Using Predictive Talent Analytics for Reducing Employee Turnover

August 27, 2014 Katharina Lochner
talent analytics

KPIs Are Not Just About Performance!

As consultants in the area of recruitment we are often asked what is the criterion for a successful hire. Job performance of course is the first criterion that comes to mind. However, there is another aspect: turnover.

In an article in the journal Talent Management Ranjan Dutta points out that turnover in companies has been rising since 2011. He makes clear that this is a problem because the cost of turnover for a new hire is 1.5 times the annual salary of the leaving employee. The costs comprise direct hiring costs like those for job ads or the recruitment process itself, but also indirect cost like loss in productivity or time required for learning. This means that the cost of turnover can be extremely high and even little decreases in turnover can save a company a lot of money.

Ranjan Dutta illustrates this with an hypothetical example: “At the 2013 median external hiring rate of 13.7 percent, a 10,000-employee organization would have hired 1,370 new employees in 2013, but would have lost 24.1 percent — or 330 — of them in the first year. Assuming an average annual salary of $50,000, this turnover would have cost at least $16.5 million. Even a 25 percent improvement — 82 fewer leave — would have been a savings of at least $4.1 million.” These are quite impressive numbers and they show how important it is to prevent turnover and that even small improvements here can have large effects.

Ranjan Dutta sees two reasons for turnover: poor selection and poor onboarding, and there can be a combination of both. In order to improve the effectiveness of recruiting he suggests using predictive analytics. And here things get really interesting because he does not suggest a one-fits-all approach. Rather he points out that companies should use predictive analytics, i.e. statistical methods, to identify what makes hires successful in certain roles. These factors might very well differ across different roles. Once identified companies should base their recruitment processes on these factors.

What can such factors be? Two weeks ago we learned that intelligence is a strong predictor of job performance. However, there are also other factors like personality, motivation, or job experience. And it might be a certain combination of these factors that makes an employee successful.

About the Author

Katharina Lochner

Dr Katharina Lochner is the former research director for the cut-e Group which was acquired by Aon in 2017. Katharina is now a researcher and lecturer at the University of Applied Sciences Europe in Iserlohn, Germany. In her role at cut-e, she applied the research in organizational and work psychology to real-world assessment practice. She has a strong expertise in the construction and evaluation of online psychometric tools.

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