saas-metrics-health-check

name	saas-metrics-health-check
description	Performs a diagnostic on a SaaS product's revenue and retention metrics from a CSV or spreadsheet of customers/subscriptions/events. Computes MRR, ARR, churn (gross/net), LTV, CAC, payback period, quick ratio, and cohort retention; flags anomalies; identifies the single highest-leverage fix. Invoked when the user asks "are my SaaS metrics healthy", "compute LTV for my data", "is my churn high", "review my MRR breakdown", or shares a billing/customer CSV.
short_desc	compute MRR/ARR/churn/LTV from customer CSV diagnostic
keywords	["SaaS metrics","compute MRR","compute churn","payback period","cohort retention","unit economics","revenue metrics","SaaS health check","are my metrics healthy","quick ratio"]
model	opus
effort	high

SaaS Metrics Health-Check (Opus)

Purpose: Turn a messy customer/subscription CSV into a defensible SaaS health report with a single recommended action.

Model: Opus 4.7.

When to invoke

Use this skill when the user:

• Hands over a billing export (Stripe, Lemon Squeezy, Paddle) and asks for a diagnostic
• Asks "is my churn high" or "what should LTV/CAC be at my stage"
• Wants a monthly metrics dashboard for the first time
• Is preparing a fundraise update or YC application and needs defensible numbers
• Shares MRR figures and asks "is this healthy"

Don't use this skill for:

• "What is MRR" — point to a tutorial; this skill assumes founder-literacy
• Real-time dashboards — this is a one-shot diagnostic; for ongoing tracking recommend Baremetrics / ChartMogul / a self-built dashboard
• Pre-revenue products (<5 paying customers) — sample size too small; do customer interviews instead

What this skill does

1. Data validation

Given a CSV, first verify:

• What's in the file: customers, subscriptions, charges, invoices, events?
• Date range covered and granularity (daily/monthly)
• Currency (assume one; if multi-currency flag for normalisation)
• Missing fields that block analysis (no churn timestamps, no plan info, no MRR field — request them)
• Deduplication issues (test customers, refunded charges, $0 transactions)

If the data is unusable, stop and ask for the right export. Don't fabricate fields.

Common providers and their exports:

• Stripe: Dashboard → Reports → Billing Overview, or /v1/subscriptions API with expand[]=customer
• Lemon Squeezy: Dashboard → Reports → CSV export
• Paddle: Subscriptions list export with status + MRR

2. Core metric calculation

Compute and report with the exact definitions used:

• MRR (Monthly Recurring Revenue): sum of monthly-normalized active subscription value. Annual plans divided by 12.
• ARR: MRR × 12 (display only; not an operationally distinct number)
• New MRR: MRR from subscriptions created this month
• Expansion MRR: MRR added via upgrades from existing customers
• Contraction MRR: MRR lost via downgrades (still subscribed, lower plan)
• Churned MRR: MRR lost via cancellations
• Net New MRR = New + Expansion − Contraction − Churned
• Gross MRR Churn % = Churned MRR / MRR at start of month
• Net MRR Churn % = (Churned + Contraction − Expansion) / MRR at start of month
  • Negative is great (expansion > churn)
• Logo (customer) churn % = Customers churned / Customers at start of month
• Quick Ratio = (New + Expansion) / (Churned + Contraction)
• ARPU = MRR / Active customers
• LTV (simple) = ARPU / Gross Monthly Churn
  • Caveat: assumes churn is constant; over-estimates LTV when churn declines with tenure (it usually does — earliest customers churn fastest). Report alongside cohort-LTV (sum of revenue across a closed cohort) when data permits.
• CAC = Acquisition spend / New paying customers (ask user for the spend figure)
• LTV : CAC ratio = LTV / CAC (target >3 for SaaS health)
• CAC Payback (months) = CAC / (ARPU × Gross Margin)
  • Solo founders: assume Gross Margin = 0.85 unless told otherwise (most SaaS run 70–90%)

3. Cohort retention table

Build a cohort table:

• Rows: signup month
• Columns: months since signup (M0, M1, M2, ..., M12)
• Cells: % of cohort still paying

This is the single most important table in SaaS metrics — it surfaces:

• Whether retention is improving over time (compare row-by-row at fixed column)
• Whether onboarding has a high M1 drop-off (M0→M1 cliff)
• Whether there's a delayed-activation curve (low M1 but high M6)

4. Diagnosis

Compare measured values against stage-appropriate benchmarks:

Stage (MRR)	Monthly churn (B2C)	Monthly churn (B2B SMB)	LTV:CAC	Quick Ratio
<$1K	"Don't worry yet"	"Don't worry yet"	n/a	n/a
$1K–10K	<6%	<4%	>2	>2
$10K–50K	<5%	<3%	>3	>3
$50K+	<4%	<2%	>3	>4

(Benchmarks are rough industry medians; individual product economics vary.)

Flag:

• Any metric >2x worse than benchmark as a "blocker"
• Anomalies in cohort table (one bad month, one cliff)
• Definitional risks (e.g. if there's no annual subscription handling, churn % will be wrong)

5. The single recommended action

End the report with one prioritised action — not a list of 10 things. The point of a diagnostic is forcing prioritisation.

Examples of well-formed recommendations:

• "Your gross churn is 8% (benchmark 4%) and clusters at month 2. The onboarding sequence is your highest-leverage fix. Audit the signup → activation → first-value flow this week; everything else is downstream."
• "Quick ratio of 1.3 means acquisition barely outruns churn. Stop building features. Spend next 30 days on retention — turn on Stripe Smart Retries (involuntary churn), add a cancel survey (voluntary), add a pause-instead-of-cancel option."
• "LTV:CAC is 5.8 — you're under-investing in acquisition. Triple your ad spend on the channel with lowest CAC; you have 4 months of runway in the math even at 50% efficiency degradation."

Inputs needed from the user

If not provided, ask for:

1. The data export (CSV preferred; can also work from numbers pasted as a table)
2. Currency and whether the numbers are normalised
3. Approx monthly acquisition spend (ads + content + tools attributable) — needed for CAC
4. Approximate gross margin if unusual — needed for CAC payback
5. Customer segment (B2C / prosumer / B2B SMB / B2B mid) — sets benchmark band
6. Time horizon of the data — anything <3 months is too short for meaningful LTV

If they don't have export data, the skill should not invent numbers — instead pivot to "let's set up the tracking first" and recommend a 1-line dashboard.

Output format

# SaaS Health-Check: <product name>

## Headline
- MRR: $X
- Net MRR growth (last 3 mo avg): $Y/mo (Z%)
- Gross monthly churn: A%
- Quick ratio: B
- LTV:CAC: C
- Overall verdict: <Healthy / Acceptable / Concerning / Crisis>

## Metrics (with definitions)
<table: metric, value, benchmark, status>

## Cohort retention
<table or text-rendered grid>

## Anomalies
- <flagged issue 1, with month + size>
- <flagged issue 2>

## What changed vs last period
<if multi-period data was given>

## Single recommended action
<one paragraph; the highest-leverage next move>

## Open questions / data gaps
<things that would sharpen the diagnosis if available>

Data hygiene rules

• Never fabricate: if a metric requires data you don't have, mark it n/a — need X
• Show your work: state the definition used for each metric (LTV is especially ambiguous)
• Flag double-counting: if the CSV mixes successful and refunded charges, separate them
• Currency: don't sum across currencies without normalisation; flag if mixed

Required reading before using this skill

• knowledge/concepts/churn-taxonomy-and-tactics.md — for interpreting churn breakdowns
• knowledge/concepts/saas-pricing-psychology.md — pricing as a churn cause
• knowledge/concepts/north-star-metric-selection.md — the NSM is upstream of revenue metrics

Anti-patterns to push back on

• "Just give me the LTV" with a 4-week-old product — refuse; LTV is meaningless before 6+ months of churn data
• "Compare me to <Notion / Stripe / Slack>" — those are post-product-market-fit benchmarks, irrelevant for an indie
• "I want to look good for investors" — the diagnostic isn't a marketing exercise; if the metrics are weak, the right move is to fix them before the deck, not to spin them
• LTV:CAC > 10 — usually means under-investing in acquisition, not "great economics"

Knowledge Systems

Full reference: ~/.claude/shared/KNOWLEDGE_SYSTEMS.md

Decision tree:

• SaaS-metric definitions and patterns → hybrid_search("SaaS metrics LTV churn definition") (Weaviate MCP)
• Stage-appropriate benchmarks → consult the table above + KG nodes
• Data parsing → standard Python via Bash; pandas if available, csv module if not

Success criteria

This skill is working well if:

• Every metric has the definition used printed next to the value
• The recommendation is one action, not a list
• Anomalies are flagged with month + magnitude, not "high"
• Output is short enough to read in 3 minutes
• The founder can take the recommendation and start work the same day