Predict Attrition in a Company by Help of Analytics
There’s always a sense of apprehension when someone walks down to the HR desk to put down their papers. More so if it is a key employee whose loss is going to be a definite setback. Then people wonder – the upper management, the line manager, the HR department – how it is that they never saw this coming. There used to be a time when employee retention processes would kick in only after an employee resigned. The HR department would have to report back to the management on what steps they took to try to make the employee stay. That is, of course, no longer the case. Many intelligent HR departments have caught on to the idea of early intervention!
Many a time, it might look like an employee’s decision to leave was made on impulse – for more money, growth, or a designation. Looking at things a little more critically will reveal that a resignation is only the final step to a series of pent up frustrations. You’re now wondering how it is even possible to track down something as intangible as an employee’s dissatisfaction and desire to move on. But that’s exactly what analytics can do for you!
The loss of an important employee translates to delayed deadlines and lost productivity, as well as lost revenue, and costs for hiring and training a replacement. Analytics will help you understand not just why employees leave, but also what can be done to prevent them from leaving. So how exactly can pre-existing data be put to use to prevent and predict employee attrition?
Step 1: Understanding what makes employees unhappy: It could be something about break timings, or mapping performance ratings with remuneration and recognition – something small could make an employee unhappy. You need to be able to recognize that employee churn is dynamic; so the factors that drive employee dissatisfaction could change over time. Nevertheless, this is what forms the foundation of your analytic model.
Step 2: Segregating the available data: Clean and valid data needs to be used to build a predictive analytics model. Using different statistical techniques such as linear/logistic regression, neural networks, random forests and decision trees, it is possible to make predictions of employees who are prone to leave a company. These statistical processes involve a lot of steps and sub-steps, such as creating training sets and test sets out of historical employee data. These days, there are companies that record so much data (both relevant and irrelevant) about their employees that can be put to use for these purposes. It is indeed an intensive process to bring all of this data under a scanner that will logically put them together, find links and patterns, and eventually predict outcomes.
Step 3: Interpreting the outcome: A company might boast of a fancy analytics system. But what matters the most is that the outcome is comprehensible and usable by HR departments. Analytics models become truly efficient when they are optimally used to understand which employees might leave, and target them with various strategies.
Step 4: Integration with employee retention campaigns: After having gone through the previous steps, you should ideally have a dashboard view of the employees who might potentially quit, and their reasons for doing so. In a world where no employee is indispensable, a company gets to make a choice to try to retain their key performers by preemptively intervening with various incentives. If some of the employees who show up on the attrition list are those the company is willing to lose, then it can save the time and energy of putting those employees through a retention campaign.
At the end of the day, attrition and churn modeling is not about visualizing data; it’s about seeing patterns. These analytic models don’t randomly pick out a disgruntled employee; rather, they gauge their behavior patterns over time to arrive at plausible results. A happy customer tracks back to a happy employee, and this system definitely gives you a chance to identify trends and work on ways to keep employees happy!