How to run a scenario analysis for a strategic decision on Starch
Scenario analysis is how you pressure-test a decision before you make it. Instead of committing to a new hire, a price change, or a fundraise timeline and hoping the numbers work out, you model two or three versions of the future side-by-side — what happens under the plan you're betting on, and what happens if revenue comes in 20% below that, or payroll grows faster than expected, or you delay the raise by a quarter. Every operator who has ever had to defend a budget, set a board expectation, or decide between two real options has felt the pull of this workflow.
What it actually looks like varies. Some operators are running lean financial models tied to bank and revenue data. Others are stress-testing a pricing decision against customer cohort behavior. Others are modeling headcount scenarios against a fundraise. The inputs and the stakes differ; the underlying need — see the consequences before you commit — is the same.
On Starch, you end up with a live scenario comparison that uses your actual revenue and burn as the baseline, not a number you typed in last quarter. Each scenario — base case, upside, downside, or any variant you name — shows runway, burn rate, and break-even under its specific assumptions. You adjust only the variables you're testing; everything else stays anchored to real data. The result is a dashboard you can pull up before a board call or share with a co-founder, with numbers that reflect what's actually happening in your accounts today.
Why it matters
Decisions made without scenarios tend to anchor on the plan — which is usually optimistic. When reality lands 15% below forecast, operators who modeled it know exactly what they committed to cutting and when. Operators who didn't are improvising. The concrete risk: you over-hire against a revenue assumption that misses, burn through 4 months of runway faster than you expected, and raise in a worse position than if you'd modeled the downside in January.
Common pitfalls
The most common mistakes: using a single-month burn figure as your baseline instead of a trailing average, which makes any scenario built on top of it structurally wrong. Modeling revenue scenarios without touching the expense side, so the comparison looks cleaner than it is. Treating the base case as fact rather than one scenario among several — which defeats the point. And building scenarios in a static spreadsheet that's already stale by the time you present it, because no one updated the actuals.
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