Forecasting Call Center Workloads

Accurate forecasting is the most important component of workforce management software and call centers are inherently susceptible to the inaccuracies of call volume forecasting. Overstaffing can lead to wasted labor expense while understaffing can result in lost sales due to abandoned calls and a longer average speed of answer. In either case, staffing mistakes can have a lasting impact on a company’s bottom line.

For a company whose call volume fluctuates according to seasonal buying, catalog drops, or other special events, forecasting capabilities should be a major consideration in any purchasing decision of workforce management software. Only the most sophisticated systems can perform correlated forecasting — that is, forecasting for specific events that cause wide fluctuations in the volume of calls that must be processed.

There are two basic methods used to forecast workload in a call center: exponential weighted moving average and historical trend analysis. Both use historical data collected from the call center’s ACD, and both take growth trends into account in their calculations.

The exponential weighted moving average calculates the average call volume over a specific time period and then bases its projections on a formula that assigns more weight to recent activity. This technique is effective for contact centers where there is little fluctuation in call volume and patterns.

But it has shortcomings when trends change. For instance, it is unable to predict a continuation of trends during periods of generally increasing or decreasing volume, or to associate changes in volume and/or call arrival patterns with specific events (pattern recognition).

Historical trend analysis not only accurately predicts the continuation of trends, but the more advanced algorithms also incorporate pattern recognition to fine-tune forecasts for special events like promotional mailings or national holidays. Each time a particular event recurs, the forecasted call volume is automatically adjusted to reflect the increase or decline in incoming work caused by comparable occurrences in the past, such as a historical 40% drop in volume on the Fourth of July.

In environments where workloads regularly ebb and flow due to marketing activities and other definable variables, historical trend analysis is the only way to ensure proper staffing because it is the only method that can incorporate complex historical trends in its calculations. Without pattern matching to predict different customer behavior for different events, the risk of over- or understaffing increases dramatically.

Bob Webb is vice president of sales for St. Louis-based contact center solutions provider Pipkins.

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