Madeline Jacobson at Creovai makes the case for de-emphasizing AHT – exploring how to balance efficiency and quality by focusing on what your agents control, and sharing how to get to the root cause of unnecessary handle time drivers.
Average handle time (AHT) has been a core metric in contact centres for decades. It’s simple to calculate, easy to track, and provides what appears to be a clear indicator of agent efficiency (at least on the surface).
But contact centre interactions look different than they did a few decades-or even a few years-ago. Creovai’s research with ContactBabel found that average call times in contact centres are getting longer as more customers take straightforward issues to self-service channels, leaving the most complex issues for the voice channel.
Many of these complex interactions are necessarily long, and pressuring agents to get through them faster to meet AHT goals can lead to rushed calls, missed steps, and a less-than-stellar customer experience.
As a result, a growing number of senior operational leaders are questioning whether AHT does more harm than good as an agent performance metric.
Before we explore the drawbacks of AHT as a performance metric, it’s important to understand why contact centres use this measurement.
The main reason for tracking AHT boils down to cost savings. Every minute an agent spends on a call represents a direct cost to your organization. When you multiply those minutes across hundreds or thousands of daily interactions, the impact adds up.
Reducing AHT by even a few seconds per call can yield substantial cost savings over time, allowing your contact centre to handle more volume with the same staffing levels or maintain service levels with fewer agents.
One of our customers, NRTC, shared that every 15 seconds of AHT reduction saves them the cost of three to four full-time equivalents.
AHT also plays a crucial role in capacity planning and forecasting. When you know your average handle times for different types of interactions, you can better predict staffing needs, set realistic service-level targets, and make informed decisions about resource allocation.
AHT’s appeal also lies in its simplicity. Unlike complex quality scores or customer satisfaction metrics that require subjective evaluation, AHT is objective and straightforward to calculate.
This makes it valuable for trend analysis, benchmarking against industry standards, and setting clear, measurable goals for your team.
The metric provides a consistent framework for comparing performance across different agents, teams, and time periods. It’s also widely understood throughout the industry, making it useful for benchmarking.
Most customers don’t want to spend any more time than necessary on a customer service call. In this sense, optimizing for lower AHT can mean less customer effort and a better experience.
However, there’s a caveat: AHT goals only serve customers when they reflect genuine efficiency. If an agent rushes through an interaction and doesn’t fully resolve a customer’s issue, those AHT goals become counterproductive.
You’ve probably sensed from the last caveat that we’re about to get into some of the cons of AHT. Despite its operational benefits, using AHT as a primary agent performance metric can create significant problems that undermine your contact centre’s effectiveness and customer experience.
When agents feel pressure to keep calls short, they may rush through interactions without fully understanding or resolving the customer’s issue.
They may miss important steps, fail to capture all the customer information they need, or address one part of the problem without addressing the root cause.
The result is a false economy: while individual call times may decrease, repeat contacts increase, potentially creating more work and higher costs over time. Even worse, customers become frustrated with the poor service quality, leading to decreased satisfaction and loyalty.
AHT-focused performance metrics can create significant stress for agents who find themselves watching the clock instead of focusing on the customer’s needs.
This constant time pressure can contribute to burnout and turnover, problems that cost contact centres far more than the savings from slightly shorter call times.
When agents are primarily evaluated on how quickly they can end calls, it fundamentally shifts their mindset from “How can I help this customer?” to “How can I get off this call?” This mentality rarely leads to better customer experiences or job satisfaction.
Not all customer issues are created equal. A simple password reset should naturally take less time than troubleshooting a complex technical problem or helping a customer understand a complicated billing issue.
However, AHT as a blanket performance metric fails to account for these natural variations in interaction complexity.
When agents are penalized for longer calls regardless of context, you’re essentially penalizing them for handling the customers who need the most help, the exact opposite of what quality customer service should achieve.
The biggest problem with using AHT as an agent performance metric is that it’s something your agents don’t have full control over.
Call duration depends on the customer’s issue complexity, their communication style, technical difficulties, and numerous other variables outside the agent’s influence.
Instead of positioning AHT as a performance metric, successful contact centres are shifting toward measuring the behaviours and actions that agents directly control, what they say, how well they adhere to best practices, and actions they take that contribute to desired call outcomes.
Conversation intelligence software allows you to measure these controllable behaviours at scale. Instead of evaluating agents solely on how quickly they end calls, you can assess their ability to:
While you shouldn’t use AHT as an agent performance metric, that doesn’t mean you should ignore handle times entirely.
The key is using conversation intelligence and root cause analysis to understand why some interactions take longer than others, and then addressing the controllable factors.
Advanced conversation intelligence platforms can predict how much time specific topics or events will add to interactions. This analysis often reveals that long handle times can be a good thing, in certain contexts.
For example, a healthcare contact centre might find that agents spend an average of 20 minutes on intake questionnaires with new patients, a necessary process that can’t and shouldn’t be rushed. These longer interactions aren’t a problem to solve but rather a natural part of providing thorough service.
But root cause analysis might also uncover unnecessary drivers of extended handle times, such as:
Once you identify the controllable factors extending handle times, you can address them through targeted coaching, process improvements, or technology enhancements. This approach allows you to improve efficiency while maintaining or even enhancing service quality.
Average handle time will likely always have a place in the contact centre for planning and cost management purposes, but if you’re using AHT as an agent performance metric, it’s time to rethink your approach.
When you hold your agents accountable for staying within “acceptable” handle time limits, you’re asking them to prioritize speed over quality and putting pressure on them to control things they can’t control.
This is a recipe for bad agent and customer experiences, which increases the risk of high agent turnover and customer churn.
By turning your focus to the factors your contact centre can control and providing agents with the tools to succeed, you can achieve efficiency gains while building a more engaged workforce and delivering better customer experiences.
Frame the conversation around total cost of ownership rather than per-call costs. Present data showing how AHT pressure leads to increased repeat contacts, higher customer churn, and greater agent turnover, all of which cost significantly more than slightly longer initial interactions.
Propose a pilot program where you track both traditional AHT metrics and behaviour-based quality metrics to demonstrate that focusing on quality actually improves efficiency over time while reducing overall operational costs.
Continue using AHT for planning and forecasting purposes, but separate it from individual agent evaluation. Monitor AHT trends at the team and centre level to identify process improvements and staffing needs.
Use root cause analysis to understand what drives longer interactions, distinguishing between necessary complexity (like thorough new customer onboarding) and addressable inefficiencies (like system issues or training gaps).
This approach maintains cost control while removing counterproductive pressure on individual agents.
Use conversation intelligence or manual call analysis to categorize interaction drivers and their typical time impact.
Look for patterns like frequent holds while agents search for information, repeated explanations due to unclear initial communication, or system-related delays.
Survey your agents about their biggest timewasters and pain points. Create a priority matrix of handle time drivers based on frequency and controllability, focus first on high-impact issues that can be addressed through training, process changes, or system improvements.
Monitor first-call resolution rates, customer satisfaction scores, and repeat contact rates as leading indicators that quality improvements are working.
Track agent adherence to key behaviours and processes that typically correlate with efficient resolution. Use handle time data contextually, comparing similar issue types and complexity levels rather than applying blanket targets.
Most importantly, measure agent confidence and job satisfaction, as engaged agents who aren’t under time pressure often naturally become more efficient while delivering better service.
This post has been re-published by kind permission of Capacity - view the original article.
Reviewed by: Megan Jones