Experience Matters Less Than You Think

When Markellos started digging into the data, he found that the correlation between job experience and performance was very low. In the world of statistics, a perfect correlation is 1. 

He found that job experience only has a .13 correlation with strong job performance—a very low correlation indeed. Even reference checks are better with a correlation of .26, fully double that of job experience. 

Despite the evidence, companies continue to base their hiring practices on the myth that experience predicts performance. Why do we ignore compelling evidence that there’s a better way to anticipate the success of our most valuable resource: our people? Markellos explains this by quoting William Gibson, who said, “The future is here. It’s just not evenly distributed yet.”

“Most people will do whatever the industry helps them get their hands on,” he says. The science has been around for a long time, and most formally trained HR staff are fully aware it exists, but they aren’t yet aware of alternatives. After all, hiring based on experience is better than hiring at random (though not by a very wide margin).

“As drivers in the industry, this is what we’re called to do,” says Markellos. “How can we distribute those uneven chances?” HR teams are spread too thin already, he explains. We can’t expect them to spend hours seeking or creating a research-backed solution, so he and his co-founder created Bryq to bring the solution to them. “HR is a freakishly complicated field,” he says. “Democratizing these processes isn’t easy, but this is what we’re good at.”

Big data can be overwhelming to teams who aren’t familiar with using it, and traditional HR is no exception. When Markellos and his co-founder started Bryq, part of their inspiration was what they saw when they looked around the market: plenty of specialized, complex, and unwieldy solutions, but hardly any user-friendly ones. Many assessments on the market took hours for candidates to complete and yielded massive reports that hiring managers could never hope to read.

“Big data is not about having a lot of data points,” Markellos says. “It’s about how to get them to come back to you with insights that are actionable.” No HR team needs to know the correlation coefficient of a data set. What they need to know is which five candidates out of a group of 100 are worth interviewing, and which of those will most likely succeed over the long run.

So what happens when hiring teams aren’t sure what they’re looking for—or, worse, when they’re looking for the wrong thing? AI tools like Bryq can help course-correct, assuming they’ve been trained correctly. 

Hiring managers often have a profoundly biased view of what a job involves and what it will take to succeed. Of course, there are many different paths to success, even when we fall out of the habit of considering them. 

Here’s how Markellos puts it: “Sometimes a client is asking for an account executive, but they really want a farmer or a hunter.” That’s why Bryq doesn’t use today’s criteria to inform its outputs; if you feed AI today’s data, you’ll get more of today’s results. Amazon’s experience with AI is infamous for precisely this reason. They created their hiring algorithm with the best of intentions, but because they fed it current data, they got more of the same: a candidate pool dominated by men with expensive educations.

Bryq’s objective is to create an AI that’s more than a machine fed by pure data. Instead, Markellos describes it as a “psychologist in a box.” The tool takes candidates, job descriptions, and success criteria, and feeds back actionable insight to hiring managers. The results far outpace those based on hiring for experience; according to Markellos, Bryq’s success rate is better than 90%. No tool is perfect, and we can’t remove the human factor entirely, but 90% and higher gets pretty darn close. 

What does all of this mean for companies hoping to do more with less in the context of the Great Resignation and the war for talent? 

First, they should look beyond the skill set of any one individual and focus on the skill set of the team as a whole. Extraordinary teams are made of people who complement each other’s strengths and weaknesses, becoming greater than the sum of their parts. Imagine the office equivalent of the “Moneyball” strategy: hiring for specific skills and qualities that will offset gaps, allowing teams to accomplish more by working in harmony together.

Second, hire for fit. Hiring for “culture fit” has become something of a taboo, but it doesn’t need to be. At its core, culture fit isn’t about hiring carbon copies of you and your current staff. It’s about hiring for the core qualities that matter most to your organization and resonate with your values. Done right, hiring for fit yields greater long-term success.

According to Markellos, companies that want to survive and thrive in today’s market must hire with the team in mind, basing their decisions on skill and fit—not experience. In the context of the war for talent and a marketplace in which even small companies have expanded their reach to global pools of talent, gaining even a slight competitive edge makes all the difference. 

Small- to mid-sized players can’t hope to compete with the Facebooks and Googles of the world on salary or brand recognition, but the proper assessments give them a unique edge.

Assessment tools like Bryq look at what people do, not at what they are. A candidate may have a background in IT, but if their skills align perfectly with an open role for a developer or an account executive, you’ve got access to insight your competition doesn’t. Candidates who might not otherwise surface as good a fit for a role will rise to the top of your search, helping you find them before someone else does. 

You may not be able to out-bid Amazon on salary. But if you can find candidates who would otherwise be lost amidst their massive pool of candidates, you don’t need to.