Product metrics

Skill Mattakushi432/Claude-Code-Skills-Custom-DevTools-Pack/plugins/devtools-pack/skills/product-metrics

A curated pack of custom Claude Code skills for developers — installable as a Claude Code plugin marketplace.

Install

npx -y skills add Mattakushi432/Claude-Code-Skills-Custom-DevTools-Pack --skill product-metrics

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When to activate: product metrics, north star metric, KPIs, retention metrics, engagement metrics, revenue metrics, metric tree, dashboard, DAU MAU

SKILL.md

6.5 KB, ~1.7k tokens by cl100k_base, as published. Nobody here has run it

Product Metrics

North Star Metric Framework

What Makes a Good North Star

Captures value delivered to customers (not just revenue)
Predictive of long-term business success
Actionable — teams can influence it
Understandable — whole company can explain it
Measurable — available in near real-time

North Star Examples by Business Model

Business Model	North Star Metric
B2B SaaS	Weekly active teams
Consumer social	DAU/MAU ratio
Marketplace	GMV (gross merchandise value)
Media/content	Total reading time per week
E-commerce	Orders per customer per year
Developer tools	Weekly active developers running builds
Collaboration	Files shared per user per week
Fintech	Transactions processed per active user

Metric Tree (Input Metrics)

North Star: Weekly Active Teams

├── Acquisition
│   ├── New signups / week
│   ├── Trial activation rate (signup → first value)
│   └── Traffic × Conversion rate

├── Activation
│   ├── Time to first key action (< 3 days target)
│   ├── Onboarding completion rate (target: 70%)
│   └── Setup step completion rates (funnel)

├── Engagement (core action frequency)
│   ├── DAU / WAU / MAU
│   ├── Sessions per user per week
│   ├── Features used per session
│   └── Core action depth (# items created, shared, etc.)

├── Retention
│   ├── Day 1 / Day 7 / Day 30 retention
│   ├── Monthly churn rate
│   └── L30 (users active in last 30 days)

└── Expansion
    ├── Seats added per account per month
    ├── Upgrade rate (free → paid)
    └── NRR (Net Revenue Retention)

Retention Metrics

Cohort Retention Curve

Cohort: Users who signed up in January

         Day 0  Day 1  Day 7  Day 14  Day 30  Day 90
Jan cohort: 100%  40%   25%    20%     18%     15%

"Flattening" = product-market fit signal
Continuous decline = no PMF

Retention Formulas

Day N Retention = Users from cohort active on Day N / Cohort size

Rolling Retention = Users from cohort active on Day N or later / Cohort size

Churn Rate (monthly) = Customers lost in period / Customers at start of period

Retention Rate = 1 - Churn Rate

Quick Ratio = (New MRR + Expansion MRR) / (Contraction MRR + Churn MRR)
  > 4 = excellent growth
  2-4 = healthy
  < 2 = leaky bucket

Retention Benchmarks by Stage

Stage	Day 1	Day 7	Day 30
Early PMF (B2C)	25-35%	12-20%	8-15%
Strong PMF (B2C)	35-50%	20-35%	15-25%
B2B SaaS monthly	—	—	90-95%

Engagement Metrics

DAU/MAU Ratio (Stickiness)

Stickiness = DAU / MAU

> 20% = good (Slack ~50%, Facebook ~65%)
10-20% = moderate
< 10% = low engagement / wrong use case

Use WAU/MAU for weekly-use products

L-Ness Framework (Active Users)

L7 = users active in last 7 days
L14 = users active in last 14 days
L28 = users active in last 28 days

L7/L28 = stickiness equivalent

Feature Adoption Metrics

Feature Adoption Rate = Users who used feature / Total active users

Feature Frequency = Avg times feature used per user per period

Feature Breadth = Avg # of features used per active user (depth of product)

Revenue Metrics (SaaS)

Core SaaS Metrics

MRR = Monthly Recurring Revenue (sum of all monthly subscriptions)
ARR = MRR × 12 (or sum of annual contracts)

New MRR = revenue from new customers this month
Expansion MRR = upgrades, upsells, additional seats
Contraction MRR = downgrades
Churned MRR = cancelled subscriptions

Net New MRR = New MRR + Expansion MRR - Contraction MRR - Churned MRR

NRR (Net Revenue Retention) = 
  (MRR from cohort at end of period) / (MRR from same cohort at start)
  > 120% = world-class (customers expand faster than they churn)
  > 100% = growth without new customers
  < 100% = leaky bucket

GRR (Gross Revenue Retention) = NRR without expansion
  Healthy: > 85% SMB, > 90% enterprise

Unit Economics

CAC (Customer Acquisition Cost) = 
  Total Sales & Marketing spend / New customers acquired

LTV (Lifetime Value) = 
  ARPU × Gross Margin % / Churn Rate
  or: ARPU × Average customer lifespan in months

LTV:CAC Ratio:
  > 3:1 = healthy (< 3 years payback)
  < 1:1 = losing money per customer

CAC Payback Period = CAC / (ARPU × Gross Margin %)
  < 12 months = excellent
  12-18 months = good
  > 24 months = concerning

ARPU (Average Revenue Per User) = MRR / # active paying customers
ARPA (Average Revenue Per Account) = MRR / # accounts

Acquisition Metrics

Traffic → Sign-up Conversion Rate (target: 2-5% for B2B SaaS)
Sign-up → Activation Rate (target: 40-60%)
Activation → Paid Conversion Rate (target: 15-25% for PLG)
Trial → Paid Conversion Rate (target: 25% for freemium, 15% trial)

Lead Velocity Rate (LVR) = 
  (Qualified leads this month - last month) / last month × 100
  Leading indicator of future revenue growth

Dashboard Design Principles

Metric Hierarchy

Executive Dashboard (weekly)
├── North Star (vs goal)
├── MRR/ARR (vs goal)
├── Churn Rate (vs target)
└── CAC Payback Period

Team Dashboard (daily)
├── DAU / WAU (vs 7-day avg)
├── Activation rate (rolling 7d)
├── Feature-specific metrics
└── Error rates / P0 issues

Experiment Dashboard (real-time)
├── Treatment vs control metric
├── Statistical significance
└── Guardrail metrics (not regressions)

Anti-Patterns

Vanity metrics: total sign-ups without activation context
Lagging-only dashboards: MRR is lagging, track leading indicators
Too many metrics: max 5-7 metrics per dashboard
No targets: every metric needs a goal and time horizon
Missing context: always show trend + vs period / vs goal

Metric Governance

Each metric has a single owner
Metric definitions documented (exact SQL/event name)
Calculation methodology agreed cross-functionally
Refresh cadence defined (real-time, daily, weekly)
Metric changes require changelog entry
Discrepancies between tools investigated and resolved

Gives 0 of the 12 instructions most analytics metrics skills give in ~1.7k tokens

Counted across 368 of the 369 authors here whose files we hold, read 2026-08-06

read product marketing context before asking questionsin 18 of 368, across 12 files
use lowercase with underscores for event namesin 16 of 368, across 6 files
track events for decisions not vanity metricsin 15 of 368, across 5 files
use object-action format for event namesin 15 of 368, across 8 files
produce a tracking plan documentin 14 of 368, across 4 files
Call RUBE_SEARCH_TOOLS first to get current schemasin 13 of 368, across 2 files
establish consistent event naming conventions before implementingin 10 of 368, across 4 files
Verify dimension and metric compatibility before reportingin 9 of 368, across 2 files
Encrypt data at rest and in transitin 9 of 368, across 3 files
use snake_case for event namesin 9 of 368, across 5 files
monitor technical health during the testin 9 of 368, across 5 files
use consistent property namesin 8 of 368, across 4 files

Said here and by no other author read

build a metric tree covering acquisition to expansion
measure user stickiness using the active user ratio
evaluate customer acquisition cost against lifetime value
assign a single owner to each metric
document exact metric calculation sql
define the metric data refresh cadence

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.