Tuesday, July 17, 2012

Making the Most of Your Turnover Data

Turnover is one of the HR metrics that we have been measuring forever...and ever.  The problem is that turnover all by itself really doesn't mean anything.  What does your leadership team do with, "Our quarterly turnover rate is 28.6%?"

The questions then become:
  1. What caused the turnover?
  2. How do we stop the turnover?
  3. How much is the turnover costing us?
  4. How much will fixing turnover cost us?
  5. Was the turnover good or bad for the company?
  6. Are we at risk for losing our high performers or our high potentials?
  7. Which managers have the most/least turnover and why?
  8. Is there differences in turnover among generations? If so, why?
  9. Is there differences in turnover among managers? If so, why?
  10. Is there differences in turnover among departments?  If so, why?
In this month's HR Magazine, Adrienne Fox wrote an article title, "Drive Turnover Down."  I was honored to be quoted in the article that talks about using data to get to root cause of turnover.  The article was interesting talking about how HR used to view turnover and what is different about turnover 2.0.  

I believe to answer the questions above you have to think like a marketer and slice and dice data until it tells the turnover story.  

In the article Fox quoted Brain Wilkinson, who pointed to general attributes that impact turnover in most organizations:
  • The local economy
  • The traits of the job
  • How often employees have been promoted
  • Pay increase frequency
I believe each company needs to understand what drives THEIR turnover as the list above implies, those are generalities and when it comes to turnover, its not a cookie cutter approach.

As Fox discusses and I concur, HR professionals should strive to be less general and more predictive when it comes to turnover.  The data is there to analyze.  Using data such as historical turnover rates, performance scores, engagement data and many other variables, you can "predict" the employees that are at risk for leaving.  Managers would really value this data as they can "course correct" the employment experience so that top performers will stay with the organization.  

What has your approach been to analyzing turnover?  What has worked for your organization?

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