HR Clairvoyance: Using Predictive Analytics to Take Your HR Process to the Next Level

Predictive HR analytics continue to be tipped as the ‘next big thing’, the year’s ‘disruptive technology’, and the cutting edge human resources ‘must-have’. And it’s not surprising, given that analytics present a new way to use data – a shift from reporting on the past to forecasting the future that doesn’t require tarot cards or a crystal ball. However, it begs the question, why are analytics so often featured in those January ‘the year ahead’ lists? Shouldn’t they have moved from “disruptive” to “business as usual” by now?

The key is to look for predictive analytics packages that not only draw on all your HR data but also mine the contents of other internal and external data sources

Possibly, the reason is that while everyone agrees that they are a ‘good thing’, businesses and users are still struggling to apply analytics in a practical fashion. Maybe a few pointers on the practical use of predictive analytics are required…

Match Your Recruitment Activity to Your Employee Turnover

Your HRMS can ease the difficulties in employee turnover. Traditionally, people leave and then the recruitment process to replace them begins. But what if you knew in advance they were leaving (not individuals but overall numbers)? Then you could have your replacements ready and waiting – minimal lag, minimal downtime, minimal loss of productivity. 2015 case study research from Bersin by Deloitte looked at a predictive turnover model. The received wisdom was that employees left for reasons related to compensation, but the model showed that other factors were more influential, including employee tenure, marital status, and their supervisor’s tenure. Knowing this, steps could be taken to bring down the turnover rate, all of this achieved via predictive analytics in your HRMS.

More Sophisticated Employee Engagement

So many organizations still see the staff survey questionnaire as the beginning and end of their employee engagement strategy. But by pooling the survey data with business information held elsewhere – such as sick leave, training, promotion, performance assessment, and even customer satisfaction, sales, and productivity statistics – deeper insights can be gained which can drive workforce deployment, process improvements, employee stakeholder involvement and longer-term strategy.

Break Free of Your Absence Cycle

Every employer tracks absenteeism and the reasons for absenteeism. Yet still, the tactics used to tackle workplace absence tend to focus on the individual, effectively a case of picking off the statistics one at a time. However, a predictive model that takes the past absence patterns and combines them with data on employee demographics, work roles and duties, and other relatively fixed variables can produce a picture of your potential trouble spots within the organization – the future risk areas. You should be able to forecast future patterns and trends of absence, even including likely causes and duration. Which, of course, gives you the opportunity to take steps to mitigate and even avoid repeating the past.

Recommended Reading: HRMS Vendor Guide - Find Vendors Applying Predictive Analytics in their HRMS

The key is to look for predictive analytics packages that not only draw on all your HR data but also mine the contents of other internal and external data sources, such as your financial and operational systems, facts and figures regarding the wider employment market, economy and your position within them. Using all this data and your HRMS you may just achieve HR clairvoyance.