As I wrote in a past article, Average Handle Time (AHT) - Metrics That Matter, there is no doubt about it, Average handle time (AHT) metric is an important metric in workforce management workload forecasting.
Now I will tell you why they can also be next to useless...
Why outliers are the enemy of the Average as a predictor
Any use of an average for assumptive forecasting can be misleading when the distribution is heavily skewed at either end of the tail. Even a small number of outliers can pull the average in that direction and give the misimpression that the handling time is clustering around a point that is higher or lower than where they truly are clustering.
Depending on where that data point lies in a normalized distribution of data points (the “bell curve”), the probability that the average is accurately describing or predicting the behavior/outcome of handle time comes purely down to how the distribution approaches the tails of the curve. The wider the outlier, the less reliable any assumption (not just AHT) as a predictor.
For example: Let’s say that the current average handle time (AHT) is 404 seconds, and you usually operate at an AHT of 450. The fact that you are running 46 seconds quicker must mean there has been a productivity improvement, right? Wrong in this case. That average number is entirely skewed by a mass of super-quick conversations. c.1000 of those contacts are taking 2000 seconds, and c.4000 of those contacts are taking a mere 5 seconds. Now imagine how alarming this insight would be. In contrast, before, we were applauding the productivity improvement. Now we know that we have an unusually large number of short contacts, indicating perhaps some sort of connectivity issue difficulty. This is probably causing the customer to reconnect, which in turn is driving up offered volume and destroying your Service Level. We also have some unusually long conversations indicating complexity and difficulty and perhaps again some sort of issue.
If you’re merely looking at averages, you’re probably missing the data that’s of greatest importance. Outliers might be your worse enemy to the “Average” as a predictor, but they are mighty useful to help you to discover problems.
How to know when the Average (mean) is a good predictor
As we mentioned earlier, when the data becomes skewed, the average loses its ability to provide the best central location because the data will drag it away from the typical value, as was shown in the above example.
So, to determine whether the Average (mean) will be a good predictor, you are looking to see how close your dataset is to following a normal distribution.
Normal distributions have the following features:
symmetric bell shape
mean and median are equal; both located at the center of the distribution
68%, percent of the data falls within 1 standard deviation of the mean
95%, percent of the data falls within 2 standard deviations of the mean
99.7%, of the data falls within 3 standard deviations of the mean
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