14 Call Center Productivity Metrics + How to Fix Them

Are your call center productivity metrics showing you what’s really slowing down your operation? Hitting efficiency targets is a constant pressure, but it’s easy to overlook a powerful tool that’s hiding in plain sight—your QA program. 

If you’re tracking key performance indicators (KPIs) like handle time, occupancy, or FCR, you’re already on the right path (and we don’t need to explain them to you). But without QA data, those numbers can’t tell you why performance is slipping or help you identify specific areas for agent improvement.

The solution: Align QA with productivity goals and make it a real driver for change—not just a report card.

In this article, we’ll break down 14 key call center productivity metrics and show how QA can help you improve each one. From cost per call to chat concurrency, you’ll learn where to focus and what to fix.

What are call center productivity metrics?

Call center productivity metrics give you a quantitative way to measure the efficiency of contact center operations. These numbers help you understand:

• Performance levels

• Workload balance

• How time is spent across teams

And key call center metrics like average handle time (AHT), occupancy rate, and agent utilization rate show how well your operations are running.

These insights are necessary to set achievable goals, measure agent performance, manage costs, and ensure you’re delivering a great customer experience. Without contact center productivity KPIs, it’s difficult to know whether agents are working effectively or simply staying busy. 

However, it’s also important to separate call center productivity metrics from quality metrics.

• Productivity metrics: Measure output and efficiency; how much is done and how fast it happens.
• Quality metrics: Evaluate how well customer interactions meet standards for service and compliance.

Call center QA teams often focus on quality metrics alone—but quality assurance software can also provide powerful data for improving overall productivity. And with a bit of strategy, QA tools offer visibility into:

• Process bottlenecks

• Training opportunities

• Coaching outcomes

• Systemic performance issues

By combining QA and call center productivity metrics, you can unlock a wealth of valuable information and drive meaningful improvements.

How QA impacts call center productivity

Quality assurance gives managers a clear view into what’s happening in calls, chats, and other customer service interactions. By reviewing this data, you can pinpoint behaviors, scripts, and processes that are hurting (or helping) individual agent performance. QA reveals where time is lost, what’s driving customer frustration, and where contact center agents need more support. 

This visibility makes it easier to streamline workflows and spot coaching opportunities that can lead to faster resolutions and less repeat contacts. It can guide process updates, tech improvements, and smarter, more effective training programs—all of which boost productivity.

When QA is tied to productivity goals, it moves from being reactive to proactive. Instead of just scoring calls, teams can use insights to drive performance.

Before we dive into our list, here’s a quick rundown of the top 14 call center productivity metrics, along with formulas for calculation and how QA can directly impact each one.

Snapshot: 14 key contact center productivity metrics + the QA impact

14 crucial call center productivity metrics + how QA improves each one

1. Average handle time (AHT)

AHT is one of the most essential contact center metrics. It measures the full length of a customer interaction, from the moment it begins to the time post-call tasks are completed. This includes talk time, hold time, and after-call work, making it a key indicator of how efficiently customer service agents are managing their time.

Average Handle Time (AHT) Formula: (Talk Time + Hold Time + After-Call Work Time) / Total number of calls

Managing AHT and keeping it low helps reduce wait times and ensure adequate staffing levels. However, if it’s too low, it could indicate that agents are rushing through customer calls and damaging service quality. This is where QA helps you find the balance.

QA reviews provide visibility into call flow, agent behavior, and points to where time is lost so you can track what trends are driving longer calls over time. It can identify:

• Friction points causing delays (e.g. agents taking too long to explain a policy)

• Unnecessary holds

• Confusing scripts

2. First contact resolution (FCR)

FCR measures how often an issue is resolved during the first interaction—with no need for any follow-ups.

It’s a direct reflection of how effective agents are at solving problems quickly and completely. High FCR rates typically mean better customer satisfaction scores, lower operating costs, and an ability to handle larger call volumes.

First Contact Resolution (FCR) Formula: (Number of customer queries resolved on first contact / Total number of contacts) * 100

It helps highlight how well your systems, training, and processes support resolution during the first touchpoint. A low FCR rate may signal that agents lack the tools or information they need, or that workflows are forcing unnecessary escalations and follow-ups.

QA helps identify where FCR breaks down. Analysts can flag instances where agents miss discovery questions, whether the customer issue was actually resolved (but not in a satisfying manner), or failures to use support resources effectively.

QA data can also expose some of the common reasons why inbound calls aren’t resolved in the first interaction, such as:

• Gaps in agent training

• Missing knowledge base resources

• Flawed call handling processes

3. Contact per hour (CPH)

Contacts per hour (CPH) tracks how many customer interactions an agent completes in one hour of active work. This includes all communication channels—calls, chats, emails, and social media—depending on how your team is structured. It’s a vital metric for understanding agent output, scheduling needs, and overall contact center efficiency.

Contact Per Hour (CPH) Formula: Total contacts handled / Total hours worked

CPH helps contact center managers assess how effectively time is being used on the floor. If this number is consistently low, it could indicate unnecessary delays, insufficient systems, or inconsistent agent workflows. Having a high CPH means your customer support agents are staying productive, as long as quality isn’t being sacrificed.

Again, this is exactly why QA makes such a big difference. Through recorded interactions and QA tools, you can find repeat contacts (and what causes them), coach agents to identify and correct verbiage and repetition, or use scorecards to reward agents for balancing speed and effective resolution.

4. After-call work (ACW) time

ACW time measures how long agents spend on wrap-up tasks after the interaction ends. This includes entering notes, updating records, assigning follow-ups, and call logging. While some ACW is always necessary, too much of it reduces agent availability for new customers.

After-Call Work (ACW) Formula: Total time spent on after-call work / Total number of calls

If your ACW is too high, then agents are spending more time on after-call work and less time with customers.

Manual and confusing processes overwhelm agents, resulting in longer customer wait times and added stress for agents, so figuring out how to balance it without cutting corners means you need a clear understanding of what is truly necessary and where time is being wasted.

QA gives you valuable insights into how agents are using their time. With QA software, reviewers can observe how long agents spend on different post-call activities. Then, coaching systems or automation technology can help streamline some of these tasks (or eliminate them entirely).

5. Cost per call

Cost per call is a key call center productivity metric for evaluating operational efficiency. It measures the average expense associated with handling a single customer interaction (including fixed and variable costs).

Cost per Call (CPC) Formula: Total operational costs / Total number of calls handled.

This metric matters because it directly connects contact center performance to financial outcomes. As volume rises or staffing shifts, your average cost per call will respond accordingly. If the number climbs without a corresponding improvement in quality or resolution rates, you’re spending more to do less.

QA can identify what’s driving cost fluctuations in your call center. Through call and agent workflow reviews, QA pinpoints inefficiencies (long handling times, excessive transfers, repeating steps, etc.) which you can tackle via workflow automation with contact center AI or agent coaching.

6. Response time

Response time measures how quickly your agents engage with customers after they’re connected. Customers want answers, and they don’t want to wait forever—77% already think that queue times are too long. The faster your response time, the more interactions your agents can handle and, generally speaking, the more satisfied customers you’ll have.

Response Time Formula: Total wait time for all interactions / Total number of interactions

This call center productivity metric is essential for workforce planning and queue management. Slow response times often mean your staffing levels aren’t meeting customer needs. But, not all delays are from volume—some can come from inefficient processes or performance gaps.

Your QA software can review call and chat recordings to see where agents pause, struggle to find information, or bounce between systems. It can also show systemic issues in your call center (tools, lack of ownership, unclear policies, etc.) are slowing them down. Then, you can use QA to help coach agents on how to:

• Use scripts and templates more effectively
• Multitask while maintaining quality service
• Create workflow shortcuts and automations

7. Agent utilization rate

Agent utilization rate shows how much of an agent’s logged-in time is spent on productive work. It factors in all direct interaction time and ACW, compared to their total work time.

This call center productivity metric matters because it helps balance agent efficiency and well-being. Underutilized agents drive up costs and waste labor, while a high rate leads to better ROI. However, overworked agents with a poor work-life balance are more likely to experience burnout, so you need to find a sweet spot between the two.

Agent Utilization Rate Formula: (Total handle time + ACW) / Total logged-in time * 100

It’s a powerful metric for identifying gaps in scheduling, forecasting, and real-time management. However, it doesn’t explain the why behind an agent’s productivity levels during a shift. Your QA fills that gap by providing context, both behavioral and process-based.

QA reveals what agents are doing when they’re not actively engaged. It spots non-productive behaviors like excessive ACW or idle time and detects when they’re disengaged by analyzing monotone voices or low-energy tones. Plus, it can spot where agent skills might fall short, allowing you to reassign or help upskill them.

8. Chat concurrency efficiency

Chat concurrency efficiency measures how well agents manage multiple chat interactions at the same time. In many omnichannel call centers, agents are expected to handle two, three, or even more chats concurrently. This call center productivity metric tells you whether or not they’re managing that workload effectively while ensuring a smoother customer experience.

Chat Concurrency Efficiency Formula: (Total chats handled / Total agent chat hours) * Average number of concurrent chats

This metric helps managers assess digital channel capacity and spot training or support gaps in real-time operations. Like many of the other call center productivity metrics on this list, it’s important to find a balance.

• Too low concurrency: You may be underutilizing your agents.

• Too high concurrency: You risk drops in customer satisfaction because of delayed replies or miscommunication.

Through QA, you can review transcripts and session timelines to assess how this type of multitasking is affecting service quality.

Evaluations can flag dropped threads, missed cues, or inconsistent tones from juggling too much at once. Then, through coaching, you can help agents use more proactive messaging when managing multiple chats, or even provide templated phrases to reduce cognitive load.

9. Transfer rate

Transfer rate tracks how often agents pass calls to other team members, departments, or supervisors. This matters because excessive transfers eat up time, signal deeper issues with training or system access, and frustrate customers—66% say good service depends on matching with the right agent.

Transfer Rate Formula: (Number of transferred calls / Total number of calls) * 100

Your transfer rate can help surface weaknesses in knowledge management, onboarding, and call routing. But without context, this metric doesn’t explain what’s going wrong. QA teams can review transferred calls to determine why they happened. For example:

• Policy reasons

• Skill gaps

• Escalations

• Missed discovery questions

QA helps flag avoidable transfers that stem from agent uncertainty and unclear processes. Through evaluations, you can find agents who are hesitant or overly reliant on scripts, and then work with them to develop confidence or the skills to handle new situations.

10. Occupancy rate

Occupancy rate measures how much of an agent’s available time is spent on active customer handling—whether talking to customers or finishing ACW. It’s an important call center productivity metric because it reflects how efficiently your team is being utilized without overloading individual agents.

Occupancy Rate Formula: (Talk time + ACW) / (Talk time + ACW + Idle time) * 100

Your occupancy rate gives a clear overview of workload distribution and staffing effectiveness. If it’s too high, agents may burn out—but if it’s too low, you may be overstaffed or having workflow issues.

By reviewing recorded interactions and desktop activity, QA software can spot hidden idle time or inefficient call flows. Quality assurance can also identify whether or not non-work factors (like systems, scripts, or unclear expectations) are forcing agents to spend more time than necessary on certain tasks.

Pair these insights with workforce management (WFM) and you can adjust scheduling levels accordingly.

11. Call abandonment rate

Average call abandonment rate measures how often customers hang up before reaching an agent. It’s a key signal of how well your contact center manages queues and meets demand—high rates mean average time spent waiting is too long, queues aren’t being managed properly, or there aren’t enough agents.

Call Abandonment Rate Formula: (Number of abandoned calls / Total incoming calls) * 100

This call center productivity metric shows how customer patience is tested and whether your staffing or call routing strategies are effective. But numbers alone don’t show why customers are abandoning calls. QA gives you that context by evaluating what happens during those initial waiting periods.

With QA that integrates with your other contact center software, you can analyze the full customer journey to find common friction points that cause call abandonment, such as:

• Confusing interactive voice response (IVR) menus

• Off-putting queue messages

• Long wait times

Then, you can look to see if callback options were offered and executed properly. These actionable insights can lead to smarter queue design, better call forecasting, more self-service options, and an understanding of where customers could have been better served.

12. Service level

Service level tracks the percentage of calls answered within a specific timeframe, like 80% of calls answered within 20 seconds, for example.

This call center productivity metric measures how well you’re meeting customer expectations. Meeting a service level target involves many different factors, but generally speaking it shows you’re delivering quality CX and staying efficient.

Service Level Formula: (Calls answered within the threshold / Total calls answered) * 100

Your service level reflects your staffing models, queue management, and scheduling accuracy. Falling short often means that agents are overwhelmed or that spikes in call volume weren’t accurately predicted.

QA doesn’t just show you that calls are picked up late, it explains why.

By analyzing call data and agent behavior during peak periods, it can spot trends that are impacting service levels—such as spending too long on specific call types or common questions not being answered quickly enough. Plus, you can feed QA insights into your WFM software to help adjust forecasting, refine call routing, and make better plans for the future.

13. Schedule adherence

Schedule adherence measures how closely agents follow their assigned schedules, including start times, breaks, and end-of-shift compliance. This matters because consistent adherence ensures the right number of agents are available to handle customer demand at all times—even small gaps in coverage can lead to longer wait times and lower service levels.

Schedule Adherence Formula: (Time agent was scheduled to work and worked / Total time scheduled) * 100

This metric highlights how well staffing plans are executed in real time. Low adherence can stem from late logins, extended breaks, or leaving early. While WFM tracks the numbers, QA reveals the why behind the patterns.

QA reviews uncover operational and behavioral causes of adherence issues. By analyzing call handling, break behavior, and idle time, QA can identify non-adherence (like agents extending wrap-up time to delay availability or late call starts) and give you qualitative insights into morale issues that might be causing stress and burnout. Then, you can use coaching to:

• Address the root causes
• Reinforce expectations
• Improve agent engagement

14. Queue management effectiveness

Queue management effectiveness tracks how well your call center routes and distributes incoming contacts across available resources. It measures how efficiently the system connects customers to the right agents, without excessive wait times or unnecessary transfers. Effective queue management leads to improvements in:

• Average speed of response

• Agent utilization

• Customer satisfaction

There’s no universal formula for measuring queue management effectiveness, but it’s often assessed by analyzing average wait time, agent utilization, and the number of transfers per queue.

This metric depends on how well routing rules, agent availability, and call types are balanced. When queues are misaligned, it can overload some agents while others are underutilized.

QA teams can track where customers are experiencing delays or get transferred between queues. They can also assess whether calls are being routed based on the right criteria, transfers being sent to the wrong department, and what friction points are leading to negative customer experiences and lower call center agent productivity.

Making QA a strategic call center productivity weapon

Keeping your call center functioning is one thing, but ensuring productivity is a completely different animal.

Thankfully, productivity metrics provide a quantitative measure of your call center's operational efficiency, so you can use these numbers to understand team performance, workload distribution, and time management.

But without QA, you’re missing crucial information that will show you why these call center productivity metrics are changing. Rather than simply tracking individual agents, QA serves as a central resource for monitoring and enhancing overall team productivity. And by adding AI to your QA, you can target these common metrics at any scale.

Ready to see how AI-powered QA can transform your call center’s productivity? Start your self-guided, interactive demo of Scorebuddy right now.