Sales compensation analytics in the U.S. have been woefully lacking, even though companies allocate more of their budget to sales compensation than to advertising. Yet while every dollar of advertising is thoroughly scrutinized to maximize the return on investment (ROI) of the marketing budget, the assessment of sales compensation spending is far less rigorous.
That’s unfortunate, because the cost of not evaluating and analyzing the sales incentive plan can be steep, given that an effective plan design can have a double-digit impact on sales as compared to a mediocre or poorly designed plan. Moreover, using analytics appropriately is the best way to objectively assess the effectiveness of your plan design.
Below are examples of sales compensation analytics that can help shed light on incentive plan effectiveness.
• Payout Ranges.
One of the most effective uses of sales compensation analytics is assessing the plan’s ability to pay for performance. The idea is simple—pay high performers more and low performers less. But this turns into a question of how much more or less. What degree of differentiation will send the appropriate message to both high and low performers and help the company maintain financial responsibility?
One simple way to assess the pay-for-performance relationship is to evaluate the incentive payments for the 10th and 90th pay percentiles and compare them to the target incentive amount.
For the 10th percentile performer, there is typically a payout range from 10 percent to 30 percent of the target incentive. Anything below this range means there may be too many people earning little to no incentive, risking turnover and a disengaged salesforce. Anything above that range may mean that bottom performers are being overpaid.
For the 90th percentile performer, there is typically a payout range from 200 percent to 300 percent of the target incentive. Anything below this means you may not be rewarding your top performers generously enough, potentially causing them to look elsewhere for a job. Anything above this range may indicate poor quota setting and/or windfalls.
• Percentage of Revenue Generated.
Another useful analytic to implement—in cases where a particular product or product group is of strategic importance to the organization—is to divide the percentage of incentive paid by the percentage of revenue generated. A ratio more than 1.2 is appropriate for an emphasized product, while a ratio less than 0.8 is appropriate for a less important product.
Take, for example, a company that sells both license and software-as-a-service (SAAS) software. The company was particularly interested in driving SAAS business in 2014, and the goal for SAAS revenue amounted to roughly 20 percent of the total while license revenue encompassed the remaining 80 percent.
To ensure focus on SAAS products, the company put 40 percent of the incentive weight on the SAAS sales and 60 percent of the incentive weight on license sales. The resulting metrics showed the relative importance of SAAS sales to the organization:
• License “Relative Importance” = 60 percent of incentive / 80 percent of sales = 0.75 (signifying low emphasis).
• SAAS “Relative Importance” = 40 percent of incentive / 20 percent of sales = 2.0 (signifying high emphasis).
• Assessing ‘Fairness.’
“Fairness” is another important incentive concept for which analytics is critical. A plan is considered “fair” when no territory characteristic other than the effort and ability of the salesperson impacts territory performance, and therefore incentive pay.
The importance of fairness cannot be underestimated for salespeople. In practical terms, designing for fairness requires the company to address two challenges: 1) know which territory characteristics to test for bias, and 2) if bias is observed, understand how to adjust the plan or quotas to eliminate or reduce it. Field sales managers can provide input about what fairness tests to run, increasing the odds of diagnosing fairness issues before faith in the plan is diminished.
To determine perceptions of unfairness in the plan, one good question to ask field salespeople and their manager is, “If you could have any territory in the country, which would you choose and why?” These answers will begin to reveal field perceptions of territory unfairness and provide the basis for further analytic evaluation.
Monitoring Performance with Critical Metrics
There is no one-size-fits-all approach for sales compensation analytics. However, ZS has found in our experience across various industries that a best practice for companies is to define target “zones” for many key metrics. Some typical target zones are shown in the table below.
Typical Target Zones for Key Incentive Compensation Plan Metrics
• SPIFFs = sales performance incentive funds, used to provide an immediate bonus for a sale.
• IC = incentive compensation.
Sales analytics are a key element of a successful sales compensation program. In addition to providing efficient, timely and accurate payout calculations, use of analytics presents companies with a big opportunity to enhance sales compensation plan diagnosis and design.
Chad Albrecht is a principal with ZS in Chicago, where he leads the firm’s business-to-business sales compensation practice. He is a Certified Sales Compensation Professional (CSCP) with more than 15 years of experience implementing motivational incentive plans in the software, business services, medical devices, telecom, distribution and manufacturing industries. He is a co-author of The Power of Sales Analytics (ZS, 2014).
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