Review SaaS metrics
An ARR, retention, NRR, CAC payback and burn-multiple review with diagnosis.
This is a practical, copy-paste process for board meetings.. Use it when you want a clear structure, then add your own details and the linked evidence.
Published by Deepstory Research. Reviewed Sep 2026.
How to use this method
- 1. Copy or downloadCopy the prompt below, or download the method file and attach it to your chat.
- 2. Paste into any AIChatGPT, Claude, Gemini or Copilot. Replace each [PLACEHOLDER] with your details, or let the AI ask you.
- 3. Add evidenceAttach the evidence files listed on this page so the AI works from sourced numbers, not guesses.
The prompt
Paste it as-is. The AI will ask for anything still in [BRACKETS].
# Review SaaS metrics Method by Deepstory Research: deepstoryresearch.com/method/saas-metrics-review You are a SaaS operating partner. Follow the method below step by step. Goal: An ARR, retention, NRR, CAC payback and burn-multiple review with diagnosis. ## My inputs - Monthly metrics: [MONTHLY_METRICS] (example: attach MRR movements, customers, spend) - Period: [PERIOD] (example: last 12 months) Before you start: if any input above is still in [BRACKETS] or missing, ask me for it in one short numbered list and wait. Do not guess. If I attached files (data, briefs, Deepstory evidence files), treat them as the primary source. ## Steps to follow 1. Build the MRR bridge (new, expansion, contraction, churn). 2. Compute GRR, NRR, logo churn. 3. Compute CAC payback, magic number, burn multiple. 4. Benchmark with stated sources. 5. Diagnose and recommend. ## Deliver the output in this structure - MRR bridge - Metric table with formulas - Benchmarks - Diagnosis - Actions ## Quality rules - Definitions stated once and used consistently. - Label every number as FACT (with source and date), ESTIMATE (show the calculation) or ASSUMPTION. - If a Deepstory evidence file is attached, use its figures as the reference values and cite its source references; tag claims that are not from it as [UNVERIFIED]. - End with: the 3 assumptions that matter most, what would change the answer, and the single best next step.
What you need to give the AI
| Input | Placeholder | Example |
|---|---|---|
| Monthly metrics | [MONTHLY_METRICS] | attach MRR movements, customers, spend |
| Period | [PERIOD] | last 12 months |
Use it when
- Board meetings.
- Fundraising preparation.
What you get
- MRR bridge
- Metric table with formulas
- Benchmarks
- Diagnosis
- Actions
The procedure, step by step
Read this before you run it, so you can check the AI actually followed the method.
Build the MRR bridge (new, expansion, contraction, churn).
Compute GRR, NRR, logo churn.
Compute CAC payback, magic number, burn multiple.
Benchmark with stated sources.
Diagnose and recommend.
Quality checks built into the prompt
- • Definitions stated once and used consistently.
- • Every number labelled FACT, ESTIMATE or ASSUMPTION; claims outside attached evidence tagged [UNVERIFIED].
Evidence to use with this method
Methods tell the AI how to think. Evidence gives it sourced numbers to think with. Download a context file and attach it with the prompt.
The 6.5% Mortgage Era
US mortgage rates settled into the mid-6% range in 2026 while home-price forecasts converged near flat — the first stretch since 2008 in which prices are expected to grow slower than wages.
US Office Vacancy
Office vacancy at successive record highs — and how far the forecast peak ran ahead of the actual.
E-Commerce Share of US Retail
Online sales as a share of total US retail, and the growth gap that keeps pushing that share up.
Run next
Methods that chain well after this one.
Deepstory methods are process guidance for AI tools. Review AI output before relying on it; legal, tax, medical and investment decisions need a qualified professional.