Free-Agency In The Workplace: Using Employee Data To Assess Flight Risk

We all know them – the colleague who’s slowly circling the drain, but won’t budge despite repeated signs that s(he) might need to move on, and the highly-valued top performer who blindsides us with a resignation when “we thought they were happy.” Truth be known, there are many reasons why employees choose to stay, and perhaps even more reasons for switching jobs. With respect to the latter, understanding the cost of free-agency (the impact on an organization of losing an employee) is imperative. While everyone who exits won’t represent a huge loss, the departure of a key “player” should raise some important questions:

  • How much will it cost to replace them?
  • How long will it take to replace them?
  • How much training will be involved in finding a replacement?
  • Will other employees be expected to take on additional responsibilities?
  • Will morale decrease among remaining staff?
  • Will other employees begin to look for work too?

To avoid surprises, a whole science of algorithmic or predictive modeling, complete with software to assess an employee’s flight risk has emerged. Aside from using an array of variables and weights, and some fairly complex computations, there are many readily available sources of data that can help determine if you’ll be receiving more resignation letters.

One of the best ways to find out if employees will leave is to analyze why others have left.  You can begin this process at the end, ironically, with a well-designed exit interview or survey. While employees are sometimes fearful that they can’t speak candidly while still on the job, when they leave a company it presents one of the best opportunities to solicit unfiltered feedback. It’s important to put comments into context, but there is likely to be value in what is disclosed. A simple checklist can point out some major “pain points” such as non-competitive pay, benefits, commute time, or lack of opportunity for advancement and promotion. By developing a short list of the most frequent reasons why employees leave, an organization can then develop a plan of action to address concerns. Digging a little deeper may also disclose some interesting trends and patterns. For example:

  • Are there differences across departments, divisions, etc. in the organization?
  • Are there positions with exceptionally high turnover?
  • Do employees leave at about the same point in their time with the company?

There is also value in analyzing some internal employee demographics. For instance, identifying long-time employees who have consistently received high performance appraisals, but who have not been promoted, received bonuses, or received recognition for their accomplishments should signal a red flag.  If similar employees have cited a failure to acknowledge their contributions on the way out, this may be an opportunity to be more proactive. And, although there may be limited monetary resources, an opportunity to work-from-home on Mondays, for example, could be a tangible and inexpensive reward for work well-done.

A company should also be proactive in its approach with under-achievers. Why not focus on improving the ROI for those who received poor performance appraisals by offering training, placing them with a mentor, or by reassigning workload? In the long-run, savvy use of consistently collected measures can help to quantify issues. This, combined with other employee data, can better ensure talent managers develop their workforce and keep stars out of free-agency.