- Understand the influence of variables. Each variable adds its own influence to a situation, and sometimes it's hard to untangle which ones do what when it's a complicated analysis. But sensitivity analysis can tease them apart and allow you to answer one question at a time about which is doing what.
- Simplify models. Often, we make the problem harder than it has to be, especially when we don't know which variables are doing what in a model. With sensitivity analysis, we can see which variables aren't contributing, and move those out of the way to focus on the ones that do matter.
- Reduce uncertainty. With the models simplified and the variables understood, it's easier to reduce risk and uncertainty. Although you can never reduce these to zero, you can minimize them, and sensitivity analysis helps.
- Achieve financial goals. Playing the "what-if" game in your romantic life can destroy you, but if you play it in your financial life, with future-looking questions, you can actually achieve your financial goals much more easily. Knowing what your plan is for eventualities like changes in interest rates or demand for a product can help you make smart decisions for the future.
Sensitivity analysis vs. scenario analysis
A sensitivity analysis and a scenario analysis are easily confused with each other, because they seem very similar on the surface. However, one looks at the impact on variables, and the other looks at how specific scenarios will affect the variables. Often, these are used together, which makes it even more confusing.
Put another way, scenario analysis looks at what happens if foot traffic to a mall increases this holiday season, and sensitivity analysis looks at what happens if sales increase by 20% due to an increase in foot traffic this holiday season. One is a scenario, and one is the actual measured variables.