saas-metrics-diligence
npx machina-cli add skill evalops/open-associate-skills/saas-metrics-diligence --openclawSaaS metrics diligence
When to use
Use this skill when:
- Diligencing B2B SaaS, PLG, or usage-based businesses
- You need to translate raw metrics into an investment view
- You want to detect metric illusions (cohort mixing, accounting artifacts)
Inputs you should request (only if missing)
- Revenue metrics: ARR/MRR by month (12–24 months)
- Customer counts and logos (by cohort if possible)
- Gross margin (and what’s included in COGS)
- Sales & marketing spend (and headcount)
- Churn/expansion data by cohort
- Pricing and ACV distribution
Outputs you must produce
- Metrics readout (1–2 pages)
- Cohort/retention assessment (what’s driving NDR)
- Unit economics summary (CAC payback, efficiency)
- Questions/risks (ranked) + recommended next data
Templates:
- assets/metrics-readout.md
- assets/metrics-worksheet.csv
Procedure
1) Normalize definitions (do not assume)
Write down:
- What counts as “revenue” (bookings vs recognized)
- Whether NDR is logo-weighted or revenue-weighted
- Whether gross margin includes hosting, support, etc.
2) Retention: start with cohorts
Compute:
- Gross revenue retention (GRR) if possible
- Net dollar retention (NDR/NRR) by cohort and by segment
Look for:
- expansion vs churn drivers
- segment mix effects (beware Simpson’s paradox)
- “one whale” distorting NDR
3) Efficiency and payback
Compute:
- CAC payback (on a gross margin basis)
- Sales efficiency / magic number (if you use it)
- Contribution margin (if available)
Check:
- whether growth is cash-efficient at current payback
- whether payback is improving or worsening
4) Pricing and ACV distribution
- Identify ACV bands
- Evaluate whether the product supports expansion (seat-based, usage-based, tier upgrades)
- Check whether pricing matches perceived value
5) Produce an investment-grade interpretation
Translate metrics into:
- “Is this repeatable?” (cohorts + pipeline)
- “Is this durable?” (retention + switching costs)
- “Is this capital-efficient?” (payback + margins)
- “What breaks at scale?” (support costs, infra costs, sales cycle)
References
- Tomasz Tunguz’s public writing is a useful mental model set for NDR, payback, and SaaS efficiency metrics.
Salesforce logging (optional)
If your Opportunity has metric fields:
- Update NDR, GRR, GM, payback, etc. Otherwise:
- Attach the metrics readout as a File/Note to the Opportunity.
Edge cases
- If data is messy: request raw exports and define one source of truth.
- If NDR is high but churn is hidden: insist on cohort-level GRR and logo churn.
Source
git clone https://github.com/evalops/open-associate-skills/blob/main/saas-metrics-diligence/SKILL.mdView on GitHub Overview
Analyzes SaaS and usage-based metrics (NDR/NRR, gross margin, CAC payback, sales efficiency, cohorts) by segment to form an investment view. It helps translate raw metrics into actionable diligence insights and flags metric illusions like cohort mixing or accounting artifacts.
How This Skill Works
First, normalize definitions (revenue counting, NDR weighting, COGS inclusions). Next, analyze retention via cohorts (GRR, NDR by cohort/segment) and identify expansion vs churn drivers. Then compute CAC payback on gross margin, assess sales efficiency, map ACV distribution, and synthesize an investment-grade interpretation.
When to Use It
- Diligencing B2B SaaS, PLG, or usage-based businesses
- Translating raw ARR/MRR, churn, and pricing data into an investment view
- Detecting metric illusions like cohort mixing or accounting artifacts
- Assessing growth efficiency and capital efficiency via CAC payback and margins
- Producing an investment-grade readout with recommended next data
Quick Start
- Step 1: Normalize definitions (revenue counting, NDR weighting, GM inclusions)
- Step 2: Build cohort-based retention and compute NDR/GRR by segment
- Step 3: Compute CAC payback and unit economics; produce metrics readout and investment-grade interpretation
Best Practices
- Normalize definitions: decide revenue counts (bookings vs recognized), NDR weighting, and what GM includes
- Start with cohort-based retention: compute GRR and NDR by cohort and segment
- Compute CAC payback on a gross margin basis and monitor trend over time
- Map ACV bands and evaluate expansion potential; verify pricing matches value
- Maintain a single source of truth and attach metrics outputs to Salesforce opportunities when available
Example Use Cases
- A B2B SaaS with strong NDR but mixed cohorts reveals churn artifacts masking true retention
- An infra/usage-based vendor shows healthy expansion yet CAC payback lengthens due to hosting costs
- Data is messy; requires raw exports to establish a single source of truth before analysis
- Cohort-level GRR uncovers retention strength in small segments but riskier performance in large accounts
- Pricing changes improve payback but raise infra costs, affecting gross margin and scalability