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"Hear has been a transformative partner for Shift, revolutionizing the way we manage customer interactions. What used to be a manual, time-consuming effort is now automated, accurate, and insight-driven. With Hear, we’ve gained both operational efficiency and deeper call compliance and quality from our representatives."
– Yuval Danin, CEO at Shift

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Hear has been a transformative partner for Shift, revolutionizing how we manage customer interactions. What was once a manual, time-consuming process is now automated, accurate, and insight-driven.

What we love most about Hear is how easy it is to use. Everything we need is right there. We can instantly see what customers are calling about, how our team is performing, and where we can improve.

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Call Resolution
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Call Issue Analysis
What’s getting in the way? Discover the top call blockers, so your team can fix what matters faster.
Compliance Tracking
Surfaces missed details in real time, helping teams close gaps, stay compliant, and avoid the backtracking that slows everyone down.
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Rethinking AI Adoption in the Contact Center: From One Off Tools to Full System Intelligence
In the current wave of enterprise AI adoption, contact centers have become a primary testing ground. It’s where automation meets urgency, where customer sentiment meets real time execution. Yet most organizations approach AI as if they’re picking tools off a shelf adding one bot here, a sentiment tracker there, an agent assist tool in the middle. This approach offers quick wins, but yields limited transformation. The real potential of AI in the contact center does not lie in incremental tools. It lies in rethinking the system entirely.
To illustrate the point, consider a simple analogy: personal AI adoption.
The Image Generator vs. the AI Operating System
Imagine you discover a cutting edge AI image generator. It’s powerful, intuitive, and drastically reduces the time it takes to produce visual content. For marketing or design work, it’s a game changer. But it touches only one part of your daily workflow.
Now imagine adopting a multimodal tool like ChatGPT. It’s not confined to one domain; it assists with writing, summarizing, brainstorming, learning, decision making, coding, image generation, and more. Its value doesn’t lie in outperforming a single tool, but in improving everything you do. It doesn’t replace one skill; it elevates your entire baseline of capability.
This is the difference between AI as a tool and AI as a system of intelligence.
The same choice confronts contact center leaders today.
Incremental AI: The Comfortable Path to Minimal Disruption
The enterprise appetite for AI is growing, but the instinct to contain it is strong. It’s easier to frame AI as a bolt-on chatbot for FAQs, an agent assist plug-in for real time scripting, or a predictive routing module for better queue management. These point solutions offer local efficiency, but they rarely shift the organization’s intelligence frontier.
Why? Because their impact is compartmentalized. They streamline functions, not systems. A bot that reduces call volume by 10% is valuable, but if the underlying training, analytics, quality assurance, and managerial workflows remain unchanged, the operation continues to behave like a legacy system.
Incremental AI tools often create more fragmentation, not less. They produce disconnected data silos, demand additional human oversight, and rarely integrate seamlessly into existing strategic workflows. The ROI is real, but shallow.
Foundational AI: Building Intelligence into the Operating System
By contrast, foundational AI doesn’t aim to optimize a part; it aims to rewire the whole. It views the contact center not as a set of functions to automate, but as an interconnected network of people, conversations, workflows, and decisions, all of which are candidates for intelligence augmentation.
This approach allows AI to touch every layer of the contact center:
- Training & Onboarding: AI dynamically adapts learning content to each agent’s performance profile.
- Live Operations: Real time copilot tools adjust based on context, customer sentiment, and escalation thresholds.
- Routing & Workflows: Conversations are dynamically routed to human or AI agents based on complexity and skill match.
- Post Call Insights: AI performs 100% QA scoring, extracts trends, summarizes calls, and feeds back to both product and CX.
- Managerial Reporting: Data becomes queryable in natural language, and insight replaces intuition.
Here, AI is not a feature. It is the organizing principle of the contact center.
Strategic Costs of a Piecemeal Approach
The hidden downside of incremental AI adoption is the operational tax it imposes. When AI tools are introduced in isolation:
- Integration debt accumulates. Each new tool demands its own data pipeline, governance layer, and training protocol.
- Context is lost between systems. A bot may know what the customer asked, but the agent may not know how the bot responded.
- Managerial complexity rises, not falls. Human supervisors end up managing not just agents, but the misalignment between fragmented tools.
Perhaps most critically, this approach reinforces the old paradigm: humans are the glue that holds the system together. In a truly intelligent contact center, that role is played by AI itself, managing AI, monitoring human performance, and continuously optimizing the orchestration of both.
From Toolstack to Intelligence Fabric
What’s needed is a shift from assembling a toolstack to constructing an intelligence fabric, a layer of AI that permeates the entire contact center, learning from every interaction, optimizing every touchpoint, and surfacing insights across every function.
This is not about replacing humans. It’s about eliminating friction, freeing human potential, and designing an environment where both people and AI can perform at their best. When AI is applied systemically, it doesn’t just make the contact center more efficient. It makes it self-improving.
Rethinking the Implementation Playbook
This reframing demands a new kind of implementation strategy. Instead of asking “Where can AI help?” we must ask:
“What would this operation look like if it were AI native from the ground up?”
- What workflows would disappear?
- What data would become instantly actionable?
- What roles would shift from supervision to strategy?
Most importantly:
What becomes possible when intelligence is no longer something we add to the edges of the system, but something we embed at its core?
AI Is Not the Upgrade; it’s Your New Foundation
The contact center is no longer a place to patch with point solutions. It’s a strategic nerve center for customer insight, brand experience, and operational excellence. Adding AI incrementally is tempting; it promises improvement with minimal disruption. But only by embracing AI holistically can we unlock the full potential of automation, intelligence, and human-machine collaboration.
In a world moving this fast, the future belongs not to those who adopt AI, but to those who rearchitect around it.

What Is Quality Automation and How to Implement It
Discover how quality automation transforms contact center performance. Learn what it is, how it works, and how to implement it for faster, smarter QA at scale.
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.

13 Call Center Quality Assurance Metrics Every Team Should Track
Discover the most important call center quality assurance metrics that impact customer experience, agent performance, business success, and operational efficiency.
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.
- 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
- 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
- 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
- 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.
- 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
- 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:
- Compliance score, which generally monitors how well your agents follow the established customer service protocols, company policies or processes, and industry or legal regulations.
- 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.
- Schedule compliance, which monitors whether an agent completes their required work hours to ensure enough total time worked rather than just punctuality.
- 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:
- 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.
- 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
- 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.)
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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.

How to Create an Effective Call Center Agent Scorecard
Discover the essential components of a call center performance scorecard to track, evaluate, and improve agent effectiveness and customer satisfaction.
Your call center agents are one of the key lifelines of your business, but staying on top of their performance at scale can be tricky.
You'll need the right system, and a call center agent performance scorecard comes in handy. With an effective scorecard, you can see the successful agents, those who need guidance, and where your processes require improvement.
Our guide today covers what makes call center scorecards effective, the metrics you need to track, and the best software to help you make your scorecards more efficient.
TL;DR - How to Evaluate Call Center Agent Performance
Let's start with a quick overview of how to evaluate how your call center agents perform.
1. Define Clear Objectives: Align scorecard metrics with business goals and customer expectations.
2. Select Key Performance Metrics: Focus on relevant key performance indicators (KPIs) such as AHT, FCR, CSAT, QA scores, and adherence to standards.
3. Use a Weighted Scoring System: Prioritize metrics based on their impact, giving more weight to the ones that have higher impact.
4. Incorporate Both Quantitative & Qualitative Measures: Balance tangible data with qualitative aspects such as call quality, empathy, and soft skills.
5. Customize for Roles: Since agents have different roles, tailor scorecards for inbound vs. outbound roles, junior vs. senior agents, and any specialized roles you may have.
6. Ensure Transparency & Agent Buy-In: Share your performance evaluation criteria and scoring logic with agents to secure their buy-in.
7. Review & Update Regularly: Refine your scorecard to ensure it remains relevant as business needs evolve.
8. Use Data and Insights for Coaching: Use the results from your scorecards to help you offer personalized feedback and training to your agents.
As mentioned, you'll need the right software to help you ensure your scorecards evaluate agent performance efficiently.
With Hear, a comprehensive call and contact center software solution, you can automate and optimise various agent performance evaluation processes.
For example, you can automate quality assurance (QA) scoring to ensure your performance tracking, service quality, and coaching recommendations are consistent.
Book a demo today to see what Hear can do for you.

What Is a Call Center Agent Performance Scorecard?
A call center agent performance scorecard is a structured system that call centers use to track, measure, and improve how their agents perform against certain predefined quantitative and qualitative KPIs.
You can look at the scorecard as a report card that shows the areas where your agents excel and where they need to improve through targeted guidance and coaching.
Call Center vs. Contact Center Scorecard
A call center scorecard focuses primarily on phone calls (voice-based agent-customer interactions), and follows metrics such as first-call resolution and average call handling time.
A contact center scorecard is broader and includes various communications channels besides voice, including chat and email.
Contact centers go beyond the key metrics for call centers to cover advanced ones such as response time, customer engagement, and resolution rates.

The Importance of Performance Scorecards in Call Centers
Whether you run a small or large call center, you'll find that scorecards are important in ways such as:
- Ensuring Consistency in Evaluation and Service Quality: Scorecards provide a standardized way to assess agent interactions and performance, ensuring consistent service delivery and adherence to industry or company quality standards.
- Enhancing Customer Experience: A good scorecard focuses on key metrics like customer satisfaction, which helps optimize customer journeys for a better experience.
- Optimizing Efficiency and Productivity: Scorecards help uncover areas where your processes are inefficient, such as long call handling times. You can address these to improve agent productivity, reduce costs, and optimize how you allocate resources.
- Motivates Agents to Do Better: Clear performance expectations and feedback allow your agents to identify their strengths and weaknesses. Recognizing and rewarding high performers can motivate them to improve their performance.
- Enabling Continuous Improvement: Scorecards provide valuable data you can use to spot trends, track progress, and make informed decisions on how to improve your processes or train your agents. Reviewing and updating them regularly ensures they evolve and adapt to meet the changing needs of customers and the business.

Call Center Agent Performance Metrics
You can include several metrics or KPIs in your scorecards as discussed below.
1. Call Efficiency Metrics
Include the following metrics to track how efficiently your agents handle calls.
- Average Handling Time: This measures the average duration of calls, including the hold time, conversation time, and post-interaction work related to the call.
- Number of Calls Handled: With this metric, you can track the volume of calls each agent handles within a given time.
- First Call Resolution: You can measure the percentage of calls resolved in the first interaction to see how effective your agents are at handling issues.
- Service Level: Use this metric to see the percentage of calls your agents answer within a specific timeframe, which is typically a set number of seconds.
2. Customer Satisfaction Metrics
You'll want to loop in various metrics related to customer satisfaction score, such as:
- Customer Satisfaction Score: You can measure how customers are satisfied or pleased with the service they received. The metric works well when you use feedback forms or surveys.
- Customer Effort Score (CES): This metric gauges how much effort a customer puts in to resolve an issue.
- Net Promoter Score (NPS): You can track this metric to gauge customer loyalty and their willingness to recommend your company or business to others.
3. Agent Productivity and Adherence Metrics
Consider the following metrics to see how your agents maintain productivity and adhere to different aspects.
- Adherence to Scripts: Monitor how closely your agents follow predefined scripts and procedures.
- Schedule Adherence: Check how closely your agents stick to their scheduled work hours.
- Occupancy Rate: This metric measures the percentage of time an agent is actively involved in customer interactions or related tasks.
- Quality Assurance Score: The QA score shows the quality of agent interactions based on various predefined criteria.
- Average Order Value (AOV): The AOV measures the average sales an agent generates during calls.

How to Create an Effective Performance Scorecard
Let's now explore the steps for creating and implementing a practical scorecard for measuring call center performance.
- Define Your Goals: Set the goals you want to achieve with the scorecard. For example, you can purpose to increase operational efficiency and improve customer satisfaction.
- Set Your Key Performance Indicators: Select relevant and measurable metrics that match business objectives.
- Establish Scoring Criteria: Set unbiased scoring criteria for each metric to ensure the evaluation is fair for each agent.
- Prioritize and Assign Weights to Metrics: Determine the importance of each metric and give more weight to high-impact metrics that align with business goals.
- Design the Scorecard Layout and Develop a Template: Use a simple format that highlights scores, weights, and agent performance per category. Create a user-friendly call center agent scorecard template that is easy to use and understand for all agents.
- Customize for Roles: Adjust the metrics and weightings based on agent roles, such as inbound vs. outbound sales, since their expected outcomes are different.
- Set Performance Targets or Benchmarks: Establish realistic, data-driven goals to measure against.
- Pilot the Scorecard: Test your scorecard with a small group of agents and tweak based on their feedback.
- Implement and Train: Roll out the scorecard fully and provide training to agents and supervisors on how to use it effectively. Ensure everyone understands how you'll use it to evaluate performance.
- Monitor and Review: Regularly track agent performance and provide feedback based on the scorecard data.
- Update and Refine: Monitor how you use the scorecard and its effectiveness. Check that it remains relevant and update it to reflect new goals, feedback, or operational changes.

Best Tools to Enhance Scorecard Efficiency
When choosing the right tool to enhance the efficiency of your scorecard, you'll want one that prioritizes automation, instant feedback, and data-driven insights.
Let's consider the top options.
1. Hear

Hear is an AI-driven call and contact center conversation intelligence platform that helps you improve agent performance, boost operational efficiency, and enhance customer experience.
With Hear, you get the following solutions:
- Practical Feedback: Hear allows for instant feedback based on calls to help you guide your agents on how to improve.
- In-depth Reporting: Hear analyzes your calls to create reports customized to your needs. The reports include call topics, customer feedback on products, churn intent, and first-call resolution.
- Automation: You can use Hear to eliminate manual call reviews. Our software automatically creates summaries for every call. Besides 100% call coverage, Hear also automates quality assurance scoring.
- Comprehensive Agent Evaluation: Hear can help you evaluate how agents perform based on 100% of their call interactions and your preset qualitative and quantitative metrics. You can monitor metrics such as empathy, problem solving, information accuracy, listening skills, self-presentation, service language, resolution, and compliance.
- Data-Driven Decision Making: Hear uses an Interactive Insights Chat to provide data-informed recommendations to help you train your agents.
Check out Hear today to see how you can use it to transform agent performance.
2. Observe.ai
While Observe.ai offers automated scorecards, Hear provides a more comprehensive solution that includes instant feedback, deeper post-call analytics, and advanced coaching recommendations.
3. Cresta
Cresta uses real-time agent assist solutions to improve agent productivity during live calls through real-time suggestions.
While Cresta offers prompt checklists that an agent might miss easily, Hear offers continuous call center agent performance monitoring, detailed AI call analysis, and advanced AI-driven insights for detailed agent development.
4. Cogito
As an AI tool specializing in real-time emotional intelligence, Cogito helps users improve scorecard efficiency based on agents' voice tone, empathy, and customer sentiment.
Hear goes beyond the emotional intelligence element by monitoring resolution quality and churn signals across calls to improve customer satisfaction scores and retention.

Common Pitfalls to Avoid in Scorecard Development
You'll want to avoid various common mistakes when developing your call center scorecards. These include:
- Overemphasizing Speed Over Quality: You might focus too much on average handling time, which might make your agents rush through calls. According to a Salesforce study, 69% of agents agree that it's difficult to balance between speed and quality in customer service. To avoid the issue, ensure you balance average handling time with quality metrics like agent empathy and customer satisfaction.
- Inconsistent Scoring: If you use subjective scoring methods, your evaluators will interpret data and criteria differently. Your agents' assessments will be biased, unfair, and erratic. Instead, use clear and objective scoring guidelines, and train your evaluators regularly on how to apply your scorecards consistently.
- Not Adapting to Change: Failing to update your scorecards to reflect changes in technology, business priorities, and customer or agent needs can lower agent performance. Review and update your scorecards regularly to ensure they still align with current business goals.

Frequently Asked Questions (FAQs)
Have questions about call center agent performance scorecards? We've got the answers.
How Is a Call Center Scorecard Different From a QA Evaluation?
A call center agent scorecard measures agent performance based on specific metrics like first-call resolution and customer satisfaction.
A quality assurance evaluation is a broader process that assesses the quality of calls based on specific preset criteria and standards, including agent performance metrics.
What's the Ideal Frequency for Updating Scorecard Metrics?
You can review and update your scorecard metrics regularly, ideally on a weekly, monthly, or quarterly basis.
The frequency depends on agent performance trends, effectiveness of the metrics, organizational culture, and the size of your operations.
Can Scorecards Be Used in Remote Call Centers?
You can use agent scorecards in a remote call center just as you would in an on-site center and achieve similar results.
As a remote call center, you'll want to add tailored criteria such as adherence to remote work policies, including punctuality and planned breaks.
Conclusion
Using a call center agent performance scorecard is a great way to not only improve agent productivity but also improve customer satisfaction and operational efficiency.
While making the scorecard can be easy, ensuring its efficiency can be tricky. You can use Hear as a modern call center software solution to automate QA scoring and identify upselling opportunities to improve your agents' average order value.
Hear also helps you track and improve various agent performance metrics like average call handling time, first-call resolution, customer satisfaction score, and compliance.
Get started with Hear today to transform agent performance and customer satisfaction.
"The system is truly amazing. The insights it provides go far beyond what I could have imagined before we started using it."
– Nethanel Avni, Contact Center Manager at Cellcom
