Using Monte Carlo Simulation to Forecast Sales Compensation Spend

Sales compensation represents a significant (if not the most significant) cost line item for many organizations. Yet, many still struggle to develop an accurate budget for the fiscal year. Rarely do companies spend what they set out to on sales compensation.

Most organizations end their year overspent — i.e., someone’s getting written up. Or underspent — you saved money but probably left revenue on the table.

Why is Budgeting Sales Compensation So Hard?

Sales compensation in most organizations represents a complex financial system. Enterprise sales organizations typically have many different compensation plan structures — sometimes 50 to 100+ incentive plan structures.

With each unique plan comes complex payment mechanic structures, such as thresholds, accelerators, kickers, and gates, where the sales rep must meet multiple criteria to earn a commission.

Among sales reps, various attributes impact sales comp spend, such as different OTEs for different roles and different quotas for different representatives. This complexity within sales compensation makes it difficult to accurately forecast how much money will be spent compensating the sales team. Without knowing how each individual will perform, there are many ways that the same top-level revenue outcome could occur.

Example: One Plan, Three Wildly Different Outcomes

To understand how much sales compensation spend can vary, consider this simple example:

Imagine you have three sales reps.

Rep 1 has a 500K quota.

Rep 2 has a $1M quota

Rep 3 has a $2M quota

The total sales target is $3.5M

Each rep earns $15K when they hit 50% of their quota.

$50K when they hit their quota.

$100K when they hit 150% of their quota

If all three reps hit their target exactly, the organization will hit their $3.5M target and payout $150K.

But this isn’t how sales organizations work.

It’s more likely that there will be some variability in each rep’s performance.

The following is more likely to occur:

Rep 3 hits 150% of the quota, Rep 2 hits 100%, and Rep 1 hits 50%.

The organization makes $4.25M in revenue on a $165K spend.

A good outcome for the budget.

Or:

Rep 1 and Rep 2 hit 150% of the quota, but Rep 3 hits 50%.

The organization only makes $3.25M in revenue but spends $215K on compensation.

A completely different outcome.

This example highlights the amount of variability in a sales comp system based on how individual territories perform and the specific structure of comp plans.

What is a Monte Carlo Simulation?

Monte Carlo Simulation is a technique used to model outcomes in a highly variable process that organizations cannot easily predict.

A Monte Carlo Simulation assumes that the probability of an outcome is dependent on random variable interference. So, a simulation using this method relies on repeating random samples upwards of 1000 times and averaging the results to reach an estimate.

Confused? Don’t worry. This TikTok video does an excellent job of explaining how using randomness and repeated trials can help you reach a surprisingly accurate estimate.

Monte Carlo Simulation for Sales Comp Budgeting

A Monte Carlo simulation is ideal for modeling results since sales compensation is a highly complex process with variable outcomes.

Let’s consider how a Monte Carlo simulation of sales compensation spending would look:

Imagine an enterprise sales organization with 1000 sales reps and 50 different comp plans.

A Monte Carlo simulation would simulate potential outcomes of their performance at an individual employee and comp plan level and output an expected sales comp spend for the organization.

The model would give each performance aspect a probability distribution from which the simulation would sample for expected outcomes.

The same simulation would be repeated 10,000 more times with varying employee-level assumptions to deliver other potential sales comp spends for the organization.

The sales comp team would aggregate results to calculate an excellent representation of the entire spectrum of outcomes that may occur.

This exercise would highlight the potential variability of the spend and allow the budget owner to summarize the scenarios into a meaningful and accurate sales compensation budget.

Why use a Monte Carlo Simulation?

A Monte Carlo Simulation of your sales compensation program presents many benefits.

Reduce risk

First, it allows you to account for every scenario within your sales compensation program and reveal aspects of your plan that expose your organization to unforeseen risk.

Gain confidence

The more confidence an organization has in its budgeting and plan modeling, the more aggressive it can set pay curves and deploy short-term incentives.

Drive revenue and loyalty

With the ability to be more aggressive with your comp plan design comes more upside for your top performers. Knowing there is money to be made, your plans will motivate your top reps hit their number and help your organization reach its goals.

How to Run a Monte Carlo Simulation for Sales Compensation Budgeting, Step-by-Step

1. Define sample — 1000 reps, 50 comp plans
2. Define individual rep and plan parameters — OTE (On-Target Earnings), quota, comp plan mechanics
3. Define performance simulation parameters —what % of reps will hit the target, miss the threshold, hit the excellence point, and everything in between.
4. Define how many simulations will run — 10,000 is ideal
5. Randomize performance across the rep population to get 10,000 potential scenarios, each with revenue performance, and compensation spend forecast.
6. Evaluate the scenarios to understand when revenue comes in at budget, what the organization can expect the potential range of comp spend, and the probability of each occurring.
7. For scenarios where revenue underperformed, assess how much risk there is of overspending.
8. For scenarios where revenue over performed, assess how much will be paid out.

“What if” Analysis vs. Monte Carlo Simulation

When searching for a sales compensation solution, you may encounter functionality for “What If” scenario planning. This functionality may bear similarities to Monte Carlo simulations, but it will not return accurate and robust results.

In “What-If” scenario planning, every unknown variable is assigned a “best guess” estimate for varying scenarios ( i.e., best case, worst case, likely case). The results for each case are recorded.

By contrast, a Monte Carlo simulation would simulate scenarios from a probability distribution of each variable and repeat the process thousands of times to produce possible outcomes.

Since the Monte Carlo simulation is sampling from a probability curve, it will rarely sample from low probability ranges. The distribution of Monte Carlo results will be generally narrower — and more realistic — than that of a “What If” scenario.

Not Recommended: Monte Carlo in Spreadsheets

You can conduct a Monte Carlo simulation to estimate your sales compensation budget in something as simple as a spreadsheet.

Unfortunately, a spreadsheet only gives you a snapshot of that moment in time rather than accounting for the dynamic nature of the sales environment.

With sales compensation software like Forma.ai, you can track actuals against scenarios on a rolling basis and re-forecast in real-time. This allows you to take action confidently based on where your spending is trending.

Recommended: The “Rolling” Monte Carlo Simulation with Forma.ai

A rolling Monte Carlo simulation opens up powerful opportunities and unlocks agility to use your sales compensation dollars strategically.

Consider this: if your rolling forecast shows that you are tracking below budget, your organization could confidently deploy a SPIF to push your sales performance to the next level while knowing you won’t go over budget.

A Monte Carlo simulation can give your organization a deep confidence level in your sales comp budget at the beginning of the year.

With a tool like Forma.ai, you can re-forecast throughout the year by supplementing the model with YTD actuals.

That allows you to present the budget with confidence to leadership at the beginning of the year and provide solid projections on an ongoing basis.