>> Announcer: Live from San Francisco, it's The Cube. Covering Spark Summit 2017. Brought to you by Databricks. >> The Cube is live once again from Spark Summit 2017, I'm David Goad, 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're you doing? >> Matt: Very well. >> You're the chief, Chief Data Science Officer, I don't see many CDSOs out there, is that a common-- >> I think to say, 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, their futures. They're creating both the mix of commercial, technical, and algorithmic skill sets, this one team, and to execute together, and that's where the title came from. There's more coming, there's a number of-- Facebook have a few, that's one for example, but it's a newer title, I think it's going to become larger and larger, as time goes on. >> David: So, the CDSO for Hotels.com, 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? >> Uh, that's not true. (laughter) I think Captain Obvious is only an expression of my brand, so there's an awesome brand team, at our office in Dallas. (crosstalk) We all love the captain, he has some 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 a, "Ooh, 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, 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-- in even better ways than that, I think it's the people. So whether it's the focus on the customer, if we did 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 delight you in every experience you have with us. And equally about people, it's about the team, so we have an internal concept called 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 the same team, and we all win if all of us pull together. That makes it a great place, a fun place to work, we're going to play with some new technologies, tech is important to us, but actually the people are even more important to us. >> In part why you love the Spark Summit then, huh? Same kind of spirit here, right? >> It's great, I think it's my third Spark Summit, my second time over in San Francisco, and the size of it is very impressive now. I just love meeting other people learning about some of the things they're up to, how we can apply those back to our business, and hopefully sharing a little bit of what we're up to. >> David: Let's dive into how you're applying it to your business, you talked about this evolution toward becoming an algorithm business, what does that mean and what part does Spark play in that? >> Matt: I think what it is, 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 it, it's 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, we have over half our traffic, comes from people using smartphones, on both the app and mobile web. That bringing together means that, be more targeted, in understanding your journey, and people are, last on to time, speed is much more important, people 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 out on a business operation side, just how do you scale? The analogy I use with, say, anomaly detection, which is a crucial feature for enterprises. Used to have a large business intelligence, lots of reports, pages of paper, now people have things like Tablo, Power BI, those are great and you need those to start with, but 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 where 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 advance in technology to really do a great job for customers. >> David: Well, 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? >> One of the next big things, I think. Yeah, I think it's already happening, in fact, our chairman, Mr. Diller made that statement very recently, 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 travel online space did, were 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, enablers like mobile, messaging and having conversations, have a machine learning how to make this happen, that you can turn the screen around and actually empower users always with the second revolution. They actually have the advice, and the benefits you had a number of years ago from travel agents: A, they had the price transparency, they have the other part now, which is the content, advice, and what's the most relevant to help them. And you can listen to what they're saying to you, as a customer, and actually we can now replay the perfect information back to them, or increasingly perfect as time goes on. (crosstalk) >> That is fascinating, 'cause in the way you broke that out, with--it wasn't actually only travel, but over the last couple decades, 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 to help you do that, you're not giving up control, you're actually creating stickiness. >> Matt: We're empowering, is the language I use. And if you empower the user, the more likely to come back to use your service in the future, and that's really what we want, we want happy customers. >> George: Tell us a little bit, at the risk of dropping a little in the wait, 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 embryonic, part of the world right now. I don't think anybody's-- have we made some great developments? I said it was a long journey we have, but it's a lot about how do you, and this is true across data science 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, and streaming is one way, that's becoming feasible, and the advances in streaming and it's great Databricks are working on that, but the advances in streaming allows it to feed that loop, to take that much--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, Netflix and Amazon were some pioneers in this space, where if you use Netflix service, often you go, "How the hell 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 microsegments, so previously everyone talked about custom segments as these very large groups, and they have their place, but increasing machine learning allows you to build microsegments. What I can start to do is actually discover from the behavior of others, things you likely-- very relevant things that 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 microsegments as well as put truly personal, personalization, I can bring that together to offer you a much more enhanced service. >> George: 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 account, to 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 customers can take, through that buying decision. And depends on the trip type, whether it's a leisure trip, seeing your family and friends, how much knowledge you may have about them, have you been there before? We look for all those signals, to try and help inspire. So a great example might be, if you stayed in a hotel on our site before, and you liked that hotel, and you come back and do a search again, we try and make it easy to continue by putting that hotel at the top. Trying to 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 back some of 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 filter list, and that's a key, and we're looking in stuff, how we do conversations in the chatbox, is this sort of future, how to have a conversation to say, "Hey, here's a list of hotels, which we used a mix of your, "the types of preferences 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, 'cause I said at the start, for example, those 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 there instead. Even now, where we've moved further up 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--with a very relevant for your use case, particularly if you're going on vacation, we can help inspire you to find that hidden gem, that hidden great prize, you may not even know it existed. Being the much better job, but to show you how busy the market is, to how fast you should be looking to book there, if it's a very compressed, busy market, you need to get in there quick to lock your price in, and we're now providing that information to help you make a better decision. And we can mine all that data, to empower you to make smart decisions with smart data. >> I want to clarify something 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 photoshopped pictures of the hotel? >> Matt: We do, yeah. >> David: What are the ramifications of that? >> So it's an interesting advancement we've made, so we've... In the last of the year, we now offer and 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, text reviews, and we can see they're really, crucially important to users, and their buying decisions. >> David: It scares the hotel owners to death though, doesn't it? >> Matt: Well, I think it does, but I think the testimony of the customer, could be one of the key things we call them, as we have verified reviews, so to leave a review on our site, you've had to stay in that hotel. We think that's a crucial step in really helping to say, "These are your customers." In recent times, we've taken that product further, to now when you actually arrive at the hotel within a few hours, We'll ask you what your first impressions were. We would ask if you want to share that with the hotel owner. To get 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 basing how can hotels--if they want happy users and customers as well, it helps them to cut a course correct, if there's an issue, and we can step in as well to help the user if it's a really deep issue. And then with the photos, the key to 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-- >> David: Possibly mistagging it. >> Possibly mistagging it on occasion, that's something we've, we've built in some skill as you've heard, on how to tackle that, but the crucial thing is how to bring these together, if you're on a mobile device, you've got to scan through each photo, and in places around the world have limited bandwidth, a 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--what we want to do, is get the customer the most lively experience they will have. As I said before, we're on the customer's kind of focus, we want to make sure they get the best photos, the most realistic of what's going to happen, and doing the most diverse. You want to see three photos, exactly the same, and we're working on the moment, you can swipe left and swipe right, we're working on how that display evolves over time, but it's exciting. >> David: Very exciting, fascinating stuff. Sorry that we're up against a hard break, coming here in just 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 about that in the keynote, totally caught the kind of out challenge. How to scale a mountain, which has been-- 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, 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 environment, whether it's marketplaces, Adtech marketplaces or websites, or in call centers, or in social media, wherever the platform needs to go, that's a hard problem right now. Or, I think it's too hard a problem right now. And I'd 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, and is actually an asset. >> David: Well I hope I'm not Captain Obvious to say, I know you're up to the challenge. Thank you so much, Matt Fryer, we appreciate you being on the show, thank you for sharing what's going on at Hotels.com. And thank you all for watching The Cube, we'll be back in a few moments with our next guest, here at Spark Summit 2017. (electronic music) (wind blowing)