You would probably jump at the chance to impact your organization’s bottom line by reducing the voluntary turnover rate of high performing employees (i.e., reducing the number of good employees who choose to leave the organization). Are you scratching your head to figure out why employees are leaving in the first place? You’re not alone! So, we set out to figure out which key drivers typically emerge with turnover risk as an outcome and how that maps to subsequent analysis of actual turnover behavior.
In a recent study, we looked at the five most recent clients with which we had conducted analytic approaches and tracked the differences in key drivers between turnover risk and voluntary turnover.
But first, turnover risk defined. Organizations can examine how the various topics on the employee survey (management, communication, compensation, job fit) drive employee commitment. We refer to this as “Turnover Risk” – the likelihood that an employee may voluntarily exit the organization. For example, how an employee feels about job fit may be related to whether they would like to be working for the organization 3 years from now – both topics that may be covered in the employee survey.
Now, see figure 1 below.
Figure 1. Turnover Risk vs. Actual Turnover Drivers
TR = Turnover Risk; Term = Voluntary Turnover
Predictors are rank ordered; 1 = Strongest predictor of the respective outcome
Analyzed drivers across 5 organizations, representing more than 150,000 employees
Commonly measured categories that were not found as drivers of Turnover Risk or Actual Turnover were: Accountability, Staffing, Tools & Resources, and Wellness
Numbers indicate statistically significant drivers of either Turnover Risk or Actual Turnover. Green when the same, red when different. Turnover Risk is measured by two items –
“I would like to be working at this organization 3 years from now.”
“I would feel comfortable referring family and friends to this organization for employment.”
Turnover Risk was consistently the strongest predictor of actual turnover, but the drivers were often different when examining Turnover Risk versus Actual Turnover. SMD found that while there were some consistently predictive topics across the two outcomes, many other topics differed in what drove commitment and what caused actual turnover. For example, SMD found that management was a strong predictor of actual turnover, but not usually a driver of turnover risk. We also found that when compensation was a driver of turnover risk, it did not actually cause an employee to ultimately leave the organization. Job Fit and Senior Leadership were among the only topics that were consistent drivers of both turnover risk and voluntary turnover. These key topics address how an employee feels they are suited for their current role and their impressions of senior leaders in the organization.
As we always say, predictive analytics is not a one-size-fits-all approach. Stay tuned for our next blog post to learn how to investigate turnover risk and voluntary turnover, with an employee survey, at your organization.