szoloth/product-data-analyst
skills/product-data-analyst/SKILL.md
Define product metrics frameworks (HEART, AARRR), create measurement plans, and quantify product improvements. Use when establishing success metrics, measuring feature impact, or building data-informed product culture.
development
Updated Apr 15, 2026
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SKILL.md
- name:
- product-data-analyst
- description:
- Define product metrics frameworks (HEART, AARRR), create measurement plans, and quantify product improvements. Use when establishing success metrics, measuring feature impact, or building data-informed product culture.
Product Data Analyst
Overview
Design metrics frameworks, measurement plans, and analytics strategies to quantify product performance and inform decisions. Apply HEART, AARRR, and other product analytics frameworks.
Core Frameworks
HEART (Google)
- Happiness: User satisfaction, NPS
- Engagement: Usage frequency, session duration
- Adoption: New user activation
- Retention: Users returning over time
- Task Success: Completion rate, time-on-task, error rate
AARRR (Pirate Metrics)
- Acquisition: How users find you
- Activation: First good experience
- Retention: Users come back
- Revenue: Monetization
- Referral: Users tell others
North Star Metric
Single metric that best captures core value delivered to customers. Examples:
- Spotify: Time spent listening
- Airbnb: Nights booked
- Slack: Messages sent by teams
Measurement Planning
For each feature/initiative:
- Hypothesis: What will improve and by how much?
- Success metrics: How we'll measure impact
- Counter-metrics: What we don't want to harm
- Instrumentation: What data we need to collect
- Analysis plan: How we'll interpret results
Good vs bad metrics
Good (actionable):
- Time to find asset (can optimize this)
- Search abandonment rate (clear signal)
- Asset reuse rate (measures efficiency)
Bad (vanity):
- Total users (doesn't show value)
- Page views (doesn't indicate success)
- Features shipped (output, not outcome)
Analytics Implementation
Event tracking design
- Name events clearly (asset_search_completed, not event_42)
- Include context (search_type, result_count, time_to_first_click)
- Track both success and failure paths
- Capture user segments for analysis
Dashboard principles
- Focus on decisions, not data
- Show trends over time, not just current state
- Include context (goals, benchmarks)
- Alert on anomalies automatically
When to Use
- Defining success metrics for new features
- Measuring impact of product changes
- Building data-informed product culture
- Investigating performance issues
- Quantifying opportunity size
Quality Checks
- [ ] Metrics directly relate to user/business value
- [ ] Instrumentation captures what's needed
- [ ] Counter-metrics defined (prevent gaming)
- [ ] Analysis plan clear before shipping
- [ ] Metrics accessible to whole team, not just analysts
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