Successful Contact Centers Track Forecast Accuracy at Frequent Intervals
While many call centers spend a lot of time and effort building schedules and forecasting using the latest solutions and apps, fewer do the work required after the forecast is created and the day is over: tracking that forecast’s accuracy after-the-fact. The best forecast in the world is worth very little if it doesn’t actually help the contact center run smoothly and keep customers happy by ensuring that the right amount of resources are available to meet call and multimedia traffic. In other words: it’s about ensuring the right resources are available at the right time for the right customers.
Forecast accuracy is an equation that measures the percent variance between the number of calls (or chats) predicted to arrive during a given period and the number of contacts that the contact center actually receives during that time, wrote Knowlagent’s Greg Levin in a recent blog post. (Levin notes that you’ll sometimes hear forecast accuracy described as “forecasted contact load vs. actual contact load.)
Most contact center managers who measure this metric consider a five percent variance to be acceptable, writes Levin. Naturally, they strive to do even better – that is, try to achieve a lower percentage variance – but a five percent variance is generally good enough to keep senior management and customers from trying to hurt them.
When contact centers do track forecast accuracy, they often do it over too long a time period for it to be of much value. Determining how accurate your forecasts were for the year won’t really help you (though the numbers might be interesting to know). You need to know where you’re succeeding and where you’re failing to a very specific degree. Levin recommends tracking forecast accuracy by half-hour intervals, noting that this interval-level tracking can provide you with a much clearer view of how badly your contact center botched the forecast…or how well you succeeded.
Read Levin’s full blog post here.
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Edited by Brooke Neuman