Average handle time, or AHT, is one of the most important contact center metrics. It characterizes the duration of call processing by agents and has a strong impact on the whole operation of the contact center. For instance, it directly influences waiting time, abandonment rate, occupancy of agents, workforce demand, and ultimately both customer satisfaction and cost of operation.
At the same time, AHT is a rough characteristic of the handle time that provides only limited understanding about its nature and ways to use it. In reality, handle time is a random value that varies from case to case and deserves a more accurate consideration. Looking at handle time, we need to be able to predict agents’ status. For example, we should be able to estimate an engaged agents’ chances of becoming available again after a specific period of time.
It would be nice if the handle time were a constant value because it would simplify everything. Imagine that all handle time values are the same and are equal to, say, 180 seconds. This means that when an agent starts talking to a customer they will definitely complete the call in 180 seconds. We could predict the behavior of the agent very precisely.
Unfortunately, this is not the case in practice. The handle time is a random value and needs to be treated as such. In this article we make an attempt to consider the handle time as a speculative value and apply a statistical approach to its treatment.
Is average handle time a good indicator of how long it would actually take to handle an interaction?
In this article, we are discussing handle time metric, its applicable statistics, and learn how to predict it. We use a real-world dataset recorded from an outbound campaign that was run in the Bright Pattern cloud contact center. We show how to treat experimental data and apply statistical methods.