With Observe.AI’s Post-interaction AI, Freeman boosts its QA scores and reduces average handle time as well as not-ready time.
Events and exhibit solutions provider Freeman manages thousands of moving parts behind every show, from tight timelines to last-minute logistics changes and high expectations from exhibitors and organizers. Its customer support team handles thousands of inquiries every week, ranging from invoices to materials handling for trade shows.
As demand for events rebounded after the pandemic, customer interactions became longer and more complex. The nature of Freeman’s business requires responsive and accurate service, but its QA process was struggling to keep up.
“Our average agent takes about 100 to 120 interactions a week,” says John Riel, Director of Exhibitor Support at Freeman. “But at the time, we were only evaluating one of those.”
This led to inconsistency in agent evaluations, little visibility into performance, and missed coaching opportunities. It also made it harder to identify patterns across events and customer segments, limiting the company’s ability to act on important customer trends.
“We knew we needed a better approach to QA,” says Riel. “We needed more data to improve our performance and make informed decisions.”
Riel’s team re-evaluated its QA process and searched for conversation-intelligent platforms that could help them coach more effectively at scale.
“We weren’t looking for a giant company. We wanted one that was smaller, more responsive, and with their own tech.” His team attended demos and assessed product roadmaps.
“After every demo, we’d ask the team: What did you like? What didn’t work? Observe.AI hit the mark for us on price, flexibility, and product capability,” adds Riel.
With Observe.AI’s Post-Interaction AI, Freeman moved from a heavily manual QA sampling approach to coaching based on actual data from 100% of customer interactions.
“We use the QA tool in our Observe.AI platform to manage coaching sessions,” says Riel. “We can track agents’ progress, connect dots, and show them actual data and examples. That kind of visibility just wasn’t possible before.”
This informed coaching strategy has improved the company’s first-call resolution and reduced repeat calls. That’s contributing to a 4% year-over-year drop in volume, even as the business continues to grow.
Two years into using Observe.AI, Freeman has made remarkable improvements across quality, speed, and customer experience.
Notably, its net promoter score has increased by almost four points to 10–11% over this period. “That’s huge for us,” notes Riel. “Our QA scores also rose about 6%, and at the same time, we saw a 22-second decrease in average handle time as well as a 10% reduction in not-ready time.
“There’s a lot we can now point to that’s been improved because of the data we’re able to surface,” he adds.
Freeman’s customer support team has become an invaluable source of insight for other departments across the company. With visibility into every customer conversation, they help identify and investigate trends that were previously buried in manual QA reviews and incomplete CRM notes.
In particular, the team gets pulled into post-event inquiries when other departments are trying to solve a problem, answer a question, or spot a trend.
Denise Brown, Vice President of Customer Experience, describes how her team is often asked to investigate. “They want us to basically help do some ‘forensics’, like go look at those calls and help us understand,” she says. “How many times did people call and actually say, ‘My driver was refused,’ or ‘My shipment couldn’t get picked up’? Whatever it is, we go in and look for those keywords.”
Freeman also uses Observe.AI to quickly confirm and understand the questions customers raise within call transcripts.
“We recently had issues with a new invoicing platform,” shares Brown. “Customers were calling and saying they had issues with duplicate charges. So we went into the platform and keyed in ‘duplicate charges.’ Sure enough, there were calls just within that period of time that were related to it.”
Previously, the team used a manually updated inquiry tracking system, which made it difficult to get a complete picture. Now, they combine both systems to cross-validate issues and identify recurring themes.
“We drill down to each of those calls,” says Brown. “And with AI, I think this is giving us every phone call in real time and probably more accurate than human effort with our other system.”
These insights now inform improvements in customer experience. For example, when customers repeatedly ask the same questions—such as how to handle freight—they’re often pointing to a gap in communication.
“Sometimes the information they need is three clicks away,” says Brown. “So we move it to just a click away. It sounds so minuscule when you’re saying it out loud, but those little things we’re doing help reduce friction and improve the overall experience for our customers.”
Now that the platform is embedded within Freeman’s daily operations, the company is exploring what else it can do with Observe.AI. It is particularly interested in its GenAI-powered insights and how broader access to analytics could support teams across the organization.
“If we’re looking for company-wide impact, we’re really hoping that we can partner with Observe.AI,” says Brown. “I really do feel that can be a game changer for us, and not just for the call center.”
Because many teams at Freeman use the same telephony platform as customer support, there’s potential to identify insights that map the full customer journey.
The company is also evaluating real-time assistance tools to help agents respond faster and more accurately.
“We think that’s where the value is, giving our agents faster answers to help customers,” says Riel. “We can see a reduced handle time, which of course, for us and the company overall, could potentially lead to a reduced full-time equivalent and cost savings by us investing more with Observe.AI and with AI in general.”
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