ncklrs/startup-os-skills/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)

HEART Framework (Google)

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

Metric Selection Criteria

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