Workload Calculation with AHT: A Call Center's Guide

In every call center organization, regardless of whether it's a predominantly synchronous channel like phone calls or an asynchronous channel like ticketing, one common Key Performance Indicator (KPI) is Average Handle Time (AHT). This is a critical metric to measure accurately because it influences workload calculations. An incorrect workload forecast can lead to downstream implications for capacity planning and affect all other workstream functions, such as scheduling and real-time monitoring.  Here are some nuanced understanding of AHT that I have learned through my professional experience.

1. In a synchronous environment, AHT data pulled for a recently concluded day likely reflects the true AHT for that day. Conversely, in an asynchronous environment like ticketing, the AHT from a recently concluded day may not be accurate. This is because, in synchronous scenarios, handle time is tied to a phone call or chat session—clearly defined by its start and end. However, in an asynchronous environment, while there is a clear start, the end time is more fluid. Ticket interactions often don't conclude after the first exchange between a client and a customer representative. If the client has follow-up questions days later, the ticket reopens, potentially elongating the handle time. Therefore, AHT can increase over time until it stabilizes after a certain period.
2. Many contact center leaders view AHT or its derivatives (like calls per hour or average handle time by ticket type) as essential KPIs. From my professional experience, I have encountered numerous arguments that focusing on AHT encourages associates to rush through interactions, which could degrade customer experience. While this can be true if AHT is considered in isolation, mature call centers also monitor other KPIs to maintain a balanced view of organizational health. Hence, when leaders propose removing AHT from the center's focus, it is vital to slow down and consider alternatives.
3. In a chat environment, it's important to account for concurrency alongside AHT to accurately forecast workload. Unlike phone interactions, chat associates often handle multiple conversations simultaneously. This ability to multitask effectively reduces the required workload hours, especially since typed responses typically take longer than verbal ones, allowing associates to switch between chats. However, achieving the target concurrency rate, such as handling two chats at once, is challenging unless the center continuously faces high demand and capacity constraints. Furthemore, the handling time of a single chat interaction of similar type will take longer in a chat setting versus a phone setting. 
4. As businesses enhance their self-service capabilities through process automation and the implementation of technologies like large language models, average handle time in aggregate is expected to increase initially. These innovations often eliminate simpler transactions that require minimal agent interaction, leaving more complex inquiries that necessitate longer handle times. This shift in transaction type should be considered when creating future capacity plans.
5. In a multi-channel environment, it's advisable not to blend the handle times across different channels into a single workload forecast. Each channel has unique characteristics that should be considered individually. As channel mixes evolve, blending handle times can obscure the specific impacts each channel has on overall workload.

Understanding and accurately measuring AHT is essential for effective call center management. By considering the specific dynamics of each communication channel and incorporating other critical KPIs, organizations can better plan their capacities and improve overall efficiency.