Pm Metrics Agent Skill

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

| Field | Required | Notes |

|---|---|---|

| Metric data | yes | Excel / CSV / pasted table / verbal description |

| Cycle | no | Weekly / monthly / quarterly review; default weekly |

| Focus | no | Full review / single-metric anomaly / experiment readout |

| Business context | no | Releases, campaigns, incidents in the period |

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:

| Product type | Recommended NSM | Typical L1 |

|---|---|---|

| Social / community | Weekly active posters | DAU/MAU ratio, interactions per user, D7 retention |

| Tools / productivity | Weekly users completing core task | Task completion rate, frequency, feature reach |

| E-commerce | Weekly transacting users | GMV, AOV, repeat rate, conversion |

| Content / media | Weekly content-consumption time | Time per user, completion rate, return rate |

| SaaS / B2B | Weekly active teams | Team penetration, feature depth, renewal rate |

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:

| Type | Definition | Focus |

|---|---|---|

| New | First-time user | Channel quality, activation rate |

| Active retained | Active in both periods | Depth, feature reach |

| Returning | Inactive last period, active this | Return reason, secondary retention |

| Churned | Active last period, inactive this | Churn cause, win-back potential |

| Dormant | Inactive multiple periods | Possibly permanent loss |

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:

| Product type | D1 | D7 | D30 | Note |

|---|---|---|---|---|

| Social / messaging | > 70% | > 50% | > 35% | High-frequency essential |

| Tools | > 40% | > 25% | > 15% | "Use and leave" pattern |

| Content / news | > 35% | > 20% | > 10% | Many alternatives, lower retention |

| E-commerce | > 25% | > 15% | > 8% | Low-frequency, watch repeat rate instead |

| Games | > 40% | > 20% | > 10% | High variance by genre |

| SaaS / B2B | > 60% | > 45% | > 30% | High switching cost, higher baseline |

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:

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

Funnel framework:

| Step | Action | Output |

|---|---|---|

| Draw | List steps + rates | Full funnel view |

| Identify bottleneck | Find lowest-rate step | Optimization focus |

| Benchmark | Compare history / industry / competitor | Gap quantification |

| Segment | By channel / device / user type | Locate problem cohort |

| Hypothesize | Why is the bottleneck there? | Optimization direction |

| Experiment | Propose A/B test | Action plan |

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

| Dimension | Standard | Note |

|---|---|---|

| Statistical significance | p < 0.05 | p > 0.05 → inconclusive, don't decide |

| Effect size | Lift > MDE | Significant but tiny lift may not be worth it |

| Sample size | Reaches pre-set N | "Significant" without N is unreliable |

| Duration | Covers ≥ 1-2 full weeks | Avoid weekday/weekend bias |

| AA check | Pre-period baselines match | Mismatch → split assignment is broken |

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

| Check | Healthy | Anomaly signal |

|---|---|---|

| Coverage | Every KR has ≥ 1 trackable metric | A KR with no measurable proxy |

| Consistency | Metric direction matches KR target | Metric up but KR no progress |

| Pacing | Linear pacing ≥ 50% by mid-quarter | Severely behind schedule |

| Attribution | Metric movement attributable to team action | Metric improved due to industry tailwind, not team |

OKR progress table:

| OKR | KR metric | Target | Current | Progress % | Trend | Risk |

|---|---|---|---|---|---|---|

| {O1} | {KR1} | {target} | {current} | {X%} | Up/flat/down | On-track / at-risk / severe |

Step 8 — Anomaly attribution

When a metric moves anomalously, work the framework:

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

Common causes:

| Category | Pattern | Verification |

|---|---|---|

| Release | Inflection aligns with deploy time | Compare per-version |

| Campaign | Up during campaign, drops after | Compare per-channel |

| Tech incident | Sudden drop + recovery | Check error logs and uptime |

| External | Industry-wide change | Compare with competitor / industry data |

| Channel mix | One channel changed dramatically | Per-channel decomposition |

| Seasonality | Same as YoY | Look at last year's same period |

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

| Type | Frequency | Time | Audience | Focus |

|---|---|---|---|---|

| Weekly | Every Monday | 15-30 min | PM | NSM + anomalies + experiments |

| Monthly | Month start | 30-60 min | Product team | All L1 + retention + funnel + OKR pacing |

| Quarterly | Quarter end | 60-90 min | Product + ops + eng | Strategy review + OKR scoring + next-quarter plan |

Quality bar

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

Common analysis pitfalls

| Pitfall | Symptom | Fix |

|---|---|---|

| Simpson's paradox | Total goes up while every segment goes down | Always segment, never just look at totals |

| Survivorship bias | Only retained users analyzed, churned ignored | Compare retained vs churned behavior |

| Vanity metric | Cumulative signups only ever grow, not decision-useful | Use active metrics (DAU/WAU) instead |

| Time-window trap | Comparison window happens to be an outlier | Cross-validate across multiple windows |

| Goodhart's law | Target becomes a metric, stops measuring well | Set guardrails to prevent gaming |

Red lines

• No fabricated data — missing data → tag "missing", don't extrapolate
• Don't conflate correlation with causation — attribution must say "highly correlated" or "confirmed causal"
• Don't over-read small swings — small fluctuation → tag "within normal noise"
• 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