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In the movie Rounders, Matt Damon plays the role of poker prodigy Mike McDermott. At one point, he says, “If you can’t spot the sucker in the first half-hour at the table, then you are the sucker.”
I’ve long been fascinated by people who take on poker players they know are much better than themselves. I’ve been even more fascinated by why people bet against the house in a casino. Similarly, I’m fascinated by companies that think they can beat giant brands at their own game when it comes to recruiting. I believe if you want to compete effectively for talent, you have to, at a minimum, understand how large firms are stacking the odds of recruiting in their favor.
Recruiting resembles poker in several respects. I recently had a hard-earned lesson in this when I participated in a small Texas Hold’em tournament with about 40 players, one of whom was Matt Damon. A friend asked me if I thought Matt would be any good. I said he was probably there just for fun, even if he was excellent in the role of Mike McDermott. A few hours later, Matt — not Mike — took home first prize, and I had to eat my words while my friend laughed at me.
Much as in poker, hiring predictions are impacted by both chance and skill. The shifting ratios of chance and skill make recruitment decisions quite challenging, especially because we often can’t know whether a hiring manager was just lucky when they landed a star performer or if their methods made all the difference.
In both poker and recruiting, a surprising number of people believe they can just wing it. When they lose, they convince themselves they were just unlucky. These individuals fail to recognize that top players rely much more on a good strategy and a disciplined game than pure luck.
Similarly, most executives I’ve spoken to dismiss the notion that investing in talent analytics can help them significantly improve their results. According to Gartner, only 21% of human resources leaders think their companies effectively use talent data to inform business decisions. This is despite strong business examples of even small investments in HR analytics paying off. Even sports teams see improvement: An analysis by FiveThirtyEight found that baseball teams with at least one analyst supporting their recruiting efforts outperformed their expected winning percentage by more than 40 percentage points in 2009.
My question to business leaders not investing in people analytics is this: In what other parts of your organization would you pass up on hiring one analyst if it could improve the performance of your company? The most common response I receive is, “The main companies that care about talent analytics are tech firms.”
Let’s take a look at this argument.
From my perspective, business leaders using this argument tend to believe that the talent who wants to work in tech would not want to work for them anyway, and it’s only tech firms that are investing in people analytics. As a result, they draw the conclusion that they don’t need to worry about other companies gaining a competitive edge over them.
Let me start by saying that I disagree with the notion that tech only recruits from a specific pool of people who would only want to work in tech. Yes, some people are only interested in working in tech. However, tech firms understand that they need superstars to achieve their goals. But LinkedIn, for example, is training food truck workers and teachers to fill in tech talent gaps. To me, it’s difficult to argue these people would not be interested in a nontech job.
The second part of the assumption — that only tech firms are investing in people data analytics — is also incorrect. My own analysis (presented in my upcoming book) from March 2019, looked at 490 companies implementing data analytics; 12 out of 17 companies employing 10 or more talent analytics professionals were not tech firms. I’m not alone to point out that nontech firms are adapting people analytics. For example, JetBlue is using talent analytics to understand attrition patterns.
How can you leverage data analytics?
It’s important to remember that using data analytics will require some initial investments in collecting better data and in team members who can truly make use of this data. To overcome these challenges, consider investing in a person to manage the data collection and ensure that the data is clean and reliable. Depending on the size of your organization, a part-time resource might suffice. For example, at my company, we have about 130 employees, and one person dedicates 20% to 30% of their time to this. Also, you can consider hiring a data-scientist, either internally or externally, who can help you look at the right data and to draw actionable conclusions from the data.
My company used analytics internally and looked at data for junior-level hires. We found out that relevant experience with the task they were being hired to do correlated negatively with job performance at this level. This was a highly surprising result, and it showed that hiring managers were overly valuing past experience when trying to hire junior-level candidates. We have since trained our hiring managers to value past experience less for junior roles.
The Bottom Line
If you have a hard time finding the talent you need, I believe you are already competing with companies that have talent analytics departments. And these firms are stacking the odds of hiring better talent in their favor. To paraphrase Matt Damon, if you do not have a repeatable and scalable way of attracting the best talent away from other firms and to your company, then those companies are stealing the best talent away from you.
Think about it this way: Would you want to be the last baseball team to hire a talent analytics person? If not, I suggest you should stop reading this article and start drafting the job description for your first talent analytics hire.
Author: Michael Fizard