Can the Moneyball Method Help You Hire Better Employees?

One entrepreneur says hiring via metrics, not just traditional resumes and job interviews, has led to a higher retention rate ... and a more successful team.
June 05, 2013

There are several factors that can influence your hiring decision when interviewing candidates, such as the impression you get during an interview or what's on their resumes. Unfortunately, relying too heavily on either doesn't always result in the perfect fit for your company. Burned too many times, some employers are now using research and analytics to predict the best person for the job.

Michael Rosenbaum, president and founder Catalyst IT services, began using the "Moneyball" method in 2001 as part of his company's hiring strategy. The term Moneyball originates from a statistical method that the Oakland Athletics baseball team used to build a winning team on a smaller budget by identifying undervalued talent. Rosenbaum did the same thing and relied more on the analysis of metrics than what was on a person's resume, and it worked to help him increase retention rates and hire better talent and. By analyzing up to "a couple thousand data points" on a person, Rosenbaum claims he can even know when an employee is ready to be promoted. 

We asked Rosenbaum how he did it, what kind of data he compiles and how he determines whether candidates will be a good fit before hiring them. 

Why do you think the traditional interviewing method is so imperfect?

The classic way of hiring is so subjective, because it tends to revolve around hiring someone similar to yourself. When I was 23, the person who hired me for my first job also went to The London School of Economics and I think that's the only reason I got my job. The idea is that people think that they're good at their jobs, so they want to hire someone who reminds them of themselves. However, teams work best when you have complementary skill sets on the team.

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What type of data do you look at?

We collect traditional data, such as what's on an individual's resume and their social network, but we also look at things like how long someone spends filling out their online application, and we analyze their keystrokes. What you can get from analyzing large data sets can better help you understand how someone works and their productivity levels.

What if someone takes a break in the middle of filling out their online application? Do you record that data? And how do you use it?

If it turns out the data we compiled doesn't seem relevant when we compare it to everything else, we probably won't consider it.

What kind of software do you use to collect the data? 
When we decided to use this method in our own company, we developed software that would allow us to use algorithms to find the best match. After some success, we spun out that software into its own company called Pegged Software, which launched in 2010 and is available for other employers. We're a software company so it makes sense that we wanted to develop our own software for hiring purposes to predict if someone's a good fit and how long they'll stay with us.
How accurate are you at predicting how long someone will stay?
Our voluntary turnover rate last year was 5.8 percent and our involuntary turnover rate was 9 percent. In the software development space, turnover is typically 30 percent. The hiring is not all metric-based and a lot of it depends on the interview and resume, but when incorporating data into the equation, the entire hiring process becomes much more efficient.
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