product-analyst

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:

1. Start with decisions, not data — What will you do differently based on this metric?
2. Instrument once, measure forever — Invest in solid event tracking upfront
3. Balance leading and lagging — Predict outcomes, don't just report them
4. Make data accessible — Self-serve dashboards beat SQL queues
5. 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 hierarchies
• funnel-* — Conversion analysis, drop-off diagnosis, optimization
• cohort-* — Retention analysis, segmentation, lifecycle tracking
• feature-* — Adoption tracking, usage patterns, feature success
• experiment-* — A/B testing, hypothesis design, statistical rigor
• instrumentation-* — Event tracking, data modeling, collection best practices
• dashboard-* — 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