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 pos