10 Jan Predictive HR Analytics For Your Company
The HR department is one of the most important departments of a company. As the saying goes, your people are your most important asset. And keeping them happy, will make your customers happy.
Besides being the most important asset, your employees are also your biggest expense. The combination of unhappy employees and biggest expense does not make for a best practice. The HR department makes sure that this does not happen. The use of HR analytics can help the department creating and maintaining happy employees. Analytics should not only be used to create simple dashboards (although also very useful), but should aim to provide insights that eventually lead to better business decisions and operational improvement. Appropriately using HR analytics can return insights which were previously unknown. The following two applications can be done with the use of advanced predictive HR analytics:
1. Decrease Employee Turnover
Employee turnover, also called employee churn, is a serious issue for companies. Retaining your best employees can be hard, especially if the exact reason for leaving is not known. The earlier the HR department knows about the reasons for leaving, the earlier it can act. With the use of analytics, the HR department can get to know these reasons. It starts with collecting all relevant historical data from the employees, both employees that are currently working as employees that have left the firm. Relevant data is for example staffing data, salary data, engagement data (collected through surveys), performance data and productivity data. After this very important task, the data is consolidated so it is ready to use. Mathematical techniques, such as logistic regression and decision trees, are often used to get an insight in the turnover reasons. These techniques are also easy to understand for employees that are not core data scientist, which makes them perfect for such analyses. If necessary, more advanced techniques can be used, such as a random forest model. The main disadvantage of the advanced mathematical techniques is that they are more of a black-box and therefore not always easily understood.
After the drivers for retention within your organization are known, is it time to see what that means for your current workforce. With machine learning the future workforce can be predicted. This is because the main drivers for retention are taken into account and plotted on your current workforce. Each employee then gets a turnover probability based on his or her characteristics. The HR department can now act on time and talk with these employees to persuade them in staying longer at the firm. As the HR department knows what the reason is that the employee wants to leave, HR can come up with solutions that fit the employee and will benefit employee satisfaction tremendously.
Our article Predict Your Future Workforce goes deeper into decreasing retention within your company.
2. Lower Absenteeism
There can be many causes for employees being absent. Illness and injuries are only a part of this. Other reasons are low workplace morale, disengagement, stress or burnout. It is important to know what the reasons for absenteeism are, so appropriate actions can be taken and absenteeism can be lowered. As research has shown, your best employees are more sensitive to stress and burnout. This makes it, from a business perspective, even more important to analyze absenteeism and its drivers.
The first step is to start with monitoring absenteeism. In some countries it is obligatory for companies to monitor absenteeism. Closely monitoring absenteeism makes it also possible to have a better employee view, which gives the HR department more power to come up with relevant business decisions. Furthermore, monitoring absenteeism gives the possibility to compare your own organization with your competitors and the rest of the market. After monitoring, the absenteeism data should be analyzed. The cases of short-term absenteeism can be filtered out, so only the severe long-term cases, such as burn-outs, are left. Employee absenteeism can then be analyzed using classification models or regression techniques. The classification models take into account whether an employee is absent or not, while regression techniques look at the number of days absent.
Emplysight was founded to give you better insight in your employees. The company aims to use both simple analytics as more advanced predictive HR analytics, such as machine learning and Artificial Intelligence, to help organizations get a better understanding of their employees. Emplysight helps all HR departments, wherever they are in their analytical journey.