Valuation models look precise. Numbers flow from cell to cell, formulas link everything together, and the final result shows a clear valuation. But behind that neat output is a web of assumptions—about growth, margins, capital costs, and terminal values—that may or may not hold up.
That’s where sensitivity analysis comes in. It’s the part of the model that asks the hard questions:
What happens if our assumptions are wrong?
How much does the valuation change if conditions shift?
In this article, we’ll break down what sensitivity analysis is, why it matters in valuation, how to build it into your models, and how to interpret the results when presenting to clients, investors, or internal stakeholders.
What Is Sensitivity Analysis?
Sensitivity analysis is the process of testing how a model’s output—like the company’s valuation—changes when you tweak one or more of the key input variables.
It helps answer questions like:
- What happens to the DCF value if WACC increases by 100 basis points?
- How sensitive is our model to the terminal growth rate?
- If EBITDA margins are lower than expected, how much does that reduce equity value?
Rather than pretending the base case is guaranteed, sensitivity analysis lets you see how much the outcome depends on the assumptions.
Why Sensitivity Analysis Matters in Valuation
Valuation isn’t about certainty—it’s about decision-making under uncertainty. Models are full of estimates. Sensitivity analysis gives those estimates context and credibility.
Here’s why it’s essential:
- It highlights the most critical drivers of value.
If valuation changes dramatically with small shifts in one input (e.g. WACC), that’s a red flag. You’ll know where to focus attention. - It helps clients and stakeholders understand risk.
A valuation range is far more credible than a single number. Sensitivity analysis builds that range and explains its boundaries. - It defends your assumptions.
If someone challenges your model, you can show how outcomes change under different scenarios—rather than debating the base case alone. - It supports better decisions.
Investors, buyers, and boards don’t just want to know what a business is worth—they want to know how fragile or robust that value is.
How to Build a Sensitivity Table
The most common way to run a sensitivity analysis is by creating a data table in Excel. This shows how the valuation (usually enterprise value or equity value) changes based on different inputs.
You typically vary one or two assumptions at a time. Common sensitivity axes include:
- WACC (discount rate)
- Terminal growth rate
- Exit multiple
- Revenue CAGR
- EBITDA margins
Example:
| WACC ↓ / Growth → | 1.5% | 2.0% | 2.5% | 3.0% |
|---|---|---|---|---|
| 7.0% | $520M | $550M | $580M | $610M |
| 7.5% | $480M | $510M | $540M | $570M |
| 8.0% | $450M | $480M | $510M | $540M |
| 8.5% | $420M | $450M | $480M | $510M |
This table lets you see how a 0.5% change in WACC or growth rate affects the valuation—instantly.
How to Interpret the Results
Sensitivity analysis isn't just about numbers—it's about narrative. When reviewing your model, ask:
- Which variable has the biggest impact on value?
That’s where your assumptions must be strongest. - How wide is the valuation range?
If the difference between high and low cases is too extreme, your model may not be useful for decision-making. - Are there any thresholds or tipping points?
For example, if IRR drops below the hurdle rate at just one small assumption change, the deal is fragile.
Use the analysis to guide conversations—not just to show math. For example:
“At our base case, the business is worth $500M. But if the terminal multiple drops from 9× to 7×, that value falls below $440M. That’s why we need to stress-test this assumption before moving forward.”
Common Mistakes to Avoid
- Using unrealistic ranges. Don't test scenarios that are too extreme just to create a wide grid. Stay close to plausible market outcomes.
- Changing too many variables at once. Start with one or two. Too much movement can blur insights.
- Forgetting to link everything correctly. If your inputs aren’t tied dynamically to your valuation, the table won’t show accurate results.
- Treating it as an afterthought. Sensitivity isn’t optional. It’s a core part of communicating valuation quality.
Closing Thoughts
A good valuation model doesn’t just answer the question “What is this worth?” It answers, “How does that answer change if we’re wrong?”
That’s what sensitivity analysis delivers. It turns a static model into a dynamic one. It gives clients confidence, investors clarity, and analysts a deeper understanding of what actually drives value.
The most trusted valuation work isn’t just about accuracy. It’s about transparency—showing not just the outcome, but the shape of the risks around it. And that’s exactly what sensitivity analysis is built to do.