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