Data Helps Firms Find the Right Workers for the Right Jobs

• 

• JOURNAL REPORTS
• September 15, 2013, 5:17 p.m. ET

Analytics help evaluate candidates, build teams and motivate everyone

By 

RACHAEL KING

Most companies would love to know if a prospective CEO will be successful or if a key marketing executive is going to quit.

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Now lots of them are crunching numbers to get answers. They're scouring data about existing employees to look for patterns of behavior and performance, so they can figure out the best ways to motivate them and see what qualities matter most in certain jobs. They're also tapping information from applications, interviews and résumés to see if prospective hires will be a good fit.

On the Rise

The practice has increased over the past five years, thanks to improvements in technology, says Elissa O'Brien, vice president of membership at the Society for Human Resource Management. The systems have gotten simpler to use and can process data from a wider variety of sources. They can also operate over the Web instead of needing to be kept on corporate servers.

Among companies with 25,000 or more employees, about 5% are using predictive analytics in human resources, according to a soon to be published study by Deloitte Consulting LLP.

Companies such as Google Inc. have created algorithms to predict which job candidates will succeed. Google identifies the skills, behaviors and values of the ideal job candidate for various roles. Google says humans still look at the résumés but the company uses algorithms in specific situations as an added layer to make sure it doesn't miss candidates who might be successful.

ConAgra Foods Inc. also leans heavily on analytics, because roughly 50% of its employees will be eligible to retire over the next 10 years. "As people leave the organization, we want to make sure we are competitive in hiring people with the right skills at the right time," says Mark Berry, vice president of people analytics.

In particular, the company looks for people who can adapt so they can keep up to speed as jobs change. Scrutinizing data changed how they look for those candidates.

"One of the general assumptions is that the younger you are, the more learning agile you are, but we've been able to disprove that," says Mr. Berry. Instead, he says, ConAgra discovered that learning easily is largely an interpersonal skill—and can be present at any age.

ConAgra also used analytics software to predict which key employees are most likely to leave the company and why, says Mr. Berry. The company scrutinized operations with high turnover and those with low attrition and mined data from human resources, looking at over 200 factors that might contribute to employees leaving.

"Pay wasn't in the top 10," Mr. Berry says. Instead, employees' relationship with their supervisor and whether or not they were recognized for their work were two of the stronger predictors.

"Understanding those factors can help us understand how we can align pay practices, benefits and cultural practices to retain the people we want to retain," he says.

Next in Line

Data can also help predict which employees may be ready to step in to replace a departing executive, says Ms. O'Brien. "Companies are asking, 'Do we invest in this person to get them from point A to point B, or do we have to look outside the organization for that expertise, and how much is that going to cost us?' " she says.

Daniel Joseph, who until recently worked in human resources at Avon Products Inc., experimented with using data to predict which employees might be ready to step into key roles. He analyzed whether it would be more cost effective to invest in training a particular employee or to hire someone who already has the skills.

Some companies, he says, invest in employees on the bench who may become disillusioned and leave if the position doesn't become available as soon as predicted.

"When I look at people, they are a resource that gets managed in a way that you would manage your money," he says. "If you think about succession planning, it's all about building supply, and you would never build supply ahead of demand."

Ms. King is a reporter for The Wall Street Journal's CIO Journal in San Francisco. She can be reached at rachael.king@wsj.com.