AI analysis, part 1: Travel tech giants – PhocusWire


The ubiquity of artificial
intelligence is evident in the fact that the abbreviation “AI” is now a common
and recognizable term. A Google search for “AI” turns up more than 820 million
results, with articles from sources as diverse as The Wall Street Journal, the Verge and Vanity Fair and topics such as AI for healthcare, astronomy and human
resources.

Just last week, Microsoft announced a reorganization
that bolsters its focus on AI, with CEO Satya Nadella calling this technology
something that will “shape the next phase of innovation.” A recent article in Harvard Business Review calls AI “the most important general-purpose technology of our
era…”

And in November, a team led by Stanford University announced
the launch of the AI Index, an open, not-for-profit project to track activity
and progress in the field. Clearly, AI is here to stay.

In travel, AI is starting to touch every business sector
and every step of a traveler’s journey, from the ideas and images in
online searches to the pricing of flights and accommodations to
experiences in-destination.

And the subset of artificial intelligence known as
machine learning holds the promise that these systems and the capabilities they
offer today will only continue to get better – faster, smarter, more helpful – with
the addition of each bit and byte.

This month we’ll be digging into artificial intelligence
to learn how it’s being used in travel and to understand its future potential.

We begin with input from the three largest global distribution
systems: Amadeus, Sabre and Travelport.

Background

The global distribution systems are the legacy travel
tech providers in the industry, at the center of distribution and other IT
services between suppliers and intermediaries.

But while the GDSs do not deal directly with travelers,
they know that travelers are the final – and ultimately most important –
customer in their workflow. So much of the research and development taking
places within the GDSs is focused on creating solutions to improve the
traveler experience.

Context for travel may be the single most important concept that needs to be addressed.

Ben Vinod – Sabre

Amadeus, Sabre and Travelport are all investing
substantial resources to implement AI into their platforms
and processes.

Their efforts spread throughout their webs of business units.
Representatives from the three companies shared a range of examples, such as using
AI to support their sales teams, to refine digital marketing, to detect fraud,
to monitor internal systems, to automate agency tasks and more.

But it’s clear the bulk of their work with AI – and where they see the most promise – is in customer-facing
solutions to drive conversions from looking to booking.

Segmentation and
personalization

The amount of choices that can be offered to a traveler
looking for flights, for example from Los Angeles to London, numbers in the
hundreds. One of the biggest challenges for suppliers is figuring out how to
provide relevant, targeted options.

It’s similar to the conundrum faced by all e-commerce
retailers, as they try to make conversions. The solution comes from
knowing each customer, and it’s something the GDSs are trying to facilitate –  but it can be a bigger challenge for travel
brands than companies in other sectors.

“We have less individualized data than in the retail space,”
says Rodrigo Acuna Agost, head of the Amadeus AI Research Group.

“Amazon has many observations for every customer, so they really
know their customer – people may buy 20 or more times per year, while in travel
you don’t have this frequency. Eighty-percent of the observations we have in
travel systems are people that travel once a year or less.”

You tend to travel because you want to enjoy the experience at the other end.

Mike Croucher – Travelport

 

But GDSs do have enough data – Sabre chief scientist Ben Vinod
says its system has about 150 to 200 TB of data going in each day from shopping
and booking transactions – to use AI to segment travelers into personas, based
on information such as day and time of departure, destination, class of booking
and length of stay.

“Even though we don’t know the customer, we can say this
is a business trip or this is a short trip for a weekend, or it could be with wife
and kids on an extended stay,” Vinod says. “We use that segmentation to drive
the recommendation engine. Context for travel may be the single most important
concept that needs to be addressed in terms of giving a targeted answer back to
the customer.”

Sabre has created a solution known as Preference Driven Air
Shopping that combines an analysis of these personas with user-selected attributes,
for example for flight duration or fare, to deliver a small number of relevant
choices.

“This replicates to a large extent what a travel agent would
do, and it basically enables you to personalize the recommendation so you can
tell the customer this is really the one you are looking for from the hundreds
of options that I could have displayed,” Vinod says.

Artificial intelligence is enabling GDSs to automate – and therefore
expedite – processes that were once manual and tedious.

“We have used AI to heavily look at our search algorithms to
learn what sells, what converts, what routings are best,” says Mike Croucher,
chief architect at Travelport.

“Rather than doing it by the old-fashioned rules based, we
are using a lot more AI to get learning back in and taking work out and then
making the end result a lot more relevant for our end customer.”

Croucher says a next step will be to better connect each
point along the traveler’s journey, for example using AI to understand the type
of hotel that would be preferred by passengers on a specific type of flight.

Other functions

Speed and accuracy of offers are also critical drivers of
conversions, and Croucher says AI is helping them improve both elements.

“We are now using it for caching
search results close to the customer and understanding using AI when the
predictability of that cache result is likely to expire,” Croucher says.

“In other words, what
refresh rate do we need in cache, and we’re beginning to build models to do that.
That we believe will really significantly
change the industry going forward.”

GDSs are also using AI for demand forecasting and fare
prediction. Ultimately all of these solutions are designed with a common goal –
to grow the travel business by meeting the needs of the travelers.

“You tend to travel because you want to enjoy the experience
at the other end,” Croucher says.

“I think what you will see artificial intelligence do is
maximize the experience you have at the other end and minimize the amount of
effort you to have to put in the processing of getting there.”



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