pm-metrics

name: pm-metrics description: Делает ревью продуктовых метрик — тренды, аномалии, root causes и рекомендации к действиям. Включает декомпозицию North Star (L1/L2), диагностику retention-кривых, анализ воронки, разбор A/B-экспериментов, проверку соответствия OKR и фреймворк атрибуции аномалий. User-invoked only — do NOT auto-trigger. Triggers on /pm-metrics, "обзор метрик", "разбор воронки", "анализ удержания", "ретеншн", "A/B результаты", "review metrics", "DAU analysis", "retention analysis", "funnel analysis", "metric anomaly".

pm-metrics — Product metrics review

Part of the Personal Corp framework — running a one-person business through AI agents. Systematically review product metrics, identify trend changes, locate root causes, output action recommendations. Includes North Star decomposition, retention diagnostics, funnel methodology, and A/B experiment reading.

Inputs

Mode: full data → complete review; single-metric change → focused anomaly analysis.

Step 1 — Data integrity check

• Confirm time coverage (current vs comparison period)
• Confirm metric coverage (which North Star / L1 / L2 are present)
• Flag missing critical data

Step 2 — North Star metric system

Decomposition: North Star → L1 → L2.

L1 dimensions:

• User growth: DAU/WAU/MAU, new, returning
• User engagement: core action frequency, session length, feature reach
• User retention: D1 / D7 / D30
• Conversion efficiency: signup → activation → paid step-by-step rates
• Business value: paid rate, ARPU, LTV
• Satisfaction: NPS, complaint rate, ratings

North Star selection guide:

Step 3 — Growth metric analysis

Definitions:

• DAU: distinct users with valid action that day
• WAU: distinct users active ≥ 1 day in 7
• MAU: distinct users active ≥ 1 day in 30
• DAU/MAU ratio (stickiness): > 0.5 very high, 0.3-0.5 high, 0.2-0.3 medium, < 0.2 low

User segmentation:

Growth identity: This-period MAU = prev-period retained + new + returning − churned

Step 4 — Retention analysis

Definitions:

• D1: % of new users who return on day 2
• D7: % of new users who return on day 8
• D30: % of new users who return on day 31

Retention benchmarks:

Retention-curve diagnosis:

• Steep drop (D1 → D7 loses > 60%): activation experience broken — users didn't find value
• Slow decay (D7 → D30 keeps falling, doesn't level): no long-term hook
• L-shape (levels off after D7): healthy, core user base formed
• Bounce-back (sudden uptick on a specific day): cyclical use pattern (e.g. weekday-only)

Retention segmentation:

• By channel: organic vs paid retention gap
• By behavior: completed activation vs not
• By cohort month: compare month-over-month curves to gauge product improvement

Step 5 — Conversion funnel analysis

Funnel construction:

1. Define start and end points (e.g. homepage visit → payment success)
2. Split into key intermediate steps (each step = a user decision point)
3. Per-step rate = arriving at next / arriving at this

Funnel framework:

Common funnels:

• Acquisition: impression → click → install/signup → activation
• Activation: signup → onboarding done → core action first-trigger
• Payment: browse → cart → order → pay success
• Sharing: trigger → share click → recipient open → recipient conversion

Step 6 — A/B experiment readout

Decision framework:

• Significant + large effect → ship to all
• Significant + small effect → weigh long-term value vs cost
• Not significant → don't ship; investigate (wrong hypothesis? sample? execution?)
• Metric conflict (A up, B down) → weigh, prioritize North Star

Common pitfalls:

• Reading results too early (before reaching N)
• Looking only at primary metric, not guardrails
• Multiple peeks → false positives
• Ignoring novelty effect (early data inflated)

Step 7 — OKR alignment check

OKR progress table:

Step 8 — Anomaly attribution

When a metric moves anomalously, work the framework:

1. Quantify: how much, starting when?
2. Decompose: segment by channel / region / version / cohort to localize
3. Time-align: what happened around the inflection? (release, campaign, incident, competitor move)
4. Eliminate: rule out causes one by one until the most likely root remains
5. Cross-check: verify the attribution via other metrics

Common causes:

Step 9 — Generate review report

# Product Metrics Review

**Period:** {date range}
**Product:** {name}
**Type:** {weekly / monthly / quarterly}

## 1. Health Overview
| Layer | Metric | Current | Previous | MoM | Target | Status |
|---|---|---|---|---|---|---|
| North Star | {} | {} | {} | {±X%} | {} | OK / warn / alert |
| L1 | {} | {} | {} | {±X%} | {} | OK / warn / alert |

**Overall judgment:** {one-sentence summary}

## 2. User Growth
- DAU: {value}, MoM {change}
- MAU: {value}, DAU/MAU = {stickiness}
- Composition: new {X}% / retained {Y}% / returning {Z}%

## 3. Retention
| Metric | Current | Previous | Benchmark | Assessment |
|---|---|---|---|---|

## 4. Funnel
| Step | Users | Rate | MoM | Bottleneck? |
|---|---|---|---|---|

**Bottleneck diagnosis:** {description}

## 5. Experiments / Feature Effects
| Experiment | Primary metric Δ | Significance | Conclusion |
|---|---|---|---|

## 6. OKR Progress
| KR | Target | Current | Progress | Risk |
|---|---|---|---|---|

## 7. Anomaly Attribution
| Anomaly | Magnitude | Start | Attribution | Confidence |
|---|---|---|---|---|

## 8. Key Insights
1. {insight 1: finding + data + meaning}
2. {insight 2}
3. {insight 3}

## 9. Action Recommendations
| Priority | Action | Linked metric | Expected impact | Owner |
|---|---|---|---|---|

Review cadence

Quality bar

1. Metric definitions clear — every metric has a calculation note
2. Data has comparisons — current always compared to previous, YoY, or target
3. Attribution evidenced — no causation from correlation alone
4. Recommendations actionable — owner-assignable
5. Limitations tagged — call out small samples or data quality issues

Common analysis pitfalls

Red lines

1. No fabricated data — missing data → tag "missing", don't extrapolate
2. Don't conflate correlation with causation — attribution must say "highly correlated" or "confirmed causal"
3. Don't over-read small swings — small fluctuation → tag "within normal noise"
4. Don't ignore negatives — flag risks even when overall is up

When input is incomplete

• Single metric only → focus on that anomaly, no full review
• No history → snapshot only, tag "no baseline, recommend establishing tracking"
• Verbal description → analyze based on description, tag "recommend exact data for verification"
• No targets → use industry benchmarks, suggest team set explicit targets

Related skills

• /pm-feedback — pair quantitative anomaly with qualitative voice-of-customer
• /pm-prioritize — adjust priority based on metric findings
• /pm-roadmap — adjust roadmap based on OKR pacing