 Live from San Francisco, it's theCUBE, covering Spark Summit 2017, brought to you by Databricks. The Cube is live once again from Spark Summit 2017. I'm David Goh, your host here with George Gilbert, and we are interviewing many of the speakers that we saw on stage this morning at the keynote. Happy to introduce our next guest on the show. His name is Matt Fryer. Matt, how you doing? Very well. You are the Chief Data Science Officer. I don't see many CDSOs out there. Is that a common time? I think it's a newer title, and it's coming, I think, where companies that feel the use of data, data science, and algorithms are fundamental to their features. They're creating both the mix of commercial, technical, and sort of algorithmic skillsets as one team to execute together. And that's where the title came from. There's more coming. There's a number of Facebook have a few, as one, for example, but it's a newer title. I think it's going to become larger and larger. The time goes on. So it's CDSO for hotels.com. Correct. Something else we learned about you that you may not want me to reveal, but I heard you were the inspiration for Captain Obvious. Is that true? That's not true. I think Captain Obvious is our inspiration of our brand. So there's an awesome brand team at our office out of Dallas. Yeah, I was kidding. You have to. I think the captain, we all love the captain. He has a good humorous moments and he keeps us all kind of happy. Oh yeah, he states the obvious. We're going to talk about some of the obvious and maybe some of the not so obvious here in this interview. So let's talk a little bit about company culture because you talked a lot on the stage this morning about customer first kind of approach rather than oh, look what I can do with the technology. Talk a little bit more about the culture at hotels.com. And that's important. And I think we're a very data-driven culture. I think most tech companies and travel and technology companies have that kind of ethos. But fundamentally, the focus and the reason we exist is for the customer. So we want to bring and actually an even better way than that I think is the people. So whether it's the focus on the customer, if we do the right thing by the customer, we fundamentally want you to use our platform time and time again. Whatever need you have booking, lodging and travel, please use our platform. That's the crucial win. So to do that, we have to always make delight you in every experience you have with us. And equally about people, it's about the team. So we have an internal concept course being supportive. So the whole part of our team culture is that everybody helps everybody else out. We don't single things out. We're all part of all the same team and we all win if all of us pull together. And that makes it a great place, fun place to work. We're going to play with some new technologies. Tech is important to us, but actually the people is even more important to us. I'm sure you love the Spark Summit then, same kind of spirit here. It's great. And I think it's my third Spark Summit, my second time over in San Francisco. And the size of it is very impressive now. And I just love meeting other people, learning about some of the things that are up to how we can apply those parts of our business. And hopefully sharing a little bit what we're up to. Well, let's dive into how you're playing it to your business. You talked about this evolution tour becoming an algorithm business. What does that mean and what part does Spark play in that? I think a lot of it is about how do you, if you think about a bit of the journey, historically a lot of the opportunity came in building new features, constantly building that there's like almost like a semi-arms race about how to build more and more features. The crucial thing I think going forward and particularly with mobile devices now, you know, over half our traffic comes from people using smartphones and both the app and mobile web. That bringing together means that being more targeted in understanding your journey and people are less tolerant to time. Speed is much more important. People are expect things to be right there when they need it. Relevance is much more important to people. So we need to bring all those things together to offer a much more targeted experience and a much more real-time experience. People expect you to have understood what they did milliseconds ago and respond to that. The only way you can do that is using data science and algorithms. You balance that on the business operations side, which is how do you scale? You know, the analogy I use with say anomaly detection, which is the crucial future for enterprises, is you used to have a lot of large business intelligence, lots of reports, pages of paper. Now people have things like Tableau, Power BI. Those are great and you need those to start with. Really as a business leader, you want to know, tell me what's broken. Tell me what's changed, because if it's changed, something caused the change. Tell me why it's slowly moving. And most importantly, tell me where the opportunity is. And that transforms the conversation when algorithms can really surface that to users. And it's about organic intelligence. It's not about artificial intelligence. It's about how would you bring together the people and the events and technology to really do a great job for customers. You mentioned AI. You made a big bold claim about AI. I'm going to ask George to weigh in on this in just a moment. You said AI was going to be the next big thing in the travel industry. Can you explain it? One of the next big things I think. Yeah, I think it's already happening. In fact, our chairman, Mr. Dilla, made that statement very recently, and also backed up by both the CEO and the brand president, where it's, if you think about 20 years ago, one of the things both Expedia and hotels.com, and the wider travels to online space did, was democratize price information and made it transparent to users. So previously, the power was with the travel agents. That power moved to the user. They had the information. And that's evolved over time. And what we feel with artificial intelligence, particularly organic intelligence, and you enable as like mobile messaging and having conversations and machine learning make this happen. You can turn the screen around and actually empower users almost with the second revolution. They actually, they can have the advice and the benefits you had a number of years ago from travel agents. A, they have the price transparency. They have the other part now, which is the content, the advice, and what's the most relevant to help them. And you can listen to what they're saying to you, send it as a customer, and actually we can now replay the perfect information back to them, or increasingly perfect since time goes on. Okay, that's unpacking a little bit, George. This is a journey to go on. Let's look into the hood a little bit. That is fascinating, because in the way you sort of broke that out with, it wasn't actually only travel, but over the last couple of decades, sort of price transparency became an issue for many industries. But what you're saying now is by giving the content to surprise and delight the customer, as long as you're collecting the data breadcrumbs that help you do that, you're not giving up control. You're actually creating stickiness. Yeah, we're empowering as a language I use. Yeah. And if you empower the user, they're more likely to come back to use your service in the future. And that's really what we want. We want happy customers. Tell us a little bit at the risk of dropping a little in the weeds. Tell us a little bit about how you empower. In other words, how do you know what type of content to serve up, and how do you measure how they engage with it? It's a great question. And I think it's quite an embryonic part of the world right now. I don't think anybody's, I think we made some great developments. I still think it was a long journey we have. But it's a lot about how do you, and this is true across data science and machine learning. Great data science is fundamental to having great feedback loops. So there's lots of different techniques and tactics around how you might discover those feedback loops. And customers demand that you use their data to help them. So we need to get faster in streaming as one way that's becoming feasible. And then the advances in streaming and it's great data bricks are working on that. But the advances in streaming allows us to feed that loop. They take those real-time signals as well as previous signals to really help figure out what you're trying to do today. What content, interesting thing is that Netflix and Amazon were some pioneers in this space where if you use Netflix service, often you go, how did they know this video was going to be right for me? And some of the comments. And you can say, well, what they're actually doing is they're looking at micro segments. So previously everyone talked about customer segments as these very large groups, and they have their place, but increasing the machine learning allows you to build micro segments. But what I can start to do is actually discover from the behavior of others things you might be very relevant things you're going to be very interested in and actually help inspire you and discover things you didn't even know existed. And by filling that gap and using those micro segments as well as truly personalization, I can bring that together to offer you a much more enhanced service. And so help make that concrete in terms of what would I as a potential, I want to plan a vacation for the summer. I have my five and a half inch or five, seven iPhone and that's my primary device. And in banking it's moved from tying everything to the checking counter, tying every interaction to your mobile device. So what would you show me on my mobile device that would get me really engaged about going to some location? So I think a lot of it is about where you are in that journey. So you think there's so many different routes and routes customers can take through that buying decision and depends on the trip type, whether it's a leisure trip, seeing family and friends, how much knowledge you may have about that money, have you been there before? We look for all those signals to try and help inspire you. So a great example might be if you've stayed in a hotel on our site before and you like that hotel and you come back and do a search again, we try and make it easy to continue by pinning that hotel at the top. Try and make it easy to task complete. We have a trip planner capability you'll see on the home screen which allows you to record and play backs from your previous searches. So you can quickly see and compare where you've been and what's interesting for you. But on top of that, we can then use the signals and increasingly we have a very advanced sort of filter list and that's a key and we're looking at sort of how we do conversations and chatbots as a sort of future to how to have that conversation to say, hey, here's a list of hotels which we used in a mix of your, the types of preferences that we understood about you and the wider thing, where you are in the world, what's going on, what time of day. We take hundreds of different signals to try and figure out what the right list is for you. And from that list, the great thing is most people interact with that list and give us more signals exactly what you wanted. We can hone and hone and hone and repeat. Because instead of the start, for example, the majority of customers will do multiple searches. They want to understand what the market is. They may not be interested in one particular place. They may have a sweeter place as they're interested in. And even now we've moved and further the funnel investing behind how can you figure out what destination you're interested in. So you may not even know what destination you're interested in or there might be other destinations that you didn't know were very relevant for your use case, particularly if you're going on vacation. We can help inspire you to find that hidden gem at that hidden great price. You may not even know it existed. We know a much better job. We can show you how busy the market is. So how fast you should be looking to book there. If it's a very compressed and busy market, you need to get in there quick to lock your price in and we now provide that information to help you make a better decision. We can mine all that data to empower you to make smart decisions and smart data. I want to clarify something quick. I saw in your demonstration this morning you were talking about detecting the differences between photos and user-generated content. So do you have users actually posting their own photos of the hotel right next to the Photoshop pictures at the hotel? We do. We post and what are some of the ramifications of that? So it's an interesting advance that we've made. So we've, in the last sort of year we now offer an asking users to submit their photos to help other users. I think one of the crucial things is about how to be authentic. Over the years we've had tens of millions of testimonial reviews, textual reviews. And we can see there's a really crucially important to users in their buying decisions. It scares the hotel owners of death though, doesn't it? Well, I think it does. But I think it's the testimony of the customer. One of the key things we call them is we have verified reviews. So to leave a review on our site you've had to have stayed in that hotel. And we think that's a crucial step in really helping to say these are your customers. I'm very recent and in recent times we've taken that product further that now when you actually arrive at the hotel within a few hours, we'll ask you what your first impressions were. And we'll ask if you want to share that with the hotel owner. To give the hotel owner a chance to actually rectify any early challenges so you can have a great stay. And one of the crucial things we have is that what's really, really important is that users and customers have a great stay. That reflects on our Net Promoter Score and their view of us. And we need to fill that cycle and make sure we have happy users. So that real-time review is super crucial in basically helping hotels, they want happy users and customers as well. It helps them to quite a course correct if there's an issue. And we can step in as well as help the user if it's a really deep issue. And then with the photos, the key I think is how to navigate and understand what the photo is. So the user helps us by tagging that, which is great. But how we- Possibly mistagging it. Possibly mistagging it. Occasionally, we're building some scale as you heard on how to tackle that. But the crucial thing is how to bring these together. If you're using a mobile device, you've got to scan through each photo and places around the world have limited bandwidth, and limited time to go through them. So what we're now working on is how to assess the quality of those photos to try and make sure we authentically, and what we want to do is get the customer the most likely experience they will have. You know, we are, as I said before, we're on the customer's kind of focus. We want to make sure they get the best photos that are the most realistic of what's going to happen. And they're the most diverse. You want to see three photos exactly the same. And we're working on them. You can swipe left and swipe right. We'll work on how that display evolves over time. But it's exciting. Very exciting, fascinating stuff. I'm sorry that we're up against a hard break coming here just in a moment, but I wanted to give you just 30 seconds to kind of sum up maybe the next big technical challenge you're looking at that involves Spark and we'll close with that. Cool, it's a great question. I think I talked a little bit about that in the keynote. In turn, we call it the kind of the outs challenge. You know, how to scale the mountain, which is there's been great advance on how to stream data into platforms. Spark is a core part of that. And the platforms that we've been building and both internally and partnering with Databricks and using their platform has really given us a large boost going forwards. But how you turn those algorithms and that competitive algorithmic advantage into a live production environments, whether it's marketplaces, ad tech marketplaces, or websites, or in court centres, or on social media, wherever the platform that it needs to go, that's a hard problem right now. Or I think it's a too hard problem right now. And I love to see, and we're going to invest behind that, a transformation that hopefully this time next year that is no longer a problem. It's actually an asset. Yeah, well, I hope I'm not captain obvious to say. I know you're up to the challenge. Awesome. Yeah, thank you so much, Matt Fryer. We appreciate you being on the show. Thank you for sharing what's going on hotels.com. We have time for the chance to talk about it. And thank you all for watching theCUBE. We're going to be back in just a few moments for the next guest here at Spark Summit 2017.