sensitivity analysis
Sensitivity analysis shows how changing one or more variables affects a financial model—helping teams assess risk and plan better.
Sensitivity analysis is a decision-making tool that shows how changes in key variables affect an outcome. In business and finance, it’s used to test assumptions: What happens to revenue if churn increases? What if cost of goods drops 10%? This process reveals which inputs matter most—and how fragile or robust a forecast really is.
Unlike scenario analysis, which tests full environments, sensitivity analysis isolates individual inputs. It breaks a model apart to see which levers truly move the result. That clarity turns vague projections into strategic insight.
How teams apply sensitivity analysis
A startup projects $2M in revenue for next year. The CFO runs a sensitivity analysis on churn, CAC, and conversion rate. Small changes in CAC barely move the forecast—but a 5% increase in churn drops revenue by $400K. The insight: retention drives growth more than acquisition.
In another case, a company considers expanding to a new region. Before deciding, the team models margin sensitivity based on logistics cost and pricing. The analysis helps them stress-test the decision before committing real resources.
What people often get wrong
Some teams run sensitivity analysis once and treat the output as permanent. But assumptions shift. The process should be revisited often—especially when strategy changes. Others test only obvious variables, missing the indirect drivers that quietly shape outcomes.
Another issue: making changes in isolation without understanding interdependencies. Not all variables are independent. Real insight comes from knowing how one input affects others—and how that ripple changes the whole system.
What you can’t predict, you can prepare for
Sensitivity analysis doesn’t eliminate uncertainty—it organizes it. It helps teams think in probabilities, not just projections. It shows which levers create risk—and which create control. And in a world of shifting assumptions, that visibility is what turns models into strategy.
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