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	1	HIGH	Comparing variants, measuring significance, shipping decisions
Cohort Retention	1	HIGH	Feature adoption curves, day-N retention, engagement scoring
Funnel Analysis	1	HIGH	Drop-off diagnosis, conversion optimization, stage mapping
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:

## 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:

-- 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:

## 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:metrics-instrumentation — Event naming, metric definition, alerting setup
• 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

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Version: 1.0.0 (March 2026)