One of the most common causes of resignation in companies is not salary, or job responsibilities but conflict with a senior. These conflicts are rarely brought to the surface, the employee in question preferring to resign. However, what if you could tell when an employee is dissatisfied? What if you would know when he has become an active job seeker from a passive one? This is where predictive analytics can help. predictive analytics takes data, applies statistical and machine learning algorithms to it to predict the future events that can help an organisation better understand their employees.
How does predictive analytics do this? At a high level, predictive analytics helps to identify high and low performing employees. The low performing employees are likely to have issues with their seniors and sometimes it is the other way around where the low performance of the employee may be because of the issues with the seniors. Thus the performance of an employee in a particular period may not be attributed to his real performance potential, In any case, once the employee has decided to leave, it is very difficult to keep them.By analysing these patterns of low and high performing employees predictive analytics can also help companies attract good talent, and make changes to their hiring, HR policies and company culture to help keep your best workers for longer and look for more top performers.
It’s a well-known fact that replacing a good employee is far more costly than to retain an employee that would perform to high potential. Replacing an employee also takes time, that includes hiring time, on boarding and most importantly, waiting for the employee to get inducted into the culture of the company
This process could take a few months to years for the new employee. Companies are therefore always looking for ways to retain their potentially good employees and technologies like predictive analytics can help.
Predictive analytics to the rescue
Every organisation is different. You also need to understand that predictive analytics will work only as good as the data it is fed. When done right, analytics can help in finding the candidates who would best fit your company culture, would be best fit for a particular role in the organisation and help find the factors that create employee satisfaction.You have to understand, that these software systems actually learn from the data that is fed and can identify patterns based on which they are able to extrapolate and predict. In essence, if you remove the gut feel factor, that is how humans also learn and predict, right?
The more data that is fed into a predictive analytics system, the more accurate it will be. So , for example, if an employee is being late consistently to office or has been missing for a few days and there has been a co-relation of this behavior with employees showing disinterest in work and ending up resigning , then these are patterns that will get locked into the system and alert the HR at a much earlier stage , much before the damage is done, so they can talk to the employee and identify and resolve problems. This does not mean that every time the system alerts of predicts based on its past learning events, it will be correct, but as more and more information about these events ( eg. number of leave, frequency of leave, in and out times, work performance dips ) is recorded into the system, it will get smarter and accuracy of its predictions will improve.
In order to feed these system accurate data, it is also important that the different functional systems of an organisation talk to each other share data. For example, it is better to have a single system that has modules like appraisal management, payroll and leave management system than it is to have independent sofware and feed data into each. A single large system with multi functional modules is a sure way to go and reduces chances of error or missing out on data, that sometimes can lead to inconsistent data in each functional module. Predictivctive analytic systems work better when data comes from various places and can easily be co-related.