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Recruitment & Retention

Published March 2009

An Algorithmic Approach to Flight Risk

 

  Derek Finkelman

Employees switch jobs for many reasons, including poor relationships with their managers, the quest for more money or improved benefits, a shorter commute, to exercise all remaining stock options, a limited or no career path, peer frustrations, lack of trust in company leadership, etc. With the free-agent mentality of today's workforce, is it really a surprise when employees leave a company? To avert such surprises, companies can apply a more algorithmic or predictive approach to assess an employee's flight risk.

Flight-risk selections in succession planning software applications often include low, medium and high as options, but what's really behind those choices? Is it a manager's gut feeling? Can a manager really be trusted to publicly note in a software application that an employee is a high flight risk? What does that say about the manager? What does that say about the employee? Does it mean the employee is noticeably disgruntled, a poor cultural fit or a highly talented worker with a lot of open doors? Without basing that flight risk calculation on any historical information, there is no way to tell.

Why not leverage all the data about why employees leave a company, develop a flight-risk formula and then attempt to calculate a  flight-risk score based on key criteria and weightings? That way, talent managers can address employee issues before they leave, better retain the best people and ensure competent candidates are ready to step up to the plate when positions become vacant.

Ironically, one of the best places to start this process is at the end, with the exit interview. When employees leave a company, it's a great opportunity to solicit an employee's true feelings. When employees are gainfully employed, they're less likely to complain loudly because it may jeopardize their employment status. The day employees turn in their resignation letters, however, true emotions and opinions are unleashed. It's important to filter for the facts, but there often is truth in what is said.

A well-structured exit interview can go a long way toward understanding the reasons for and patterns of employee departures. By consolidating exit-interview data and information from other internal employee surveys, talent managers can develop a comprehensive list of the most frequent reasons why employees leave. This data can be manipulated and analyzed, by position, department and across the entire organization.

Once the reasons for employee departures are identified, they can be weighted for in-depth analysis. For example, if 10 percent of the people who leave cite commute as their No. 1 reason, commuting should be worth 10 percent of a person's flight-risk score. A scale such as 1-100 could be developed in which employees with a commute of less than 10 miles receive a score of 10, between 10-20 miles receive a score of 40, between 20-30 miles receive a score of 70, and more than 30 miles receive a score of 100 —based on real, company-specific data.