Transcript of a discussion on how lastminute.com manages massive volumes of data to support a cutting edge machine-learning algorithmic approach to matching the best experience in travel with end user requirements.

1. How Lastminute.com Uses Machine Learning to Advance
the Use of Big Data for Real-time Travel Booking
Transcript of a discussion on how lastminute.com manages massive volumes of data to support a
cutting edge machine-learning algorithmic approach to matching the best experience in travel
with end user requirements.
Listen to the podcast. Find it on iTunes. Get the mobile app. Sponsor: Hewlett
Packard Enterprise.
Dana Gardner: Hello, and welcome to the next edition to the Hewlett Packard Enterprise
(HPE) Voice of the Customer podcast series. I’m Dana Gardner, Principal Analyst at Interarbor
Solutions, your host and moderator for this ongoing discussion on digital
transformation. Stay with us now to learn how agile businesses are fending off
disruption in favor of innovation.
Our next case study highlights how online travel and events pioneer
lastminute.com group leverages big-data analytics with speed at scale to
provide business advantages and travel booking. We'll explore how
lastminute.com manages massive volumes of data to support cutting-edge
machine-learning algorithms and certain instances to allow for speed and
automation while buying services online.
So, join us as we learn how a culture of IT innovation helps make highly dynamic customer
interactions for online travel a major differentiator for lastminute.com. To describe how machine
learning is improving the travel fulfillment in the buying area we're joined by Filippo Onorato,
the Chief Information Officer at lastminute.com group in Chiasso, Switzerland. Welcome
Filippo.
Filippo Onorato: Thank you very much. Thank you for the opportunity of being here.
Gardner: Let’s look a little bit at disruption. Most people these days are trying to do more things
more quickly with higher complexity. What is it that you're trying to accomplish in terms of
moving beyond disruption and being competitive in a highly competitive area?
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Onorato: The travel market, and in particular the online travel market, is a very fast-moving
market, and the habits and behaviors of the customers are changing so rapidly that we have to
move fast.
Gardner

2. Disruption is coming every day from different actors that make an enormous growth out of a
different way of constructing a customer experience. In order to do that, you have to rely on very
big amounts of data just to style the evolution of the customer and their behaviors.
Gardner: And customers are more savvy; they really know how to use data and look for deals.
They're expecting real-time advantages. How is the sophistication of the end user impacting how
you work at the core, in your data center, and in your data analysis, to improve your competitive
position?
Onorato: Once again, customers are normally looking for information, and providing the right
information at the right time is a key of our success. The brand we came from was called
Bravofly and Volagratis in Italy; that means free flight. The competitive
advantage we have is to provide a comparison among all the different airline
tickets, where the market is changing rapidly from the standard airline behavior
to the low-cost ones. Customers are eager to find the best deal, the best price for
their travel requirements.
So, the ability to construct their customer experience in order to find the right
information at the right time, comparing hundreds of different airlines, was the
competitive advantage we made our fortune on.
Gardner: Let’s edify our listeners and reader a bit about lastminute.com. You're global. Tell us a
bit more about the company and perhaps your size, employees, and the number of customers you
deal with each day.
Most famous brand
Onorato: We are 1,200 employees worldwide. Lastminute.com, the most famous brand
worldwide, was acquired by the Bravofly Rumbo Group two years ago from Sabre. We own
Bravofly; that was the original brand. We own Rumbo; that is very popular in Spanish-
speaking markets. We own Volagratis in Italy; that was the original brand.
And we own Jetcost; that is very popular in France. That is actually a
metasearch, a combination of search and competitive comparison
between all the online travel agencies (OTAs) in the market.
We span across 40 countries, we support 17 languages, and we help almost 10 million people fly
every year.
Gardner: Let’s dig into the data issues here, because this is a really compelling use case. There's
so much data changing so quickly, and sifting through it is an immense task, but you want to
bring the best information to the right end user at the right time. Tell us a little about your big-
data architecture, and then we'll talk a little bit about bots, algorithms, and artificial intelligence.
Onorato

3. Onorato: The architecture of our system is pretty complex. On one side, we have to react almost
instantly to the search that the customers are doing. We have a real-time platform that's grabbing
information from all the providers, airlines, other OTAs, hotel provider, bed banks, or whatever.
We concentrate all this information in a huge real-time database, using a lot of caching
mechanisms, because the speed of the search, the speed of giving result to the customer is a
competitive advantage. That's the real-time part of our development that constitutes the core
business of our industry.
Gardner: And this core of yours, these are your own data centers. How have you constructed
them and how do you manage them in terms of on-premises, cloud, or hybrid?
Onorato: It's all on-premises, and this is our core infrastructure. On the other hand, all that data
that is gathered from the interaction with the customer is partially captured. This is the big
challenge for the future -- having all that data stored in the data warehouse. That data is captured
in order to build our internal knowledge. That would be the sales funnel.
So, the behavior of the customer, the percentage of conversion in each and every step that the
customer does, from the search to the actual booking. That data is gathered together in a data
warehouse that is based on HPE Vertica, and then, analyzed in order to find the best place, in
order to optimize the conversion. That’s the main usage of the date warehouse.
On the other hand, what we're implementing on top of all this enormous amount of data is
session-related data. You can imagine how much a data single interaction of a customer can
generate. Right now, we're storing a short history of that data, but the goal is to have two years
worth of session data. That would be an enormous amount of data.
Gardner: And when we talk about data, often we're concerned about velocity and volume.
You've just addressed volume, but velocity must be a real issue, because any change in a weather
issue in Europe or a glitch in a computer system at one airline in North America changes all of
these travel data points instantly.
Unpredictable events
Onorato: That’s also pretty typical in the tourism industry. It's a very delicate business,
because we have to react to unpredictable events that are happening all over the world. In order
to do a better optimization of margin, of search results, etc, we're also applying some machine-
learning algorithm, because a human can't react so fast to the ever-changing market or situation.
In those cases, we use optimization algorithms in order to fine tune our search results, in order to
better deal with a customer request, and to propose the better deal at the right time. In very
simple terms, that's our core business right now.

4. Gardner: And Filippo, only your organization can do this, because the people with the data on
the back side can’t apply the algorithm; they have only their data. It’s not something the end user
can do on the edge, because they need to receive the results of the analysis and the machine
learning. So you're in a unique, important position. You're the only one who can really apply the
intelligence, the AI, and the bots to make this happen. Tell us a little bit about how you
approached that problem and solved it?
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Onorato: I perfectly agree. We are the collector of an enormous amount of product-related
information on one side. On the other side, what we're collecting are the customer behaviors.
Matching the two is unique for our industry. It's definitely a competitive advantage to have that
data.
Then, what you do with all those data is something that is pushing us to do continuous
innovation and continuous analysis. By the way, I don't think something can be implemented
without a lot of training and a lot of understanding of the data.
Just to give you an example, what we're implementing, the machine learning algorithm that is
called multi-armed bandit, is kind of parallel testing of different configurations of parameters that
are presented to the final user. This algorithm is reacting to a specific set of conditions and
proposing the best combination of order, visibility, pricing, and whatever to the customer in order
to satisfy their research.
What we really do in that case is to grab information, build our experience into the algorithm,
and then optimize this algorithm every day, by changing parameters, by also changing the type of
data that we're inputting into the algorithm itself.
So, it’s an ongoing experience; it’s an ongoing study. It's endless, by the way, because again, the
market conditions are changing and the actors in the market are changing as well, coming from
the two operators in the past, the airline and now the OTA. We're also a metasearch, aggregating
products from different OTAs. So, there are new players coming in and they're always coming
closer and closer to the customer in order to grab information on customer behavior.
Gardner: It sounds like you have a really intense culture of innovation, and that's super
important these days, of course. As we were hearing at the HPE Big Data Conference 2016, the
feedback loop element of big data is now really taking precedence. We have the ability to
manage the data, to find the data, to put the data in a useful form, but we're finding new ways. It
seems to me that the more people use our websites, the better that algorithm gets, the better the
insight to the end user, therefore the better the result and user experience. And it never ends; it
always improves.
How does this extend? Do you take it to now beyond hotels, to events or transportation? It seems
to me that this would be highly extensible and the data and insights would be very valuable.

5. Core business
Onorato: Correct. The core business was initially the flight business. We were born by selling
flight tickets. Hotels and pre-packaged holidays was the second step. Then, we provided
information about lifestyle. For example, in London we have an extensive offer of theater,
events, shows, whatever, that are aggregated.
Also, we have a smaller brand regarding restaurants. We're offering car rental. We're giving also
value-added services to the customer, because the journey of the customer doesn't end with the
booking. It continues throughout the trip, and we're providing information regarding the check-
in; web check-in is a service that we provide. There are a lot of ancillary businesses that are
making the overall travel experience better, and that’s the goal for the future.
Gardner: I can even envision where you play a real time concierge, where you're able to follow
the person through the trip and be available to them as a bot or a chat. This edge-to-core
capability is so important, and that big data feedback, analysis, and algorithms are all coming
together very powerfully.
Tell us a bit about metrics of success. How can you measure this? Obviously a lot of it is going
to be qualitative. If I'm a traveler and I get what I want, when I want it, at the right price, that's a
success story, but you're also filling every seat on the aircraft or you're filling more rooms in the
hotels. How do we measure the success of this across your ecosystem?
Onorato: In that sense, we're probably a little bit farther away from the real product, because
we're an aggregator. We don’t have the risk of running a physical hotel, and that's where we're
actually very flexible. We can jump from one location to another very easily, and that's one of the
competitive advantages of being an OTA.
But the success overall right now is giving the best information at the right time to the final
customer. What we're measuring right now is definitely the voice of the customer, the voice of
the final customer, who is asking for more and more information, more and more flexibility, and
the ability to live an experience in the best way possible.
So, we're also providing a brand that is associated with wonderful holidays, having fun, etc.
Gardner: The last question, for those who are still working on building out their big-data
infrastructure, trying to attain this cutting-edge capability and start to take advantage of machine
learning, artificial intelligence, and so forth, if you could do it all over again, what would you tell
them, what would be your advice to somebody who is merely more in the early stages of their
big data journey?

6. Onorato: It is definitely based on two factors -- having the best technology and not always
trying to build your own technology, because there are a lot of products in the market that can
speed up your development.
And also, it's having the best people. The best people is one of the competitive advantages of any
company that is running this kind of business. You have to rely on fast learners, because market
condition are changing, technology is changing, and the people needs to train themselves very
fast. So, you have to invest in people and invest in the best technology available.
Gardner: I'm afraid we will have to leave it there. We've been exploring how online travel and
events pioneer lastminute.com group leverages big-data analytics with incredible speed and at
huge scale to provide business advantages in travel and other bookings.
And we've learned how the group manages these massive volumes of data to support a cutting-
edge machine-learning algorithmic approach to matching the best experience in travel with end
user requirements.
So, please join me in thanking our guest. We've been here with Filippo Onorato, the Chief
Information Officer at lastminute.com group in Chiasso, Switzerland. Thank you, sir.
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Onorato: Thank you very much.
Gardner: And thanks to our audience as well for joining us for this Hewlett Packard Enterprise
Voice of the Customer digital transformation discussion.
I'm Dana Gardner, Principal Analyst at Interarbor Solutions, your host for this ongoing series of
HPE sponsored interviews. Thanks again for listening, and please do come back next time.
Listen to the podcast. Find it on iTunes. Get the mobile app. Sponsor: Hewlett
Packard Enterprise.
Transcript of a discussion on how lastminute.com manages massive volumes of data to support a
cutting edge machine-learning algorithmic approach to matching the best experience in travel
with end user requirements. Copyright Interarbor Solutions, LLC, 2005-2017. All rights
reserved.
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