High-quality interactions are essential for superior customer satisfaction and loyalty. And that requires a comprehensive contact center quality assurance (QA) strategy. Without it, contact centers struggle to understand — and improve — the customer and employee experience.

This blog explores the advantages of implementing a modern QA strategy supported by AI-driven solutions. It also provides guidance on overcoming the limitations of manual quality assurance processes so you can enhance customer experience and elevate agent performance.

Importance of High-Quality Interactions

Every contact center has goals and metrics to enhance interaction quality so they can keep customer satisfaction levels high. But measurable results often are limited.

Typically, there’s a lack of visibility into what’s happening within an interaction. Without that view, it’s very difficult to understand what customers really need. You won’t see what problems they’re facing and why they’re asking for assistance. To proactively resolve those problems, support teams need clear and actionable data.

Let’s say you’ve recently launched a new product and it seems to be causing problems for customers. If the call center can pick up on those indicators during its quality and performance monitoring, you can make adjustments.

You might launch a campaign to explain how to use the product. Or you could do a recall before the product has a widespread effect on customer experience.

Comprehensive Interaction Monitoring

Manual QA processes in contact centers are typically limited to a random sampling of interactions. Quality evaluators select a few calls or chats each month to assess. But this approach inherently misses key interactions that might reveal significant issues or opportunities for improvement. The result is an incomplete understanding of the customer experience and agent performance.

A modern quality assurance strategy uses automated systems that record and analyze 100% of interactions. This type of comprehensive monitoring uses advanced technologies like speech and text analytics. These contact center quality assurance tools provide detailed insights into every customer interaction, highlighting trends and helping to identify areas for improvement.

By analyzing all interactions, contact centers gain a complete picture of customer issues and how well agents perform in resolving them. This holistic view gives you more accurate and actionable insights.

Automated systems also help identify patterns across all interactions, enabling proactive problem-solving and continuous improvement. Performing root cause analysis helps you understand why certain issues arise and how to address them effectively.

The Limitations of Manual Analysis

Evaluating contact center agent interactions without automation is a very time-consuming process. As a result, conducting a truly comprehensive analysis becomes impossible. Sample sizes are simply too small to provide useful insights, particularly when dealing with a high volume of voice and digital interactions.

A quality evaluator might select five interactions for their random sample. Perhaps everything sounds wonderful in those five. Agent performance is amazing! But your business overall could be failing in some ways that aren’t detected in a few calls.

A manual analysis won’t be able to identify data points across all interactions, such as positive or negative interactions within a specific topic, and then examine details that might indicate a trend.

If you don’t understand the root cause of any type of business issue, you can’t make smart decisions on how to address the problem or track the improvements you put in place. Insights are limited in scope and in how they can be applied in your business.

Data-Driven Decision-Making

Smart decision-making relies on data — and the best data already resides in your systems. Because you can now monitor, analyze and evaluate the quality standard and the performance of interactions from multiple perspectives, you can fully understand what’s happening. From this, you can plan where to go in the future and how to get there.

For example, businesses entered the pandemic believing that most customer interactions would move to digital channels. It seemed logical, and no recent precedent suggested otherwise. So, many focused on how to use digital channels to sell products and services.

But as the pandemic dragged on, the data showed something else happening: Yes, customer support interactions on digital channels were growing, but so was the use of voice. And its volume hasn’t gone down since.

These are the very consequential results and only apparent when you have a call center quality assurance strategy that can analyze all types of interactions.

Let’s say people aren’t responding to a campaign that’s offering a discount. Initially, you might think that it’s not a popular product. But what if it’s simply too expensive for certain target markets? You’d know that because you’re seeing those trends in the conversations you’re monitoring.

Contact center quality monitoring software enables you to find correlations, such as an issue related to regional differences. Using this data, you could segment your campaign and tweak messaging to match the known preferences of different regions.

The Costs of a Traditional QA Approach

Once you’re analyzing all interactions, there’s even less value in a traditional approach to quality assurance. In fact, you must have a quality management program because you’re no longer evaluating just for the sake of doing an evaluation. It’s about what you can take from those evaluations to apply to your business.

Traditionally, businesses might have 10 different tools for interaction recording, quality management, speech-and-text analytics, lead management and more — creating inefficiencies, inconsistencies, and additional maintenance costs. Some systems require manual start and stop for recording interactions, different tools for transcription and separate tools for analyzing that transcription. Additionally, some of these processes are also manually initiated.

In a traditional approach, information about an interaction would be sent to another system and some information would be lost, especially with digital information. Or you’d have to stitch together all the various elements of an interaction to have the full picture.

It’s not possible to do this for 10,000 interactions per month. It requires too much human intervention to make it work.

Benefits and Key Features of Modern QA Tools

In a modern approach to QA, all the components are integrated. Your recording system is part of your contact center solution, as is the quality management software. And that recording is necessary for compliance. It’s the same system that analyzes the quality of interactions, how your employees are performing, how customers are responding — and whether your customers are satisfied.

With modern artificial intelligence (AI)-powered capabilities working together on a cloud platform, you have all these critical insights and a holistic view of what’s happening. That information is never lost.

Now you can put automation to these capabilities and your policies. You define when to record an interaction, when to store that interaction, when this type of interaction gets an evaluation and even when you’re not going to evaluate it.

Because these policies and processes are standardized across all interactions, the comparisons are always going to be in sync with everything else. This removes the chance of someone forgetting to include some part of a manual analysis process. But this advantage goes beyond just removing human error.

Getting Rid of Bias and Rewarding Agent Success

Bias is always lurking in decision-making. Consider this: A quality evaluator hasn’t been happy with an agent’s performance. So instead of evaluating five interactions, she decides to evaluate 10, and she’s going to be harder on the evaluation than typical evaluations for other agents. The process has a huge potential for bias.

But with a modern QA strategy, evaluation assistants help reduce bias and manual evaluation time by pre-answering questions based on the content of the interactions, rather than preconceived notions. Evaluators can override these answers, providing human understanding and context. Automating which interactions you evaluate and selecting which ones to examine based on certain policies can further eliminate bias. You’ll also ensure that you’re evaluating the performance of multiple agents – without focusing solely on one or two.

You can base evaluations on predefined policies, such as interaction duration, wrap-up codes, new agents, queue type and more. Additionally, by analyzing topics from interactions, agent empathy and customer sentiment, businesses can achieve a better understanding of agent pain points and identify when and why potential “worst outcomes” occur.

Cancellations are a good example. They’re difficult for agents to handle because customers are often very angry. When indicators show an unexpected number of them, you dig deeper.

If you review these interactions for a single agent using a manual approach, it doesn’t look good for the agent. But a modern QA strategy might reveal that the agent’s subpar performance in preventing accounts from cancellation is due to a lack of training.

You can work on improving skills through learning modules. You can also empower the agent by showing him how he handled an interaction and how he could handle it better next time.

Then you can show him an interaction in which another agent properly handled a similar cancellation interaction. Compare as much information as you want to build understanding and set expectations.

A structured QA process can also recognize and reward good performance. This boosts agent motivation and engagement, leading to higher job satisfaction and retention.

Expectations Are Changing for Quality Evaluators

The role of QA evaluator is becoming easier in many ways. While they’ll no longer spend hours evaluating agents, they must ensure that AI-provided answers are correct, empathetic and meet high standards of human interaction. This requires stronger analytical skills.

During a summary analysis, they might note an indicator that matches a similar indicator from a different interaction, and both are tied to the trends that the evaluator sees for customer sentiment. And that’s tied to agent empathy trend scores. Correlating this data is a powerful tool for gleaning timely, accurate insights.

Analytical skills are more critical than ever because businesses are still interacting with a machine that’s trying to mimic human reactions and how the models have been trained.

But AI can’t hear specifics in the voice of the other person. For example, I might say a certain word because I’m happy and I’m celebrating. Or I might shout the same word because I hit something, and now I’m in pain. It’s different than understanding sentiment based on the tone of voice.

This is why we call quality managers the new “knowledge workers.” Their roles will continue to expand as more opportunities surface for improving outcomes.

Meeting Today’s Needs with Modern Contact Center Quality Assurance Software

Investing in a comprehensive QA solution is not only a best practice, it has quickly become a necessity for contact centers that want to deliver exceptional customer service and achieve long-term success.

It enables deep visibility into the interactions that drive your business, giving you numerous benefits over traditional manual processes. These include comprehensive interaction monitoring, actionable data and insights, enhanced employee performance, and strategic quality management.

Learn more about how the Genesys Cloud™ platform can help you get more from the data you already own to improve customer and employee experiences.