What is average handle time?
Average handle time (AHT) is the average amount of time support agents spend on a single customer interaction from start to finish. It’s one of the most commonly tracked metrics in contact center operations because it directly drives staffing costs: lower AHT means each agent can handle more contacts per shift.
AHT has three components:
- Talk time: The time the agent and customer are actively speaking.
- Hold time: Time the customer spends on hold while the agent searches for information, consults a colleague or processes a request.
- After-call work (ACW): The time the agent spends completing tasks after the call ends, like updating the CRM, logging notes or filing a ticket.
Reducing any one of these components reduces the overall metric. The most effective AHT improvements tend to target hold time and ACW, not talk time, since cutting talk time often means cutting resolution quality.
How is AHT calculated?
AHT = (Total talk time + Total hold time + Total after-call work) ÷ Total number of interactions
Example: Over one shift, an agent handles 40 calls. Total talk time: 120 minutes. Total hold time: 20 minutes. Total ACW: 20 minutes. AHT = (120 + 20 + 20) ÷ 40 = 4 minutes per interaction.
AHT is typically reported in seconds or minutes and tracked at the agent, team and contact center level. It’s also tracked by interaction type: AHT for billing inquiries looks very different from AHT for technical troubleshooting.
What’s a good AHT benchmark?
AHT benchmarks vary significantly by industry and interaction type. General industry ranges:
- Simple transactional contacts (billing, order status): 2–4 minutes
- Standard support contacts: 4–6 minutes
- Complex technical or account issues: 8–12 minutes
- Cross-industry average: approximately 6 minutes
Healthcare and financial services contacts tend to run longer due to compliance requirements and issue complexity. Retail and e-commerce contacts tend to run shorter.
Comparing your AHT against an industry average is good context, but comparing it against your own historical data is more useful for operational management.
Why AHT isn’t the only metric that matters
AHT is easy to measure, easy to understand and easy to set targets around. But it’s important to be aware that AHT tells only part of the story, and prioritizing AHT over other metrics can lead to worse outcomes.
If agents are under pressure to reduce AHT, they typically have three ways to do it:
- Resolve issues faster (the goal)
- Rush through calls without fully diagnosing the issue (creates repeat contacts)
- Wrap calls prematurely without completing ACW accurately (creates data quality problems)
Options 2 and 3 are both worse than a slightly higher AHT. A contact center that reduces AHT by 30 seconds but increases repeat contacts by 5% is spending more, not less.
The right way to consider AHT is alongside first contact resolution (FCR) and customer satisfaction (CSAT).
What causes high AHT?
The most common contributors to high AHT:
- Agents searching for answers. If agents have to switch between five systems, search a knowledge base and wait for a subject matter expert to respond, hold time and talk time both suffer.
- Complex or poorly structured knowledge. Even when the information exists, if agents have to search through long documents or inconsistent articles to find the relevant section, every call takes longer.
- Inefficient ACW processes. After-call work that requires manual data entry across multiple systems drives up AHT. Agents who can’t find the right fields or who have to summarize calls from scratch spend significantly more time post-call than those with streamlined wrap-up tools.
- Insufficient agent training. New agents or agents without deep product knowledge might require more hold time per call. AHT tends to decrease as agent training quality and tenure increase.
- Complex interaction types. Some issue types just take longer. AHT analysis should segment by contact type to distinguish between systemic inefficiency and inherent complexity.
How do contact centers reduce AHT without hurting quality?
Some interventions that reduce AHT without degrading FCR or CSAT include:
- Real-time agent assist. Tools that surface the right answer during the call — based on what the customer just said — eliminate hold time. Instead of putting the customer on hold to search, the agent has the answer in front of them before they need it.
- Automated ACW. AI-generated call summaries that auto-populate CRM fields reduce after-call work from several minutes to seconds. Agents who don’t have to manually summarize every call spend more time on the next customer.
- Knowledge orchestration. A knowledge orchestration layer that surfaces answers to agents in real time increases accuracy, consistency and experience quality. Agents who can’t find the right answer on the first search don’t put customers on hold.
- Improved routing. Getting the customer to the right agent the first time eliminates warm transfer time — a significant AHT contributor in contact centers with specialized queues.
How Capacity reduces AHT
Capacity’s real-time agent assist tools surface relevant knowledge during live calls, reducing handle time by giving agents the answer before they have to search. Capacity’s broader CX automation platform automates customer interactions and post-call work to improve experiences and lower costs. DSW used Capacity to reduce average handle time by 19%, contributing to $1.5M in annual savings.
See how Capacity reduces AHT →
See also
Frequently asked questions about AHT
No. Lower AHT is a positive signal only when FCR and CSAT remain stable or improve. AHT reductions that come at the expense of resolution quality are counterproductive, because they create repeat contacts and increase total cost, even as individual handle times drop.
Not necessarily. New agents typically have higher AHT than experienced ones. Agents handling complex issue types have higher AHT than those handling transactional requests. AHT targets should account for tenure, specialization and interaction type.
Self-service deflects the simplest, fastest-to-resolve contacts from live agents. When self-service works well, the interactions that remain with live agents tend to be more complex, which can make aggregate AHT appear to increase even as the operation becomes more efficient. This is normal and expected.
