Build Scenario Forecasts

Prepare best / base / worst plans so you’re not surprised.

What this is for

Giving you a structured way to see how key variables (sales, costs, conversion, churn) play out under different futures—so you can plan actions instead of reacting to shocks.

What you get

Three calibrated outlooks: optimistic, expected, and downside

Clear trigger points for each scenario

Predefined responses mapped to deviations

Confidence in pacing spend, hiring, and growth

Core logic

Reality swings. By modeling what happens if things go better, as expected, or worse, you surface the range of outcomes, identify breakpoints, and give yourself decision rules instead of guesswork.

Step-by-step

Select the key drivers

Pick the 2–4 variables that move your business most (e.g., sales volume, average order value, acquisition cost, churn rate).

Set the base case

Use current performance and conservative assumptions to build your “most likely” forecast. Calculate revenue, profit, and cash flow over your planning horizon.

Build the upside (best) case

Adjust drivers for stronger performance (e.g., +20% conversion, stable costs) and project the upside outcome. Identify what would need to happen to unlock it.

Build the downside (worst) case

Stress critical variables (e.g., sales drop, cost increase, slower collection) and see how quickly runway, margin, or growth stalls.

Define thresholds & triggers

For each scenario, set measurable trigger points (e.g., “If revenue falls 15% below base for two weeks, activate cost pause plan” or “If CAC drops 10%, accelerate spend”).

Map responses

Create a simple playbook:

Upside play: Invest incremental margin into scaling.

Base play: Stay the course, monitor closely.

Downside play: Pull levers (cut discretionary spend, tighten collections, activate contingency).

Review regularly

Update actuals against forecasts weekly. Shift which scenario is in play and execute the corresponding plan.

Decision thresholds / guardrails

Performance deviates from base by X% (define your tolerance, e.g., ±10%) → Re-evaluate which scenario is unfolding.

Downside triggers hit → Pause non-essential spend and activate mitigation.

Upside triggers hit → Validate capacity and scale with guardrails (ensure LTV:CAC and margin hold).

Forecasts stale (assumptions drift) → Refresh inputs; outdated models mislead.

Examples

E-commerce:

Base: 1,000 monthly orders at $50 AOV.

Upside: 1,200 orders + successful upsell increases AOV to $55.

Downside: 20% drop in traffic and 10% lower conversion—runway compresses, inventory risk rises.

Service business:

Base: 30 clients per quarter at $2,000 each.

Upside: Referral surge adds 10 clients with same retention.

Downside: Two major clients delay renewals—cash flow tightens and hiring freeze triggers.

Thinking checks

Are your forecasts driven by the real levers you can influence?

Do you have clear action rules tied to deviations from the base case?

Are you preparing for the downside without being paralyzed by it?

Is upside opportunity captured with scalable guardrails?

If the answer is no…

Start with a one-variable “what if” (e.g., what if sales drop 20%) and build outward.

Define one concrete response for each direction (better/worse).

Iterate: add complexity as confidence grows.

What to track (minimum)

Actual vs. base forecast variance

Trigger hits (which scenario is activating)

Response execution time

Impact of actions taken under each scenario