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• #Stack ranking vs bell curve how to#

There are at least two unspoken dimensions here. Larry performs amazing work, but is lacking this exposure. Luke performs more solidly and has much more experience with our product and our practices.

For us, this usually meant there was some dimension that we were (or weren’t) factoring in.įor example, maybe we actually think Luke is closer to getting a promotion despite believing that Larry is a stronger performer. Do we actually believe Larry is better?Īgain, lots of possible answers. Larry’s only been here for 2 months but he’s doing amazingly well, whereas Luke’s been here for 1.5 years and is doing about average. We ranked Larry as a stronger performer and Luke as a medium performer. When you try to evaluate people into a single dimension, you’re going to see some odd results. Sometimes we need to dig further and come up with more data points about the people we’re trying to compare. Sometimes we realize that we value certain qualities as equally important, even though the qualities themselves are very different. The conversation is around better understanding what we find to be important, and ensuring that we agree. Maybe Sally and Susan were ranked by different people and they’re miscalibrated. Maybe there isn’t, and she shouldn’t be ranked higher. Maybe there’s something we value more in what Sally does. The conversation starter: why did we rank Sally higher? She focuses a lot on details and puts out high quality work. Susan’s really strong when it comes to individual work. She helps coordinate team members well and ensures that we’re making progress towards shipping our feature.

Sally’s really good at seeing the big picture. Sally and Susan are both strong performers. Naturally, this raised lots of different discussion topics. The value of this exercise came afterwards: we then discussed and debated why we felt these rankings made sense. In fact, we aimed to create an absolute ranking across multiple teams. We did aim to create an absolute ranking. We did aim to bucket people into high, middle, and low performers. There is also more focus on consistent feedback and how people can improve. Employees may still be rated or ranked, but not along a bell curve or with strict cutoffs. Most companies have shifted to systems that are more flexible. Our process was actually closer to this change described in the article above: What matters here isn’t the tool we’re using, but how it’s being used. We were using it to calibrate our standards and come to (more of) a consensus on our values. With all that, why do I think our stack ranking exercise was useful? Fundamentally, we weren’t using it to decide who gets what compensation.

#Stack ranking vs bell curve how to#

That’s because people are incentivized to be more competitive and cutthroat, focusing on how to make themselves look better. Perhaps most importantly, this form of stack ranking hurts performance overall. If the bottom 25% of team A is still above average, they shouldn’t be punished for being on a strong team. There are also, naturally, calibration concerns. You should get less compensation because you’re bad at your job. You shouldn’t get less compensation because everyone else on your team is better than you. The concern becomes an actual problem when performance and compensation gets tied to these measures. It’s just that most of the other people in your team are better. Someone being in the bottom 25% of your team doesn’t necessarily mean they’re bad. The immediate concern with this is that stack ranking is a relative measure. They’re typically required to put some people in the top and/or bottom X%. In this form, managers are asked to rank their reports. Stack ranking hurts when it’s directly tied to performance and compensation, with quotas. Before we look at that, let’s first recap why stack ranking has (rightfully) earned a bad rap. Now, I actually believe the stack ranking exercise that we ended up doing was useful. As I’ve happened to read a bit about stack ranking, I felt some warning bells going off. Over the past month I’ve heard this sentiment in two different calibration meetings. We’re going to try to bucket everyone into strong, meets, and low performers, then essentially try to stack rank everyone.