Call Center Forecasting

Call center forecasting is the act of predicting or estimating future volume coming into the call center via calls, email, chat, or whatever other channels a call center may rely on. The purpose of call center forecasting is to optimize productivity and ensure the agency has the appropriate number of agents to handle the contact volume.

When done right, forecasting helps call centers avoid losing money due to having too many agents on staff or on schedule at the same time. It also helps increase customer satisfaction by ensuring that the call center never has too few agents to handle the volume of calls or emails coming in.

What Is Call Center Forecasting Used For?

There are many applications for call center forecasting. The technique can be used to recruit, create agent schedules, or improve KPIs like average response time or average wait time. Accurate forecasting leads to happier customers and agents since employees are less likely to feel overburdened or undervalued.

How Do You Forecast a Call Center?Call center agent call forecasting.

Call centers use a variety of methods to create accurate forecasts. Typically, the best method depends on the reason forecasting is needed and the type of forecasting being conducted. For example, short-term, daily forecasting requires a different technique than long-term forecasting.

Some call centers rely on spreadsheets or workforce management software to create accurate forecasting predictions, while others may use specific equations to calculate staffing needs.

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What Methods Are Commonly Used for Forecasting?

Though call center forecasting methods evolve constantly, there are currently four popular models used across most call centers.

1. Triple Exponential Smoothing

Sometimes known as the Holt-Winters technique, triple exponential smoothing has been used since the 1960s and is widely applied to forecasting tools within workforce management systems. This method splits call center data into three categories: levels, trends, and seasonality. This data is then “smoothed,” or average, over each data period (which could be monthly, daily, or even hourly).

Triple exponential smoothing is best suited for long-term forecasting since random events like a natural disaster or outage could skew the data in a short-term forecast.


ARIMA is an acronym for autoregressive integrated moving average. This newer, more complex method of call center forecasting has become increasingly popular over the past decade. The method encompasses three categories:

  • Auto Regression: Comparing data with patterns of the past
  • Integrated: Comparing the current and past observation
  • Moving Average: Smoothing the data over several periods from the past

3. Neural Networks

Though neural networks have been around for more than 20 years, the method has recently spiked in popularity since Google began using it in speech recognition and other artificial intelligence projects. This model is built from nodes that analyze a string of data inputs and then aim to match the output. The method can be applied to call center forecasting to create several models based on a range of factors.

4. Multiple Temporal Aggregation

The newest method for call center forecasting, multiple temporal aggregation, can be used for both short- and long-term forecasting in tandem. Call center teams can gain a wide scope view of staffing needs by looking at annual, daily, or hourly data all at once.

How Do You Calculate Forecast Accuracy in a Call Center?

Though there are numerous ways to calculate forecast accuracy in a call center, one equation offers the most reliable data. To calculate, subtract the actual number of calls by the forecasted calls offered within a given time period. Divide the difference by the number of actual calls, then multiply the answer by 100 to get your percentage.

So let’s say there were 152 calls in a certain period, and you had forecasted 145 calls for that period. That’s pretty close! Plug those numbers into our formula like this:

((152 actual calls – 145 forecasted calls = 7) / 152 actual calls = .046) x 100 = 4.6%

So there was a 4.6% difference between what was forecast and what actually happened. Although it’s possible to use any time period for this formula, we recommend using smaller increments of time—no more than an hour—for greatest accuracy.

Applying Forecasting to Your Call Center

Call center forecasting is an ever-evolving and important part of running a productive, efficient call center. No matter which methods or KPIs you use, it’s important to always analyze forecasting accuracy and find ways to improve measurement techniques. Check out ROI Solutions to learn more about important call center performance metrics you should know.


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