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):

| Metric | Value | Benchmark | Read | |---|---|---|---| | 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:

bash
# 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.