I have to calculate the monthly turnover rate of my team.

The formula I am using is the same as described in (http://www.payscale.com/compensation-today/2010/02/how-to-calculate-employee-turnover):

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The point is in the beginning of the month I had no employees and in the end of this month I have no employees. We had 5 people that have joined the team and the same 5 people that have left the team in the same month. Replacing this data into the equation I have:

Monthly turnover rate = [ 5 / ( (0 + 0) / 2 ) ] * 100

However, division by 0 does not exist. How should I report this turnover rate?


  • Turnover implies a comparison against expectations. How many employees did you expect to retain for the one month period?
    – Todd A. Jacobs
    Dec 17 '14 at 15:35
  • Let's say in a ideal situation we would like to retain all employees. Loosing people is loosing know-how. Dec 17 '14 at 15:57
  • You're missing the point. Did you expect to have 1 resource for a year (500% monthly turn-over), 2 people for 15 days each, or 5 people for one month (0% turn-over)? Context matters.
    – Todd A. Jacobs
    Dec 17 '14 at 16:08
  • @CodeGnome Please take a look at Ashok Ramachandran, he answered my question. Then, you will understand what I wanted with this turnover question. Thanks, Dec 17 '14 at 16:16

At a high level the max turnover rate is 100% if everyone leaves. It can exceed 100% if you hire new people during the month and they are also gone by the end of the month.

The formula you are using is good for typical (low) turnover rates, not for extreme situations like this. In your example simply taking the headcount at the beginning of the month and end of the month and averaging that won't work. You should do a more granular average - by number of weeks or even days.

Let us say the five employees stayed for 12 days each. Your average employees during the month would be = 5x12/30 = 2

Monthly turnover rate = (5/2) x 100 = 250%

  • you are right about that. Actually, I've committed a mistake when asking the question. Please, check the question again, could you answer that? Thanks, Dec 17 '14 at 14:41
  • Oh thanks for your answer !! So we need to normalize by fine grained method. Thanks a million, Dec 17 '14 at 16:15

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