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From NPR News, this is ALL THINGS CONSIDERED. I'm Audie Cornish.
MELISSA BLOCK, BYLINE: And I'm Melissa Block. When companies are looking to hire a new employee, they typically rely on a very low-tech tool: the resume. Resumes include the seemingly important information, where the person went to school, their previous employers and the skills they have, but some employers are trying out a new approach to hiring that makes resumes seem antiquated. It uses massive amounts of data and complex algorithms to help predict which job applicants are likely to work out and which are not. Here's Lisa Chow with our Planet Money team.
LISA CHOW, BYLINE: The company Xerox runs 175 call centers around the world, filled with customer service agents who deal with questions about everything from cell phone bills to health insurance. Fifty thousand people work in these call centers. Terry Moore(ph) is in charge of recruiting them. She says a lot of people they thought would work out didn't.
TERRY MOORE: We were spending a lot to recruit and even more to train. And people were in the training classes sharing with us that they weren't right for the position. You have to be able to deal with a frustrated customer, hang up the phone and get on to the next and not have to excuse yourself to the ladies' room and cry.
CHOW: So a couple of years ago, Xerox hired a company to help them do a better job at finding the right people, people who could hack it in the call centers. This company, called Evolve, began an experiment. When people applied for jobs at Xerox call centers, this company gathered a bunch of data about these candidates that went far beyond resumes. All applicants had to answer extensive surveys with questions like which word better characterizes you, consistent or witty. Also, can you name three pieces of computer hardware?
Applicants were also tested on pattern recognition and multitasking, and they had to respond to a challenging customer service call.
UNIDENTIFIED MAN: And I know for a fact that you sneak these charges in because people don't call about $1.10 because it's just $1.10, and you sneak this into people's bills, and everybody pays it. But this is criminal. It's awful. I just want this $1.10 removed from my bill, and I never want to be charged for data usage again.
CHOW: Some of these people got hired, some worked out great, some did not, but now Xerox had a way to tell what exactly should they be looking for when they were screening candidates. And there were some surprises. Moore says prior experience in a call center, for instance, didn't really matter.
MOORE: We've proven that just because you worked in a call center, and possibly even your reference came out well, it doesn't necessarily mean that you are going to be good in the future.
CHOW: On the other hand, if you had a retail background, that turned out to help unless you were a cashier or worked at a restaurant. Those people tended to do worse. With these new techniques, Xerox has been able to significantly reduce turnover. Michael Rosenbaum(ph) works at Pegged Software, which is developing big data techniques to find the right workers in hospitals. He says they've used it on themselves and overturned a basic piece of conventional wisdom, one of the things that people put at the top of their resume: where they went to school.
MICHAEL ROSENBAUM: The question is does a college degree or even a graduate degree tell you whether or not someone's going to be good in a particular job. And we find zero statistically significant correlation between a college degree or a master's degree and success as a software developer.
CHOW: Rosenbaum thinks companies are going to be doing a lot more data analysis like this in the future, which is way better than trying to read the tea leaves in a resume. Barbara Marter(ph) agrees. She works at Mercer, a consulting firm that specializes in recruiting.
BARBARA MARTER: And I think a lot of it will be looking at, you know, people who are performing really well in the job and finding out what is it about them that's making them so successful and equally the low performers or the people who either leave on their own or are asked to leave, what is it about them that makes them, you know, not really well-suited for the job.
CHOW: It is of course possible that data mining will miss some good people, but she says think about the flawed way in which we hire people now. There are all kinds of biases now. People tend to hire people who are like them or went to schools they know or who were referred to them by a friend.
MARTER: A lot of these new techniques do have the potential to eliminate biases.
CHOW: Marter thinks in the near future employers are going to be using a lot more data and relying a lot less on old paper resumes. Lisa Chow, NPR News.
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