The act of anticipating or projecting future volume coming into the call center via calls, email, chat, or whatever other channels a call center may use is known as call center forecasting. The goal of call center forecasting is to increase productivity and guarantee that the agency has enough employees to manage the volume of calls.
Forecasting, when done correctly, may save contact centers money by preventing them from losing money owing to having too many employees on staff or on schedule at the same time. It also contributes to improved customer satisfaction by ensuring that the call center never has too few agents to manage the number of calls or emails received.
Uses of call center forecasting
Call center forecasting has a wide range of uses. The method may be used to recruit, schedule agents, and enhance KPIs like average response time and average wait time. Customers and agents are happier as a result of accurate forecasting since personnel are less likely to feel overworked or underappreciated.
Commonly used methods for call center forecasting
Despite the fact that call center forecasting methodologies are continually evolving, most call centers today employ four common models.
1. Smoothing using three exponentials
Triple exponential smoothing, often known as the Holt-Winters approach, has been utilized in labor management systems since the 1960s and is commonly employed in forecasting tools. Call center data is divided into three categories using this method: levels, trends, and seasonality. After then, the data is "smoothed," or averaged, over each time period (which could be monthly, daily, or even hourly).
Because unpredictable occurrences like a natural catastrophe or outage might distort the data in a short-term prediction, triple exponential smoothing is best suited for long-term forecasting.
2. ARIMA
The auto regressive integrated moving average (ARIMA) is an abbreviation for auto regressive integrated moving average. Over the last decade, this newer, more complicated approach of call center forecasting has grown in popularity. The technique is divided into three categories:
Auto Regression is the process of comparing data to historical trends.
Comparing the present and previous observations
Smoothing the data over various time periods in the past with a moving average
3. Neural Networks
Despite the fact that neural networks have been present for more than 20 years, the technology has lately gained traction as a result of Google's use of them in voice recognition and other artificial intelligence initiatives. This model is made up of nodes that each assess a string of data inputs before attempting to match the output. The approach may be used to generate many models based on a variety of criteria in contact center forecasting.
4. Aggregation of Multiple Temporal Events
Multiple temporal aggregation, a new approach for call center forecasting, may be utilized for both short- and long-term forecasting at the same time. By looking at annual, daily, or hourly data all at once, call center teams may get a broad picture of staffing needs.
Resource Library
Blogs
