Your customers expect fast responses and resolutions to their inquiries and complaints. In fact, 46% of customers expect a response within four hours, and 12% want a response within 15 minutes or less.
Contact center key performance indicators (KPIs) like average handle time (AHT) impact your customer experience. And your overall customer experience contributes to customer satisfaction, retention, and revenue.
There are several ways to improve your average handle time, including using artificial intelligence (AI). AI can reduce the amount of work your agents must do, lowering the time it takes to handle a customer interaction from start to finish.
In this article, we’ll explain what average handle time is, how you can improve it, and why you need AI to help.
What Is Average Handle Time?
Average handle time is a contact center KPI that measures the duration of an interaction between a customer and a support agent. It includes the entire time taken to manage the call, from the moment the customer initiates the contact to the completion of the interaction.
This KPI typically includes:
🕙 Average talk time: The time the agent spends speaking directly with the customer.
🕙 Hold time: Any time the customer spends on hold while the agent gathers information or consults other departments.
🕙 After-call work (ACW): The time agents spend completing tasks related to the call after it has ended, such as updating customer records, writing summaries, logging the interaction, or processing follow-up actions.
It’s worth noting that average handle time isn’t the same as first call resolution (FCR). Your first call resolution rate is about resolving customer issues the first time they reach out to your contact center.
AHT focuses on the efficiency of handling individual calls, and a shorter average handle time means agents resolve calls quickly. FCR focuses on the effectiveness of resolving customer issues on a single call.
🤔 How can I calculate average handle time?
The formula to calculate average handle time will vary depending on the support channel you’re measuring. Here are three AHT formulas for different customer service channels:
📞 Call AHT formula
The average handle time for phone calls considers call handling and follow-up times. You could also swap out follow-up times for after-call work for a more complete overview of your call AHT.
Here’s how to calculate the average handle time for phone calls:
📧 Email AHT formula
Since email does not involve live interaction, this AHT formula only considers follow-up time. It assesses the time it takes to resolve an email inquiry or complaint from the moment a customer first sends the email.
Below is the formula for calculating AHT for email support:
💬 Live chat AHT formula
The live chat AHT assesses the total handle time across various live chats. Here’s the formula:
🤔 What is a good AHT?
A good average handle time depends on your industry. For example, the AHT for consumer or professional services is three minutes and 36 seconds, and the AHT for financial services is four minutes and five seconds.
We’ve found that a good average handle time is around six minutes per interaction, but a higher AHT isn’t always a bad thing. In some cases, it simply indicates that agents take a more comprehensive approach to resolving customer issues.
Why You Should Pay Attention to Your Average Handle Time
There are several reasons why it’s important to measure and monitor your average handle time continuously:
👍 Meet customer service expectations
Customers value quick and efficient service, and a shorter AHT can reduce wait times as agents become available more quickly to handle new interactions. This can improve overall customer satisfaction, as no one enjoys long wait times or extended interactions.
👍 Improve contact center efficiency
Average handle time helps you understand how long agents spend on each interaction. By reducing AHT without compromising service quality, you can handle more interactions with the same number of agents, increasing productivity.
👍 Provide a better customer experience
Over 70% of consumers say that customer experience is the main factor in their purchasing decisions, yet only 49% say that companies provide a good experience.
By reducing your AHT, agents can handle interactions more efficiently, meaning customers get their issues resolved faster, improving their experience.
👍 Identify where training and support is needed
Average handle time helps you identify where agent training and support are needed. This KPI reveals patterns in agent performance and pinpoints where they may need help with call handling.
If certain agents consistently have longer AHT than others, it may indicate that they’re struggling to handle interactions efficiently. This could be due to a lack of product knowledge, unfamiliarity with your processes, or difficulty using your technologies.
By identifying agents with a higher AHT, you can provide tailored agent training sessions focused on areas like product knowledge, call management, or technical skills.
👍 Better workforce management
Measuring average handle time provides data-driven insights into how much customer call volume your team can manage and helps you optimize staffing.
AHT allows you to forecast contact center staffing needs by giving you a clear understanding of how long agents spend on each call.
With accurate average handle time data, you can estimate the number of agents required to handle expected interaction volumes, ensuring you allocate the right number of agents to avoid over- or understaffing.
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5 Ways to Improve Your Average Handle Time
Now that you know what AHT is and why it’s important to measure this KPI, let’s look at some ways you can improve it:
1. Use AI conversation intelligence
We’ve found that one of the main factors contributing to high AHT is after-call work, such as taking notes after an interaction and adding tags to categories that came up during the conversation. While this is important, it also takes agents away from their main role in serving customers.
Agents typically have to spend a lot of time after interactions summarizing the conversation. AI conversation intelligence platforms allow you to generate an automated summary of the interaction, answering questions like:
👉 Why the customer contacted you.
👉 What the agent did.
👉 Whether the issue was resolved.
In our experience, AI-powered conversation summarization can save up to 12% of time on every customer interaction, significantly reducing after-call work and AHT.
Additionally, by using AI to categorize conversations by Contact Drivers – the reason for the customer reaching out – agents don’t need to learn which tags to apply to topics, there’s more consistency in how labels are applied, and it removes the need to review calls to label them.
2. Provide proactive customer service
More than 70% of customers prefer proactive customer support, and 72% of those who have experienced it report high satisfaction levels.
Proactive customer support means providing your customers with what they need to answer their questions or issues without them reaching out to your contact center. This could mean updating self-service options or even chatbot resolution flows to address known problems.
This decreases the number of incoming requests you receive, allowing agents to better focus on more complex issues and tackle customer interactions more efficiently.
You can anticipate common issues and create customer knowledge bases, guides, and self-service options so that customers can get the support they need without having to contact you. But first, you need a way to find these issues at scale quickly.
For example, if you’re a mobile service provider and you receive a lot of calls from people asking to upgrade their package, you can create a mobile app that allows customers to upgrade themselves without the help of an agent. But in order to find Frequently Asked Questions (FAQs), you need accurate tagging of those conversations so you can see how prevalent a specific issue is.
Additionally, over 75% of contact centers currently leverage self-service chatbots to support their operations and provide proactive customer service. An AI chatbot can answer common customer inquiries quickly so that your agents don’t have to deal with them, reducing the number of basic questions they have to handle.
But understanding which use case and question types can be easily automated is often trial and error. By using conversation intelligence, you can understand the relationship between Contact Drivers, complexity, and customer sentiment, making it easy to see which issues are straightforward enough for a chatbot and which are better handled by a human agent.
3. Empower your agents
As much as it’s important to create knowledge bases for your customers, you need to be doing the same for your agents.
You should develop comprehensive, up-to-date resources for agents to use when answering repetitive questions so that they know exactly what to say to customers without having to search for answers first.
Beyond resources, you should also focus on providing your agents with comprehensive coaching and training to improve their product knowledge and customer service skills.
In a recent webinar with Jon Helin, the VP of Customer Support at Calendly the scheduling platform, he noted that using Loris his team was able to reduce their Average Handle Time (AHT) by two-minutes. While this number may seem small, they handle about 35,000 customer cases per month, making this improvement a significant boost in both efficiency and customer experience.
4. Implement a call-back solution
A call-back solution for contact centers is a system that allows customers to request a call in return instead of waiting on hold during peak times or extended wait periods.
How do call-back solutions work?
When a customer chooses the call-back option, their position in the queue is held, and the system automatically contacts the customer once an agent becomes available.
One major factor of average handle time is the time customers spend on hold. A call back solution reduces or eliminates this waiting period, lowering the overall time spent on each interaction. By avoiding long hold times, your AHT is reduced.
How does a call-back system help?
During peak times, contact centers often face surges in call volume, which can increase AHT as agents become overwhelmed. A call-back system helps manage the flow of calls by offering customers the option to receive a call later when agent availability is higher. This spreads out the workload, preventing AHT from increasing due to bottlenecks in busy periods.
Additionally, when agents return a call, they typically have time to review the customer’s information or complaint beforehand. This allows them to handle the call more efficiently, reducing the time spent on resolving issues during the conversation. As a result, your call duration will decrease, leading to lower AHT.
What are the drawbacks of a call-back system
One potential issue with call-back systems is customer impatience. If too much time elapses, the customer may call again or go to another channel creating a double ticket – and double work – for the support organization.
5. Help your agents avoid lengthy escalations
Using a conversation intelligence platform, you can quickly identify which customer interactions were escalated and why. Platforms like Loris provide these insights in real time, meaning you can identify emerging issues as they arise and resolve them before they escalate.
Once you’ve identified the reasons for these escalations, you can uncover which hurdles prevent agents from resolving customer issues on their own without this happening.
One call center with over 1,500 agents that received thousands of calls per day was able to reduce its AHT by 18%. They did this by uncovering the unnecessary steps that prevented agents from resolving customer complaints on their own without escalations. The company analyzed the top three call types with the longest AHT and removed non-value-added steps from the process, preventing unnecessary escalations.
The Limitations of the Average Handle Time Formula
It’s important to remember that a low average handle time doesn’t always mean a contact center is performing as well as it should. You need to strike a balance between average handle time and customer service quality.
Your AHT should be high enough that agents can still deliver quality service but low enough that your contact center isn’t losing money.
You shouldn’t push your customer service team to end customer calls and live chats as quickly as possible at the expense of not providing excellent customer service. Pressurizing your agents to lower their AHT means they may rush through troubleshooting or cut corners to save time. This often leads to them making mistakes that cost more time and effort as a result, impacting other metrics like FCR and CSAT.
Rather than motivating your agents to provide the fastest customer support possible, focus on delivering the most effective support, and your AHT will naturally improve.
Improve Your Average Handle Time with a Powerful AI Platform
Improving your average handle time is important because it helps you meet customer expectations, improve the customer experience, and achieve better operational efficiency.
However, it can be difficult to lower your average handle time when there is so much after-call work for agents to tackle before the ticket is resolved.
With the help of an AI-powered conversation intelligence platform like Loris, you can empower your agents and help reduce their after-call work.
Our platform automatically generates summaries after calls, saving your agents time. Our contact drivers also automatically categorize conversations, eliminating the need for agents to find the right tags for calls.
Speak to a Loris expert today to find out how our platform can help you reduce your average handle time by reducing after-call work and automating your call center operations.
