For years, Central Bank’s customer service team relied on processes that had served them well but struggled to keep up with the organization’s growing operations.
“When I started, we used big metal binders—what I call ‘parts books’—as a resource for everything,” recalls Mary Beth Gillum, Senior Vice President of Central Bank’s Customer Service Center (CSC).
This manual setup made investigating and responding to customer concerns cumbersome. It was also more difficult to analyze customer interactions and capture the insights needed to improve customer service. With more than 3,000 daily interactions across various channels, the CSC team needed to streamline operations to deliver what they call “legendary service” while maintaining “that local look and feel.”
The team turned to Observe.AI to automate quality assurance (QA) processes.
“What swayed us was Observe.AI’s user-friendly interface,” says Jeffrey DeBourge, Contact Center Manager and AI Administrator. “Whether it’s an intern in college or a senior analyst, everyone can use it with no issues.”
By implementing Post-Interaction AI, Central Bank gained instant visibility into customer interactions. Features like Auto QA, Screen Recording, Moments, and searchable transcripts enabled the CSC team to evaluate 167,000 calls in a single quarter, up from just eight per month.
“The team first focused on streamlining its QA processes. “The two other key components were automatically capturing the information from a call and making it reportable, and sentiment analysis or understanding what people are calling about,” says Gillum.
Faster call analysis
Before Observe.AI, the team typically spent hours investigating customer issues. Now they can search keywords and pull up relevant interactions within minutes, making it easier to identify trends and understand customer concerns.
“During a global IT outage, I searched keywords like ‘global IT outage’ and a couple of other related phrases,” shares Gillum. “Within two minutes, I identified calls related to the outage and gave the leadership an accurate count [of customer issues].”
Reducing after-call work
One of the CSC team’s biggest efficiency gains has come from eliminating manual disposition codes. Instead of relying on these labels to categorize calls, agents now use Observe.AI’s Moments function to automatically track key events and themes within conversations.
Switching to AI-based tracking has reduced after-call work from an average of 30 seconds to just 10 seconds, “which is exceptionally low for a financial call center,” says DeBourge.
Smarter agent coaching
The team has likewise identified and addressed agent behaviors that unnecessarily increase the time spent on calls.
“We had an agent who was staying on the line to make sure the transfer was successful, but it was increasing her handling time,” says DeBourge. “We uncovered this through Auto QA, and she immediately adjusted her behavior after seeing the data.”
AI-powered analytics has also helped supervisors detect excessive hold times and long silences.
“Some agents don’t put customers on hold, while others use hold for everything,” DeBourge explains. “Observe.AI helps us find the right balance and rethink our strategies.”
More robust fraud detection
In addition to improving operational efficiencies, Central Bank is leveraging Observe.AI to strengthen its fraud detection.
“We’ve built Moments to identify fraud-related calls, and we’re exploring deeper insights to understand their root causes,” explains DeBourge.
With analytics and insights from Moments, the bank is developing self-service tools to help customers manage fraudulent charges. The CSC team also uses Moments to identify service opportunities during calls. For example, when a customer reports debit card fraud, agents can suggest setting up online account alerts for added security.
Expanding AI capabilities
Central Bank is exploring more ways to use the Observe.AI platform. It plans to expand call driver analysis, boost fraud detection capabilities, and extend AI-powered insights to other departments.
For example, the bank’s internal resolution center—a help desk that provides advice to staff members on bank procedures—is considering using transcription and analytics to identify call drivers and improve employee training.
“It’s exciting to see other business units adopting Auto QA for coaching and increasing team performance,” says DeBourge. “Everyone in our company wants Observe.AI.”
