Every call centre has them; bad days.
Call volumes are higher than expected and staff don’t show up. The result is a mile long queue of frustrated customers; a group of deeply fed up agents and a set of supervisors running around futilely trying to “manage” the situation.
If the bad day wasn’t enough, then you get the post-mortem, the inquisition into why you had such a bad day.
The answer is always the same:
- The operations guys will point at the forecast and say “the forecast was wrong, I can’t be held responsible for performance if the forecast is wrong.”
- In return the forecasting guys (guys who have their heads so far buried in spreadsheets that you can only see the tops of their glasses), mutter something darkly about regression coefficients and poisson distributions that nobody really understands and then sink back further into their calculations.
The only outcome is that relationships become more strained than they already were. Then the world spins relentlessly on towards the next bad day.
The secret the forecasters won’t tell you
As a man who has done his fair share of forecasting in the past let me tell you a little secret:
The forecast is always wrong.
If your forecasters were the sort of people who could precisely nail the outcome of a random, chaotic, complex system, day in and day out, then they wouldn’t be forecasting your call centre. They would be playing the euro-millions lottery for 5 minutes every Friday and spending the rest of the time cruising the shores of the Mediterranean in a Ferrari.
Try as they might they will never get it consistently right.
100% accuracy won’t solve your problem
But what if your forecasters were always 100% right, could you answer all the calls then? Would you be able to get all the people into the right place at the right time to answer the calls? Or is customer demand so volatile that you are on a hiding to nothing?
There is a simple way to see how volatile the demand is into your call centre. Look at the data. Take a year’s worth of weekly actual call volumes and plot it out. Does your demand look like:
For the operations analysts among you (or the statistically needy) it is possible to calculate a measure of volatility (if you would rather not know feel free to skip to the next section):
- Work out the average volume
- Calculate the standard deviation
- Divide the standard deviation by the average and show the result as a %
You now have a magic volatility number. If you feel the need you can benchmark it.
But so what? What does that tell you? The bigger the % age the more volatile the system. The more volatile the system the more likely you are to have a bad day.
How does that help with the bad days?
If you want to avoid the bad days there are only two levers you can pull:
- Work out why your demand is so volatile, and do something to smooth it out.
- Look at your staffing patterns and contracts and do something to make them more flexible.
You will notice that blaming one another doesn’t figure too high on the list above.
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