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Don't Just 'Flip the Switch' and then Fall Asleep at the Wheel!

By: J. Mark Davis, Managing Principal, Valitus Group, Inc.

Whether your sales incentive plan was just implemented or has been in place for some time, it's important to regularly monitor performance and payout results to ensure the plan is performing as intended. This is particularly true after implementing a new sales incentive plan. There are numerous potential analyses to perform - some quantitative and others qualitative in nature. However, this article will focus on the numbers; i.e., those analyses which are purely quantitative or financial in nature. Those suggested here are a good start, but you'll likely need to tailor your assessment to fit your sales compensation plans' objectives.

Overall Performance Trends

A logical place to start is to assess whether performance results are consistently trending with overall business objectives and the key performance measures designed into the incentive plan. For example, are revenue and/or profit levels growing at expected rates; are the expected shifts in product mix evident; is there an increase in new account sales? Of course, if results aren't what they should be, the incentive plan likely isn't the only, or even primary, culprit (even though it's often the easiest target). You'll need to dig a bit deeper to make that determination.

Aggregate-Level Analyses

Next, look at the aggregate cost of incentive compensation relative to budget and performance. There are a number of analyses to perform here, including:

1. Effective commission rate - What's the total cost of incentive compensation as a percentage of top-line revenue (i.e., how much of each revenue dollar is allocated to incentive pay)? How is it trending relative to past performance as well as budget? Example: I once had the CFO of a successful corrugated cardboard company complain that, despite good revenue growth, the salespeople were earning too much and eating their profits. When asked how long the current commission plan had been in place, he replied, to my amazement, "50 years". I then asked, "Do you think the economics of the business have changed in those 50 years?" He didn't appreciate my question, nor should you stop with just this basic analysis. Just because your effective commission rate has been constant for some time doesn't mean you can still afford to pay that same percentage of each revenue dollar.


2. Pay dispersion - Does the dispersion between the 90th percentile and 50th percentile payout levels approximate the plan's upside leverage ratio (i.e., how much as a percentage of the target incentive you are willing to pay for "excellence" or 90th percentile performance)? It's not unusual to see the excellence performance level yield a 3x multiple on the target incentive. Well-below or well-above the defined leverage multiple may be a bad sign.


3. Performance range integrity - Testing the various points of the performance range, are 50% to 60% of incumbents in a given role at/above target or quota, 10% at/above excellence, and only 5% below the minimum performance threshold? Deviations here could point to a performance range problem or a fundamental quota setting problem.


4. Incentive component breakdown - Is the percentage of total incentive paid by incentive component (or performance measure) aligned with the prescribed incentive weighting for each? In other words, are the reps earning their incentive pay where you intended them to vis-à-vis the plan design?

Incumbent-Level Analyses

Testing the aggregate results is important. However, you must also "peel the onion" to see how the aggregate results are dispersed on an individual incumbent basis. Here are some critical analyses of incumbent-level data.

1. Sales force ranking - Segment the incumbent data by role/position, then sort on incentive payout results from high to low. Are those at and near the top and bottom of the ranking the individuals you'd expect to see there on the basis of current and past performance?


2. Performance and pay correlation - Sort on the key performance metrics from high to low. Does this performance ranking correlate positively to incentive earnings? The easiest way to see this is to graph a scatter plot that shows the (hopefully positive) relationship between performance and incentive pay for a given metric. If you don't see a tight distribution of data points along the regression line, something may be amiss.


3. Period over period comparison - Are individual salespeople earning more or less incentive pay versus prior year, same period results? Considering relative performance differences, do the earnings differences (up or down) make sense?

Again, you'll likely need to tailor these analyses to fit your unique situation. However, be careful not to lapse into "analysis paralysis!" Also keep in mind that these quantitative analyses are a great start, but will provide only part of the picture. You'll need to complement these analyses with some more qualitative assessments - and that's a subject for another article.