How a Soccer Player Hired Data Scientists for Contract Negotiations
Kevin De Bruyne secured a 4-year contract and got a 30% pay hike by leveraging data analytics. What can HR learn from this? In today’s talk, Erik van Vulpen shares De Bruyne’s story and what we in HR can learn from it!
Kevin De Bruyne, a Belgian midfielder playing for Manchester City, worked with FC analytics, a data analytics company to renegotiate his salary. Based on the outcome, it appears he was able to make a convincing, data-driven case.
This is a unique example of how compensation analytics could play out. How would you as an HR professional handle an employee who comes to you with hard data on their performance? And if you turn this around, how can we in HR start to quantify performance and compensation in a more objective and data-driven way? We all know that we can’t rely on the traditional, annual performance cycle anymore. Different data points, continuous feedback, and new tools like social network analysis promise to provide a completer and more comprehensive picture. This will likely impact how we conduct compensation discussions.
Today I want to share a short story with you that I read in the news late last week. And it’s a story that HR can learn something from. And that’s why I’m so excited about it. It’s a story about Kevin Brown, who is a Belgian midfielder playing for Manchester City. And even when you’re not interested in football, or as the Americans call it, soccer, I think it’s still a story that HR can learn something from. So hang with me while I give you a brief overview.
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Kevin de Bruyne thought he was under compensated. So what did he do? He didn’t just talk with HR, he went to an analytics company called FCM analytics. This is a company that analyses all kinds of football matches, and looks at different variables to predict someone’s role on the team and predict someone’s performance and analyse how well they are doing. Now, using this company, Kevin was able to show that his performance increased significantly since joining Manchester City. And it resulted in him not only getting a 30% pay bump, but also in his contract being extended by two whole years leading to a four year contract, which is a very, very long time in football.
And I think this is a brilliant story that poses three challenges for HR. The first challenge being how will you as an HR professional deal with an employee who comes to you with a report showing you that they’re underpaid? That is something that you need to learn how to deal with. And I know I’m preaching to the choir, but it’s another piece of evidence that HR needs to become more data driven.
But I think the second formidable challenge that this story teaches HR, or provides HR, I should say, is about compensation analytics. How do you go about predicting someone’s performance when you know they’re not the goal scorers? They’re not Messi, they’re not Ronaldo. They’re not scoring the goals themselves. They’re a midfielder. They create opportunities. So what is the data that you look at? You look at, you know, how fast can they run? How much distance do they cover in a match? How many passes do they give? And how many opportunities do they create, in order to help the team win the match? So how do you as HR assess performance and look at compensation of skills that are unique, that are very, like very valuable? And we see that happening more and more where people have unique skills that are hard to quantify? How do we still quantify it in HR? I think that’s the second challenge for HR.
And the third one is how do we look at performance, given Kevin’s use of publicly available data? And a lot of the data that we have in companies is not publicly available? Maybe good for us, but maybe also not good for us. The thing is that how do we measure performance? Shouldn’t we stick to the annual performance cycle that we all know is kind of broken and doesn’t really work anymore? How can we look at other data points that go beyond that annual performance review? continuous performance reviews and continuous feedback? How can we analyse it? How can we make these intangibles more tangible? So the number of opportunities that you create for the goal scores in HR, we could measure that, for example, using social network analysis, how crucial is an individual employee in connecting different departments, or in enabling our top performers to perform even better? And I think that’s the third formidable challenge for HR. How can we not just look at money and hard outcomes, but how can we also look at these more intangible processes to make a good assessment of someone’s performance and what someone is worth? I would love to continue this discussion in the comments.
Let me know your thoughts. What are the lessons HR can learn from this specific case? And how HR can improve being good at compensation analytics, and being able to measure performance in an accurate way? I’ll see you in the comments. Wishing you a great day. See you later. Bye bye.