Average Handle Time (AHT) is the average duration of the entire customer call transaction, from the time the customer initiates the call to ending the call, including all hold times and transfers, as well as after call work.
51% of customers will never do business with a company again after just one poor service experience.¹
AHT is a metric that impacts a number of critical call center KPIs across CSAT, operational efficiency, and agent effectiveness. It’s a strong indicator of everything from the impact of agent training programs to organization processes and resources. And it’s a defining metric in understanding and improving the customer experience.
Simply put, AHT shows how well equipped is the agent to handle customer queries. That’s why consistent measurement, monitoring, and taking action on AHT is an essential KPI for any call center.
The AHT benchmark varies from industry to industry. According to this report from Cornell, the AHT benchmark for telecommunications is just over 8:30 minutes, while the AHT benchmark for financial and IT services is 4:45 minutes. Pending the complexity or high-value nature of calls, AHT will be higher.
In some cases, companies include After Call Work (ACW) into the AHT calculation. ACW is the average duration after each call an agent takes to carry out post-call processing, including data entry and updates, scheduling follow-ups, and any other communication requirements.
To calculate average handle time, add total talk time with total hold time, then add ACW. Lastly, divide that by the total number of calls to get the AHT.
AHT can be assessed per agent, per department, or across the organization.
The easiest way to immediately improve AHT is to uncover and determine the root cause of interactions that increase the length of agent conversations. To do that, it starts with 100% call coverage, to ensure that every interaction is monitored and identified.
Common monitorable interactions, analyzed with speech analytics, that impact AHT include dead air, hold-time violations, and supervisor escalations. In some cases, customer sentiment can also shed light into AHT, with negative interactions lengthening the call, and leading to the interactions listed above.
With a clear understanding of the interactions impacting AHT, supervisors can more rapidly train agents on how to better handle those interactions, including critical agent soft skills.
For example, if an agent is has a high number of supervisor escalations, supervisors can quickly hold micro-training sessions to coach on tactics to reduce it, like empathy statements or de-escalation tactics. It’s all about providing context side-by-side with the agent feedback. Additionally, if the resolution of the query is out of the agent’s control, you can quickly identify why and fix the operational efficiency that’s keeping the agent from resolving the customer’s issue.
In some cases, AHT isn’t just impacted by agent performance, but rather the resources available to them. That’s where a dynamic, easy-to-navigate internal knowledge base (IKB) comes into play. Agents need to be able to quickly search and find the answers to their questions, and additional content to handle more complex queries.
Check out our 7 ways to up-level your contact center IKB blog post for some actionable tips.
Check out our new Driving Contact Center Disruption with Contact Center AI eBook, a collection of best practices and insights from contact center leaders, specifically around building data-driven coaching programs, driving operational efficiencies, and crushing their KPIs.
Joe Hanson leads content marketing at Observe.AI. Want to guest blog? Or maybe you have some expertise you want to share? Connect with him on LinkedIn.