Best practice guide to predictive HR analytics

HRM talks to Raytheon deputy director of HR, Janette Coulton, about using the power of predictive analytics to look at the future of your workforce

Best practice guide to predictive HR analytics

Traditionally, HR metrics report on something that’s already happened – why a person has resigned or what the staff turnover has been.

But for HR professionals willing to dig deeper and use predictive analytics, it’s possible to identify trends in advance that will lead to certain outcomes.

“It’s understanding your data and being able to determine in advance things like the drivers to a termination, for example,” Raytheon deputy director of human resources Janette Coulton told HRM.

“It’s really important for the HR team in an organisation to understand how to take the various HR metrics reports and go through a process of what’s called data mining – actually drilling down into that data to understand and identify key activities that might have impacted on a termination.”

One of the advantages of using predictive analytics is being able to get a greater understanding of the workforce and the impact of things like life events and changes to their professional role will have on an employee, as well as understanding how long some professions may stay in a particular role.

“If you’re in a growth industry and you’re looking to increase your market share in a particular area or go into a new market area, you want to make sure that you’re able to hold on to particular skill sets.

“You also want to understand what those skill sets find attractive about your organisation. You use predictive analytics to help identify what might attract someone to come and work for your organisation and what will help retain them,” said Coulton.

Adopting a marketing mindset and properly segmenting a workforce to understand it is a key factor.

“Sometimes organisations are a bit too general in their analysis. It may break down into gender but when you start breaking down into generational differences, age group differences, demographic and location, it really does start so show a different story. You’re not trying find a solution that’s one size fits all, you’re trying to understand every element of your workforce and what is driving it.

“Sometimes with HR is it about that marketing mindset of segmenting your market, and the workforce is just a market that HR needs to understand. Segment it and analyse those segmentations if you truly want to make a difference to preparing for things like future growth.”

You don’t need to have a mathematical background to use predictive analytics, Coulton said.

“I’m not a statistician. I think there are a lot of people in HR and coming into the HR world who do have that background and do a whole lot of analytic modelling and trends analysis. I’m not of that background and I don’t think that you necessarily have to be to get some of that basic segmentation happening.

“I wouldn’t want a HR professional to be scared of moving forward with predictive analytics. There are lot of tools that are readily available and a lot of training that is easily accessible that can get a HR professional started on that pathway of predictive analytics.”

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