/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

| 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 we