All Financial Models Require Assumptions

All models require assumptions.

The problem is, actual results nearly always deviate from these assumptions. The question is, by how much?

Most modeling uses the guidelines of ±10% as an acceptable variance threshold. Variances within ±5% are considered a top of the line model. We have actionable data as to how we can make improvements in the business thanks to Unit Economics and Cost-Volume-Profit (CVP) analysis already covered in previous content.

But our assumptions may not play out as we expect.

We may assume a 10% price increase may cause sales volumes to drop by 5%. But will that actually happen?

How far can we deviate from our assumptions until we miss our profit target? How far until we're worse off than how we operated before making any changes? How far until we dip below our breakeven point?

There are many factors that can cause assumptions to not pan out as we originally thought. It could be an execution problem within the business. It could be a significant change in economic conditions that are outside of the company's control. But that doesn't mean we should be blind to what could happen when assumptions go sideways.

The Future Is Uncertain

Up to this point, we're just doing spreadsheet math.

Some assumptions will indeed happen. For example, instead of an across the board price increase, we may implement a price increase on a specific customer. There will only be two results:

1. They accept the price increase and our profits increase as a result.
2. They stop doing business with us and the impact from them leaving may still increase our profits.

But what about other assumptions, such as trying to grow profits with new sales? This may require additional marketing spend or hiring an additional sales rep. Will these produce the anticipated increases in sales volume? How far can our actual sales volume fall below our assumptions? At what level does the increased investment fail to increase profits from our current level?

Things don't always go according to plan.

On top of that, we may have several options available to us. How do we choose which ones to pursue? How do we determine which options are safer bets compared to others?

Navigating the Fog of Assumptions

This is where two new types of analyses come into play.

Sensitivity Analysis

Some proposed changes to increase future performance are highly sensitive. This means a slight dip below the expected sales volume may put the business in the red. Other investment options are less sensitive to deviations from our underlying assumptions. This means actual results can deviate further from our assumptions and the underlying performance would be relatively unchanged.

Sensitivity analysis highlights how outcomes change when actual results deviate from expectations. In CVP analysis, this means assumptions surrounding price, sales volume, and costs.

Let's go over an example. Here's our current state and profit target CVP analysis using our current unit economics:

Our unit economics remains unchanged:

• $100 revenue per unit
• $50 variable costs per unit
• $50 contribution margin per unit
• $4,000 fixed costs

Our profit target is $2,000. Using profit point analysis, we determine we need to sell 120 units to hit our target ($4,000 fixed costs + $2,000 profit target ÷ $50 unit contribution). This represents a 20% increase in sales volume from our current level.

But what is the impact to profits when actual results are above or below our 120 profit target units?

This is where we can use sensitivity analysis to see how our profits change when underlying assumptions deviate from expectations. We'll start with sales volume. A good model should have a variance within ±10% of expectations. This means our actual results for sales volume should in the 108 to 132 unit range if we expect 120 units. How does that affect profits? Let's see the table below:

As you can see, our 120 unit expectation in the middle is required to hit our $2,000 profit target. At the ends of the table, we can see what our profits will be if sales volumes come in 10% under expectations (108 units) and if sales volume comes in 10% over expectations (132 units). All this table does is take our $50 unit contribution, multiply it by the relevant sales volume, and subtract our $4,000 fixed costs to calculate profits for that level of sales. We can see if sales come in 10% below expectations, our profits will be $1,400. This is still a 40% increase over our current $1,000 profit level. All sales volumes above 120 units results in profits above our $2,000 target.

We can expand this sensitivity analysis to see how profits change over specific ranges, such as ±10.0%, ±7.5%, ±5.0%, and ±2.5%. We can also add the average selling price for additional context as seen below:

Our expectations are $100.00 revenue per unit and 120 sales volume, highlighted in orange. We can see the middle of the table produces our profit target of $2,000. I've added conditional formatting so easily show what revenue per unit and sales volume levels produce profits at different thresholds:

• Below our current $1,000 (highlighted in red)
• In between our current $1,000 and our $2,000 profit target (highlighted in yellow).
• At or above our $2,000 profit target (highlighted in green)

The red cells in the table highlights the combination of volume and revenue per unit that result in profits below current performance. This helps identify the specific scenarios that pose a risk to profitability. We can take proactive measures to mitigate these potential losses at these volume and revenue per unit levels.

The yellow cells in the table highlights the scenarios where our profits improve from current levels, but still fall short of our profit targets. Understanding this range of outcomes helps in setting more realistic goals or helps identify areas where minor adjustments to sales efforts or pricing could push profits into the desired range.

The green cells in the table highlights our most favorable scenarios. It shows the combinations of volume and revenue per unit that not only meet our profit targets, but exceeds them. This can help with strategy as we could implement efforts to increase production capacity or adjust pricing to maximize profitability.

If we believe our selling prices will hold firm at $100 per unit, our sales volume can fall 10% below our expected 120 units and we'd still increase profits above our current $1,000 level by 40%. We can use Margin of Safety to calculate how much sales volumes can decrease from our 120 unit expectations and still stay flat to current level of profits or breakeven point.

Margin of Safety

Margin of safety is the percentage or amount our assumptions can deviate from expectations and still hit either our profit point target, breakeven point, or maintain our current level of profit.

Below is how to calculate Margin of Safety for breakeven point:

You can use breakeven, profit point target, or your current sales revenue or volume in this calculation. We'll be using breakeven point.

Using our above example, here's how it plays out:

• Breakeven Units = $4000 fixed costs ÷ $50 unit contribution = 80 units
• Margin of Safety = 120 planned units - 80 breakeven units = 40 units
• Margin of Safety Ratio = 40 unit Margin of Safety ÷ 120 planned units = 33.3%

The lower the Margin of Safety Ratio, the higher the risk associated with actual or planned revenue or sales volume hitting the breakeven point.

Since our Margin of Safety Ratio is 33.3%, this means our sales volume can fall up to 33.3% from our 120 expected volume and we'd still breakeven. I consider this to be low risk because it is more than 3 times what a good forecast or projection variance should be within (±10%). A higher risk Margin of Safety Ratio would have breakeven or another benchmark fall at or below that 10% variance tolerance. Even when we use our current state sales volume (100 units), our Margin of Safety Ratio would be at 16.7% (120 planned units - 100 current units; 20 margin of safety units ÷ 120 planned units = 16.7%).

Use Sensitivity Analysis and Margin of Safety to Identify Risk

Sensitivity analysis shows the range of outcomes when actual results deviate from our underlying assumptions. Margin of Safety shows how far sales volume or revenues can fall while still hitting a benchmark (current state, profit target, breakeven, etc.).

Use these to highlight risk in any proposed changes you may look to implement to improve future performance. You can use the results from this to find ways to mitigate any potential downside. This sets you up to be more likely to achieve your projected results as you are proactively ensuring any issues are identified and addressed as early as possible.