116 lines
3.1 KiB
Markdown
116 lines
3.1 KiB
Markdown
---
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name: lean-analyze-usage
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description: Analyze post-launch usage data to generate improvement recommendations with prioritized action items. Use after a product has launched and usage data is available, when identifying feature adoption gaps or funnel drop-offs, or when planning the next iteration based on real user behavior. Trigger phrases: "analyze usage data", "post-launch analysis", "lean usage analysis".
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---
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## Overview
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Generates a usage analysis report from analytics data (CSV or manual input) to inform the next product iteration. Outputs `plans/reports/usage-analysis-{date}.md` with findings, funnel analysis, retention data, and prioritized recommendations.
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## When to Use
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- Product has launched and 30+ days of usage data are available
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- Identifying which features are underused or causing drop-off
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- Planning the next improvement cycle based on real behavior
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- Presenting data-driven recommendations to stakeholders
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## Don't Use When
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- Product has not launched yet (use lean-user-research instead)
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- No usage data is available
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## Input
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Supported:
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- CSV file path (exported from GA, Mixpanel, Amplitude, etc.)
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- "manual" (guided input via questions)
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## Workflow
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### Step 1: Collect Data
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If CSV provided: parse user_id, event, timestamp, properties.
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If "manual": ask for:
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- Total users (DAU/WAU/MAU)
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- Key events (signups, activations, feature usage)
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- Funnels (conversion rates)
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- Retention (D1, D7, D30)
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### Step 2: Analyze Patterns
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- Feature usage: most/least used features, power vs casual users
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- Drop-off analysis: where users abandon flows, conversion per step
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- Retention: return rates after 1/7/30 days, cohort quality
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- Correlations: features correlated with retention, acquisition source vs conversion
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### Step 3: Generate Report
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```markdown
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# Usage Analysis Report
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**Period:** {start} to {end}
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**Generated:** {date}
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## Executive Summary
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**Key Findings:**
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1. Finding 1 (Impact: HIGH)
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2. Finding 2 (Impact: MEDIUM)
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**Top Opportunity:** [Highest impact improvement]
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## Metrics Overview
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| Metric | Value | Benchmark | Status |
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|--------|-------|-----------|--------|
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| MAU | X | 1,000+ | pass/warn |
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| Signup → Activation | X% | 40%+ | pass/warn |
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| D7 Retention | X% | 20%+ | pass/warn |
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## Feature Adoption
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| Feature | Users | % of Total | Trend |
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|---------|-------|------------|-------|
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## Funnel Analysis
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| Step | Users | Conversion | Drop-off |
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|------|-------|------------|----------|
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## Cohort Retention
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| Source | Users | D1 | D7 | D30 | Quality |
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## Recommendations
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### P0 (Do First)
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**1. [Issue Title]**
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- Issue: [Problem]
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- Root Cause: [Why]
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- Solution: [Fix]
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- Expected Impact: [Metric improvement]
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### P1 (Medium Priority)
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### P2 (Low Priority)
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## Next Steps
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1. Review with team
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2. Create improvement plan: lean [improvement]
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3. Implement P0 recommendations
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4. Re-analyze in 30 days
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## Data Sources
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- Platform: {name}
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- Date Range: {range}
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- Limitations: {data quality notes}
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```
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## Integration
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```
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Launch MVP
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↓
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Collect usage data (30+ days)
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↓
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lean-analyze-usage → plans/reports/usage-analysis-{date}.md
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↓
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lean [improvement] → Next iteration
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↓
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plan → Implement improvements
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```
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