Augmented Analytics for HR: Employee Churn Management
Traditionally, gathering data and insight about employee attrition has required a lot of heavy lifting and technical support from IT. Now, with augmented analytics, IT can empower HR leaders to access this insight on their own.
If you’re an HR manager and your company has an aggressive growth strategy, you need to retain your high performing employees and hire additional top talent to drive your strategy forward.
As such, HR needs to keep an eye on attrition rates, identify areas where attrition poses significant risk, evaluate the reasons why, and take measures to rectify the situation.
Answers at your fingertips
With augmented analytics, the data for answering these questions – and, critically, the ability to predict what’s coming next – is readily available. The dashboard below represents attrition at a midsize company. The top right part of the screen provides KPIs on overall number of employees (8,681), average salary (US$43,266.54), the number of male (5,071) versus female (3,610) employees, and the attrition ratio (7.03%).
In the lower right, the pie charts break down attrition by various factors: age, gender, employment type, department, employment level, and recruitment source. The map to the left provides an at-a-glance view of attrition by geographic location. Notice that the circles on the map are color-coded according to region and sized to quickly communicate relative attrition rates. A quick mouse over on Russia, for example, shows an attrition of 74 employees.
This view is a great starting point that already provides a lot of insight. For example, you can identify that the bulk of employees leaving the company (51.88%) are between the ages 26-35 and tend to be associate-level (45.57%) compared to specialist- (24.43%), senior- (29.02%), and expert manager-level (0.98%) employees.
With augmented analytics, built-in machine learning capabilities help you detect patterns in historical data and easily identify potential attrition risks by various categories (geography, age, salary, and so on). HR planning is more effective because you can easily forecast – or predict – attrition for the next quarter with the help of machine learning algorithms.
The Influencer Contributions pie chart in the screen below shows the factors that lead to employee churn at the company. In this scenario, one of the most significant factors (13.96%) is a language skills bonus.
Imagine that a big part of the company’s business is running call centers that have a global reach. By looking at the first screen, it’s not difficult to see that younger employees are more prone to leave than others. But understanding the specific reasons – in this case, the language bonus – takes augmented analytics.
In addition, the underlying model on which the analytics are based is open to further investigation by business analysts or data-savvy HR employees. If analysts, for instance, require a higher level of detail or confidence, they can dig into the underlying model and adjust where appropriate.