How does Call Center Forecasting Work?
Forecasting isn’t just for weather anymore. Nowadays, many industries use various forecasting levels to predict market trends, sales, costs, profit margins, etc. While it might not immediately come to mind, call center forecasting is something the industry relies on heavily as well.
What is Call Center Forecasting?
Call center forecasting uses historical industry data to find patterns in contact volume to help businesses predict future levels. This information assists in tasks such as staff scheduling since the forecast models will predict which days of the week are most likely to be busy or slow. An accurate forecast can help a business by decreasing staffing costs, increasing employee retention, improving CSat scores, and providing more engaged and efficient employees. While these forecasts are never guaranteed to be 100% accurate, the rewards of call center forecasting are well worth the work.
Types of Forecasting
Call centers do more than predict what days will be busy in the upcoming week. There are several different types of forecasting.
Long-term call center forecasting can be extraordinarily helpful, but short-term forecasting can also be insightful. For example, let’s pretend a business offers a particular discount or runs a sale on products or services at different points throughout the year. Short-term forecasting allows the business and its customer service department (call center) to know what the call volume will most likely be like during those periods. This helps with staffing, training, and time management.
Call center forecasting allows your business to analyze collected data as far back as you wish. When comparing data points from the past, you may learn things about your business you did not already know – including data about specific trends and seasons. For instance, when reviewing forecasted data, you may notice a decrease in calls around less-popular holidays or long weekends followed by sharp increases in the following days. If this pattern continues from year to year, your business has a good idea that these actions will likely continue.
Triple-smoothing exponential forecasting handles the time series data containing a seasonal component. This method uses three smoothing equations: stationary component, trend, and seasonal.
Autoregressive Integrated Moving Average
Autoregressive integrated moving average, also known as ARIMA, is one of the most intricate models. ARIMA is a generalization of an autoregressive moving average model. It uses historical data to highlight current trends within your statistical data and make predictions for the future.
How Can a Call Center Help?
This is the process of predicting anticipated work and allocating tasks and resources to call center representatives depending on skills, capabilities, and experience.
Your business must use historical data to determine incoming contact volumes – the more recent and accurate, the better. Your business must factor in the number of customers, call types received, and average handle time. Regular events, such as day of the week, day of the month, and the season, must also be considered. Contact volume variations based on the types of calls, average handle times for each call type, and abandon rates are predictable events that affect demand.
Average Handle Times
This involves a representative’s time on the phone for a successful or failed contact. To measure the time, you must include everything from when the call starts to when the representative finishes their post-call tasks.
Service Level Goal
This goal refers to the percentage of incoming calls answered by a call center representative within an established time.
There are three steps you need to follow in workforce forecasting:
- Find the total number of workers – Your business’s human resources system will help you know the total number of workers available in your call center. To forecast correctly, you must only count the workers you will schedule, such as representatives, team leaders, etc.
- Calculate FTEs (Full-Time Equivalents) – FTEs are staffing arrangements that total one person working full-time. Calculating this helps compare supply and demand.
- Adjust for attrition – Staff attrition involves employees leaving within your forecasting period. There is voluntary attrition (staff leaving on their own) and involuntary attrition (call center terminates an employee). Your business must review historical data to identify when employees will quit voluntarily. For involuntary attrition, consider when the business has fired employees for poor performance or policy violations in the past.