Big data can take the guesswork out of the hiring process

Big data leads to big insights and occasionally, big chairs.
Big data leads to big insights and occasionally, big chairs.
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By Teri Morse
Vice President, Human Resources, Xerox

On average, U.S. companies spend around $46,000 each year on training and developing new employees. It’s even more expensive when companies struggle to retain people.

If you want to improve employee retention, and reduce training costs, your recruiting process must change, even for hourly employees, according to the Corporate Learning Factbook 2013.

Cracking the code

Data and software programs can help crack the code of hiring the right employees, based on input gathered from tens of thousands of employee files on hourly workers.

Applicants are put through a series of tests and their job performance is tracked, which helps the technology learn about and identify the ideal customer care worker. The test asks questions to see how many days per  month a candidate anticipates being off of work and ask questions about reliable transportation and how many miles candidates are willing to travel to work; ascertain attitude about overtime, as those that work one to three hours overtime per week are 15 times more likely to stay longer.

The stronger people do on the typing test, the better they are at providing customer service, by being able to maneuver through the pages of background information.

The testing also helps predict longevity. People that have had a customer service role where they have had to show empathy, tend to stay longer than those who just take orders. Those that have an associates or bachelors degree stay longer 5% longer than those with a high school diploma. Those with technical diplomas stay 26% longer than those with high school degrees.

Those technologies, that gather this data, like the one we use at Xerox from Evolv, help us assess all of our candidates (and we have a lot) and rank them from high to low potential for the job. Then we make the final decision on whom to hire.

Not only does it help during the application process, but it also shows us where applicants have weaknesses. This provides recruiters with areas to dive deeper into during the interview process, and it lets us know how we can focus training to enhance skills.

If the test determines that typing skills are light, we need to improve their keyboard skills to help them become more successful. We know if they are weak in what we call the soft skills or do not have customer relationship skills, we can work on that to improve their ability in customer services.

Less guesswork, more data
So who is the ideal customer care worker? The data tells us it’s someone who lives close to his or her place of work, has reliable transportation, uses social media (but not too often), and is creative.

This data has proven that when we hire people who are graded as “most likely to succeed,” they stay on the job longer and perform better. For one of our clients, it helped increase revenue by 10 percent because we were able to hire better people and keep them on the job longer.

Software like Evolv helps take the guesswork out of the hiring process and replaces it with real, scientific, data. While hiring managers have the final say in whom they bring on, instinct and intuition now play a much smaller (and more reliable) role in personnel decisions.

We’ve learned from this process that it’s not about the number of job applicants, it’s about the quality of those applicants and how many we hire.

Employees that stay less than six months cause a loss for us, due to the expense of training them. With the knowledge drawn from our data, our turnover is lower, which means less time and money spent on recruiting and training new hires.

Big data saves us time and money, and allows my team to focus on efficiency and customer satisfaction. What’s your team working on?

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This article was produced by Xerox and not the Quartz editorial staff.