metrics
    - Confidence levels for impact
    - Platform fit assessment
  </Card>
  <Card title="About The System" icon="cog">
    - Classification changes
    - Flagging reasons
    - How recommendations work
    - Revenue sharing drivers
  </Card>
</CardGroup>

### Never Share

<Warning>
- Other creators' exact metrics
- Unsubstantiated causation claims
- Predictions without confidence intervals
</Warning>

## Technical Integration

### Data Sources

```sql
-- From questions table
health_discrimination, abandon_rate, 
median_time_ms, health_status

-- From user_stats
mastery_by_cell (Beta parameters)

-- From creator_analytics tables
creator_exposures_daily, creator_impact_daily
```

### Dashboard Architecture

<Steps>
  <Step title="Data Layer">
    Pull from existing analytics rollups
  </Step>
  <Step title="Confidence Layer">
    Apply established calculations and sample thresholds
  </Step>
  <Step title="Presentation Layer">
    Cache for performance, progressive disclosure
  </Step>
  <Step title="Recommendation Engine">
    Hook into coverage analytics and mastery flows
  </Step>
</Steps>

<Tip>
This approach uses everything already built in the quiz architecture, presents it progressively so creators aren't overwhelmed, and maintains system integrity while scaling naturally with the Phase A→B→C plan.
</Tip>