Scenario planning is the practice of running your financial model under several plausible futures instead of one. You build a base case (your realistic plan), a best case (things go better than expected), and a worst case (things go worse), then read what each one does to the number that actually matters at an early stage: your cash runway. Scenario analysis flexes several drivers together to tell a coherent story; sensitivity analysis flexes one driver at a time to find which assumption matters most. The strongest models use both. This guide shows how to build SaaS scenarios, the difference between the two methods, and a worked runway example.
A single-number forecast is a guess wearing a suit. You already know it will be wrong; the question is by how much, and what you would do about it. Scenario planning answers that by pricing out a range of outcomes ahead of time, so a bad quarter triggers a plan you already wrote rather than a panic. It is a discipline you apply to a model, not a model itself. If you have not built the underlying model yet, start with the SaaS financial model guide; this page is about stress-testing it.
The three cases: base, best, worst
Almost every scenario exercise starts with three cases. Keep them plausible, an implausible case teaches you nothing.
- Base case. Your anchor: current trends continue, growth is steady, nothing dramatic happens. This is the plan you actually run and the one you walk investors through in detail.
- Best case. The most favorable outcome that is still realistic. For a SaaS company that might mean a faster MRR growth rate, lower churn after a retention fix, or a new channel that lowers CAC.
- Worst case. The most challenging outcome that is still realistic: growth stalls, churn ticks up, a round slips, or a key hire takes longer to pay back. The point is not doom, it is knowing where the model breaks and how much warning you would get.
Showing a credible downside makes you more fundable, not less. It signals you have thought about what could go wrong and already know your response.
Scenario analysis vs sensitivity analysis vs what-if
These terms get used interchangeably, but the distinction is worth getting right because it changes what you learn. The standard framing (see Corporate Finance Institute) is simple: sensitivity analysis changes one input at a time; scenario analysis changes several at once.
| Method | What changes | Question it answers | Typical output |
|---|---|---|---|
| Sensitivity analysis | One driver at a time | Which single assumption moves the outcome most? | A range or tornado chart per driver |
| Scenario analysis | Several drivers together, as a coherent story | What happens if the world goes base / best / worst? | Two to four full cases |
| What-if analysis | Umbrella term for either | "What if X changes?" | Varies |
They work best in sequence: run sensitivity analysis first to find the two or three drivers that actually swing your outcome, then build scenarios around coherent combinations of those drivers. In most SaaS models a handful of inputs (conversion rate, churn, and price) move runway and valuation far more than the rest, so that is where scenario effort belongs.
Heavier techniques exist. Monte Carlo simulation runs thousands of random draws across your assumptions to produce a full probability distribution of outcomes, but it is usually overkill before Series A. For most early-stage SaaS, base, best and worst cases plus a sensitivity check on the top two or three drivers cover the need.
How to build scenarios for a SaaS model
- Pick three to five drivers. For SaaS these are usually MRR growth rate, churn, CAC, sales or engineering hiring pace, and price. More than five and the scenarios stop being legible.
- Set the base from reality. Anchor each driver to actuals or a defensible run-rate, not to the number you wish were true. See revenue forecasting for how to set the base revenue line.
- Flex the drivers for best and worst. Move each driver up for best and down for worst. Resist flexing everything by the same symmetric percentage; churn and growth do not move in lockstep, and the realistic downside is often a combination (slower growth and higher churn) rather than one bad number.
- Read the consequence that matters. At an early stage that is almost always cash runway and the month you run out of cash. Valuation and profitability matter later; survival matters now.
- Weight the cases if it helps (optional). Some teams assign rough probabilities, for example an illustrative base 55%, worst 30%, best 15%, and compute an expected value. This can sharpen a decision, but the probabilities are judgment, not data, so treat the weighted number as a discussion aid rather than a forecast.
Worked example: three cases, one runway
The inputs below are illustrative, use your own. Say a seed-stage SaaS has $600,000 in the bank. Its net monthly burn (cash out minus cash in) depends on how growth and churn play out, so each scenario produces a different burn and therefore a different runway. Runway = cash on hand divided by net monthly burn.
| Scenario | What's assumed | Net monthly burn | Runway |
|---|---|---|---|
| Best | Faster MRR growth, churn drops after a retention fix | $50,000 | $600,000 / $50,000 = 12 months |
| Base | Current growth and churn continue | $75,000 | $600,000 / $75,000 = 8 months |
| Worst | Growth stalls, churn rises, costs unchanged | $100,000 | $600,000 / $100,000 = 6 months |
Same starting cash, and the honest range is 6 to 12 months. That spread is the point: the worst case tells you the earliest date you would need to either raise or cut, and the six-versus-eight-month gap is what turns "we're probably fine" into a dated trigger. In a full model you flex the drivers (growth, churn, CAC) and the burn falls out of them; here we show the burn outcomes directly so the arithmetic is clear. The calculator below lets you run the same base-versus-downside comparison on your own numbers.
Turn scenarios into trigger points
Scenarios only earn their keep if they change what you do. The step most teams skip is pre-deciding the action each case triggers, a tripwire you write down before you need it: "if net new MRR misses the base case for two consecutive months, we pause the two planned hires and start the raise." That converts the worst case from a background worry into a dated decision, and it takes the emotion out of the moment you actually have to act.
Then keep the scenarios alive. Revisit them monthly against your actuals, and refresh the underlying assumptions each quarter or whenever something material changes (a pricing move, a new channel, a round that slips). A scenario set that is not updated goes stale within a couple of months and quietly stops guiding anything.
Common scenario planning mistakes
- Presenting a single case. One number hides the risk and reads as naive to anyone who has raised before.
- Symmetric plus-or-minus on everything. Moving every driver by the same percentage produces tidy but unrealistic cases. Real downsides cluster.
- Stopping at revenue. A scenario that does not carry through to cash and runway has skipped the decision-relevant part.
- Too many scenarios. Five slightly different cases are harder to act on than three distinct ones. Base, best, worst is usually enough.
- Building them once. Scenarios go stale the moment actuals arrive. Revisit them each month against what really happened.
Scenario planning tools
You can run scenarios in a spreadsheet, Excel and Google Sheets both have what-if data tables for one-off comparisons. The problem is durability: cloning tabs for each case means the versions drift, a formula fix in one is forgotten in the others, and by the third month nobody trusts which tab is current. A purpose-built model keeps the scenarios wired to the same underlying logic, so you change an assumption and every case updates together. That is the idea behind Adlega: base, best, and downside cases run off one model, and the AI CFO can explain why a given scenario changes your runway. For a broader tool comparison, see best SaaS financial modeling software.
Frequently asked questions
What is scenario planning in finance?
Scenario planning is running your financial model under several plausible futures, typically a base, best, and worst case, by changing a coherent set of assumptions and reading how each future affects outcomes like cash runway, profit, and valuation. It turns a single forecast into a range you can plan against.
What is the difference between scenario analysis and sensitivity analysis?
Sensitivity analysis changes one driver at a time to find which assumption moves the outcome most. Scenario analysis changes several drivers together to model a coherent situation (base, best, worst). The two are complementary: use sensitivity to find the drivers that matter, then build scenarios around them.
How many scenarios should I build?
Three is the standard: base, best, and worst. That is enough to show a range without becoming unreadable. Add a fourth only when a specific decision needs it, such as a "raise slips two quarters" case.
What percentages should I assign to best, base, and worst cases?
There is no fixed rule; the weights are judgment. A common illustrative split is base 55%, worst 30%, best 15%, summing to 100%, but you should set your own based on what you know. Treat any probability-weighted result as a discussion aid, not a forecast.
How often should I update my scenarios?
Revisit scenarios monthly against your actuals, and refresh the underlying assumptions each quarter or whenever something material changes, a pricing move, a new channel, or a round that slips. Scenarios that are not updated go stale within a couple of months and stop guiding decisions.
Should I share my scenarios with investors?
Sharing a credible base and downside case with existing investors builds trust; it shows you know where the model breaks and have a plan. For new investors in a raise, lead with the base case you can defend and keep the downside ready for the questions that follow, rather than opening with it.
What is the best scenario planning tool for startups?
For one-off comparisons, Excel or Google Sheets what-if data tables work. For ongoing planning where scenarios must stay in sync with the model, a purpose-built tool like Adlega keeps base, best, and downside cases wired to the same logic and shows the runway impact of each.
Related: SaaS financial model, revenue forecasting, cash runway and burn rate, and SaaS financial projections.