To address this problem, organizations are turning to data science to help predict and prevent employee turnover. Advancements in data science have made it possible to accumulate and analyze a variety of data points that can indicate the likelihood that a valued employee is contemplating resigning.
One approach that has been used is workforce behavior analytics. This approach involves the analysis of employee data to identify patterns and trends that may indicate a likelihood of turnover. For example, analysis of employee demographic data, such as age, tenure, and education level, may reveal that employees in certain age groups or with certain levels of education are more likely to leave the organization. Similarly, analysis of performance metrics, such as productivity and attendance, may identify employees who are at risk of leaving due to dissatisfaction with their job or workplace.
Another approach is the use of investigation management software, which can help organizations identify potential turnover risks by flagging unusual or concerning behavior. For example, if an employee suddenly starts accessing confidential information outside of their normal work hours, this may be a red flag that they are considering leaving the organization and taking sensitive data with them.