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skills/mohitagw15856/pm-claude-skills/saas-metrics

saas-metrics

2
mohitagw15856/pm-claude-skills·Audit passed·Snapshot 3703f115137e

Summary

This source did not publish a separate summary. Review SKILL.md before using the skill.

SKILL.md

SaaS Metrics Skill

Investors and boards judge a SaaS business on a standard metric set — and getting the definitions right matters as much as the numbers. This skill computes MRR/ARR, growth, net and gross revenue retention, churn, the quick ratio, and the magic number from your movement data, each with its benchmark and a plain read — so a board update or investor snapshot is correct and defensible.

Required Inputs

Ask for these only if they aren't already provided:

  • Starting MRR and the month's movement: new, expansion, contraction, churned MRR.
  • Customer counts (start, churned) if you want logo churn too.
  • S&M spend (prior period) if you want the magic number.
  • Or just paste what you have — the skill computes what the inputs allow and flags the rest.

Output Format

SaaS Metrics: [company], [period]

A computed dashboard (use the helper script):

MetricValueBenchmarkRead
MRR / ARR
MRR growth %
Net Revenue Retention≥ 100% (great ≥ 110%)
Gross Revenue Retention≥ 90%
Revenue churn %
Quick ratio ((new+exp)/(churn+contr))≥ 4 strong
Magic number (if S&M given)≥ 0.75 efficient

What it says — 2–3 lines: the health story the numbers tell, and the one metric to fix first.

Definitions used — state each formula explicitly (NRR excludes new customers; GRR caps at 100%), so the numbers are comparable and audit-proof.

Programmatic Helper

scripts/saas_metrics.py (stdlib only) computes the set from the MRR movement:

# in.json: {"starting_mrr":100000,"new":12000,"expansion":6000,"contraction":2000,"churned":4000,"sm_spend_prior":40000}
python3 scripts/saas_metrics.py in.json
python3 scripts/saas_metrics.py in.json --json

Quality Checks

  • NRR excludes new MRR (it measures the existing base only) — the most-botched definition
  • GRR is capped at 100% (it can't exceed retention of what you had)
  • Each metric is shown against its standard benchmark
  • The formulas used are stated, so the numbers are comparable across reports
  • Metrics that can't be computed from the given inputs are flagged, not guessed

Anti-Patterns

  • Do not include new customers in NRR — that's a different (and misleadingly flattering) number
  • Do not mix monthly and annual figures without converting — label MRR vs ARR clearly
  • Do not report a metric without its definition — "120% retention" is meaningless without the formula
  • Do not vanity-pick metrics — show churn and contraction alongside the growth numbers
  • Do not present computed values to false precision — round sensibly and flag assumptions

Based On

Standard SaaS metrics definitions (Bessemer / a16z / KeyBanc) — NRR/GRR, quick ratio, magic number.

Related skills

capacity-planningcompetitor-teardowncontext-engineering-reviewrunbook-writerreceipts-audit