After designing a sales comp plan, the big question is how it will work in different situations.  Will the cost of sales skyrocket and break the bank in a good year?  Likewise, will a bad year starve the sales force and trigger massive turnover?

Our sales compensation consultants typically answer these questions by conducting stress tests on the pay curves for extreme situations – asking what the sales expense would be if, say, 90% of reps blew their quotas.   These tests help reassure our clients that their new comp plans are sound, but the test results cannot form the basis of a sales cost forecast, because the scenarios are hypothetical, and often overly simplistic.

For more advanced analysis, those same questions can be readily addressed using Monte Carlo simulation techniques. There are three simple steps.

  1. By looking at the historical sales data, we build statistical models to fit the performance distribution of sales regions or even individual sales reps.
  2. Using the modeled parameters, we simulate tens of thousands of sales outcome scenarios that traditionally would take decades or longer to produce in the real world.
  3. From there, we visualize the possible sales scenarios by plotting simulated results on a scatter chart.

The data points might tell a story like, “If you have a good year, there’s an 80% chance you’ll see 55% of reps exceed their quotas, resulting in a revenue increase of 10% and a sales cost increase of 15%.”

Powerful stuff. The stuff that strategic sales forecasting and sales compensation decisions can be based on.

What’s more, this type of analysis doesn’t require expensive software or hardware investments.  Excel has built in a flexible random number generator that facilitates the design of these simulations, and modern laptops are now powerful enough to carry them out.

Setting appropriate parameters for testing pay curves and conducting meaningful simulations will give you the ability to make your sales compensation plan decisions with confidence.  Contact Alexander Group’s Sales Benchmarking practice to learn more about using these advanced analysis techniques.


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