Millions of calls distilled
into answers

We’re obsessed with measuring speed and efficiency. But new AI lets us finally measure what truly matters: value, trust, and intelligence.

If you manage or lead a customer-facing team, you know the drill. You live and die by a sacred set of acronyms: AHT, FCR, SLA, CSAT, NPS.

• Average Handle Time (AHT): How fast did we get them off the phone?
• First Call Resolution (FCR): Did we solve it in one go?
• Service Level (SLA): How quickly did we pick up the phone?

These are the stopwatch metrics. For decades, they’ve been the gold standard for running an “efficient” contact center. They keep the machine humming and the costs under control.

But they also tell a dangerous lie.

They tell you your operation is successful when agents rush through complex problems. They tell you you’re winning when you’re just deflecting customers. They tell you the speed of the car, but not if it’s heading toward a cliff.

The Problem: We’re Measuring Logistics, Not Value

The old KPIs were designed for a different era, an era of telephony, not technology. They are fundamentally incapable of measuring the true value of a customer conversation.

• They optimize for cost, not growth. A low AHT looks great on a spreadsheet, but what if it comes at the cost of a customer who leaves, confused and frustrated, for a competitor?
• They measure speed, not substance. They can’t distinguish between a 5-minute call that solves a root cause and prevents future calls, and a 5-minute call where an agent just reads a script and closes the ticket.
• They keep managers focused on firefighting, not strategy. When your primary goal is to keep AHT down, you’re not looking for trends, insights, or opportunities. You’re just watching the clock.

In the experience economy, your brand is defined by the quality of these interactions. Measuring them with a stopwatch isn’t just outdated; it’s strategic malpractice.

The Game-Changer: Agentic AI and Deep Data Access

For years, we had no choice. How could you possibly know what was really said in ten thousand hours of calls? You couldn’t. So we measured the container, the call’s duration, instead of the content.

That has now changed, completely.

The revolution is being driven by new Agentic and Autonomous AI technologies. This isn’t just a simple transcription. This is AI that can listen, understand, and analyze 100% of your customer conversations in real-time. It provides deep access to unstructured data that was previously a black box, turning every conversation into a rich stream of intelligence.

This technology allows us to move beyond the stopwatch and ask bigger, better questions:

• Did we build trust on that call?
• Did we reduce future effort for the customer?
• Did we uncover an insight that could help our product team?

To answer these questions, we need a new set of metrics.

From Stopwatch Metrics to Value Metrics

These new conversation-centric KPIs are designed to measure business impact, not just operational efficiency. While there are many emerging metrics in this space, here are some of the powerful new KPIs that are becoming the new standard:

1. Average Value Time (AVT) Instead of measuring the total handle time, AVT measures the percentage of the call spent on actual value-creating activities—like problem-solving, educating the customer, and building rapport—versus wasted effort like long silences, hold times, or repeating information. A long call with a high AVT is infinitely more valuable than a short, useless one.

2. Trust Signal Score This goes way beyond basic sentiment analysis. The AI is trained to detect specific phrases of relief, confidence, and gratitude. Think of hearing a customer say, "Oh, that makes perfect sense now," or "Thank you, I really appreciate you explaining that." This KPI quantifies whether you strengthened or weakened the customer relationship on a call.

3. Customer Love % Forget chasing a 7 or 8 on the NPS scale. This metric focuses only on your true promoters (the 9s and 10s) and enriches that data with conversational proof. The AI verifies the high score by finding moments of unsolicited praise within the conversation itself. This helps you understand exactly what behaviors create die-hard fans.

4. Insight Yield What percentage of your calls contain actionable intelligence for the rest of the business? This KPI tracks every time a customer mentions a competitor, points out a product flaw, or reveals a misunderstood marketing message. It turns your contact center from a cost center into a priceless R&D and business intelligence hub.

5. Effort Elimination Index First Call Resolution tells you if you solved today's problem. This metric tells you if you prevented tomorrow's. It measures how effectively an agent proactively solves the next issue, educating the customer on how to self-serve or anticipating a follow-up question. It’s the ultimate measure of a proactive, customer-centric experience.

The Shift: From Cost Center to Intelligence Hub

When you change what you measure, you change your business.

By adopting these value-centric KPIs, the contact center transforms. It’s no longer a line item on the budget to be minimized. It becomes a growth engine.

Managers stop being taskmasters and start being coaches, guiding agents on how to build trust and eliminate effort. And agents, freed from the tyranny of the stopwatch, are empowered to deliver genuinely fantastic experiences, knowing their success is measured by the value they create.

The future of customer experience won’t be judged by how short your calls are. It will be judged by how valuable your conversations become. It’s time to put down the stopwatch and start listening.

‍

Struggling with manual quality management processes in a contact center is so 1990s. Given modern technological advancements, quality automation is a necessity now more than ever before. 

After all, it's the 21st Century, and you must be able to automate agent performance evaluations, tracking and reporting metrics, and compliance monitoring. 

You can use Hear, a modern AI-powered conversation intelligence platform for contact centers, to streamline quality assurance automation. 

Hear can automatically analyze and score interactions, monitor compliance, and offer actionable insights for agent coaching and training.

Can't wait to automate quality management? Book a comprehensive Hear demo today. 

What Is Quality Automation and Why It Matters

Quality automation in a call or contact center is the use of technology or software to streamline and improve how the center monitors, evaluates, and manages agent-customer interactions. 

For these centers, quality assurance (QA) is the most important part of quality management, which is why they prioritize it over automation in quality control for several reasons:

• Promotes Better Efficiency: Automating reduces manual effort and allows your quality management team to focus more on strategic tasks using the time they free up. 
• Reduced Human Error: Automating repetitive tasks minimizes human error to ensure your QA processes, such as evaluating agents, are always consistent, fair, and accurate. 
• Improved Agent Performance: Evaluating agents automatically leads to timely feedback and data-driven insights that CX leaders can use to provide targeted coaching and training. As a result, your agents improve their performance and deliver better customer service. 
• Enhanced Compliance: Modern quality automation systems monitor interactions and flag potential issues to help you ensure your agents comply with company, legal, and industry standards or regulations. 
• Improved Customer Satisfaction: One of your ultimate goals is to ensure your customers' issues are resolved and they are happy with your service. With quality automation, you get a framework for improving your processes on an ongoing basis to deliver consistent high-quality service. Better service increases customer satisfaction and loyalty. 

Core Components of Quality Automation

Contact center quality automation can include the key elements below.

• Automated Interaction Monitoring: Modern quality assurance automation tools can automatically record, transcribe, and analyze calls based on established criteria. For example, you can prioritize customer sentiment, adherence to scripts, and certain keywords. These tools can also handle other forms of agent-customer interactions, including chat and emails. 
• Automated Agent Evaluation and Scoring: Instead of manual evaluation, you can use modern tools to automatically analyze and evaluate customer interactions. You can also score them against predefined criteria through automated quality assurance scoring. 
• Performance Analytics and Reporting: Quality automation software can generate detailed reports and provide dashboards that track key metrics related to call and contact center quality assurance. You can see where your agents need to improve, which means you can make data-informed decisions for continuous improvement. 
• Compliance Management: Your contact center must comply with specific internal and external policies and regulations. Quality automation helps you ensure that agents adhere to these requirements by flagging potential violations and sending instant alerts. 
• Multichannel Support: Modern quality automation platforms can handle interactions across multiple channels like voice (calls), chat, and email. You get a holistic view of agent performance and customer experience, which allows for consistent quality assessment and management across different touchpoints. 
• Automated Insights for Personalized Coaching: You can use modern conversation intelligence software to get instant insights that can help you tailor feedback, coaching, and training to individual agent needs based on their performance data. 

Types of Quality Automation

You can use different types of quality automation to become an autonomous contact center. These automations mix technological enablers like AI with functional applications like speech analytics for better results. Let's check out a few.

1. Artificial Intelligence (AI)

Artificial intelligence (AI) recognizes patterns in agent-customer interactions or agent performance, personalizes agent coaching, and helps you make data-informed decisions. 

AI is useful in:

• Predictive analytics to forecast quality issues
• Evaluating agent performance without manual input
• Auto-scoring interactions at scale across calls (voice), chat, and emails
• Generating summaries, insights, and suggestions based on customer conversations using large language models (LLMs) in Generative AI

2. Machine Learning (ML)

A branch of AI, machine learning is the use and development of self-improving systems that learn from historical data.

In a contact center setup, you can use ML in:

• Anomaly detection, such as unusual agent behavior 
• Refining your scoring models based on QA outcomes
• Trend analysis to optimize agent coaching. 

3. Natural Language Processing (NLP)

Natural Language Processing is another branch of AI that helps you interpret human language in both text and speech, and can be used in:

• Speech-to-text transcription for calls
• Churn intent and sentiment analysis to understand customer emotions
• Detecting compliance phrases and soft skills, such as empathy

4. Speech and Text Analytics 

Text and speech analytics help you analyze interactions on a large scale and can be used in:

• Identifying keywords, topics, and customer sentiment
• Flagging low-quality or non-compliant interactions
• Estimating customer satisfaction

5. Software Integrations 

Your contact center conversation intelligence software should connect with your other software for uses such as:

• Synchronizing QA tools with CRMs, analytics tools, and telephony systems 
• Centralizing performance data for detailed reporting and analytics

6. Co-Pilot Assistance

While most contact center tools act as agent-side assist platforms, some software systems act as co-pilot assist platforms for CX team leads. 

A co-pilot assist platform can be used in:

• Visibility into agent performance through interactive reporting or conversational dashboards with natural language querying
• Predictive analytics for customer churn, performance trends, seasonal interaction volume spikes, and high-performing agents
• Instant coaching recommendations based on live or post-interaction speech/text analysis and sentiment analysis
• Instant alerts to help reduce churn risk, customer frustration, and compliance breaches
• AI-driven quality assurance, including monitoring and improving various QA metrics
• AI-based compliance monitoring 

How to Implement Quality Automation Effectively

You'll need a strategic approach that combines automation with human input to succeed at quality automation. What must you do?

• Establish Smart Quality Standards and Metrics: Set quality standards that you can measure and achieve across all customer interactions. Prioritize metrics or Key Performance Indicators (KPIs), such as customer satisfaction and agent productivity, that are critical to your operations. Align these metrics and quality standards with your overall business goals and customer experience objectives. 
• Choose the Right Tool: Select the best automation tool that integrates with your technology stack and aligns with your business objectives. 
• Conduct Automation Tests with the Tool: Perform different automation tests for your most critical and frequent tasks. You can test things gradually, beginning with analyzing interactions, scoring, and generating reports on selected metrics. 
• Balance Automation with Human Input or Oversight: Use quality assurance and automation analysts to periodically review the results of the tool you've chosen. Monitor its automated scores to see if it's accurate and identify potential biases in the automated processes themselves. That's not all. Combine the insights from the tool with human knowledge to guide your agents on areas where they need to improve. 
• Monitor, Maintain, and Improve the Process Continuously: Ask your agents and supervisors for feedback on the effectiveness of the automation and the tool itself. Monitor how automating things affects quality metrics. Adjust your automation rules, scoring criteria, and feedback systems to maximize the results you expect. 

Metrics to Track in Quality Automation

You can track a mix of efficiency, performance, and impact metrics across QA, agent performance, and customer experience to see whether your quality automation process is effective. 

Evaluate crucial metrics such as:

• QA Coverage: Check whether you can now analyze 100% of interactions, especially across multiple channels. A higher coverage means more data, better insights, and increased visibility into agent performance and customer sentiment. 
• QA Efficiency: Analyze how long you now take to analyze and score interactions. Automated QA significantly reduces the time to free up your team for other strategic activities. 
• Agent Performance Trends: Look for significant changes in quality scores, empathy, and script adherence, among others. You should be able to identify and improve agent behavior faster. 
• Improvement in KPIs: Monitor how automation improves metrics such as first-call resolution (FCR), compliance adherence, customer satisfaction scores (CSAT), and average handling time (AHT). For example, ensure QA scores are objective and consistent across all interactions and fair across all agents. Your average handling time should reduce without compromising first-call resolution and customer satisfaction. 

How to Choose the Right Quality Automation Tools

Besides identifying your QA pain points and goals, let's discuss other important considerations when selecting quality automation software. 

• Analytics and Reporting: Choose a tool with an interactive dashboard, detailed reports, and comprehensive performance tracking. 
• Ease of Use: The tool should have an intuitive or user-friendly interface that your agents can navigate and use with minimal learning required to optimize adoption.
• Flexibility and Scalability: You should be able to customize various aspects of the tool to adapt to your specific QA scoring criteria, workflows, or business rules. The tool should also accommodate increasing interactions and agent numbers as your business grows.
• Integration with Systems: The software should connect with your CRM, analytics tools, and telephony systems. You'll want to avoid manual data entry and switching between systems, which can make achieving consistent data a mere imagination.
• Investment and Return on Investment: Evaluate the total cost of the tool, including the initial purchase price, implementation, ongoing subscriptions, and training costs. Estimate the potential return on investment without forgetting factors like increased agent productivity, improved customer satisfaction, and reduced operational costs. 

As a quality automation tool for contact centers, Hear meets these criteria perfectly. 

You can integrate Hear with your existing CRM and telephony systems and adapt it to your scoring criteria. 

You can also improve key metrics, increase operational efficiency, and generate reports from an easy-to-use, chattable dashboard.

Check out Hear today to turn your contact center into an autonomous entity and value-generating hub. 

Common Mistakes to Avoid in Quality Automation

Making inevitable mistakes in the automation of quality assurance can render your efforts fruitless. You must avoid the following costly mistakes. 

• Lack of a Clear Strategy and Goals: Automation efforts without a well-defined strategy and goals can be ineffective and result in wasted resources and limited impact. Establish clear goals from the onset regarding which processes you want to automate, the metrics to measure, and how you'll integrate the results into your overall quality management strategy. 
• Not Consulting Stakeholders: Implementing automation without the input of your agents and supervisors can lead to poor ownership. Consult with your agents, supervisors, and top leaders to get enough buy-in and contextual feedback based on real needs. 
• Delayed Coaching and Training: Delivering feedback, coaching, and training to your agents when it's too late can dilute effectiveness. Your agents may repeat the same mistakes, further reducing effectiveness. Use instant feedback mechanisms that allow for timely feedback shortly after the interaction or analysis. 

Frequently Asked Questions (FAQs)

Here are answers to common contact center quality automation questions to wrap up the guide:

How Does Quality Automation Differ from Test Automation?

Test automation is a subfield of quality automation that specifically deals with automating the execution of tests to verify the practicality of a solution and identify defects before deployment. 

As the broader aspect, quality automation continuously monitors and evaluates agent interactions to ensure consistent service quality. 

Is Cloud-Based Automation Better Than On-Premise?

Cloud-based quality automation generally offers superior benefits over premise-based automation. The former is more scalable, cost-effective, flexible, and faster to deploy. 

An on-premise automation strategy is ideal if you prioritize infrastructure and data control, particularly if your industry has strict security requirements or compliance needs. 

Can Automation Improve Regulatory Compliance?

Automation can significantly improve regulatory compliance in a contact center through automated interaction analysis and compliance monitoring. 

Monitoring interactions for compliance helps you identify mistakes sooner and address them in time before your agents repeat them. 

Conclusion

Effective quality automation can be a gateway to improving contact center operational efficiency, agent performance, and customer satisfaction. 

You must first understand your needs, choose the right automation tool, and implement it correctly to achieve these results. 

Hear stands out as a highly effective contact center quality automation tool. With Hear, you can automatically analyze and score 100% of conversations across voice, email, and chat at scale. 

You can also integrate Hear with your existing systems, automate compliance monitoring, and improve various QA metrics. 

Request a demo today to discover why top contact centers use Hear for quality automation.

Like most call centers with inefficient manual processes, you might be struggling with limited visibility into agent performance, incomplete data, and compliance risks resulting from inconsistent monitoring. 

Assessing the right call center quality assurance metrics can solve these and many other problems, but it can be tricky. 

In today's guide, we'll explore the most essential metrics you need to monitor to ensure better agent performance, operational efficiency, customer experience, and business success. 

Pro Tip: You can use Hear, our call center conversation intelligence software, to analyze 100% of calls for the crucial data you need to measure different metrics. 

Hear also helps you improve key metrics like first-call resolution and customer satisfaction score through coaching recommendations and monitoring resolution quality across calls. 

Discover why top call centers use Hear to streamline QA — get a detailed demo today.

What Are Call Center Quality Assurance Metrics?

Call center quality assurance metrics are key data points CX leaders use to measure, evaluate, and improve the effectiveness and efficiency of call center customer service and agent performance. 

These metrics help assess how agents perform, indicating where you can improve the overall customer service and experience. 

Why Quality Assurance Metrics Matter

Quality assurance (QA) metrics or key performance indicators (KPIs) come in handy for CX leads, call center directors, and QA leads struggling with poor agent performance, manual QA processes, and poor compliance. 

The metrics are important in:

• Ensuring Service Quality Is Consistent: Quality assurance KPIs help you establish and maintain consistent service standards across all calls, regardless of the agent handling them. 
• Promoting Customer Satisfaction: With the right QA metrics, your agent-customer interactions can meet or exceed certain expectations, which leads to customer satisfaction scores and positive experiences. 
• Optimizing Operational Efficiency: QA metrics can show where your processes are inefficient, allowing you to apply targeted improvement solutions. Optimal processes and agent performance can reduce the operational costs related to call handling and customer service in general. 
• Supporting Business Goals: Positive customer experiences help build a strong brand reputation. You can also increase sales and revenue from repeat customers and upsell opportunities identified in calls. Improved customer satisfaction, compliance, and operational efficiency promote the overall success of your call center.

13 Call Center QA Metrics to Track

We can break down the most important call center quality assurance metrics into the main categories below. 

A) Agent Performance and Efficiency Metrics 

You can track the following agent-related metrics.

1. Average Handling Time (AHT), which measures the average number of minutes an agent spends on a call and its follow-up activities (After-Call Work or ACW). 

AHT = (Total Talk Time + Total Hold Time + Total After-Call Work Time) ÷ Total Number of Calls

2. Average Speed of Answer (ASA), which measures responsiveness in terms of the number of seconds that pass before an agent answers a call. 

ASA = Total Wait Time of All Answered Calls ÷ Total Number of Calls Answered

3. First Call Resolution (FCR), which monitors the percentage of calls in which an agent resolves a customer's issue in the first interaction without the need for an escalation or follow-up call. 

FCR = (Total Calls Resolved on First Interaction ÷ Total Calls) x 100

4. Agent Turnover Rate, which measures the rate at which agents leave your call center since this has a direct impact on training costs and service quality. 
5. Agent Occupancy Rate, which tracks the percentage of time your agents are actively handling customer calls or related activities. 

Agent Occupancy Rate = (Total Call Handling Time ÷ Total Logged-In Time) x 100

6. Quality Assurance (QA) Score or Quality Score, which is a broader grade an agent gets depending on a QA checklist that can include aspects such as tone, empathy, script adherence, problem resolution, and more. 

B) Compliance and Adherence Metrics

These include:

7. Compliance score, which generally monitors how well your agents follow the established customer service protocols, company policies or processes, and industry or legal regulations. 
8. Schedule adherence, which measures how closely your agents stick to their assigned schedules (such as shifts, log-in time, and breaks), ensuring there are enough agents available when the need arises. 
9. Schedule compliance, which monitors whether an agent completes their required work hours to ensure enough total time worked rather than just punctuality. 
10. Script compliance, which checks if your agents follow your predefined call scripts to meet company and legal requirements. 

C) Customer Experience and Satisfaction Metrics

These include:

11. Customer Satisfaction Score (CSAT), which is usually collected using surveys and feedback forms to measure how happy customers are with their call interactions. Based on the American Customer Satisfaction Index, Apple currently has the highest CSAT score of 85% against an average score of 81% in the personal computers industry. 
12. Net Promoter Score (NPS), which is usually collected through surveys to monitor customer loyalty and the likelihood that they will recommend a company on a scale of 1-10. You then group them into Promoters (Score 9-10), Passives (Score 7-8), and Detractors (Score 0-6). 

NPS = % of Promoters - % of Detractors

13. Customer Effort Score (CES), which tracks customer experience in terms of how much effort a customer had to apply to get their issue resolved. (The idea is to reduce customer effort such that they don't have to exert themselves to reach their goal and be happy with your service.)

How to Measure QA Metrics

When it comes to measuring QA metrics correctly, you'll need a holistic approach to get an all-around view of agent performance, operational efficiency, and customer experience. 

Consider the steps below.

1. Identify Your Preferred Goals and Metrics

Set clear goals regarding the specific call center performance aspects you want to improve, such as resolving more issues on the first interaction. Go for useful metrics that match the goals.

2. Create Scorecards, Checklists, and Customer Surveys

Create a detailed call center quality assurance scorecard or a checklist to monitor performance against the selected metrics. 

Emphasize key elements such as call handling, soft skills, product knowledge, and adherence to standards or procedures. 

You'll use the scorecard to uncover areas of strength and weakness and offer constructive feedback to your agents. 

You can also create surveys and feedback forms to collect customer feedback on satisfaction and areas where you can improve service. 

3. Monitor, Analyze, and Score Calls

Listen to live calls or review the interactions after the call to evaluate how your agents perform based on your established criteria and the scorecard or checklist. 

4. Analyze Customer Feedback

Review the customer data from the surveys and feedback forms to gauge the relevant metrics and identify patterns, trends, and areas of concern. 

5. Continue Monitoring and Reporting Metrics

Keep monitoring the metrics you've selected to assess the overall performance of your call center on an ongoing basis. 

Look for trends and patterns related to your metrics to identify potential issues and opportunities to improve quality assurance, agent performance, and customer service. 

6. Use Call Center QA Software to Scale

If you handle a high volume of calls, consider using call center quality assurance software to scale the process. 

For example, you can use Hear, a conversation intelligence platform for call centers, to streamline how you measure metrics and monitor quality. 

Hear uses AI to analyze 100% of calls, automate QA scoring, flag compliance risks, and offer actionable business insights to improve agent performance and overall customer service. 

Hear also helps reduce call handling time, increase FCR, and improve customer satisfaction, which drives operational efficiency. 

Ready to monitor and improve various call center QA metrics? Book a demo today.

Challenges in Measuring QA Metrics

Regardless of the size of your call center, you are likely to encounter the following challenges when measuring QA metrics.

• Subjective Evaluations: Your human evaluators may interpret quality standards and scorecard aspects differently, leading to inconsistent feedback and coaching recommendations.  
• Limited Call Sampling: If you use manual processes or traditional QA methods, you only analyze a small percentage of calls. Reviewing random calls can result in poor agent evaluation and biased insights.
• QA KPI Manipulation: Some agents may prioritize certain metrics for QA that favor them more and compromise genuine customer satisfaction. The customer experience is likely to suffer if agents rush calls or leave issues unresolved. 

Best Practices for Enhancing Call Center QA Metrics

To overcome these and many other challenges, below are some best practices you can turn into ongoing habits.

• Set Clear Quality Standards: Identify clear and measurable quality standards with objective evaluation criteria and minimal room for subjectivity. 
• Use Conversation Analytics Software: Implement interaction analytics software, such as Hear, to automate call center quality assurance. For instance, Hear can analyze 100% of your calls and assign QA scores automatically. 
• Offer Comprehensive Training: Adequate training is necessary to sensitize your agents on the importance of measuring the metrics. 
• Prioritize Customer Experience: Balance operational efficiency with positive customer experiences based on first-call resolution, customer satisfaction, and other relevant metrics. Encourage your agents to prioritize customer needs and build strong relationships with them as necessary. 
• Review the Metrics and Measurement Process Regularly: Liaise with the agents and evaluators to review and update the metrics and measurement process regularly. Consistency is critical to ensuring fair measurement, evaluation, and training. 

Frequently Asked Questions (FAQs)

Here are answers to common questions leaders often have regarding call center QA metrics. 

Are There Industry Benchmarks for Call Center QA Metrics?

Different industries have different benchmarks for various call center QA metrics. For example, a CSAT score of 85% or higher is generally seen as advantageous. 

Most call centers typically create their own benchmarks. For instance, you can set an FCR target of 75%, allow only 2% of compliance errors, or choose a minimum acceptable QA score of 80%. 

How Do You Ensure QA Metrics Are Aligned with Business Goals?

To ensure QA metrics align with business goals, you must first identify and understand the goals. The next step will be to select or create relevant metrics that directly match those goals. 

When identifying business goals, engage all the relevant stakeholders, including business analysts, top leaders, and agents. 

Can QA Metrics Be Used in Multilingual Call Centers?

You can use QA metrics in multilingual call centers if you apply the right adaptations. Standardize the metrics and adapt them to each language while considering cultural contexts, regional dialects, and communication styles. 

You can also ensure your QA team is linguistically diverse, such that you have native speakers or staff members fluent in the languages you use to monitor different metrics. 

Can QA Metrics Predict Customer Churn?

QA metrics can predict customer churn in different ways. Low CSAT scores and Net Promoter Scores can stem from low customer satisfaction levels, acting as strong indicators of potential churn. 

As an AI-powered tool, Hear can analyze various QA metrics and criteria like customer sentiment to predict and help you reduce churn risk.

Conclusion

Monitoring and analyzing relevant call center quality assurance metrics can significantly impact how your agents perform and ensure consistent service quality. 

Since there are so many metrics to consider, you'll want to focus on the most important ones. In this guide, we've covered 13 critical QA KPIs, including average handling time, customer satisfaction scores, and compliance scores. 

The right call center software can save the day by automating various aspects of the process. For example, you can use Hear to automate quality assurance scoring, an important overall metric. 

Hear also analyzes all your calls at scale, ensuring you have enough data to measure your preferred metrics and uncover areas for improvement. 

Request a demo today to see Hear in action and learn more about its QA capabilities. 

‍