product-analytics

# Product Analytics Frameworks for turning raw product data into ship/extend/kill decisions. Covers A/B testing, cohort retention, funnel analysis, and the statistical foundations needed to make those decisions with confidence. ## Quick Reference | Category | Rules | Impact | When to Use | |----------|-------|--------|-------------| | [A/B Test Evaluation](#ab-test-evaluation) | 1 | HIGH | Comparing variants, measuring significance, shipping decisions | | [Cohort Retention](#cohort-retention) | 1 | HIGH | Feature adoption curves, day-N retention, engagement scoring | | [Funnel Analysis](#funnel-analysis) | 1 | HIGH | Drop-off diagnosis, conversion optimization, stage mapping | | [Statistical Foundations](#statistical-foundations) | 1 | HIGH | p-value interpretation, sample sizing, confidence intervals | **Total: 4 rules across 4 categories** ## A/B Test Evaluation Load `rules/ab-test-evaluation.md` for the full framework. Quick pattern: ```markdown ## Experiment: [Name] Hypothesis: If we [change], then [primary metric] will [direction] by [amount] because [evidence or reasoning]. Sample size: [N per variant] — calculated for MDE=[X%], power=80%, alpha=0.05 Duration: [Minimum weeks] — never stop early (peeking bias) Results: Control: [metric value] n=[count] Treatment: [metric value] n=[count] Lift: [+/- X%] p=[value] 95% CI: [lower, upper] Decision: SHIP / EXTEND / KILL Rationale: [One sentence grounded in numbers, not gut feel] ``` **Decision rules:** - **SHIP** — p < 0.05, CI excludes zero, no guardrail regressions - **EXTEND** — trending positive but underpowered (add runtime, not reanalysis) - **KILL** — null result or guardrail degradation See `rules/ab-test-evaluation.md` for sample size formulas, SRM checks, and pitfall list. ## Cohort Retention Load `rules/cohort-retention.md` for full methodology. Quick pattern: ```sql -- Day-N retention cohort query SELECT DATE_TRUNC('week', first_seen) AS cohort_week, COUNT(DISTINCT user_id) AS cohort_size, COUNT(DISTINCT CASE WHEN activity_date = first_seen + INTERVAL '7 days' THEN user_id END) * 100.0 / COUNT(DISTINCT user_id) AS day_7_retention FROM user_activity GROUP BY 1 ORDER BY 1; ``` **Retention benchmarks (SaaS):** - Day 1: 40–60% is healthy - Day 7: 20–35% is healthy - Day 30: 10–20% is healthy - Flat curve after day 30 = product-market fit signal See `rules/cohort-retention.md` for behavior-based cohorts, feature adoption curves, and engagement scoring. ## Funnel Analysis Load `rules/funnel-analysis.md` for full methodology. Quick pattern: ```markdown ## Funnel: [Name] — [Date Range] Stage 1: [Aware / Land] → [N] users (entry) Stage 2: [Activate / Sign] → [N] users ([X]% from stage 1) Stage 3: [Engage / Use] → [N] users ([X]% from stage 2) ← biggest drop Stage 4: [Convert / Pay] → [N] users ([X]% from stage 3) Overall conversion: [X]% Biggest drop-off: Stage 2→3 ([X]% loss) — investigate first ``` **Optimization order:** Fix the largest drop-off first. A 5-point improvement at a high-volume step is worth more than a 20-point improvement at a low-volume step. See `rules/funnel-analysis.md` for segmented funnels, micro-conversion tracking, and prioritization patterns. ## Statistical Foundations Plain-English explanations of the stats every PM needs. Load `references/stats-cheat-sheet.md` for formulas and quick lookups. **p-value in plain English:** The probability that you would see a result this extreme (or more extreme) if the change had zero effect. p=0.03 means a 3% chance you're looking at random noise. It does NOT mean "97% probability the change works." **Confidence interval in plain English:** The range where the true effect probably lives. "Lift = +8%, 95% CI [+2%, +14%]" means you are fairly confident the real lift is somewhere between 2% and 14%. If the CI includes zero, you cannot claim a win. **Minimum Detectable Effect (MDE):** The smallest lift you care about detecting. Setting MDE too small forces impractically large sample sizes. Anchor MDE to business value — if a 2% lift is not worth shipping, set MDE = 5%. **Statistical vs practical significance:** A result can be statistically significant (p < 0.05) but practically meaningless (lift = 0.01%). Always check both. A 0.01% lift that costs 6 weeks of eng time is not a win. ## Common Pitfalls 1. **Peeking** — stopping an experiment early because results look good inflates false-positive rate. Commit to a runtime before launch. 2. **Multiple comparisons** — testing 10 metrics at p < 0.05 means ~1 false positive by chance. Apply Bonferroni correction or pre-register your primary metric. 3. **Sample Ratio Mismatch (SRM)** — if variant group sizes differ from expected split by > 1%, your experiment is broken. Fix before analyzing results. 4. **Novelty effect** — new features get inflated engagement in week 1. Run experiments long enough to see settled behavior (minimum 2 full business cycles). 5. **Simpson's paradox** — aggregate results can reverse when segmented. Always check results by key segments (device, plan tier, geography). ## Ship / Extend / Kill Framework | Signal | Decision | Action | |--------|----------|--------| | p < 0.05, CI excludes zero, guardrails green | SHIP | Full rollout, update success metrics | | Positive trend, underpowered (p = 0.10–0.15) | EXTEND | Add runtime, do not peek again | | p > 0.15, flat or negative | KILL | Revert, document learnings, re-hypothesize | | Guardrail regression, any p-value | KILL | Immediate revert regardless of primary metric | | SRM detected | INVALID | Fix assignment bug, restart experiment | ## Related Skills - `ork:product-frameworks` — OKRs, KPI trees, RICE prioritization, PRD templates - `ork:monitoring-observability` — Metric definition, alerting, and drift monitoring - `ork:brainstorm` — Generate hypotheses and experiment ideas - `ork:assess` — Evaluate product quality and risks ## References - `rules/ab-test-evaluation.md` — Hypothesis, sample size, significance, decision matrix - `rules/cohort-retention.md` — Cohort types, retention curves, SQL patterns - `rules/funnel-analysis.md` — Stage mapping, drop-off identification, optimization - `references/stats-cheat-sheet.md` — Formulas, test selection, power analysis --- **Version:** 1.0.0 (March 2026)