6 takeaways for transforming contact center QA with artificial intelligence

Madeline Jacobson

September 12, 2023

Imagine going to an art gallery and seeing a large canvas covered with a tarp, with only a narrow strip of the painting visible at the bottom. If someone asked you to describe the scene in the painting, you could make your best guess, but you would be hard-pressed to give them an accurate answer.

Unfortunately, many contact center leaders are playing a similar guessing game, attempting to piece together insights into the customer experience based on low-response-rate surveys and time-consuming, manual reviews of only 2-3% of customer interactions. 

But as artificial intelligence continues to advance, contact center leaders now have the conversation intelligence technology to automate the QA process, analyze 100% of their customer conversations across all channels, and view the full picture of the customer experience. 

Our Chief of Operations and Product, Steve Trier, recently participated in a panel webinar called Transforming Contact Center QA with AI (you can watch it on-demand now). The panel was moderated by Rick DeLisi, Lead Research Analyst at Glia and co-author of “Digital Customer Service: Transforming Customer Experience for an On-Screen World,” and also included Char Sears, VP, MX & Product Management, Unitus Community Credit Union, and Garrett Jorewicz, VP, Member Solutions at Credit Union 1. The group had a wide-ranging discussion about the impact of QA automation on the customer and member experience, how AI can enhance the work of QA managers and agents, and overcoming resistance to AI in the contact center.

We’ve rounded up six of the biggest takeaways for contact center leaders that the panelists shared. 

1. Conversation insights bridge the divide between the customer and contact center’s perceptions of effort.

One of the biggest shortcomings of the typical manual QA process is that the information the business gets from it is limited to the criteria they have established. Nuance in the conversation can be lost, leading to discrepancies between how the customer felt the interaction went and how the business scored it. And, as DeLisi pointed out, “Any time there’s a mismatch in how an organization perceives an interaction versus how the customer or member perceives it, there’s likely to be trouble.”

For example, a customer might call your contact center and state that they had already called five times to try to get their issue resolved. As long as the agent on the call resolves their issue, the call would likely receive a passing QA score and be logged as a positive interaction. But even though that one interaction was scored as positive, the overall experience for the customer was frustrating. 

When you automate the QA process using a conversation intelligence platform like Tethr, the software tracks not just your custom QA criteria, but also customer sentiment, agent behavior, and other factors that influence the customer experience. So, for instance, if a customer mentions that they have called five times already, this statement would be flagged as demonstrating “chronic effort.” This gives you information about the customer experience that goes beyond the standard QA scorecard, helping your contact center uncover more opportunities for improvement. 

2. QA automation helps contact centers prioritize actions to improve CX.

When you’re responsible for improving the customer experience while managing your contact center’s operational costs, you need to consider the return on investment (ROI) of every CX initiative. In other words, if you’re evaluating ten potential initiatives but only have the budget and resources to implement two, you must determine which initiatives will have the biggest impact on the customer experience relative to the cost.

QA automation gives you a data-backed approach to prioritization by measuring how often customers bring up certain topics or pain points, or how often certain customer or agent behaviors occur. 

Jorewicz shared that this prioritization is one of the things his team at Credit Union 1 is most excited about as they prepare to roll out Tethr’s automated QA. “We’re hoping to start stack-ranking insights, like, ‘We’re getting a lot of interactions related to this [topic].’ So now we can zoom in on that and say, ‘What content do we need to create? Or how can we improve that process? Or how can we better arm our agents to serve our members more efficiently?’”

QA automation and conversation intelligence enable businesses to diagnose the most common customer issues or contact center cost drivers so they can prioritize the right actions to resolve them.

3. Customer (and member) experience is a team sport–and insights need to be shared.

By automatically reviewing all of their customer conversations, businesses can unearth insights that go beyond the contact center. And that gives contact center leaders new opportunities to work with other department leaders to improve the customer or member experience at every stage of the customer journey.

For example, contact center leaders might share insights with their product teams to help them address frequently mentioned product issues, or they might share insights with their technology teams to help them identify points of friction in the digital customer experience.

“Member experience is a team sport,” said Sears. “You need everybody in the conversation, and these member insights should be communicated and distributed [across departments],” Sears explained that communicating insights across departments would allow her organization to take a more proactive approach to experience management. “We can start working much farther upstream before that next call comes in and anticipate and overcome some of those potential pitfalls that come down the line.”

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4. Eliminating manual QA frees managers up to spend more time on high-value activities.

While some contact center employees may worry that AI has the potential to replace them, the webinar panelists shared that rather than thinking about replacing employees, they were considering how QA automation could enable employees to focus on more complex, high-value activities.

Jorewicz shared that his QA team is paired with learning and development, so he anticipates that after rolling out QA automation, the team will focus more on coaching agents using insights from their conversations. “I think this is going to enable our team to do more of those human activities and not just have to have headphones in, listening and taking notes,” he said.

Sears noted that her team has already been shifting away from checklists and taking a more nuanced approach to manual QA (for example, by evaluating calls for member sentiment), and she anticipates that QA automation will allow them to go deeper with this analysis while freeing managers up to make changes or implement initiatives based on the generated insights. 

5. Change management is essential to get buy-in for QA automation and other AI tech.

Despite the promise of conversation intelligence and QA automation, the panel acknowledged that it’s natural for employees to feel some hesitation about new technologies in the contact center. Sears and Jorewicz agreed that a strong change management program is critical to getting buy-in from team members and that one of the most effective strategies is to get frontline employees involved with the rollout of new technologies.

Sears noted that Unitus encourages employees to volunteer for project teams that they’re interested in so they can be part of the pilot program, play with the new technology, and provide feedback that’s incorporated into the design. “Allowing folks to be part of that process is really helpful in getting buy-in and producing champions across the organization,” she said.

Jorewicz emphasized the importance of education, transparency, and demonstrating to employees that their feedback matters. “Every single person on our team has a voice and can shape the future of Credit Union 1,” he said. “And when somebody comes up with a good idea, we make it a point to implement that idea as fast as humanly possible and give them all the credit in the world.”

6. AI-powered intelligence platforms help businesses meet their customers where they are. 

One of the major themes of the webinar was the idea of the effortless experience, something that DeLisi literally co-wrote the book on. In a world of multichannel customer service, customers want to be pointed to the lowest-effort channel that will allow them to resolve their issues. And if they do need to switch channels, the transition needs to be as seamless as possible to avoid creating a negative, high-effort experience.

Businesses can marry operational data from an interaction platform like Glia with conversation data from a platform like Tethr to better understand what’s happening in each channel, what’s causing customers to escalate, and what opportunities exist to improve the overall customer experience.

As Trier pointed out, businesses can use their operational and conversation data to create low-effort, self-service experiences for their customers. “Once you have that data–’What’s the number one reason customers contact me? What’s the number one way that agents resolve that?’--you can use it to train virtual assistants,” he said. “You can use it to improve virtual experiences and impact the future of how you design your customer experiences.”

Final takeaways

Conversation intelligence technology gives businesses the power to go beyond the 2-3% visibility of manual quality monitoring and capture the voice of the customer in every conversation and channel. One of the most immediate benefits is the reduction in manual QA time, but the long-term benefits go much further. Businesses can leverage their conversation insights to improve agent performance, gain a deeper understanding of customer sentiment, prioritize CX improvements across the organization, and ultimately deliver the experiences their customers want.  

Interested in seeing the full webinar? Watch it now.

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