--- 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 ```markdown # SaaS Health-Check: ## 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: ## Metrics (with definitions) ## Cohort retention ## Anomalies - - ## What changed vs last period ## Single recommended action ## Open questions / data gaps ``` ## 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 " — 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`](~/.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