For any organization, employee turnover can be a nightmare impacting profitability as well as eroding brand value. If not prevented, it can result in huge financial losses for the company, among other challenges. This is why, HR executives are always on the lookout for strategies which will help them reduce employee turnover and ultimately lead to retaining people and controlling cost.
Apart from working on controlling attrition, a firm’s HR also tries to figure out ways to predict which of their current employees might be gearing up to leave the company, and what steps need to be taken to prevent the same from happening. This is called employee retention strategies or employee retention models. Typically, such strategies to reduce employee turnover rely on three main points of action – exit interviews, environment sensing and trying to find early warning signals of attrition among the workforce.
The company’s HR, by way of exit interviews, tries to get a better idea as to why the employees have chosen to quit. At times, although not often, the HR might try to coax the employee to re-think their decision to leave by offering to negotiate depending upon the reason for resigning, however most of the time it is already too late to retain them. On the other hand, another traditional tool i.e annual or bi-annual employee surveys, are conducted to evaluate whether conditions are favourable to ensure all employees are actively engaged (meaning committed to the firm’s vision & mission, motivated to give their best and to contribute to the success of the company as well as personal growth). The results are later analysed against the data collected during other employee churn prediction processes.
You might also be interested to read: Role Of Artificial Intelligence In Talent Management
Despite all these efforts at managing employee turnover, the HR is still struggling to provide business leaders with a real-time predictive analytics around leavers. The above traditional employee turnover prediction models have failed to yield relevant data for effective employee turnover management. Thankfully, all’s not lost.
Recent studies and research on employee retention models has revealed that Artificial Intelligence can be employed successfully in employee churn prediction on real-time basis. Specifically, machine learning algorithms can be applied to build special index that would correctly measure the tendency for employee turnovers. How machine learning can be used for reducing employee turnover, can be understood by looking at past research data.
Previous research on why people quit their jobs threw light on two major factors. The first one being shocks of professional or personal nature; for instance, new reporting manager or closing of a process, or shifting to another city, marriage, having children or getting another job offer. The other determinant that was found to lead to employee churn was job embeddedness or factors that cause an employee to be strongly connected to the company. Machine learning makes use of these predictors of workplace turnover to develop indices that can effectively track those employees who are most likely to leave the organisation, thus helping the HR to take appropriate steps to prevent the same.
In conclusion, organisations can easily ascertain how to reduce attrition, by employing Artificial Intelligence & machine learning to conjure specialized indices, and use them alongside internal employee data that their HR already possesses and build better strategies to reduce employee turnover.
References:
- “HR News: Machine Learning Can Reduce Turnover,” HR Exchange Network Editorial Team, 23rd August 2019
- “Better Ways to Predict Who’s Going to Quit,” Brooks Holtom & David Allen, 16th August 2019
