1.4 Scenario Planning

Time: ~25 minutes

What You'll Learn

• How to build best-case, worst-case, and base-case scenarios
• Sensitivity analysis — which variables matter most
• Stress-testing your model to find breaking points
• How to present scenarios to leadership without overwhelming them

Key Concepts

Why Scenarios Matter

A single-point budget is a guess. Scenarios turn that guess into a range of informed possibilities. Instead of saying "we'll do $5M in revenue," you say "we'll do $4M-$6M depending on these three factors."

This is dramatically more useful for decision-making.

Sensitivity Analysis

Not all assumptions are equal. Some variables, if they change by 10%, barely move the needle. Others can swing your entire P&L. Sensitivity analysis identifies which variables matter most so you know where to focus your attention.

Common high-sensitivity variables:

• Customer acquisition cost — Small changes compound across every new customer
• Churn rate — Losing 2% more customers per month can be devastating
• Average deal size — Especially if you have a concentrated customer base
• Payment terms — 30 vs. 60 day collections can make or break cash flow

Stress Testing

Stress testing asks: "What would it take to break this model?" It's not about predicting doom — it's about knowing your limits so you can set up early warning systems.

What You'll Do

In this lesson, you'll:

1. Take your budget from lesson 1.3 and create three scenarios (base, optimistic, pessimistic)
2. Run sensitivity analysis on key variables
3. Build a sensitivity table showing how changes in assumptions affect the bottom line
4. Stress-test the model to find the breaking point
5. Summarize your scenarios in a format leadership can quickly understand

How to Start

start lesson 1.4

Your AI will help you systematically vary assumptions and see how the numbers respond.

Skills You'll Use Later

• Scenario modeling (provides the "expected" numbers for variance analysis)
• Sensitivity tables (included in financial summaries in 1.6)
• Identifying key drivers (essential for explaining variances in 1.5)