Improve Average Handle Time in the Call Center: A 6-Point Action Plan
Wondering how to improve average handle time (AHT) in your call center? If you always seem to be asking yourself, “How to improve my AHT?,” here is a six-point action plan for improvement:
Average handle time (AHT), sometimes also called average handling time, is a key contact center metric that indicates how long agents take to resolve cases.
Many contact centers struggle to reduce AHT while maintaining or improving customer satisfaction.
When AHT is low, it signals an increase in productivity among agents. When the agent is encouraged to solve issues as efficiently as possible, they can work with more customers. The more customers they work with, the more problems they can solve. This may not sound like a bad idea when considering efficiency, but consider the most important person in this equation: the customer.
Even though it is an important metric, AHT can fall flat in oversimplifying customer interactions by failing to account for whether the issue was actually solved. While you want to measure agent performance with AHT, focusing on it alone could cause overall customer service to suffer. When looking at overall call center KPIs, AHT is important but should not exist in a vacuum.
How can you improve your agents’ handle time and also provide a truly great customer service experience? Provide them with the tools, training, and resources they need to succeed.
Here are six steps to improving AHT while still providing high-quality customer service.
Your contact center agents are the voice of your brand, so if they’re frustrated or can’t find answers, it contributes to a negative customer experience. But the right training and consumer engagement CRM software can address the issue.
When shopping for software, make sure you choose an agent desktop CRM designed to shorten training time. In-context guidance is a key feature, keeping even the newest agents moving swiftly through cases without having to follow a script. Another feature to look for in a CRM is an integrated knowledge management system that provides agents with fingertip access to product and company information, as opposed to making them memorize it. The ability to quickly find the right answer goes a long way toward shortening average handle time in the call center.
The more applications the agent has to use, the longer the customer has to wait for the answers they need. By presenting the information on a single, integrated interface, agents can see everything that matters about that consumer in one place.
Also take into consideration how much stress multiple applications can cause agents. The International Customer Management Institute (ICMI) found in a survey that 71% of contact center leaders recognized system and tool inefficiencies and difficulties as the top factor in making agents feel stressed.
Streamlining their process of data entry with the right technology improves efficiency based on the issue the customer is having. With dynamic field configuration, you can configure your consumer engagement CRM to gather only the information pertinent to the interaction or issue. This not only improves the quality of data but reduces average handling time.
With customer service automation growing more sophisticated, having human agents handle every aspect of every customer issue is quickly becoming a thing of the past. Of course, there will always be certain customer interactions that need the level of empathy and complex problem-solving that live agents excel at providing. But so many routine customer service-related tasks can and should be automated in order to reduce AHT and improve a host of other call center metrics.
For example, email process automation can read incoming customer emails, code the case, draft a meaningful reply based on machine learning from what human agents have done in similar situations, and present the response to an agent for QA. Instead of an agent performing all these steps, taking an average of five minutes per email, automation is doing the bulk of the work, requiring only one minute of the agent’s time to review and send.
As another example, let’s think about self-service chatbot interactions. Bots are an excellent way to divert traffic from agents, since they can be programmed to resolve the most routine, high-volume customer requests. But even for cases they don’t know how to handle, bots can triage and escalate customers to live agents in a way that saves valuable handling time. When a bot hands off a customer to an agent, it can automatically create and populate a new case based on what it already knows about that customer – their contact information, the nature of their issue, etc. – shaving minutes off data entry time but also quickly getting the agent up to speed on the situation. Agents can handle cases more efficiently, and customers can avoid the frustration of repeating themselves.
When customers are talking with a live agent, they are looking for immediate help from someone knowledgeable about the product or service. By using agent desktop software that guides agents through interactions by recommending the next best action, you are not only ditching canned, artificial-sounding scripts but you are simplifying follow-up.
This strategy improves speed, accuracy, and consistency as the tool reviews existing customer data to anticipate their next move and provide the agent with the appropriate response. With this tool, agents are able to be more efficient in their interactions and ultimately reduce AHT. In the video below, an agent is presented with key information about the customer as well as recommended next steps for a follow-up email and coupon enclosure.
Monitoring how your agents are performing will identify areas of improvement and will give them the feedback they need to improve their interactions. This increases their comfort, competence, and job satisfaction.
Recording your agents’ calls is one measure you can take to monitor performance. This way, you will be able to fully understand their style of communicating with customers as well as how long it takes them to handle each call. For example, while listening to an agent’s recent phone call with a customer, you notice they have many moments of silence during the conversation. You can address this with the agent to determine if they were lost for words, updating the CRM, or experiencing a moment of technical difficulty. Then you can discuss how to best resolve those moments in future conversations. Some CRM systems also enable keystroke tracking to understand how agents move through case coding tasks and pinpoint areas where they may be getting stuck. Identifying these friction points can help you address instances that are causing longer average handle times in your call center.
Also, consider implementing short post-interaction surveys, like the one shown above, to capture customer feedback right as their conversation ends. Sharing their customer ratings with each agent helps them understand how helpful, accurate, and empathetic customers found them during their live interactions, and can be an excellent source of near-real-time feedback for improvement.
The agent desktop software you choose should make sure they have easy access to all information when they need it. This should be done in an unobtrusive way that keeps them focused on the customer. Visually, customer context should be contained within one section of the interface instead of pop-ups that need to be constantly resolved and closed. This helps the agent have smoother, more efficient conversations without technology getting in their way. Remember, call center technology exists to support the work your agents are doing, not force them to change their processes to suit the limitations of the system.
Astute’s end-to-end customer experience platform powers customer interactions for thousands of brands. Want to see how Astute’s agent desktop CRM can help improve efficiency and shorten average handle time in the call center while enhancing customer satisfaction? Schedule a personalized demo today.
This post was originally published in February 2017 and updated in January 2021.