Calculate CAC payback, magic number, and burn multiple for my SaaS
Your core SaaS efficiency metrics calculated correctly, benchmarked by stage, and turned into a short diagnosis.
The prompt
Calculate the go-to-market efficiency metrics for my SaaS business.
Period data (quarterly preferred): [new ARR, expansion ARR, churned and contracted ARR, sales and marketing spend, gross margin %, net burn, starting and ending ARR]
Number of new customers and average ARR per new customer: [DATA]
Business model: [self-serve / sales-led / hybrid] Stage: [e.g. seed, Series A, $5M ARR]
How we account for S&M: [what is included - salaries, commissions, tools, agencies]
Calculate, showing the formula and inputs for each:
1. CAC (blended, and paid if I give channel data).
2. CAC payback in months = period S&M spend / (new ARR added in the period x gross margin %) x 12, using prior-period spend if I specify a lag. Keep units consistent (ARR is annual) and state them.
3. Magic number = (this quarter's revenue minus last quarter's revenue) x 4 / prior-quarter S&M spend (net new ARR / prior-quarter S&M as an approximation).
4. Burn multiple = net burn / net new ARR.
5. Net revenue retention and gross revenue retention, if data allows.
6. LTV:CAC, only with a stated churn assumption, and a note on why it can mislead at early stage.
Then:
- Compare each metric to commonly cited ranges for my stage and model, labeled as rough rules of thumb, not standards.
- Diagnose the biggest drag (low win rates, high churn, low margin, overspend) and what data would confirm it.
- List 3 actions ranked by likely impact.
- Flag any inconsistent or missing inputs instead of guessing.
Output: a metrics table, a 150-word diagnosis, and the action list.
Highlighted [brackets] are blanks to replace with your own details.
Fill in the blanks and use this promptMore Finance & Accounting prompts
What to fill in
- DATA
- self-serve / sales-led / hybrid
- e.g. seed, Series A, $5M ARR
- what is included - salaries, commissions, tools, agencies
How this prompt is built
This prompt follows the RTF structure (Role, Task, Format). RTF tells the model who to be, what to do and what shape the answer should take. It is the quickest structure that still gives noticeably better results than a bare request.
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