Roughly 36 percent of employees leave their positions just over a year after their hire. When factoring the loss of production, cost of recruiting, hiring and onboarding, Gallup research found that the cost of replacing an individual employee can range from one-half to two times the employee’s annual salary. Although the specific amount varies across employers, the range makes sense. It also sends executives down rabbit holes as they attempt to figure out how to keep their talent in a tight labor market.
Presently, exit interviews and performance reviews are the primary methods firms use to address retention concerns, but they don’t address retention issues in real-time. However, a data-driven method to improve retention is currently on the table—one that HR professionals will gladly welcome.
The Economic Minority Report
The latest research by the Harvard Business Review (HBR) focused on using big data and machine learning algorithms to identify which employees were most likely to quit. Through their research, they demonstrated that it is possible to develop a process that predicts the real-time likelihood of employee turnover.
Using machine learning, combined with an assessment of turnover factors, each individual was given a turnover propensity index (TPI) score. From there, two concurrent studies predicted the employee’s likelihood of quitting.
Data-Driven Decision Making
According to Gallup research, 51 percent of employees said that in the three months before they left, neither their manager nor any other leader spoke with them about their job satisfaction or future with the organization. In addition, 52 percent of those who quit said that their boss could have done
something to prevent them from leaving. What does that say? Turnover is preventable—just so long as employers know that there is an issue.
By using big data, firms can track indicators of turnover propensity and identify which employees are most likely to leave their company. This proactive approach will allow managers to mediate potential losses and reengage employees before they put in their two-week notice.
Data-driven decision making, when used correctly, has the potential to decrease the potential of firms losing their most talented employees. In a tight labor market where talent is scarce, HR departments and executives should make data-driven decisions to give them an edge on keeping the right people in the right seats.