Legal, Finance & People · Chapter 3 of 4
Finance in a technology business
SaaS metrics, revenue recognition, and why the finance analyst is expected to write SQL.
Technology finance has its own vocabulary and its own failure modes, and data and analytics language appears in 91% of these postings for a concrete reason: the numbers live in systems, not in a ledger anyone can read.
The metrics you must be fluent in
| Metric | What it is | The trap |
|---|---|---|
| ARR / MRR | Recurring revenue, annualised or monthly | Non-recurring revenue quietly included |
| Net revenue retention | Revenue from existing customers, including expansion, minus churn | Above 100% is the headline; segment it or it hides churn in one cohort |
| Gross margin | Revenue minus cost to serve | AI features change this materially — inference cost is COGS |
| CAC payback | Months to recover acquisition cost | Sensitive to which costs you include; compare like for like |
| Rule of 40 | Growth rate plus profit margin | A heuristic, not a target, and easy to game short term |
| Burn multiple | Cash burned per unit of net new ARR | Increasingly the number investors actually look at |
The AI-specific point worth carrying: a product whose marginal cost per use is meaningful changes the software margin assumption that most SaaS financial models rest on. Finance people who can model that — cost per resolved task, distribution rather than mean, and what the tail does to margin — are unusually valuable right now.