Sensitivity Analysis: Pre-Building the Questions Investors Will Ask
Sensitivity analysis is the practice of testing how your model outputs change when key input assumptions change. Done well, it pre-answers the most important investor questions before they are asked: what does the business look like if growth is slower, if CAC increases, if churn worsens? Founders who build sensitivity analysis into their models arrive at investor meetings with credibility. Founders who have not done it get surprised by questions they should have anticipated.
Author: Yanni Papoutsi · Fractional VP of Finance and Strategy for early-stage startups · Author, *Raise Ready*
Published: 2025-03-08 · Last updated: 2025-03-08
Reading time: \~7 min
What Is Sensitivity Analysis in a Startup Financial Model?
Sensitivity analysis shows how a change in one or more input assumptions flows through to a change in the model's key outputs --- typically runway, gross margin, and revenue. It is distinct from scenario planning (which changes multiple assumptions together to reflect different business environments) and is instead focused on isolating the impact of individual variables.
Key distinctions:
What | One variable at a time | Multiple variables together changes
Purpose | Identify which inputs drive the Show coherent future states most variance
Output | Tornado chart or sensitivity | Conservative / Base / table | Aggressive cases
Best for | Identifying risk concentration Investor-facing narrative Builds | Model robustness | Investor confidence
Why Investors Use Sensitivity Analysis in Diligence
When a VC analyst goes through a financial model in diligence, one of the standard checks is to change the key assumptions by 10-20% and see what happens to the bottom line. This is not adversarial. It is how experienced investors understand which assumptions the model is most dependent on.
If the model has not been built with this flexibility in mind --- if assumptions are hard-coded rather than centralised --- the analyst cannot run the test and flags this as a model quality issue. If the model has sensitivity analysis pre-built, the analyst can see the founder has already done the work and understands their own key risks. The question "what are the two or three assumptions your model is most sensitive to?" is one of the most common investor diligence questions. Pre-building the sensitivity is the equivalent of pre-building the answer.
The Four Most Important Sensitivities for Early-Stage Models
1. CAC (Customer Acquisition Cost)
If CAC is 20% higher than modelled, what happens to marketing spend, to the CAC payback period, to LTV:CAC, and to monthly burn? For most early-stage businesses, CAC is one of the most uncertain and highest-impact variables. Build a sensitivity that shows the impact across a range of CAC scenarios.
2. Churn rate
If monthly churn is 1 percentage point higher than modelled, what happens to net revenue retention, to total customer count at year-end, and to the revenue line? For SaaS businesses especially, the compounding effect of churn means a small change in churn rate produces large changes in the revenue line over 18-24 months.
3. Revenue growth rate
If the core growth assumption is 20% below plan, what is the revised runway? What is the new point at which the company needs its next round? This is often the most powerful single sensitivity because it directly addresses the question investors most want answered: what is the downside scenario?
4. Gross margin
If COGS are 10% higher than modelled (due to higher infrastructure costs, pricing pressure, or operational inefficiency), what happens to gross margin and operating leverage? For businesses targeting high gross margins, the sensitivity shows how robust the unit economics are to cost pressure.
How to Build a Sensitivity Table in Excel
A sensitivity table (or data table in Excel terminology) shows the output of a model formula across a range of input values. The structure is straightforward:
One-variable sensitivity (1D data table):
Column header: range of values for the input (e.g. CAC from $200 to
$400 in $25 increments)
Row output: the metric being measured (e.g. 18-month runway, gross
margin)
Built using Excel's Data > What-If Analysis > Data Table Two-variable sensitivity (2D data table):
Row header: range of values for input A (e.g. CAC)
Column header: range of values for input B (e.g. churn rate) Cell values: output metric (e.g. monthly burn at month 18) Built the same way but using both row input cell and column input
cell
The key requirement for both: the input must flow from a single cell reference in the model (not be hard-coded in multiple places). Centralised assumptions tabs are what make sensitivity analysis possible without rebuilding.
Key insight: Sensitivity analysis is only possible if the model is built with centralised assumptions. A model where growth rates, CAC, and churn are hard-coded in multiple tabs cannot be stress-tested efficiently. The assumptions tab is what makes sensitivity analysis fast.
The Tornado Chart: Visualising Which Inputs Matter Most
A tornado chart ranks input variables by the size of their impact on a key output (typically revenue, runway, or gross margin). The widest bar is the variable the business is most sensitive to. The narrowest is the least critical.
Building a tornado chart requires running a high and low test for each variable (e.g. ±20%) and recording the change in output. The result is a ranked list that immediately communicates the model's key risk concentrations.
For most early-stage companies, the ranking looks roughly like this: 1. Revenue growth rate (widest bar, highest impact)
2. Churn rate (for subscription businesses)
3. CAC (for sales-led or performance-marketing-driven businesses) 4. Gross margin
5. Headcount growth (typically lower impact in early models) A tornado chart in the investor deck communicates two things simultaneously: the founder has done the work of understanding their key uncertainties, and the business is not dependent on a single fragile assumption.
Common Sensitivity Analysis Mistakes
Running sensitivities on outputs that are already robust, ignoring
the fragile ones
Hard-coding assumptions that prevent efficient testing
Showing only upside sensitivities (what happens if CAC is lower than
expected)
Not including sensitivity results in investor materials, doing the
analysis only internally
Sensitivity ranges that are too narrow to reflect realistic
uncertainty
Frequently Asked Questions
How wide should sensitivity ranges be?
Wide enough to reflect realistic uncertainty. For CAC, a ±30% range around the base assumption is reasonable for an early-stage business. For churn, ±1-2 percentage points per month. For growth rate, ±20-30% of the base assumption. If the model breaks materially within the realistic range, that is important information --- not something to hide.
Should sensitivity analysis be shared with investors?
Yes. Including a sensitivity tab or a tornado chart in the model shared during diligence signals that the founder has thought rigorously about uncertainty. It also pre-empts investor questions about downside scenarios, which is a time-saving and trust-building move.
What is the difference between sensitivity analysis and stress testing?
Sensitivity analysis typically tests realistic ranges around base assumptions. Stress testing pushes inputs to extreme values to identify where the model breaks down entirely --- for example, what churn rate would cause the business to run out of cash within 12 months? Both are useful and together give a complete picture of model robustness.
Summary
Sensitivity analysis turns a financial model from a static forecast into a tool for understanding risk. The four variables most worth testing in early-stage models are CAC, churn, revenue growth rate, and gross margin. Build sensitivity tables using centralised assumption cells, visualise the results in a tornado chart for investor materials, and use the analysis to pre-answer the questions investors will ask. A founder who has run their own sensitivities and can speak to the results is in a structurally stronger position in any diligence conversation than one who has not.
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