In a recent LinkedIn Influencer post, Josh Bersin took aim at the bell curve – introduced into performance management by GE, used widely around the world and now a subject of a raging debate.
The main contention against the bell curve is that performance does not follow a bell curve or normal distribution. While there are numerous research studies to prove that point, my own simple analyses often point to the same conclusion. When I look at distribution of performance data (e.g. sales per employee, profitability per branch / store etc.), the raw data seldom looks like a bell curve. Indeed, it looks like what Bersin calls the Power Law distribution.
A “Power Law” distribution is also known as a “long tail.” It indicates that people are not “normally distributed.” In this statistical model there are a small number of people who are “hyper high performers,” a broad swath of people who are “good performers” and a smaller number of people who are “low performers.” It essentially accounts for a much wider variation in performance among the sample.
It has very different characteristics from the Bell Curve. In the Power Curve most people fall below the mean (slightly). Roughly 10-15% of the population are above the average (often far above the average), a large population are slightly below average, and a small group are far below average. So the concept of “average” becomes meaningless.
In fact the implication is that comparing to “average” isn’t very useful at all, because the small number of people who are “hyper-performers” accommodate for a very high percentage of the total business value.
A key implication here is that companies must seek to reallocate rewards and developmental investments disproportionately to hyper-performers, rather than letting bulk of it go to the middle of the bell curve. If we don’t do this, we fuel the tendency to “hang in there” and kill the tendency to “strive” to do great things. Another important point is not to limit the number of hyper-performers by rationing of ratings, but instead building an army of them through development, coaching and empowerment. Rationing of top performance ratings can have significant downsides. For instance, if we say that there is a quota of 10% for the top performance rating, then companies implement it as “10% of the workforce must get a top performer rating” and “no more than 10% can get it”, even if some folks turn in stellar performance!
What do you think? Have you made a shift from the bell curve? What have the learnings been?