We'll explore how lastminute.com manages massive volumes of data to support cutting-edge machine-learning algorithms to allow for speed and automation in the rapidly evolving global online travel research and bookings business.
Listen to the podcast. Find it on iTunes. Get the mobile app. Read a full transcript or download a copy.
To learn how a culture of IT innovation helps make highly dynamic customer interactions for online travel a major differentiator, we're joined by Filippo Onorato, Chief Information Officer at lastminute.com group in Chiasso, Switzerland. The discussion is moderated by BriefingsDirect's Dana Gardner, Principal Analyst at Interarbor Solutions.
Here are some excerpts:
Gardner: Most people these days are trying to do more things more quickly amid higher complexity. What is it that you're trying to accomplish in terms of moving beyond disruption and being competitive in a highly complex area?
Disruption is coming every day from different actors ... [requiring] a different way of constructing the 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?
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| Onorato |
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 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: 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 a data warehouse. That data is captured in order to build our internal knowledge. That would be the sales funnel.
Right now, we're storing a short history of that data, but the goal is to have two years worth of session data.
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, for example, 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.
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 own 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.
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.
It's endless, because the market conditions are changing and the actors in the market are changing as well.
So, it’s an ongoing experience; it’s an ongoing study. It's endless, because 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.
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?
We can jump from one location to another very easily, and that's one of the competitive advantages of being an OTA.
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.
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?
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.
Listen to the podcast. Find it on iTunes. Get the mobile app. Read a full transcript or download a copy. Sponsor: Hewlett Packard Enterprise.
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