Sensitivity analysis is the practice of testing how financial model outputs change when key input assumptions vary, typically one at a time. Inputs include customer acquisition rate, churn, ARPC, gross margin, and hiring pace; outputs include revenue, EBITDA, runway, and valuation. It's used to understand which assumptions matter most (high-sensitivity drivers vs low-sensitivity), how robust the plan is to uncertainty, and where to focus operational attention. The discipline is one of the most-useful additions to financial models and one of the most-overlooked when models are built for fundraising rather than for operating. It separates rigorous financial modeling from optimistic projection.
The mechanics:
One-variable ...