What Is Sensitivity Analysis?

Sensitivity analysis is the "what if we change X?" question applied systematically across all key model inputs. It reveals which assumptions are critical — small changes in these create large outcome differences — and which are relatively unimportant.

The most common presentation is a data table or tornado chart. A data table shows how an output (e.g., net income, cash balance, IRR) changes across a range of values for one or two input variables. A tornado chart ranks variables by their impact on the output, with the most impactful at the top.

FP&A teams use sensitivity analysis for several purposes. In investment appraisal, it identifies the assumptions that must hold true for a project to be viable. In budgeting, it reveals which cost or revenue assumptions carry the most risk. In valuation, it shows how the implied value changes with growth rate and discount rate assumptions.

Two-way sensitivity analysis (sometimes called a data table or sensitivity matrix) varies two inputs simultaneously, producing a grid of outcomes. This is particularly useful for pricing decisions (price x volume) and valuation (growth rate x discount rate).

The key limitation of sensitivity analysis is that it changes variables one at a time, ignoring correlations between inputs. In reality, if demand drops, both volume and pricing may be affected. Scenario analysis addresses this limitation by changing multiple variables simultaneously.

For UK businesses, common sensitivity variables include sterling exchange rate (for importers/exporters), UK base rate (for borrowing costs), employee cost inflation (salary growth, NI changes), and commodity prices (for manufacturers).

To build sensitivity tables into your planning workflow, try the scenario planning template. For a broader look at modelling approaches, read Scenario Planning: Three Models Every CFO Should Build or explore how driver-based planning pairs with sensitivity analysis to focus on the assumptions that matter most.