Product Analyst
Strategic product analytics expertise for data-driven product decisions — from metrics framework selection to experimentation design and impact measurement.
Philosophy
Great product analytics isn't about tracking everything. It's about measuring what matters to drive better product decisions.
The best product analytics:
- Start with decisions, not data — What will you do differently based on this metric?
- Instrument once, measure forever — Invest in solid event tracking upfront
- Balance leading and lagging — Predict outcomes, don't just report them
- Make data accessible — Self-serve dashboards beat SQL queues
- Experiment before you ship — Validate hypotheses with real users
How This Skill Works
When invoked, apply the guidelines in rules/ organized by:
metrics-*— Frameworks (AARRR, HEART), KPI selection, metric hierarchiesfunnel-*— Conversion analysis, drop-off diagnosis, optimizationcohort-*— Retention analysis, segmentation, lifecycle trackingfeature-*— Adoption tracking, usage patterns, feature successexperiment-*— A/B testing, hypothesis design, statistical rigorinstrumentation-*— Event tracking, data modeling, collection best practicesdashboard-*— Visualization, stakeholder reporting, self-serve analytics
Core Frameworks
AARRR (Pirate Metrics)
| Stage | Question | Key Metrics |
|---|---|---|
| Acquisition | Where do users come from? | Traffic sources, CAC, signup rate |
| Activation | Do they have a great first experience? | Time-to-value, setup completion, aha moment |
| Retention | Do they come back? | DAU/MAU, D1/D7/D30 retention, churn |
| Revenue | Do they pay? | Conversion rate, ARPU, LTV |
| Referral | Do they tell others? | NPS, referral rate, viral coefficient |
HEART Framework (Google)
| Dimension | Definition | Signal Types |
|---|---|---|
| Happiness | User attitudes, satisfaction | NPS, CSAT, surveys |
| Engagement | Depth of involvement | Sessions, time-in-app, actions/session |
| Adoption | New users/features uptake | New users, feature adoption % |
| Retention | Continued usage over time | Retention curves, churn rate |
| Task Success | Efficiency and completion | Task completion, error rate, time-on-task |
The Metrics Hierarchy
┌─────────────────┐
│ North Star │ ← Single metric that matters most
│ Metric │
├─────────────────┤
│ Primary │ ← 3-5 key performance indicators
│ KPIs │
├─────────────────┤
│ Supporting │ ← Diagnostic and health metrics
│ Metrics │
├─────────────────┤
│ Operational │ ← Day-to-day tracking
│ Metrics │
└─────────────────┘
Retention Analysis Types
┌───────────────────────────────────────────────────────────┐
│ RETENTION VIEWS │
├───────────────────────────────────────────────────────────┤
│ N-Day Retention │ % who return on exactly day N │
│ Unbounded │ % who return on or after day N │
│ Bracket Retention │ % who return within a time window │
│ Rolling Retention │ % still active after N days │
└───────────────────────────────────────────────────────────┘
Experimentation Rigor Ladder
| Level | Approach | When to Use |
|---|---|---|
| 1. Gut | Ship and hope | Never for important features |
| 2. Qualitative | User research, feedback | Early exploration |
| 3. Observational | Pre/post analysis | Low-risk changes |
| 4. Quasi-experiment | Cohort comparison | When randomization hard |
| 5. A/B Test | Randomized control | Optimization, validation |
| 6. Multi-arm Bandit | Adaptive allocation | When speed > precision |
Metric Selection Criteria
| Criterion | Question | Good Sign |
|---|---|---|
| Actionable | Can we influence this? | Direct lever exists |
| Accessible | Can we measure it reliably? | <5% missing data |
| Auditable | Can we debug anomalies? | Clear calculation logic |
| Aligned | Does it tie to business value? | Executive cares |
| Attributable | Can we trace changes to causes? | A/B testable |
Anti-Patterns
- Vanity metrics — Tracking what looks good, not what drives decisions
- Metric overload — 50 dashboards, zero insights
- Lagging only — Measuring outcomes without predictive indicators
- Silent failures — No alerting on data quality issues
- HiPPO-driven — Highest-paid person's opinion beats data
- P-hacking — Running tests until you get significance
- Ship and forget — Launching features without success criteria
- Segment blindness — Looking only at averages, missing cohort differences