Should employers use data to predict workers' needs?

To improve the employee experience, some organisations are striving for a better use of data to predict employee needs

BY HRD 15 May 2018

Modern CHROs are taking steps to handle challenging HR demands while remaining a key partner to business leaders, according to Pat Wadors, chief talent officer at ServiceNow.

“Nearly two-thirds of (global) CHROs (surveyed by ServiceNow) said it’s their responsibility to drive corporate performance,” she said.

“CHROs also expect their success to be defined by the consumer-like employee experience.

“In fact, more than half (56%) of CHROs say the ability to create a digital consumerised experience will define their roles in the next three years.”

In order to improve the employee experience, many global organisations are striving for a better use of data to help predict employee needs, which can then lead to greater employee satisfaction and lower attrition rates.

Indeed, more than half of the CHROs said they have the data and technology to predict employee needs. However, success of use is not consistent.

Even though 55% said HR technology allows them to predict the services and information employees need, 59% said the HR function is unsuccessful or just somewhat successful at using predictive analytics to respond to employee data.

This is backed up by research by Deloitte which said that 77% of executives rate people analytics as a top priority, but only 44% are using workforce data to predict business performance. Moreover, just 29% are performing well in leveraging external data.

So what’s an example of how this could work effectively for an organisation?

According to the 2017 McKinsey report, ‘The CEO’s Guide to Competing through HR’, a major US insurer facing high attrition rates gathered data to help create profiles of at-risk workers.

After implementing data analytics, the company found that lots of workers in smaller teams, with longer periods between promotions and with under-performing managers, were more likely to leave.

However, once these high-risk employees had been identified, more informed efforts were made to convince them to stay. Consequently, attrition rates fell.