Predictive analytics show potential for employee retention and recruitment

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Analytics can be used for a wide variety of business data. They can be used to track payroll, benefits enrollment, growth projection and even potentially predict the growth or longevity of employees within a company.

The use and influence of workforce analytics, also known as “big data,” is experiencing a transformative revolution. Analytics have a growing impact on the future composition of the workforce, HR and talent management and overall business strategy that will be evident in the next five to 10 years, according to a recent EIU and SHRM Foundation report.

Beth McFarland, director of the SHRM foundation, says predictive analytics will likely attract increasing attention over the coming years, within two areas in particular: employee retention and recruitment.

“What [administrators] can do with [predictive analytics] is get a sense of which employees are likely to leave,” McFarland says. “Maybe they find a certain category of [employees] who are likely to leave and the company can focus more their efforts on finding a way to make them feel more engaged, like finding a new assignment to put them on.”

Michael Fauscette, chief research officer for G2 Crowd, agrees predictive analytics could be used to extend the length of time an employee chooses to remain with a company by using behavioral analytics.

“If you had analytics that could help you predict the success of a candidate based on their past performances, their skill levels, their personality and their cultural fit it could paint a better picture of how they will fit into your company,” Fauscette says. “If the analytics can predict the success of a candidate, then it could be a huge benefit to the hiring process, and if that fits, then it is a huge benefit to retaining an employee.”

On the recruiting side, McFarland says SHRM spoke with executives at Johnson & Johnson to answer if it is better for their company to recruit new college graduates or recruit more experienced candidates.

“[Johnson & Johnson] tracked those recruits to determine which were more successful and which stayed with the company longer, and that gave them some insight on where they should be focusing their recruiting,” McFarland says.

Intuition and experience
Despite the growing usage around analytics, many employers are still basing many of their major decisions either on intuition and experience or on the advice and experience of others, rather than on data and analysis, according to the results of a 2014 EIU survey.

Fauscette says there needs to be a compromise between the data received from analytics and the human interaction between the employer and employee.

“An interaction is a very good indication of behavior and engagement,” he says. “Sometimes peers see you in a different way than how you would evaluate yourself or how your manager would evaluate you, too. It’s different kinds of data, but it’s behavioral and not solely based on numbers.”

Some recommendations from the SHRM analytics report that organizations and businesses can use to overcome obstacles pertaining to workforce analytics include:

  • Improve the analytical skills of the HR function.
  • Ensure that data is clean, organized and ready for analysis.
  • Keep projects focused on solving key business problems.
  • Maintain rigor; do not confuse correlation and causation.
  • Strike a balance; perfectionism is a drawback.
  • Seek small wins at first.
  • Establish cross-functional cooperation for data gathering, storage and analysis.
  • Reassure staff that analytics is an aid to human decision-making, not a replacement.
  • Understand the legal and ethical complexities of employee monitoring.
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Predictive analytics Payroll Data sharing Benefit management