Sensitivity analysis measures how much a forecast changes when you vary one input assumption while holding the others fixed. In revenue forecasting, those inputs are the pipeline assumptions behind the number: win rate, average deal size, sales-cycle length, and the volume of new pipeline created inside the period. Running the analysis tells you which of those assumptions the forecast actually depends on and which ones barely move it. That ranking shows you where to spend your attention.
Most teams stress every variable equally or none at all. Sensitivity analysis replaces the guesswork with a ranked list.
How sensitivity analysis works
Start with a base-case forecast, your current best estimate for each input. Flex one input up and down by a set amount, say plus or minus 10 percent, and record how far the output moves. Repeat for every input. The variable that produces the largest swing is the one your forecast is most sensitive to.
If a five-point drop in win rate cuts the forecast by $1.2M while a five-point drop in pipeline creation cuts it by $200K, your quarter lives and dies on win rate. Defend that assumption first. Analysts often draw the result as a tornado chart, widest bars on top. The top bars are the assumptions worth stress-testing. The narrow bars below can hold rough estimates without much risk.
Which pipeline assumptions move the forecast most
For most B2B SaaS forecasts, two inputs dominate: win rate and average deal size. Both multiply directly into booked revenue, so a small percentage error in either flows straight to the number. Deal size is the quieter risk. ORM flags this exact gap: a pipeline averaging $80,000 per deal while closed-won deals average $40,000. Every forecast built on the pipeline figure then overstates revenue by half.
Timing ranks next. When sellers push close dates, real deals slide into the following quarter, and a forecast that ignores slippage is sensitive to a variable no one is tracking.
| Assumption | Why it moves the forecast |
|---|---|
| Win rate | Multiplies directly into closed revenue |
| Average deal size | Pipeline value routinely exceeds closed-won value |
| Close-date timing | Slipped dates shift revenue across periods |
| In-quarter pipeline creation | Sets how much not-yet-visible pipeline closes |
Turning sensitivity into a stress test
Sensitivity analysis is a diagnosis, not a fix. Once you know the two or three assumptions that carry the forecast, track them as live signals. Deal size should be measured against closed-won history rather than pipeline entry values. A changed close date is the earliest sign of slippage, so flag it the moment it appears, and watch win rate for the competitive pricing pressure that shrinks it. A forecast that updates when those assumptions shift stays accurate through the quarter instead of only in the last week, by which point the quarter has already happened.
Frequently Asked Questions
What is sensitivity analysis in revenue forecasting?
Sensitivity analysis measures how much a forecast changes when you adjust one input assumption, such as win rate or average deal size, while keeping the others fixed. It shows which assumptions your forecast depends on most, so you know which numbers to defend and which you can estimate loosely.
Which forecast assumption has the biggest impact?
For most B2B SaaS teams, win rate and average deal size move the forecast most because both multiply directly into booked revenue. A small error in either flows straight to the projected number. Close-date timing ranks next, since slipped deals push revenue into the following period.
How is sensitivity analysis different from scenario planning?
Sensitivity analysis flexes one variable at a time to isolate its individual effect. Scenario planning bundles several assumptions into named cases like best, base, and worst. Run sensitivity analysis first to find the variables that matter, then build scenarios around those variables.
How often should you run a sensitivity analysis?
Run it at the start of each quarter during forecast planning, then re-run it whenever a key condition changes, such as a new competitor pressuring price or a drop in win rate. The assumptions that carry the forecast can shift mid-quarter, and a stale sensitivity ranking sends attention to the wrong variables.
Put these metrics to work
ORM builds custom revenue forecast models that turn concepts like sensitivity analysis into prescriptive action for your team.
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