“AI” is a buzzword being thrown around in the business travel industry a lot these days. But as Marilyn Markham, global product director at American Express Global Business Travel (GBT), explains, when you look at what artificial intelligence actually is, and its applications in business travel today, it’s basically driven by data science and analytics.
While that may not sound as exciting as what’s portrayed in “West World,” such technology can dramatically enhance the business travel experience. Today Markham explains a few ways how.
With machine learning, a subset of AI, we can begin using data from previous trip itineraries to create a more personalized booking experience through AI business travel. We’re specifically employing this technology with our Trip Recommender™ solution, which is currently available to clients in the United States, United Kingdom, India, and Australia for hotel bookings.
If a traveler books a flight through a company-approved channel without a hotel attached, a notification automatically will be sent to the traveler with a curated list of hotels that Trip Recommender suggests booking. The whittled-down choices are based on the traveler’s previous business trips, usual booking habits, and room availability.
“Historically, the preferences that were offered were based on the company’s preferred suppliers. But Trip Recommender is more dynamic and personalized,” Markham explains.
Not only will this save travelers significant time from having to narrow down the options themselves, but by making the booking process convenient and fast, it will also likely lead to a higher hotel attachment rate, which then can translate into more savings for the company and improved data capture for duty of care purposes.
And what if a traveler doesn’t see a hotel they wish to book? That’s when machine learning comes in handy.
With this AI business travel technology, Trip Recommender can begin to understand the traveler’s preferences over time and will self-adjust in order to deliver better results with no human intervention required!
So if a traveler doesn’t like the first two options that Trip Recommender offers but goes ahead and books the third, the program will work to establish the pattern as to why a traveler selected one hotel over the other so that the next time it will feed the best option the very first time. And if a traveler’s own booking habits begin to change, so will the suggestions Trip Recommender provide, thanks to its machine learning capability.
Expediting the onboarding process
Markham also sees how AI and automation can be useful for the onboarding process. Traditionally, when a travel management company (TMC) takes on a new client, there has been a slew of emails with attachments bouncing back and forth as the TMC establishes the contracts the client has with preferred suppliers, the billing structure, policy, and travel preferences, etc. It can be a lengthy procedure filled with a lot of follow-ups and fact checking before all that information is manually loaded into the proper systems.
But now, instead of exchanging documents via email, we have an online portal through which they can provide the right information the first time around with step-by-step support and validation for travel booking even as they supply the information.
“Then,” Markham says, “the robotics kick in to replicate the information to systems that are not yet fully integrated. For the systems that do have API (application programming interface) connectivity, the data will just flow through to them accurately and consistently.”
Essentially, she says, it accelerates the entire onboarding process and “the go-live date is going to be much earlier than waiting on people to do all that work.”
Providing meaningful data
Big data tools also can be useful to travel managers (TM) with their optimization goals. Instead of humans having to eyeball loads of data to identify travel patterns and arrive at cost-saving solutions on their own, a data visualization tool like Premier Insights™ can instantly deliver actionable results in an easy-to-digest format.
For instance, Markham says that with such a tool, TMs may see that a lot of T&E dollars are being spent in one particular destination where business is growing. Armed with this kind of information, then they can proactively negotiate deals with suppliers to make it cheaper to travel to this hotspot location.
Or perhaps you are trying to figure out ways to prevent travel burnout and health issues among frequent business travelers, which is a rising concern today. Markham says you can review certain KPIs, such as the frequency of travel at an employee level, distances they have traveled, and time they have spent on the ground away from home.
“When you start to crunch all of that together, the quality of your business travel program starts to surface,” she says.
From there, you can look at what policies and perks the company has to offset travel fatigue and meet with senior management to make decisions whether more needs to be done to minimize the impact of frequent travel.
But that is just scratching the surface. With nearly 40 million bookings made annually with American Express GBT, we have a tremendous amount of data that may be extremely beneficial for TMs when strategically culled.
Markham says this is why we have hired a team of data scientists: to continually find interesting and new ways to cross-reference the wealth of data we have, in order to deliver even more meaningful analytics and actionable insights to our clients.