Call center KPIs and agent performance metrics are essential tools for assessing productivity and identifying opportunities to improve efficiency with AI and automation. For decades they’ve served as a benchmark for measuring key operational markers, like call handling time, average wait time, abandonment rate, and others. However, there’s a problem: they only tell part of the story.
Want to know the average call time during peak hours? Call center metrics and KPIs have you covered. Want to know what drivers impacted that number? That’s going to take some digging. Maybe it’s shear call volume—or maybe it’s something else. In any case, it’s up to humans to make sense of the data. Or at least it was until now.
With generative AI, humans (i.e. call center workers) can dig deeper into call center metrics and KPIs than ever before, identifying hidden call drivers and confirming suspected correlations with explainable data.
The best part? AI does the digging for them. With advanced solutions, like Uniphore’s Conversation Insights Agent, call center workers can ask the data anything—in plain language—and get the insights they need. No more manual searching, comparing, and cross-indexing call center metrics and KPIs. Just ask, analyze, and act.
Let’s explore how call center metrics and KPIs measure different areas of operational efficiency—and how AI is transforming that data into valuable, actionable intelligence.
How is call center efficiency measured?
Call center efficiency is evaluated using a variety of Key Performance Indicators (KPIs) that measure both overall contact center performance and individual agent effectiveness. These metrics provide actionable insights into service quality, customer satisfaction, and operational efficiency.
Call center KPI data is derived from various sources, including:
- Customer Satisfaction Surveys – Measure perceived service quality and customer sentiment.
- Call Diagnostics – Provide objective metrics such as AHT, average wait time, and first response time.
- Conversation Intelligence – AI-powered solution that analyzes and extracts insights from customer interactions across voice, chat, and digital channels.
Key call center metrics and KPIs
Call center KPIs fall into three main categories, each playing a crucial role in customer experience, agent efficiency, and business impact:
Customer Service
KPIs
Measure how well the contact center meets customer expectations and delivers a positive experience.
Agent Productivity
KPIs
Assess the efficiency and effectiveness of individual agents in handling interactions.
Operational & Financial KPIs
Evaluate overall call center performance, cost efficiency, and long-term business impact.
Customer service KPIs
Customer service KPIs are performance metrics that measure how effectively a contact center delivers support and meets customer expectations. These KPIs help organizations assess service quality, identify pain points, and improve customer experiences.
Customer Satisfaction Score (CSAT)
CSAT measures how satisfied customers are with a company’s service, typically gathered through surveys and direct feedback. It reflects whether a contact center is meeting, exceeding, or falling short of customer expectations. Depending on the format, CSAT can be a highly subjective call center metric, particularly if it involves customer-written feedback. Conversation intelligence can quickly make sense of that feedback, taking the cognitive load off of agents (more on that later).
Call Abandonment Rate
Call abandonment rate is the percentage of inbound calls where customers hang up before speaking with an agent. This customer service metric is often influenced by factors like long wait times or customer frustration. High call abandonment can have a snowball effect on other call center KPIs, like CSAT, so it’s important to accurately identify and correct its root cause.
Average Wait Time (AWT)
Also known as average speed of answer (ASA), AWT tracks how long customers spend in the queue before connecting with an agent. The industry benchmark is 80% of calls answered within 20 seconds. Reducing wait time can lower call abandonment rates and improve customer satisfaction.
Peak Hour Traffic (PHT)
PHT monitors call volume spikes during peak periods. Proper staffing strategies and AI-driven automation (such as self-service options) help manage high call traffic more effectively. Because of the influx, PHT also presents an opportunity to gather valuable conversation intelligence. Insights gleaned from peak hours can help call centers identify friction points and improve customer journeys.
Agent Productivity KPIs
Agent performance KPIs provide insights into individual efficiency, effectiveness, and impact within a contact center. By measuring agent performance, organizations can gauge productivity, pinpoint coaching needs, and streamline operations. Tracking these metrics enables contact centers to enhance customer experiences, improve agent support, and drive cost-effective performance improvements.
First Contact Resolution (FCR)
First Contact Resolution measures the agent’s ability to resolve a customer issue during the first interaction. High FCR rates correlate with higher CSAT and lower customer churn. Low FCR indicates a problem in the process. Conversation intelligence can help diagnose the root cause and get this critical call center metric back on track.
First Response Time (FRT)
FRT tracks how quickly an agent responds once a customer enters the queue. Delays often indicate process inefficiencies, such as excessive after-call work or outdated knowledge management systems. Conversation intelligence can help identify the culprit here as well.
Average Handle Time (AHT)
Average Handle Time is the average time it takes to handle a transaction from start to finish. AHT measures the total time spent on a call, including talk time, hold time, and after-call work. While lower AHT is ideal, it must be balanced with ensuring a high-quality, empathetic customer experience.
Average Call Transfer Rate
This call center metric measures how often calls are transferred between agents or departments. High transfer rates may indicate gaps in agent training or a lack of real-time guidance tools. Uniphore’s Real-Time Guidance Agent can help call centers accelerate agent proficiency with AI-driven in-call guidance, minimizing skill-related transfers.
After-Call work (ACW)
After-Call Work refers to the tasks agents must complete immediately after a customer interaction before handling the next call. These tasks often include:
- Updating customer records – Adding new information to the CRM or internal systems
- Call summarization – Logging key details from the conversation
- Disposition tagging – Categorizing the call for reporting and follow-up
- Scheduling follow-ups – Setting reminders or next steps for unresolved issues
Operational & financial KPIs
Beyond service and productivity metrics, operational KPIs gauge cost efficiency and business sustainability. Business leaders rely on these call center metrics when making critical budgeting, staffing, and investment decisions.
Cost per contact
This call center KPI calculates the total operational cost of handling a single call, which includes labor and infrastructure expenses. Industry benchmarks range from $2.70 to $5.60 per call.
Service Level Agreement (SLA) compliance
SLA compliance tracks whether the contact center meets contractual service expectations, such as response time guarantees for industries like healthcare and finance.
Agent attrition rate
Agent attrition rate is the percentage of agents leaving the organization within a given period. Contact centers typically experience high turnover rates (30-40%), leading to increased hiring and training costs. Organizations that use conversation intelligence can often flag attrition indicators and address them with tailored coaching and reengagement initiatives.
Agent utilization rate
This call center metric gauges the percentage of an agent’s working hours spent on productive tasks, such as answering calls and resolving issues. Real-time agent guidance can help organizations struggling with efficient agent utilization.
Call Volume Trends
Analyzes fluctuations in inbound call volume, helping organizations optimize staffing levels and self-service automation.
Real-world impact: AI-driven KPI optimization in action
One leading U.S. health insurer, handling 1 million+ calls annually, faced high AHT, excessive after-call work, and inefficiencies in agent workflows. By deploying Uniphore’s Real-Time Guidance Agent, they achieved:
80%
reduction in after-call work through automated call summarization.
20%
decrease in AHT, allowing agents to assist more customers.
$6M
in annual cost savings, improving operational efficiency
FCR
Higher First Contact Resolutionand reduced escalations.
This case highlights how AI-powered real-time agent guidance not only improves KPIs but also delivers tangible cost savings and customer experience enhancements.
What’s next: going beyond call center metrics and KPIs with AI
Tracking call center KPIs isn’t just about measuring performance—it’s about unlocking smarter, more efficient customer service. The right metrics reveal inefficiencies, highlight coaching opportunities, and guide strategic improvements that directly impact customer satisfaction and operational costs.
But tracking alone isn’t enough. That’s where conversation intelligence comes in. AI-driven solutions like Uniphore’s Conversation Insights Agent can help call center leaders uncover valuable insights hidden in all that tracked data. And they can do it without a lot of heavy lifting—thanks to generative AI.
Now, leaders can identify, analyze, and understand the reasons behind their call center metrics and KPIs—and take the actions needed to impact real change. The result? Faster service, higher FCR, lower AHT, and a more empowered workforce.
Want to know what’s hiding in your call center metrics and KPIs?
Book a demo of our Conversation Insights Agent today and see how you can turn your data in action!