6 Best Practices for Call Center Quality Assurance

Implement AI-driven solutions for real-time insights and integrate both human and virtual agents to enhance quality assurance. Utilize predictive analysis and unify customer conversations to create a continuous improvement cycle.

Introduction to Call Center Quality Assurance

Delivering exceptional customer experiences is crucial. In fact, 73% of customers say their experience influences their purchasing decisions. But if you’re reading this, you already know that call center quality assurance is important. And you probably already have some kind of quality assurance (QA) process in place. But you also realize that, to keep up with rising customer expectations, your team needs to look beyond simple scorecards and embrace AI-driven solutions for real-time analytics and insights.

Here are the 6 tips to help you revitalize your QA practices, to empower your teams to exceed those customer expectations.

1. Use AI to Strengthen Quality Monitoring

Implementing AI in quality monitoring can feel overwhelming. There are no shortage of options and places to start. But with the right approach, it’s both manageable and effective.

Lay the Groundwork for Robust Data Collection

Start by identifying where to collect data—calls, chats, and emails from all customer interactions. AI can analyze these channels seamlessly. This is where you can capture the customer side of the interaction. While most call center quality assurance programs have focused more on the agent, it’s critical to have the customer’s perspective so you can understand how issues are handled, not just conversations. It also provides greater context for QA review when you know what the expected response or procedure should be, based on what drove customer contact.

Choose a Conversation Intelligence Platform

Select a solution capable of processing large amounts of conversation data and providing insights you can use. A platform like Loris, recognized as a 2024 Gartner Cool Vendor and a Leading Provider by CMP Research, can identify recurring issues faster than manual methods or tools that only use keywords to detect issues. Look for integrations that accommodate various communication channels, especially in industries like fintech or retail.

Track Trends and Pinpoint Performance Gaps

With AI, you can detect emerging patterns, such as never-before-seen issues, as well as complaints from specific customer segments. This advanced analysis provides a broader view of issues as well as team performance beyond random call sampling.

Focus on Actionable Improvements

Use insights to enhance training and processes. If AI identifies confusion about a product, create targeted coaching sessions. Shift from problem-hunting to solution-finding to keep motivation high and route issues to the people in your organization who can actual fix them.

Tips for Making It Work

• Introduce AI tools that give you the big picture before targeting too narrow of an area. 
• Monitor success metrics like average handle time and first-contact resolution.
• Take the results from the AI project to understand what’s wrong and where to focus next.

2. QA Should Include Both Human and Virtual Interactions

Balancing human and virtual agents is essential for modern customer support. Each plays a unique role in delivering great experiences.

Human Agents

Human customer service representatives excel in empathy and problem-solving. With structured call center quality assurance methods—like coaching sessions and call evaluations—managers can enhance adaptability, leading to better First Call Resolution (FCR) and higher Customer Satisfaction (CSAT) scores.

Virtual Agents

Virtual agents, such as chatbots, reduce wait times and handle repetitive inquiries. However, they require monitoring to ensure consistent messaging and accurate responses. After all, if they consider frustrating customer enough for them to abandon their call as “successful deflection”, they’re not saving you as much as they’re costing you.

A robust virtual agent monitoring system helps maintain quality, understand effectiveness, and improve customer experience.

Comparison of Human and Virtual Agents

Key Takeaways

Focusing on quality in both human and digital interactions enhances the customer journey. Whether you’re ready for instant quality insights without the scorecard or just want to start more simply with AI-driven conversation summarization, leveraging AI can make identifying errors and maintaining consistent quality that much easier.  

3. Act on Real-Time Insights to Increase Efficiency

An efficient call center quality assurance strategy relies on real-time data for quick decision-making.

Embrace Real-Time Monitoring

Implement advanced monitoring tools to capture all conversations as they happen. This proactive approach helps identify issues early, allowing for immediate fixes.

Interpret Data Quickly

Traditionally, it could take days or week to go from when the call took place to actually having that conversation assessed. Now, you can instantly analyze customer interactions for signs of negative sentiment or recurring problems. 

Provide Immediate Coaching

Having these insights more quickly means, you don’t have to wait for the scorecard to act. You can deliver on-the-spot feedback to agents using the AI data to pinpoint issues that an agent might be having, like not spending enough time on discovery or forgetting the standard greeting. 

Evaluate and Adapt Rapidly

Regularly assess changes to gauge their impact on performance and customer satisfaction. Use consistent metrics and measures to identify successes and keep improvements on track.

Tips

• Prioritize interactions with unusual spikes in call length or negative feedback for coaching.
• Consider using AI call center quality assurance tools to gain real-time insights into agent actions and customer sentiment.

4. Go Beyond Scorecards with Predictive Analysis

Predictive analysis gives you what you need to know without having to wait for the traditional quality scorecard approach. This helps you identify, address, and mitigate issues before they have a larger impact.

First, Rethink Your Quality Scorecards

With the ability to instantly gain quality data, the role of your scorecards changes. Again, using AI, you can automate your QA policies to find what matters most to you, like whether agents are using specific wording or following up customer frustration with empathy or an apology. This helps you fine-tune your quality program to have multiple layers of agent performance insight.

Next, Connect the Dots with Data

Analyze both historical and current interactions to uncover trends beyond scoring and policies. This could reveal recurring issues that your agents might be great at handling, but still lead to customer frustration. Having this visibility can illuminate larger issues that live beyond the call center.

Then, Spot Emerging Patterns Early

Predictive analysis transforms data into an early-warning  system. Organizations use this for monitoring potential issues after a launch or responding to confusion around a marketing campaign or website change. Getting faster feedback enables you to fix the issue, improve communications to customers, or respond in a consistent way. 

Finally, Put Predictive Analysis into Action

Within the call center, you can use this information to refine scripts and macros, update your FAQs, or organize coaching sessions. But when you get buy-in from other organizations, you can develop a feedback loop whereby the call center spots issues and routes them to the appropriate team to address them. If you follow this approach, you’ll find fewer issues, and happier customers, are the output.

Tips

• Identify common pain points first and expand from there.
• Use AI to rethink how you use, automate, and act on quality insights to drive improvement.
• Get the rest of the organization involve to really see greater impact.

5. Unify Customer Conversations into One Intelligence Stream

Many contact centers manage scattered customer communications, which can lead to missed details. Unifying these conversations enhances the efficiency of your QA process.

First, Centralize Interaction Channels

Each customer service interaction platform likely has some analytics and reporting. But having fragmented information limits insights. Bring all communications into one system to unify data from multiple channels, making it easier to identify recurring issues.

Then, Leverage Conversation Intelligence

A single view of all customer interactions means you can see all customer issues as well. This visibility into the customer journey provides a way to not only understand top issues but also prioritize them. And findings like recurring issues across multiple issues can help determine gaps in FAQs, on-boarding, or other obstacles in the customer journey. 

After That, Optimize with Virtual Agent Intelligence

Having a clear picture of customer issues gives you a customer experience map. Once you have this map, you can better understand which paths can be automated, for example, those with straightforward or transactional responses. As you implement more and more chatbot-driven use cases, this oversight helps identify whether chatbot escalations are handled well or if customers frequently switch to phone support.

Finally, Close the Loop with Actionable Feedback

Every customer service interaction has two sides. And by unifying both the customer and agent conversations, you can enable targeted feedback across all channels. 

Tips

• Bring conversations together to have more holistic analysis.
• Unified data reveals top issues and highlights coaching opportunities.
• Regularly revisit workflows to implement automation and improvements.

6. Create a Continuous Improvement Cycle

Establishing a dynamic feedback loop in your call center is crucial for keeping quality measures relevant and effective.

First, Analyze Performance Data Regularly

Scorecards are essentially one-time assessments of agent behavior that get used for coaching and then discarded. With a call center quality assurance and customer analytics platform you can identify ongoing patterns and trends in both high and low-performing engagements. This helps you benchmark issues across your organization, not just stop at individual agents.

Next, Adjust Your Training on the Go

Incorporate faster agent insights with both QA policy automation and traditional agent assessment. This approach lets you triage small issues, provide coverage for compliance and policies, and drill into more systemic performance issues. 

Finally, Measure Outcomes and Recalibrate

Monitor quality scores to track progress and identify areas for improvement. Use Quality Assurance dashboards to determine if and when to coach, along with updating scripts and responses as new challenges arise.

Tips

• Combine call center quality assurance and customer analytics platforms to get greater value.
• Collaborate with cross-functional teams to implement effective solutions.
• Use continuous improvement cycles to drive down costs and enhance customer loyalty.