(spirited music) >> Hey, welcome back everybody. Jeff Frick here with theCUBE. We are in the Palo Alto Studios for a CUBE Conversation. Part of our Western Digital Data Makes Possible Series, really looking at a lot of cool applications. At the end of the day, data's underneath everything. There's infrastructure and storage that's holding that, but it's much more exciting to talk about the applications. We're excited to have somebody who's kind of on the cutting edge of a next chapter of something you're probably familiar with. He's Steven Webster, and he is the founder and CEO of Asensei. Steven, great to see you. >> Likewise, likewise. >> So, you guys are taking, I think everyone's familiar with Fitbits, as probably one of the earliest iterations of a biometric feedback, for getting more steps. At the end of the day, get more steps. And you guys are really taking it to the next level, which is, I think you call it connected coaching, so I wondered if you could give everyone a quick overview, and then we'll dig into it a little bit. >> Yeah, I think we're all very familiar now with connected fitness in hindsight, as a category that appeared and emerged, as, like you say, first it was activity trackers. We saw those trackers primarily move into smartwatches, and the category's got life in it, life in it left. I see companies like Flywheel and Peloton, we all know Peloton now. >> [Jeff] Right. >> We're starting to make the fitness equipment itself, the treadmill, the bike, connected. So, there's plenty of growth in that category. But our view is that tracking isn't teaching, and counting and cheering isn't coaching. And so we see this opportunity for this new category that's emerging alongside connected fitness, and that's what we call connected coaching. >> Connected coaching. So the biggest word, obviously, instead of fitness tracker, to the connected coaching, is coaching. >> Yeah. >> So, you guys really think that the coaching piece of it is core. And are you targeting high-end athletes, or is this for the person that just wants to take a step up from their fitness tracker? Where in the coaching spectrum are you guys targeting? >> I saw your shoe dog, Phil Knight, founder of Nike, a book on the shelf behind you there, and his co-founder, Bill Bowerman, has a great quote that's immortalized in Nike offices and stores around the world: "If you have a body, you're an athlete." So, that's how we think about our audience. Our customer base is anyone that wants to unlock their athletic potential. I think if you look at elite sports, and elite athletes, and Olympic athletes, they've had access to this kind of technology going back to the Sydney Olympics, so we're really trying to consumerize that technology and make it available to the people that want to be those athletes, but aren't those athletes yet. You might call it the weekend warrior, or just the committed athlete, that would identify, identify themselves according to a sport that they play. >> So, there's different parts of coaching, right? One, is kind of knowing the techniques, so that you've got the best practices by which to try to practice. >> [Steven] Yep. >> And then there's actually coaching to those techniques, so people practice, right? Practice doesn't make perfect. It's perfect practice that makes perfect. >> [Steven] You stole our line, which we stole from someone else. >> So, what are you doing? How do you observe the athlete? How do you communicate with the athlete? How do you make course corrections to the athlete to move it from simply tracking to coaching? >> [Steven] I mean, it starts with, you have to see everything and miss nothing. So, you need to have eyes on the athlete, and there's really two ways we think you can do that. One is, you're using cameras and computer vision. I think most of us are familiar with technologies like Microsoft Connect, where an external camera can allow you to see the skeleton and the biomechanics of the athlete. And that's a big thing for us. We talk about the from to being from just measuring biometrics: how's your heart rate, how much exertion are you making, how much power are you laying down. We need to move from biometrics to biomechanics, and that means looking at technique, and posture, and movement, and timing. So, we're all familiar with cameras, but we think the more important innovation is the emergence of smart clothing, or smart apparel, and the ability to take sensors that would have been discrete, hard components, and infuse those sensors into smart apparel. We've actually created a reference design for a motion capture sensor, and a network of those sensors infused in your apparel allows us to recover your skeleton, but as easily as pulling on a shirt or shorts. >> [Jeff] So you've actually come up with a reference design. So, obviously, begs a question: you're not working with any one particular apparel manufacturer. You really want to come up with a standard and publish the standard by which anyone could really define, capture, and record body movements, and to convert those movements from the clothing into a model. >> No, that's exactly it. We have no desire to be in the apparel industry. We have no desire to unseat Nike, Adidas, or Under Armour. We're actually licensing our technology royalty-free. We just want to accelerate the adoption of smart apparel. And I think the thing about smart apparel is, no one's going to walk into Niketown and say, "Where's the smart apparel department? "I don't want dumb apparel anymore." There needs to be a compelling reason to buy digitally enhanced apparel, and we think one of the most compelling reasons to buy that is so that we can be coached in the sport of our choice. >> [Jeff] So, then you're starting out with rowing, I believe, is your first sport, right? >> [Steven] That's correct, yeah. >> And so the other really important piece of it, is if people don't have smart apparel, or the smart apparel's not there yet, or maybe when they have smart apparel, there's a lot of opportunities to bring in other data sources beyond just that single set. >> [Steven] And that's absolutely key. When I think about biomechanics, that's what goes in, but there's also what comes out. Good form isn't just aesthetic. Good form is in any given sport. Good form and good technique is about organizing yourself so that you perform most efficiently and perform most effectively. Yeah, so you corrected a point in that we've chosen rowing as one of the sports. Rowing is all about technique. It's all about posture. It's all about form. If you've got two rowers who, essentially, have the same strength, the same cardiovascular capability, the one with the best technique will make the boat move faster. But for the sport of rowing, we also get a tremendous amount of telemetry coming off the rowing machine itself. A force curve weakened on every single pull of that handle. We can see how you're laying down that force, and we can read those force curves. We can look at them and tell things like, are you using your legs enough? Are you opening your back too late or too early? Are you dominant on your arms, where you shouldn't be? Is your technique breaking down at higher stroke rates, but is good at lower stroke rates? So it's a good place for us to start. We can take all of that knowledge and information and coach the athlete. And then when we get down to more marginal gains, we can start to look at their posture and form through that technology like smart apparel. >> There's the understanding what they're doing, and understanding the effort relative to best practices, but there's also, within their journey. Maybe today, they're working on cardio, and tomorrow, they're working on form. The next day, they're working on sprints. So the actual best practices in coaching a sport or particular activity, how are you addressing that? How are you bringing in that expertise beyond just the biometric information? >> [Steven] So yeah, we don't think technology is replacing coaches. We just think that coaches that use technology will replace coaches that don't. It's not an algorithm that's trying to coach you. We're taking the knowledge and the expertise of world-class coaches in the sport, that athletes want to follow, and we're taking that coaching, and essentially, think of it as putting it into a learning management system. And then for any given athlete, Just think of it the way a coach coaches. If you walked into a rowing club, I don't know if you've ever rowed before or not, but a coach will look at you, they'll sit you on a rowing machine or sit you on a boat, and just look at you and decide, what's the one next thing that I'm going to teach you that's going to make you better? And really, that's the art of coaching right there. It's looking for that next improvement, that next marginal gain. It's not just about being able to look at the athlete, but then decide where's the improvement that we want to coach the athlete? And then the whole sports psychology of, how do you coach his improvements? >> Because there's the whole hammer versus carrot. That's another thing. You need to learn how the individual athlete responds, what types of things do they respond better to? Do they like to get yelled? Do they like to be encouraged? Did they like it at the beginning? Did they like it at the end? So, do you guys incorporate some of these softer coaching techniques into the application? >> Our team have all coached sport at university-level typically. We care a lot and we think a lot about the role of the coach. The coach's job is to attach technique to the athlete's body. It's to take what's in your head and what you've seen done before, and give that to the athlete, so absolutely, we're thinking about how do you establish the correct coaching cues. How do you positively reinforce, not just negatively reinforce? Is that person a kinesthetic learner, where they need to feel how to do it correctly? Are they a more visual learner, where they respond better to metaphor? Now, one of the really interesting things with a digital coach is the more people we teach, the better we can get at teaching, because we can start to use some of the techniques of enlarged datasets, and looking at what's working and what's not working. In fact, it's the same technology we would use in marketing or advertising, to segment an audience, and target content. >> Right. >> [Steven] We can take that same technology and apply it how we think about coaching sports. >> So is your initial target to help active coaches that are looking for an edge? Or are you trying to go for the weakend warrior, if you will? Where's your initial market? >> For rowing, we've actually zeroed in on three athletes, where we have a point of view that Asensei can be of help. I'll tell you who the three are. First, is the high school athlete who wants to go to college and get recruited. So, we're selling to the parent as much as we're selling to the student. >> [Jeff] That's an easy one. Just show up and be tall. >> Well, show up, be tall, but also what's your 2k time? How fast can you row 2,000 meters? That's a pretty important benchmark. So for that high school athlete, that's a very specific audience where we're bringing very specific coaches. In fact, the coach that we're launching with to that market, his story is one of, high school to college to national team, and he just came back from the Olympics in Rio. The second athlete that we're looking at is the person who never wants to go on the water, but likes that indoor rowing machine, so it's that CrossFit athlete or it's an indoor rower. And again, we have a very specific coach who coaches indoor rowing. And then the third target customer is-- >> What's that person's motivation, just to get a better time? >> Interesting, in that community, there's a lot of competitiveness, so yeah, it's about I want to get good at this, I want to get better at this. Maybe enter local competitions, either inside your gym or your box. This weekend, in Boston, we have just had one of the largest indoor world, it was the World Indoor Rowing Championships, the C.R.A.S.H B's. There's these huge indoor rowing competitions, so that's a very competitive athlete. And then finally we have, what would be the master's rower or the person for whom rowing is. There's lots of people who don't identify themselves as a rower, but they'll get on a rowing machine two or three times a week, whether it's in their gym or whether it's at home. Your focus is strength, conditioning, working out, but staying injury-free, and just fun and fitness. I think Palaton validated the existence of that market, and we see a lot of people wanting to do that with a rowing machine, and not with a bike. >> I think most of these people will or will not have access to a primary coach, and this augments it, or does this become their primary coach based on where they are in their athletic life? >> [Steven] I think it's both, and certainly, and certainly, we're able to support both. I think when you're that high school rower that wants to make college, you're probably a member of either your school rowing crew or you're a member of a club, but you spend a tremendous amount of time on an erg, the indoor rowing machine, and your practice is unsupervised. Even though you know what you should be doing, there's nobody there in that moment watching you log those 10,000 meters. One of our advisors is, actually, a two-times Olympic world medalist from team Great Britain, Helen Glover. And Helen, I have a great quote from Helen, where she calculated for the Rio Olympics, in the final of the Rio Olympics, every stroke she took in the final, she'd taken 16,000 strokes in practice, which talks to the importance of the quality of that practice, and making sure it's supervised. >> The bigger take on the old 10,000 reps, right? 16,000 per stroke. >> Right? >> Kind of looking forward, right, what were some of the biggest challenges you had to overcome? And then, as you looked forward, right, since the beginning, were ubiquitous, and there's 3D goggles, and there'll be outside-in centers for that whole world. How do you see this world evolving in the immediate short-term for you guys to have success, and then, just down the road a year or two? >> That's a really good question. I think in the short-term, I think it's incumbent on us to just stay really focused in a single community, and get that product right for them. It's more about introducing people to the idea. This is a category creation exercise, so we need to go through that adoption curve of find the early adopters, find the early majority, and before we take that technology anywhere towards our mass market, we need to nail the experience for that early majority. And we think that it's largely going to be in the sport of rowing or with rowers. The cross participation studies in rowing are pretty strong for other sports. Typically, somewhere between 60-80% of rowers weight lift, bike, run, and take part in yoga, whether yoga for mobility and flexibility. There's immediately adjacent markets available to us where the rowers are already in those markets. We're going to stick there for awhile, and really just nail the experience down. >> And is it a big reach to go from tracking to coaching? I mean, these people are all super data focused, right? The beauty of rowing, as you mentioned, it's all about your 2k period. It's one single metric. And they're running, and they're biking, and they're doing all kinds of data-based things, but you're trying to get them to think really more on terms of the coaching versus just the tracking. Has that been hard for them to accept? Do you have any kind of feel for the adoption or the other thing, I would imagine, I spent all this money for these expensive clothing. Is this a killer app that I can now justify having? >> Right, right, right. >> Maybe fancier connected clothes, rather than just simply tracking my time? >> I mean, I think, talking about pricing in the first instance. What we're finding with consumers that we've been testing with, is if you can compare the price of a shirt to the price of shirt without sensors, it's really the wrong value proposition. The question we ask is, How much money are you spending on your CrossFit box membership or your Equinox gym membership? The cost of a personal trainer is easily upwards of $75-100 for an hour. Now, we can give you 24/7 access to that personal coaching. You'll pay the same in a year as you would pay in an hour for coaching. I think for price, it's someone who's already thinking about paying for personal coaching and personal training, that's really where the pricing market is. >> That's interesting, we see that time and time again. We did an interview with Knightscope, and they have security robots, and basically, it's the same thing. They're priced comparisons was the hourly rate for a human counterpart, or we can give it to you for a much less hourly rate. And now, you don't just get it for an hour, you get it for as long as you want to use it. Well, it's exciting times. You guys in the market in terms of when you're going G80? Have a feel for-- >> Any minute now. >> Any minute now? >> We have people using the product, giving us feedback. My phone's switched off. That's the quietest it's been for awhile. But we have people using the product right now, giving us feedback on the product. We're really excited. One in three people, when we ask, the metric that matters for us is net promoter score. How likely would someone recommend asensei to someone else? One in three athletes are giving us a 10 out of 10, so we feel really good about the experience. Now, we're just focused on making sure we have enough content in place from our coaches. General availability is anytime soon. >> [Jeff] Good. Very exciting. >> Yeah, we're excited. >> Thanks for taking a few minutes of your day, and I actually know some rowers, so we'll have to look into the application. >> Right, introduce us. Good stuff. >> He's Steven Webster, I'm Jeff Frick. You're watching theCUBE. We're having a CUBE Conversation in our Palo Alto Studios. Thanks for watching. (bright music)

>> Hey, welcome back everybody, Jeff Frick here with theCUBE. We're having a Cube conversation in our Palo Alto studio, the conference season hasn't got to full swing yet, so we can have a little bit more relaxed atmosphere here in the studio and we're really excited, as part of our continuing coverage for the Data Makes Possible sponsored by Western Digital, looking at cool applications, really the impact of data and analytics, ultimately it gets stored usually on a Western Digital hard drive some place, and this is a great segment. Who doesn't like talking about sports, and football, and advanced analytics? And we're really excited, I have John Pollard here, he is the VP of Business Development for Zebra Sports, John, great to see you. >> Jeff, thanks for having me. >> Absolutely, so before we jump into the fun stuff, just a little bit of background on Zebra Sports and Zebra Technologies. >> Okay well, first, Zebra Technologies is a publicly traded company, we started in the late 1960s, and really what we do is we track enterprise assets in industries typically like healthcare, retail, travel and logistics, and transportation. And what we've done is take that heritage and bring that over into the world of sports, starting four years ago with our relationship with the NFL as the official player tracking technology. >> It's such a great story of an old-line company, right? based in Illinois-- >> Yeah, Lincolnshire. >> Outside of Chicago, right? RFID tags, and inventory management, and all this kind of old-school stuff. But then to take that into this really dynamic world, A, of sports, but even more, advanced analytics, which is relatively new. And we've been at it for a few years, but what a great move by the company to go into this space. How did they choose to do that? >> Well it was an opportunity that just came to them through an RFP, the NFL had investigated different technologies to track players including optical and a GPS-based technologies, and now of course with Zebra, our location and technologies are based on RFID. And so we just took the heritage and our capabilities of really working at the edge of enterprises in those traditional industries from transactional moments, to inventory control moments, to analytics at the end, and took that model and ported it over to football, and it's turned out to be a very good relationship for us in a couple of ways. We've matured as a sports business over the four years, we've developed more opportunities to take our solutions, not just in-game but moving them into the practice facilities for NFL teams, but it's also opened up the aperture for other industries to now appreciate how we can track minute types of information, like players moving around on the football field, and translating it into usable information. >> So, for the people that aren't familiar, they can do a little homework. But basically you have a little tag, a little sensor, that goes onto the shoulder pads, right? >> There's two chips. >> Two chips, and from that you can tell where that player is all the time and how they move, how they fast they move, acceleration and all the type of stuff, right? >> Correct, we put two chips inside of the shoulder pads for down linemen, or people who play with their hands on the ground, we put a third chip between the shoulder blades. Those chips communicate with receiver boxes that have been installed across the perimeter or around the perimeter of a stadium, and they blink 12 times per second. And that does tell you who's on the field, where they are on the field, and in proximity to other players on the field. And once the play starts itself, we can see how fast they're going, we can calculate change of direction, acceleration and deceleration metrics, we can also see, as you know with football, interesting information like separation from a wide receiver in defensive back, which is critical when you're evaluating players' capabilities. >> So, this started about four years ago, right? >> Yes, we started our relationship with the league in-game, four years ago. >> Okay, so I'd just love to kind of hear your take on how the evolution of the introduction of this data was received by the league, received by the teams, something they'd never had before, right? Kind of a look and feel and you can look at film, but not to the degree and the tightness of tolerances that you guys are able to deliver. >> Well, like any new technology and information resource, it takes time to first of all determine what you want to do with that information, you have an idea when you start, and then it evolves over time. And so what we started with was tagging the players themselves and during the time, what we've really enjoyed in working with the NFL is that the league has to be very pragmatic and thoughtful when introducing new technologies and information. So they studied and researched the information to determine how much of this information do they share with the clubs, how much do they share with the fans and the media, and then what type of information sharing, what does that mean in terms of impact of the integrity of the game and fair competition. So, for the first two years it was more of a research and testing type of process, and starting in 2016 you started to see more of an acceleration of that data being shared with the clubs. Each club would receive their own data for in-game, and then we would start to see some of that trickle out through the NFL's Next Gen Stats brand banner on their NFL.com site. And so then we start to see more of that and then what I think we've really seen pick up pace certainly in 2017 is more utilization of this information from a media perspective. We're seeing it more integrated into the broadcasts themselves, so you have like kind of a live tracking set of information that keeps you contextually involved in the game. >> Right. And you were involved in advanced analytics before you joined Zebra, so you've been kind of in this advanced stats world for a while. So how did it change when you actually had a real-time sensor on people's bodies? >> Yeah it does feel a bit like Groundhog Day, right? I started more in the stats and advanced analytics when I worked for STATS LLC. In 2007, I developed a piece of software for the New Orleans Saints that they used to track observational statistics to game video. And it was a similar type of experience in starting in 2009 and introducing that to teams where it took about three or four years where teams started to feel like that new information resource was not a nice to have but a need to have, a premium ingredient that they could use for game planning, and then player evaluation, and also the technology could provide them some efficiencies. We're seeing that now with the tracking data. We just returned from the NFL Combine a couple weeks ago, and what I felt in all the conversations that we had with clubs was that there was a high level of appreciation and a lot of interest in how tracking data can help facilitate their traditional scouting and player evaluation processes, the technology itself how can it make the teams more efficient in evaluating players and developing game plans, so there's a lot of excitement. We've kind of hit that tipping point, if I may, where there's general acceptance and excitement about the data and then it's incumbent upon us as a partner with the league and with the teams for our practice clients to teach them how to use the analytics and statistics effectively. >> So I'm just curious, some of the specific data points that you've seen evolve over time and also the uses. I think you were talking about a little bit off camera that originally it was really more the training staff and it was really more kind of the health of the player. Then I would imagine it evolved to now you can actually see what's going on in terms of better analysis, but I would imagine it's going to evolve where coaches are getting that feedback in real-time on a per-play basis and are making in-game adjustments based on this real-time data. >> Well technically that's feasible today but then there's the rules of engagement with the league itself, and so the teams themselves, and the coaches, and the sideline aren't seeing this tracking data live, whether it be in the booth or on the sidelines. Now in a practice environment, that's what teams are using our system for. With inside of three seconds they're seeing real-time information show up about players during practice. Let's take an example, a player during practice who's coming back from injury. You might want to monitor their output during the week as they come back and they make sure that they're ready for the game on a week to week basis. Trainers are now able to see that information and take that over to a position coach or a head coach and make them aware of the performance of the player during practice. And I think sometimes people think with tracking data it's all about managing in the health of the player and making sure they don't overwork. Where really, the antithesis of that is you can actually also identify players who aren't necessarily reaching their maximum output that will help them build throughout the week from peak performance during a game. And so a lot of teams like to say okay, I have a wide receiver, I know their max miles per hour, is, let's use an example, 20.5 miles an hour. He hasn't hit his max yet during the entire week, so let's get him into some drills and some sessions, where he can start hitting that max so that we reduce the potential for injury on game day. >> Right, another area that probably a lot of people would never think is you also put sensors on the refs. So you know not only where the refs are, but are they in the right positions technically and kind of from a best practices to make the calls for the areas that they're trying to cover. >> Right. >> There's got to be, was their a union pushback on this type of stuff? I mean there's got to be some interesting kind of dynamics going on. >> Yeah as far as the referees, I know that referees are tagged and the NFL uses that information and correlates that with the play calls themselves. We're not involved in that process but I know they're utilizing the information. In addition to the referees I should add, we also have a tag in the ball itself. >> [Jeff] That's right. >> 2017 season was the first year that we had every single game had a tagged ball. Now that tagged information in the ball was not shared with the clubs yet, the league is still researching the information, like they did with the players' stuff. A couple years of research, then they decide to distribute that to the teams and the media. So we are tracking a lot of assets, we also have tags in the first down markers and the pylons and I'll just cut to the chase, there are people who will say okay, does that mean you can use these chips and this technology to identify first down marks or when a ball might break the plane for a potential touchdown? Technically you can do that, and that's something the league may be researching, but right now that's not part of our charter with them. >> Right, so I'm just curious about the conversations about the data and the use of the data. 'Cause as you said there's a lot of raw data, and there's kind of governance issues and rules of engagement, and then there's also what types of analytics get applied on top of that data, and then of course also it's about context, what's the context of the analytics? So I wonder if you could speak to the kind of the evolution of that process, what were people looking at when you first introduced this four years ago, and how has it moved over time in terms of adding new analytics on top of that data set? >> That's one of my favorite topics to talk about, when we first started with the league and engaging teams for the practice solution or providing them analytics, they in essence got a large raw data file of XY coordinates, you can imagine (laughs) it was a gigantic hard drive-- >> Even better, XY coordinates. >> And put it into a spreadsheet and go. There was some of that early on and really what we had to do through the power of software, is develop and application platform that would help teams manage and organize this data appropriately, develop the appropriate reports, or interesting reports and analysis. And over the last two or three years I think we've really found our stride at Zebra in providing solutions to go along with the capabilities of the technology itself. So at first it was strength and conditioning coaches, plowing through this information in great detail or analytics staffs, and what we've seen over the last 24 months is director of analytics now, personnel staff, coaches as well, a broadening group of people inside of a football organization start to use this data because the software itself allows them to do so. I'll give an example, instead of just tabular information, and charts and graphs, we now take the data and we can plot them into a play field schematic, which as you know as we talked off camera you're very familiar with football, that just automates the process of what teams do today manually, is develop play cards so they can do self-study and advanced scouting techniques. That's all automated today, and not only that, it's animated because we have the tracking information and we can merge that to game video. So we're just trying to make the tools with the software more functional so everybody in the organization can utilize it beyond strength and conditioning, which is important, but now we're broadening the aperture and appealing to everybody in the organization. >> Do you do, I can just see you can do play development too, if you plug in everybody's speeds and feeds, you have a certain duration of time, you can probably AB test all types of routes, and timing on drops and now you know how hard the guy throws the ball to come up with a pretty wide array of options, I would imagine within the time window. >> Exactly, a couple of examples I could give, when we meet with teams we have every player, let's say on a team and we know all the routes they ran during an entire season. So you can imagine on a visualization tool, you can imagine, it's like a spaghetti chart of different routes and then you start breaking down the scenarios of context like we talked about earlier, it's third down, it's in the red zone, it's receptions. And so that becomes a smaller set of lines that you see on the chart. I'll tell you Jeff, when we start meeting with teams at the Combine and we start showing them their X or a primary receiver, or their slot receiver tendencies visually, they start leaning forward a bit, oh my goodness, we spend way too much time on the same route when we're targeting for touch down passes. Or we're right-handed too much, we have to change that up. That's the most gratifying thing, is that you're taking a picture and you're really illuminating and those coaches who intrinsically know that, but once they see a visual cue, it validates something in their head that either they have to change or evolve something in their game plan or their practice regimen. >> Well, that's what I was going to ask, and you lead right into it is, what are some of the things that get the old-school person or the people that just don't get that, they don't get it, they don't have the time, they don't believe it, or maybe believe it but they don't have the time, they're afraid to understand. What are some of those kind of light bulb moments when they go okay, I get it, as you said, most of the time if they're smart, it's going to be kind of a validation of something they've already felt, but they've never actually had the data in front of them. >> Right, that's exactly right. So that, the first thing is just quantifying, providing a quantifiable empirical set of evidence to support what they intrinsically know as professional evaluators or coaches. So we always say that they data itself and the technology isn't meant to be a silver bullet. It's now a new premium ingredient that can help support the processes that existed in the past and hopefully provide some efficiency. And so that's the first thing, I think the visual, the example I showed about the wide receiver tendencies when they're thrown to in the red zone, that always gets people leaning forward a little bit. Also with running backs, third down in three plus yards, or third down in short situations, and my right-hander to left-hander when I'm on a certain hash. Again the visualization just allows them to really mark something in their head-- >> Just in the phase. >> Where it makes them really understand. Another example that's interesting is players who play on special teams who are also wide receivers, so as we know, linebackers and tight ends tend to be, and quarterbacks tend to be involved in special teams. Well is there an effect when they've covered kick offs and punts, a large amount of those in a game, did that affect them on side a ball play, for instance? Think about Julian Edelman two Superbowls ago, he played 93 snaps against the Atlanta Falcons. and when you look at the route-- >> [Jeff] He played 93 snaps? >> Yeah, between special, because it went into overtime, right? It was an offensive game-- >> And he's on all the-- >> He played a lot of snaps, he played 93 snaps. how does that affect his route integrity? Not only the types and quality of the route, but the depth and speed he gets to those points, those change over time. So this type of information can give the experts just a little bit more information to find that edge. And I have a great mentor of mine, I have to bring him up, Gill Brant, former VP of Personnel to Dallas Cowboys, with Tex Schramm and Tom Landry, he looks at this type of information and he says, what would a team pay for one more victory? >> So as we know, all coaches and professional organizations and college are looking for an edge, and if we can provide that with our technology through efficiencies and some type of support information resource then we're doing our job. >> I just wanted to, before I let you go, just the human factors on that. I mean, football coaches are notoriously crazy workers and, right, you can always watch more films. So now you're adding a whole new category of data and information. How's that being received on their side? Is it, are they going to have to put new staff and resources against this? I mean, there's only so many hours in a day and I can't help but think of the second tier or third tier coaches who are going to be on the hook for going through this. Or can you automate so much of it so it's not necessarily this additional burden that they have to take on? 'Cause I would imagine if the Cowboys are doing it, the Eagles got to do it, the Giants got to do it, and the Washington Redskins got to do it, right? >> Right, right, well each team as you might expect, their cultures are different. And I would say two or three years ago you started to see more teams hire literally by title, director of analytics, or director of football information, instead of sharing that responsibility between two or three people that already existed in the organization. So that staffing I think occurred a couple, two or three years ago or over the last two or three years. This becomes another element for those staffs to work with. But also along that process over the last two or three years is, really, I always try to say in talking to teams and I'll be on the road again here soon talking to clubs after pro days conclude, is forget about staffs and analytics and that idea. Do you want to be information driven, and do you want to be efficient? And that's something everybody can grasp onto, whether you're the strength and conditioning coach, personnel staff or scout, or a position coach, or a head coach, or a coordinator. So we try to be information driven, and then that seems to ease the process of people thinking I have to hire more people. What I really need to do is ask my people that are already in place to maybe be more curious about this information, and if we're going to invest in a resource that can help support them and make them more efficient, make sure we leverage it. And so that's our process that we work with, it varies by team, some teams have large, large expansive staffs. That doesn't necessarily mean, in my opinion the most effective staff is using information. Sometimes it's the organizations that run very lean with a few set of people, but very focused and moving in one direction. >> I love it, data for efficiency, right? In God we trust, everybody else bring data. One of my favorite lines that we hear over and over and over at these shows. >> In fact, I might borrow that next week. >> You could take that one, alright. >> Thank you, Jeff. >> Well John, thanks for taking a few minutes and stopping by and participating in this Western Digital program, because it is all about the data and it is about efficiency, so it's not necessarily trying to kill people with more tools, but help them be better. >> That's what we're trying to do, I appreciate the opportunity and love to talk to you more. >> Absolutely, well hopefully we'll see you again. He's John Pollard, I'm Jeff Frick, you're watching theCUBE from Palo Alto studios, thanks for watching, we'll see you next time. (Upbeat music)

>> Hey, welcome back everybody, Jeff Frick here with theCUBE. We're in our Palo Alto studios for a Cube Conversation as part of the Western Digital Data Makes Possible program it's very gracious of Western Digital to sponsor us to go out and talk to a lot of different companies that are doing a lot of cool innovations. At the end of the day it's all powered by data, at the end of the day all software is just an algorithm sitting on data with a nice display for a specific solution. But this one we're diving into sports and there's so much going on with sports and technology and this is a great company that's actually been kind of flying under the radar for 10 years unless you're into the space. But we're happy to have them as GumGum and we're joined here by Brian Kim he's a senior vice president of Product from GumGum. Brian, great to see you. >> Thanks for having me Jeff. Appreciate it. >> Absolutely. So for the folks that aren't familiar with GumGum give 'em kind of the quick overview. >> Sure, so GumGum's a artificial intelligence company with a expertise in computer vision. And what that means in kind of common language is that we focus on building algorithms that allows computers to identify what's happening in imagery. And then we apply that into different businesses that we feel like the usage of computer vision could essentially automate scale or drive significant value to those different businesses themselves. >> Right, but you guys have been at this for awhile you're almost 10 years old, like you said your 10 year anniversary's coming up. >> Yeah we were founded in 2008 and a lot of the core team still together from the original team that worked there. And we were doing computer vision before computer vision was probably sexy. >> Right right. >> So. We've been working on it for a long time we've built a lot of expertise around that and with a lot of the improvements that have happened in machine learning and A.I. these days it's just been kind of the big, hot thing that has continued to accelerate and helped us grow significantly over the last few years. >> Yeah all of our social media feeds are filled with the picture with the chihuahuas and the blueberries right? Trying to figure out which is which. But you guys have a very different approach then kind of what we read about now in the popular press. You built computer vision capabilities but you built them for a very specific application not just as a generic kind of computer vision so I wonder if you can tell us a little bit about your strategy and how you guys got started in the ad space. >> Sure, so you know, to your point Jeff I think a lot of companies have focus on what we like to call A.I. as a service and what that means is that they build the capability of using computer vision but it's really up to the end user to build it use it as a tool in to their actual business and figure out where it'll actually apply. Our path forward has been focusing on building what we like to call full stack vertical solutions meaning that we decide to go into a specific industry or division like ads for example. We build an ad solution that's using computer vision itself and we actually sell that ourselves to agencies and brands direct and then we work with the publishers on the other side of the coin to actually deliver those ad experiences and continue to kind of build our businesses around that model. >> Right, and how is a computer vision, A.I. driven ad experience different than the alternative? >> Well I think there's a few things that we've focused on that make it different. The biggest I would say is the placement of where the ads actually are themselves. So compared to your standard display ads that are around the content on a web page what we've focused on is actually building ad experiences that are within the content itself. What we call in-image ads and that's an ad that overlays on top of that photo editorial content that's on a publisher website. And what we've done there is we've applied our computer vision to understand what's going on in the actual images. And we leverage that tech to be able to then contextually match the ads from our clients to the actual pages themselves so that they're completely relevant to the page. And we make them look very slick and that's kind of the design of it so that it feels very sticky and natural to the way that the page is actually designed. >> Right >> And built. >> So what's different just so I'm clear is that unlike a typical kind of an ad placement in a page which is going into a dedicated spot as soon as the page loads it takes some data. >> Yep. >> And goes to auction. You guys are actually looking at the page as I the consumer of the page am looking at the page, and based on the things that are loaded regardless of where I am on the page: top, middle, bottom, that's the stuff that drives your ad placement. >> Yeah, perfect example would be you might be reading an article about cats and, you know, PetSmart wants to advertise with us so we'll understand that the image itself is about cats and put like a cat food, PetSmart ad that's placed within the actual image itself you might scroll down a little bit farther and find another article that's about a completely different topic. And we would actually match that to the relevant advertiser that fits that example as well. >> So how are advertisers measuring the delta in the value? I mean some of the stuff researching for this I saw a great quote that you guys in your ad "Delivery want to be seen and frequent and respectful" which I think is a really interesting way to take a point of view because clearly ads run a risk of being way too obtrusive, popovers and popunders and popups. I go to a page, I'm a huge customer in two seconds they're asking me if I want to buy something, I'm already your biggest customer! (Brian laughs) So how are the content publishers measuring this different type of an engagement with an ad presentation the way you guys do it? >> Yeah I think what you talked about is the right approach of how we want to go to market which is that we want to provide a premium offering that's high impact but not obtrusive to the end customer, and provides value to both sides of the ecosystem. So to the advertiser it might be a premium CPM that they're paying in order for that ad placement but because of how relevant it is and how viewable it is 'cause if you think about where your eyes are on the webpage, you're not looking at scanning the sides of the page you're focusing on the content that's going down the image is right in the middle of that. So if an ad pops up right in the middle of that image its much more viewable than say all the ads that are typically around the content itself. And on the publishers side to kind of end frequency we don't want to blast an ad on every single image we want to match it to the most contextual and right placement and then therefore to the publisher when they get an ad, that ad is actually paying out a pretty significant price point back to them. And then they're happy with that experience because their users not saying I'm seeing 50 ads on one page which is kind of the traditional problem that they deal with right now. >> So that's a ad tech market and you guys have been doing that for awhile but the reason we reached out to you specifically is your activity in sports which is a relatively new business for you guys. So how did you get into sports, how did you guys identify this opportunity? And then we'll dig into it a little deeper. >> Yeah, so GumGum as a whole has always been looking at different ways that we think computer vision can be applied in a myriad of different industries. And the opportunity that about 18 months ago we identified was that there was a very legacy business that was being built around providing media evaluation around sports sponsorships. So what I mean by that is how do you quantify the value of a sponsor showing up, State Farm for example showing up in a basketball game. Whether that's an LED placement there on the basket stanchion arm, or they're part of the half-time show. And the measurement that was being done traditionally was essentially done by people. So, people were watching those clips, they were timing how long the different brands were showing up, they were measuring how big those actual placements were. And they were then calculating some value off of it. And we really thought that computer vision can one, automate that entire process, so take the humans out of the loop and get it to a point that its completely automated and you don't have to have people involved. We can deliver it faster, so a computer can do something a 1000 times faster than a human being can probably analyze it. And your providing much more accuracy and efficiency of the actual data that you're providing back. You know that exact dimensions pixel by pixel that a computer is telling you versus a human being trying to eyeball where and when certain ads are showing up. So, what we've done there is then built a business that is now called GumGum Sports, where we provide media evaluation to sports sponsorships so both on the team side, which we call rights holders, and on the brand side and we're essentially the middle man who's providing third party reporting to both sides so that they understand the value of what they're getting across their sports sponsorships, both on digital which is essentially broadcast T.V. but also across social media which has been a huge gap that nobody's addressed to date. >> So just before we go into the impact before it was just a person, they're watching the game and every time that State Farm ad pops up on the stanchion they're writing down approximately how big was it could I see it, was it blurry, was it moving? Was it in the center or the side? You guys, obviously that's just right for algorithmic treatment 'cause you know, like you said you know, the pixels. You're doing time, you're doing placement, you're doing quality, you've added a number of things beyond just simple, that is there. >> [Brian] Correct. >> In terms of metrics to measure. >> Yeah so we look at things like where's the action happening? So if we can identify in a basketball game where the actual ball is that's probably where people are focused on 'cause the action's happening near that ball. So the closer you are to the ball the better score that you'll possibly get. To your point, how clear is it if you're panning back and forth through a game of your logo showing up? How big is it, how prominent is it and we've factored that into what we call our MVP factor of media value percentage which helps calculate what that end value looks like to the client. >> And was the demand driven on the suppliers side or the buyers side, were they looking for validation of this money. >> I think both. >> Value or were you saying it was both really? >> Yeah sorry to interrupt you. >> No, it's okay. >> Both sides were looking for somebody who's not favoring one or the other to give you validation so they wanted an arbitrator who would basically say this is what I think the value is in the ecosystem so that both sides know how to negotiate when they want to put together their deals next year. >> [Jeff] Right. >> You know the value to the teams are the more value you can generate obviously the higher that they can increase their price points. And I think to the brands what they're focused on is how do I optimize my ROI? Buy the placements that are generating the most value for me and not waste my money in other placements that don't generate value for them. >> Right, any big surprises in terms of the value of a stanchion versus the value of a half-time show versus a electronic thing on the scoreboard? >> So I think more than the placements themselves I think the biggest surprise that we found was how big social media actually has become in the valuation of all of media in general. You know, there's a lot of talk about subscribers numbers going down on T.V. That broadcast is declining, that nobody's watching. >> Super Bowl was down I think this year? Which doesn't happen very often. >> You know, live sports is just not what it used to be. But the reality is I think consumers have just changed their habits of where they're consuming that content. So instead of having to sit in front of a T.V. for two hours they might go check the highlights on YouTube, they might go look at their Instagram stream and see a bunch of posts that are coming from fan accounts that they're following that give you the highlight clip. So, being able to measure that piece of it that nobody's done before what we found is that that value is actually as big if not bigger than the broadcast side of it. Which nobody has really quantified to this date. >> That's really interesting. So you're what sniffing hashtags or something around a particular event to grab that data how are you grabbing all the social data around say a basketball game or whatever? >> So that's where the computer vision actually gets applied so we don't even need to look at specific hashtags or specific accounts. We can look at the full stream literally all of social media that's available publicly and we're able to sift. >> [Jeff] Just plug into the API. >> Yes sift through all that with computer vision and say oh this is not a sport, oh this one happens to be a sport now I know that it's NFL, now I know it's tied to the Super Bowl. And then you now classify all that data and then figure out the actual post that you want to analyze and the ones you don't want to analyze. >> It's so interesting I can't help but think back to like the Grateful Dead back in the day they were the only band that would allow people to record at the concerts, right. There was this huge, you can't record and no pictures! And then they would trade the tapes in the parking lot before the game and you saw that too with a lot of professional teams, no phones, I was at a concert the other day and they're like no phones! No phones, I mean that is the way that people experience and expand and amplify these live events. And it sounds like what you guys are doing is really validating how important that is to all the people that are participating in that live event. >> I'll give you a perfect example with the NBA all-star week just happened in Los Angeles recently. If you look at the slam dunk contests half of the all-stars that were in the crowd had their phones up (Jeff laughs) and are basically recording something that they're probably going to post on their social media account later. >> With massive, massive, massive followers. >> Each one of them might have two to 10 million people who follow them so you multiply all that and that's probably a bigger audience then actually who'll tune in to TNT or whichever channel that happened to be watching live the actual slam dunk contest itself. >> That's crazy. So, I'm curious to know what the response is as you come back to this data obviously it's great news for the publishers right a bunch of value that they didn't even know they were delivering no one's even capturing. At the same time I would imagine the advertisers are thrilled to actually to see that they are getting this whole nother traunch of activation that they had no clue or at least no way to measure. >> Correct. I think that's been the biggest surprise to everybody is how much value has been unlocked to them and both sides are thrilled about it because now they can start to measure that on a consistent basis and then moving forward they can figure out how that fits into their overall plan for whether they want to charge more for their sponsorships or whether they want to price certain things in like social media that they never did before. >> Right right. So, my mind is going all kind of places, so could you on sniffing that feed find say the State Farm logo stay on the same thing. Where's State Farm logo showing up in a billboard that's on the 101 that happens to be in front of a pretty spot where people take bike paths. Are you seeing or even attempting to look for other kind of secondary social impacts of other forms of advertising outside of your core solution within the sports? >> Yeah, I mean we've started to get feedback of people who are interested in solutions like that whether it's digital out of home different kind of businesses that have built themselves around wanting to track this type of ROI and we've looked at a few use cases and talked to a couple clients that we're starting to dabble in now that might be interesting for us to build new businesses around. Just like the use case that you talked about with the digital out of home example. >> Right, another one of my favorite lines that gets thrown around a lot these days is in God we trust everybody else better bring data. So I'm just curious as to the feedback you're getting from both sides of that equation within the sports application of now we have this data I mean how is that impacting peoples evaluations, how is that impacting their business decision, just kind of generally how does moving from I think this is a good value, we bought it last year we're going to re-up this year, to here's all the impressions you got the quality of the impressions, a score, plus we've uncovered all this additional value I would imagine data driven decision making has got to be so refreshing in these environments. >> It is and I think the challenge that a lot of them had was that they were getting the data six to eight weeks later, so if you think of it from a brand prospective I'm already off to my next sponsorship six to eight weeks later I can't even think about what I previously did so for us to be able to give them a solution where they can get their data back in a week or less really helps them make smarter decisions to your point about taking data driven decision making and figure out real time how they want to adjust to how their audience is adjusting. >> And do they make a lot of real time corrections in those types of packages or are those like annual deals I would imagine in the sports thing. >> Yeah I think a lot of them at this point are still annual deals the way that they sign up for it but I think now that they're having access to this data they're starting to rethink that model and trying to figure out how do we need to change the way that we purchase these things in the future to better fit how they're getting the data around it. >> Has anyone repriced the inventory based on the data that's come out of the research. They increase the price of a stanchion and decrease, I'm just making stuff up, decrease the price of some other ad unit within the stadium based on some of the data that's come out of your system. >> We've had a lot of our clients talk about their plans of how they plan to go do that. I think we're only 18 months into this business so a lot of them are still in the first season or maybe halfway through the second season of working with us so they're still trying to figure out how to message that properly and what the right channel is for them to recoup those gains but I think the ability for them to start those conversations is something they've never had before so exposing that to them now allows them to really rethink how their business model is. >> It's such a cool example of how data actually allows both halves of the equation to do a better job It's really beneficial to everybody right it's not just one sided information that's giving somebody a big advantage over the other one. >> Exactly. >> All right so Brian before I let you go we're in 2018 still hard to believe I can't believe we're almost through the first quarter, we're ripping through it. Some of your priority's for 2018 what is GumGum working on what are you excited about if we sit down a year from now what are we going to be talking about? >> Yeah I mean we've been doing this advertising business since 2011 it's our most mature business so I'm definitely continually scaling that business from a automation standpoint and continually growing that particularly internationally has been one of our main goals for this year. As I said GumGum Sports is a pretty new business to us but we're expecting that to start to bring in significant revenue for us this year and want to see that growth happen. And we're also looking into new emerging areas where we potentially think computer vision can be applied just like we did in GumGum Sports. It could be the medical space it could be television there's a lot of different applications there that we haven't quite tapped into yet but we're starting to noodle around what are the right ways that we want to go after that and potentially where we want to invest in with how successful we've been so far. >> Yeah, the exciting opportunity ahead. >> Yeah. >> All right. All right Brian, he's Brian Kim, he's the senior vice president of Product from GumGum. Thanks for taking a few minutes out of your day and stopping by. >> Thanks Jeff. >> All right, pleasure. I'm Jeff Frick you're watching theCUBE catch ya next time, thanks for watching. (instrumental music)