Read on for the key insights Kurt shared with us.
Understanding the customer’s urgency
In the fast-paced world of travel, every second counts. Customers facing time-sensitive issues need quick and accurate solutions.
“What we see a lot is customers calling in when there are situations where time is of the essence. One of the key things we’re trying to do at Hopper, is take the customer in mind. And what’s more important to anyone but time?” says Kurt.
“We obviously want to be an industry leader in this space. There’s a lot of anxiety around travel; people don’t understand it, and when they call you, they just want an answer as quickly as possible. And we try to do that in the most empathetic way possible and try to get the customer with that resolution as quickly as we can.”
“We want to get down to the solution as quickly as possible. PolyAI is helping us with this upfront with a lot of smaller requests.”
On adapting to meet customer needs
“There was a big shift within Hopper. Previously, customers could only call through if they had a live booking,” says Kurt.
This realization prompted Hopper to reassess its approach, acknowledging that limiting customer access to those with live bookings created a gap in service.
“But that also left other customers unable to access customer service, so we knew there were a lot of low-level questions that we wouldn’t necessarily want to route to an agent because we need those agents to be focusing on things like flight changes.”
Enhancing customer service by leveraging AI
Central to Hopper’s strategy is the collaboration with artificial intelligence, particularly PolyAI.
“There are so many disruptions in the travel industry, and some of them are very complex. So how do we make sure that our agents are dealing with the most complex, most time-sensitive issues but allow customers to still access customer service in a way where they can still get the information they require to move forward,” says Kurt.
“PolyAI really fits that very cleanly because there are a lot of questions that people have that don’t require a huge amount of expertise. Fix issues, questions about payment, and questions about credits. So, PolyAI really fits that mold to help. It allowed us to open up to more people to the customer service experience without necessarily needing to leverage very highly skilled agents.”
Service automation: Expectation vs. reality
“I’ve experimented with different AIs and bots over time. So I wasn’t sure how good it was going to be,” says Kurt.
“It was a night and day difference for me from what I’ve seen in the past. The first call that we listened to was absolutely flawless and exactly the way we wanted it to go.”
For Hopper, AI was unchartered territory.
“We weren’t 100% sure what kind of call mix we were going to get,” says Kurt.
“We didn’t know what to tell PolyAI needs to be handled in this space. So we had to learn really quickly. I’m really impressed with the speed at which PolyAI has worked with us.”
Reflecting on the journey, Kurt acknowledged the desire for a deeper understanding. “If I could do anything differently today, it’d be, how do I get a temperature check of what we’re building the AI for?”
Managing uncertainties requires a delicate balance. Kurt described their approach, “If the flows that we built weren’t suiting the needs of the customer, we still routed those through to the agents so that we could collect that data and build flows that start containing those calls.”
On striking the balance between cost efficiency and customer experience
“People usually call customer service when something’s broken, right? No one’s calling you because they’re telling you they want to tell you you’re doing a great job,” says Kurt
“With the automation, what you can do is very clearly cut down to the issues that need a lot of attention versus those that don’t need a lot of attention.”
“But what PolyAI does for us in this space is it frees up our high-skilled agents to deal with critical problems, to deal with customers that are very anxious about something.”
“Both customers could be calling at the same time. They both get their answers. They’re both really satisfied with the output.”
“I think that we get the efficiency, we get the speed, and then because we’re able to get the calls to the place where they need to be, we also get the customer satisfaction of finding the right use cases for AI so that your agents can do more.”
Thank you so much to Kurt for joining us and sharing his insights at PolyAI VOX! You can watch all the talks on-demand now here.