Modern Quality Assurance: How to do customer service QA the right way

Customer service best practices are ever-evolving but always focused on the same North Star: increase sample review size, decrease audit time, and invest the recouped time into agent development. Unfortunately, manual QA processes are the biggest blocker of this goal. The audits are spread across multiple tools, which takes a significant amount of time. These time constraints limit the number of interactions that can be reviewed, and as a result, training opportunities slip through the cracks.  

We get it; you’re not sticking with old-school methods without reason. Your analysts have spent time perfecting their QA processes and are hesitant to move into the unknown. That’s why we recommend a hybrid approach where cutting-edge AI technology meets human expertise. This powerful combination not only streamlines customer service QA, but also fosters a deeper understanding of interactions and still maintains a high level of customisation to meet your business’ unique needs. 

As customer expectations skyrocket, embracing modern agent QA practices is no longer optional; it’s essential. So, take the first step and explore how blending AI and human insight can transform your quality assurance processes by: 

• Scanning 100% of customer conversions for full insights
• Monitoring compliance with company standards in real time
• Tracking standard KPIs, like customer satisfaction (CSAT) and average handling time (AHT), and integrating them into an all-encompassing quality performance overview, alongside sentiment and review scores
• Gauging conversational qualities, like empathy, frustration, or confusion
• Improving agent performance with carefully targeted reviews as well as spotting issues with agent-to-agent and team-to-team metric comparisons

The changing face of agent QA in customer service

The sheer volume of customer interactions has been a top challenge in QA for decades. Manually reading and listening to all conversations is inefficient and out of the question, but randomly selecting a few conversations a week for each agent only provides a very limited and incomplete view of their performance. 

With multiple analysts, there’s also the issue of each quality specialist approaching reviews differently, which can lead to unfair grading. Plus, when agent QA reviews are spread across multiple systems, getting the feedback to agents takes time and can prevent them from making quick and effective positive changes to their service style. So, not only does this labour-intensive approach put your agents at a disadvantage, but it also prevents your customers from getting the best experience possible. 

Today, the landscape is shifting dramatically as businesses look to improve operational efficiency and boost customer satisfaction scores with higher-performing agents. Around 54% of customer service leaders now leverage AI-powered tools to tackle complex support issues. Based on early use cases, industry experts predict that harnessing AI in the quality assurance process can cut QA costs by 50% and increase agent efficiency by up to 30%.

As more businesses embrace AI-powered agent QA, the focus is moving from basic compliance checks to improving agent performance and overall customer satisfaction. 

Why AI alone isn’t enough for effective QA

What makes AI-driven tools so impactful is their ability to identify patterns, trends, and anomalies within large datasets, offering insights that manual reviews alone can’t match. For instance, AI can analyse every customer interaction to pinpoint compliance with protocols; perform sentiment analysis, like understanding subtle queues in customer feedback to detect confusion; and flag potential issues that need immediate attention. These results are impossible for a human analyst on their own. 

But here’s the catch: the world of customer conversations is very much a human game. While AI does an excellent job at working within tight parameters and well-defined policies, the subtle nuances and complexities in interactions often need a human touch to fully understand what’s going on for an accurate assessment. 

For example, you can ask AI to answer highly pointed questions, like “Did the agent open the call in a friendly manner?”, and it can scan and mark every conversation for having done it or not.  A human, on the other hand, offers value where greater context is needed, such as “Did the agent showcase high knowledge of our products and approach the conversation in a structured way?”

If we lean solely on AI for agent QA reviews, we risk missing the mark on feedback quality. This can create a gap between agents and customers, leading to lower metrics and potential regulatory issues related to using AI for employee evaluations.

The Hybrid Model: AI + human reviews for superior QA

In our view, AI isn’t meant to take over; it should team up with human expertise to build a solid customer service QA process that benefits all parties involved. When you blend AI’s knack for data analysis with the invaluable insights of real people, you get a much clearer picture of customer interactions. This hybrid approach offers numerous benefits:

• Comprehensive View: The two methods work together to provide the full picture of each interaction, allowing for highly targeted coaching that’s impactful – no more guesswork or operating with only half of the information. 
• Consistent Results: AI isn’t swayed by personalities or biases, so you can trust it to always produce reliable results. 
• Detection of Automatic Fails: If there are any auto-fails included in your scorecards, like the use of profanity, then AI can detect and flag the issue for 100% of conversations whereas a fully manual review process would likely miss many because of random interaction selection. 
• Multilingual: AI’s multilingual capabilities can easily detect an interaction’s language and translate it accordingly in the same platform, saving the analyst time by avoiding the inefficient process of jumping between tools for manual translation. 
• Regulation Compliance: A blended approach also keeps the agent QA process in line with regulations, like the European Artificial Intelligence Act (AI Act) and Germany’s Federal Data Protection Act (BDSG), preventing costly fines and legal ramifications. 

Modern QA in action

Sure, the benefits are great on paper, but what do they look like in the real world? Fair question, and luckily, we have a real-world use case on hand.

Since launching our Agent QA Reviews feature, our customers have begun implementing a modern customer service QA process and are seeing the impact. One iGaming client has already experienced measurable results in under seven weeks of transitioning from manual to hybrid agent reviews, noting agent response time is down 11%. They’ve also experienced a drop in customer frustration and an increase in praise. 

Best practices for implementing modern customer service QA in 2025

If your organisation has a complex traditional system in place that’s taken a lot of time and effort, the thought of transitioning to a more modern approach can feel daunting. So, naturally, you want to make the transition worth it, right? The good news is that it doesn’t have to be complicated. With the right setup, you can ensure a smooth shift that benefits your company for years to come. 

1. Pinpoint your QA goals

Start by getting clear on what you want from your QA process. Are you aiming for more consistent scoring across evaluators, faster turnaround on agent feedback, or more thorough insights from customer interactions? Maybe all three? 

Also, take a look at what’s slowing your team down now, like random sampling, feedback delays, or inconsistent evaluations. Knowing your goals and pain points helps you focus your efforts where they’ll have the biggest impact. 

2. Choose an all-in-one QA platform

A major efficiency pain point when dealing with QA is juggling spreadsheets, multiple tools, and CRMs. Opting for a system that pulls the full QA process into one place helps analysts stay focused and organised to complete agent reviews faster, resulting in a greater number of conversations covered. That’s why we’ve incorporated every aspect of QA into EdgeTier’s Coach tool, including scorecards, grading guidelines, and built-in translation. 

3. Determine tagging criteria

AI has the power to review 100 % of all interactions and tag conversations that need further review by a human. To take full advantage of this power, you need to set criteria that matter the most to your organisation. Determine what flags indicate the need for closer inspection, such as: 

• Profanity 
• Aggressiveness
• Escalation Request
• Threats of Legal Action
• Customer Sentiment, like frustration, praise, or confusion 
• Agent Empathy

Use your QA goals to help direct this list to make sure they’re aligned.

4. Customise your QA tools

Tailor your agent coaching tools to reflect your organisation’s standards and objectives. This step can mean weighting certain criteria more heavily or including guidelines for reviewers to follow. Compare your old scorecards to your new goals and tagging lists to make sure what’s important to your company is reflected in the grading categories before uploading the criteria to your new system. 

5. Deliver real-time feedback 

Once a review is complete, make sure the feedback is sent to agents as soon as possible. Fast, actionable feedback enables your customer support team to make quick improvements. It also fosters a sense of support and growth. Consider setting up regular feedback loops with team leads or coaches so that agents can ask questions, share insights, and continuously improve. 

Boost agent performance and cost savings with hybrid QA

When it comes to your brand, customer experience can truly make or break you. It all starts with having a modern QA strategy in place. Ignoring the need for a balanced approach puts your company’s reputation and bottom line at risk. By embracing a hybrid model that allows AI to work hand-in-hand with human insights, you can create a QA process that’s not only effective but also enriches the customer experience and keeps you compliant with regulations. 

At EdgeTier, we recognise the critical value of modern customer service QA. Our AI-powered solutions are built with input from real customer service teams, making them incredibly user-friendly and time-saving. Imagine a platform that gives you 100% coverage of conversations and an all-in-one review process, complete with built-in scorecards to streamline agent evaluations — that’s what we’ve created!

  

Let’s work together to elevate your QA strategy and make real, meaningful improvements in your customer interactions.