pm-metrics

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:

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:

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:

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:

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

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

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

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