The Bell Curve has long served as the basis for many enterprise evaluations, including sales mobility. This model operates on the theory of “normal distribution” – the idea that an arithmetic mean for performance has an equal distribution above and below the curve. In short, it asserts that the performance of most people in a variety of activities – sales performance and compensation among them – cluster around an average level, with the same number of above- and below-average performers. It gets drilled into us beginning in school – the phrase “grading by the curve” might ring a bell.
Many companies adhere to this model for assessing – and rewarding – employee performance, and it’s actually causing them to lose some of their best employees, wrote Josh Bersin, principal and founder of Bersin by Deloitte, in a recent LinkedIn post. When evaluation time comes, an organization may distribute its sales performance totals, find the employee average and form the curve. They then use these findings to inform sales mobility strategies, rewards for top performers and punishments for those turning in below-average marks.
Why sales mobility doesn’t ring the Bell Curve
There are several problems with using the Bell Curve, Bersin argued. Foremost among them is that it requires evaluators to grade according to rigid definitions of the curve. On a five-point scale, for example, this would mean that only 10 percent of employees could possibly merit a perfect “five” rating, while 10 percent would have to be forced into a low “one” score. The rest are treated as average.
This practice can create a wildly inaccurate picture of true performance. It may be that 25 percent of employees performed at a level an employer would consider exemplary, but only a fraction of these will be rewarded as such. On the other hand, forcing 10 percent to earn fives can obscure overall lackluster results.
Empowering power users
Bersin opined that a more effective metric for measuring performance and influencing sales mobility strategies is the “Power Law” distribution, first developed through research conducted by Ernest O’Boyle Jr. and Herman Aguinis on some of society’s “power users” – athletes, entertainers, politicians and researchers among them. This model posits that there are a small number of “hyper performers” – people who do a lion’s share of the work. There are also a relatively small number of “lower performers,” with a broad range of those who fall in the “average” category.
There are a few ways that make this the better model. First, it can help businesses get away from using numbers exclusively as a rating tool in performance and sales mobility assessments. It is a tactic that many employees feel uncomfortable with or outright despise. Additionally, employees who know their employers use a Bell Curve understand that only a select group earn the top rankings, no matter if 10 percent or 80 percent perform at a high level. These productive employees may grow discouraged and seek their fortunes elsewhere.
This model also isolates the “hyper performers” into a class by themselves. It is these employees who often do the bulk of the work and they are the ones who companies should try to retain and empower. Performance rewards and bonuses can be rewarded accordingly – instead of a hyper-performing “five” receiving a small increase over a “four” who did much less work, he or she can earn money commensurate with performance. It also builds a culture more open to sales mobility en masse, leading to high performance across the board.
“The distribution reflects the idea that ‘we want everyone to become a hyper-performer’ if they can find the right role, and that we don’t limit people at the top of the curve – we try to build more of them,” Bersin wrote. “Companies that understand this model focus very heavily on collaboration, professional development, coaching, and empowering people to do great things.”