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Product Metrics & Analytics

metrics-analytics

Product analytics: AARRR framework, North Star Metric, A/B testing with statistical significance (95% confidence, minimum detectable effect), PostHog event tracking, funnel analysis, retention cohorts, feature flags for experiments. Vanity metrics vs actionable metrics. Use when defining KPIs, de...

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SKILL.md

Full skill instructions

Product Metrics & Analytics

When to use

  • Defining KPIs and North Star Metric for a product
  • Designing A/​B tests with proper statistical rigor
  • Implementing event tracking architecture
  • Interpreting funnel drop-off and cohort retention
  • Avoiding vanity metrics that feel good but don't drive decisions

Core principles

  1. North Star Metric is singular — the one number that best captures value delivered to users; everything else is a lever
  2. Actionable > vanity — "weekly active writers" beats "total registered users"; you can act on the former
  3. A/​B tests need pre-registered sample sizes — running until you see p<0.05 is p-hacking
  4. Events track actions, not pages — document_published reveals intent; /​dashboard does not
  5. Retention is the metric that matters most — acquisition without retention is a leaky bucket

References available

  • references/​aarrr-funnel.md — AARRR stage definitions, benchmark targets, North Star examples by product type
  • references/​ab-testing.md — sample size calculation, significance levels, sequential testing, peeking problem
  • references/​event-tracking.md — event taxonomy ([object]_[verb]), PostHog setup, funnel SQL, cohort retention queries

Assets available

  • assets/​metrics-dashboard-template.md — North Star + input metrics dashboard layout, KPI tracking table