>> Narrator: Live from Las Veags, it's theCube, Covering NAB 2017. Brought to you by HGST. >> Welcome back everybody, Jeff Frick here with theCube. We're at NAB 2017 with 100,000 of our closest friends. Talking all about media, entertainment and technology. The theme this year is MET, cause the technology is so mixed in with everything else that you can't separate it anymore. And we're really excited to do a deep dive into kind of the customer, or not the customer, excuse me, the consumer side of this whole world with Yves Bergquist. He's the project director, Data and Analytics Entertainment Technology Center at USC. So Yves welcome. >> Thank you, thanks for having me. >> So when I was doing some research on your segment, really interesting to see that you're involved very much in trying to figure out what people like to watch how they like to watch and get a bunch of data because now the choices for the consumers of media and entertainment are giant, like never before. >> Yeah. There's a, very very basic question that I think not a lot of people in media and entertainment can answer. Is that why are people watching your stuff? And they have sort of surface level answers, but there's ways that the content out there, that we watch, resonates cognitively with us, that is really important, is very fundamental in how we consume media and entertainment. And even the decision making of why we decide to go watch a show on Netflix, or play a mobile game, or watch a Youtube video. Why do we make these specific choices? What drives those choices? All these questions don't have a lot of really good answers right now, and that's where I, where we're focus all of our work at ETC. Is to really understand people's drive to entertain themselves or decisions to entertain themselves at a very deep level. And really understand how various narrative structures in film and trailers and brands and advertising resonate with people at a cognitive level. >> So it's pretty intersting, it really goes with the whole big data theme and the AI theme. Because now you can capture, collect, measure data in ways, and consumption in ways you couldn't ever do before. >> Yeah, that's a good point. So, you know, there's three things that are really impacting the media and entertainment industry and every industry, really. It's, number one, the ability to think in systems, right? We used to think about problems in a very sort of siloed manner, right, we think about a problem in isolation with other forces. Like we look at the flu in isolation with the environment that we're in, so like that. There's another way to look at things, in a more holistically, it's a system called systems thinking. And the ability to think of audiences as a system, just like your body's a system inside a system, right, is really revolutionizing the way we're looking at entertainment and media. The second thing thing is the availability of data, just there's an enormous amount of data out there. A lot of it is unstructured, but there's, the good thing about entertainment and media is that it drives passion and drives conversation. And anything that drives passion and conversations get very rich in data. And the third thing that is impacting the industry is machine living and AI. And the ability to really look at all of these data points across the system holistically in a very intelligent more semantic manner. And make sure that you're measuring the right things. For a very very long time the media and entertainment industry has been measuring the wrong things. And it's really now catching up very very fast and making sure that it's measuring the right things. For example, how do we measure how specific narrative structures in film resonate with people cognitively in a way that translates into the box office? Is there a specific character journey that resonates better in an action movie with males versus females. How does that matter for how a story's being told? Where do you innovate in script, right? Interesting point is the entertainment industry is very unique in that it has two major problems. Number one, its clients, its customers are absolute experts in the product. Because if you're 25 or 35, how many movies have you watched? Thousands of movies, right? So you're an expert in movies. >> Jeff: Certainly the ones you like. >> Exactly. If you're 25 you haven't bought hundreds or thousands of cars, right? So, but on the other hand the supplier of the content doesn't know as much of the customer as the customer knows about the product. So you have two problems. You have a really really really highly expert client, and, but you don't know a lot about that client as a studio, right, or a network or a media company. So that's very very unique distinct challenge that they're starting to get very very smart and very advanced in thinking about. >> The other thing is, that I see in the movie industry and I'm no expert by any stretch of the imagination but it seems like the compression pressure is huge. The budgets have grown to be giant. And the number of available weekends for your release are small. And the competition for attention and eyeballs around those weekends, it just seems to really have a really high kind of risk reward profile that's getting more and more extreme. And is that driving people more to kind of the known? Or is it just my perception that they're taking less risks on modifications from the script or modifications of kind of the norm especially around these big budget? I mean just the fact that you've got version 1,2,3,4,5,6, pick your favorite theme seems to be a trend that continues and gets even more, I mean Superman. How many Superman movies are there, or Spiderman? >> So you know, that's really interesting right? So the very natural tendency of the media and entertainment industry is when it doesn't know, as I was mentioning, it doesn't know as much as it could or should know about who its audience is. The tendency is then becomes to just take less and less risk in telling stories exactly the same way that's why you see a lot of really really formative very formulaic movies. What we're trying to do is, and the challenge with that is that, again you have an audience of experts and so if every single movie looks like the same one, look like the other one, you're going to have a problem. People aren't going to go see, going to go gravitate towards another kind of entertainment or some of your competitors. So you have to know where do you meet peoples expectations in a movie and where do you innovate? Deadpool is a really interesting example. Deadpool has the structure of a basic superhero movie but it has a lot of innovation underneath that. And so for the studios knowing where do you stick to the formula and where do you innovate in telling a story when you make a billion dollar movie, is going to become more and more interesting. Because if you innovate too much you're going to turn people off. If you don't innovate enough, you're going to turn people off. So we actually have some research looking at the mathematical definition of why we think certain things are interesting and certain things are not interesting so we can separate. These are the things you need in your movies, this is some aspects, if you go back to Deadpool, there's some aspects of Deadpool as a movie that are very traditional to the superhero genre. And a lot of other aspects that are very very innovative. So you have to innovate in certain areas and you have to no innovate areas. And that's a real challenge, and so that's why we're really applying our work to looking at narrative structure in storytelling at ETC is because that's where a lot of the revenue opportunities and the de-risking opportunities are. >> And it's interesting before we went live you were talking about thinking of storytelling and narrative as a little bit less art and a little bit more science in terms of of thinking at in terms of algorithms and algorithmically. Because there are patterns there, there is data there. So what does some of the data that you measure to get there? You mentioned earlier that in the past people were measuring the wrong thing. What are the right things to measure? What are some of the things you guys are measuring now? >> Yeah, so you know, it is still very much an art, right? It's making it, making art a little bit more optimal, and optimizing art is what we're doing, but it's, it will remain art for a very long time. I think for, and since we're at NAB, sort if in a broadcasting environment, I think a lot of the measurements and systems that have been in place for decades now are looking at demographics. And demographics, whether you're a male or female, Your age, your ethnicity, or your income, used to predict what you would watch. It doesn't do that anymore, and if you have kids, you know like me, you watch the same thing that they're watching, you're playing the same video games that they're playing. I think there's a new way to measure things more cognitively and semantically and neuroscience is starting to get into the issue of why do we think certain stories are more interesting or more appealing than others. Why do certain stories lead us to make actual decisions more than others? And so I think at a very very basic level you have to unpack this notion of why do people go see this movie? And it's a system, you know, that decision happens in a system where some of the system is demographics, demographics aren't going to go away they're still predictive to a certain extent. But it's also, you know, cast, it's also who has recommended this movie. And what are the systems of influence in driving certain people to see a movie? And all these things, and of course, what we're focusing on, which is storytelling and narrative structure and how that, sort of translates to making decisions to see this movie. A lot, you know, we're still in the infancy of measuring all of the system in a very scientific granular way, but we're making very very quick progress. And so even things like understanding the ecosystem of influence around why certain communities are influenced to go see certain movies by other communities and what happens there, right. So I'll give you an example, we did, we pulled months of data on Reddit about where supporters of Hillary Clinton and where supporters of Donald Trump would engage on that topic. Are they talking about that amongst each other or are they really going out there and trying to convince other people to vote for Trump or to vote for Hillary Clinton? And we saw some, two radically different patterns. So pattern number one, the Clinton people would mostly engage with each other on Reddit. So that's cool and that has very little value because you're not being an ambassador. On the other hand, the Trump people were engaging far outside of the Trump subReddit and trying to convince people to join the movement, to donate, to vote for Trump. So we think there's a model there that can be ported to the entertainment industry, where if your fans, if your fan base is mostly engaging with each other it has less value than if your fan base is really going out there and really trying to get other people excited about your movie. And why do certain people get excited and how do your fans, what argument do your fans use out there to convince others to go see your movie. All these things we're looking at, and it's brand new world now for media because of all of these data points. >> The systems conversation is so interesting because it's not only the system, but the individual. But it's like you said, it's all these systems of influence today. Look at the Yahoo reviews, the Rotten Tomato reviews, you know, what are there, Reddit, you know, as a system of influence, who would have ever thought? >> Yeah and we're getting it, we're going into a world very quickly, we're going to be able to understand entertainment and storytelling and narrative and it's cognitive power almost on a neural network base. In looking at what kind of neural network in our brains get fired when we are exposed to this type of character, or this type of storyline, or this type of narrative mechanics. And so this is a really exciting time. >> The other thing that's interesting, we talked again a little bit before we turned the cameras on, is about the trailers. Because that's kind of the story within the story. And depending on your objectives, and the budget, you know, they can make all kinds of number of trailers, in very different way, to approach or to target very specific audiences. I wonder if you can get into that a little bit. >> Yeah so, you know in the media and entertainment industry decisions have been made, and if you think about it it's amazing that the media and entertainment industry has made so much money, so I think it's a testament of the enormous creative talent that's involved. But, you know, especially for trailers a lot of the decisions about trailers are made sort of looking what's worked in the past in a very sort of haphazard way. There really isn't a lot of data and analytics and science applied to, hey what kind of trailer, what structure of trailer do we need to put out there in each channel for each target audience to get them really excited about the movie? Because there's many different ways you can present a movie, right, and we've seen, we've all seen many different types of trailers for many different types of movies. What we're doing, and nobody's really worried about hey let's analyze, for example, the pace, right, the edit cuts, the structure of the edits for the trailer and how that resonates with people. And now we have the ability to do that because people, you know, we will count views on YouTube for example, or there will be a way to measure how popular a trailer is. So what we're doing is we're just measuring everything that we can measure about a trailer. Is it a complete story? What is the percentage of the trailer is the main character in? What is the percentage of the trailer that the influence character is in? We're looking at cast. Does a trailer with Ben Affleck, you know, work better if Ben Affleck is a lot in the trailer, or not a lot in the trailer? And what kind of trailer types work better for specific genres, specific target audience, specific channels? So we're really unpacking that into a nice little spreadsheet. And measuring all the things that we can measure. And the thing about this is, if you think about the amount of money that's involved in making these decisions, you know if you're a studio and you're spending 3,4,5 billion dollars a year in marketing expense, and my work can make it even 10 percent more efficient, that's like half a billion dollars in savings. >> That's a real number. >> That's enormous right? So it's a really exciting time for media and entertainment because there are all these things on the horizon to help them make better decisions, more data driven decisions. And really free up creators, because if we can tell the people who tell the stories in film every, you can innovate so much more now because we've, we know that we've boiled it down to a science, and we know that in this, if you have these four or five things in your script, everywhere else you can innovate, go nuts. I think it's going to free up a lot of creative talent. We're going to see a lot more interesting movies out there. >> The other piece I think, I mean obviously a trailer for a movie's one thing, but take that little genre of creative that's purely built to drive behavior and that's a commercial. And I always joke with my kids, I watch a lot of sports, and there'll be a car ad and I'm like, just think if you're the poor guy that gets the assignment to make another car ad, I mean, how many car ads have been made, and you've got to think creatively. But the data that you're talking about, in terms of the narrative, what types of shots, the cutting, based on the demographic that you're trying to go after for that specific ad. That must be tremendously valuable information. >> Yeah it is really valuable. So you know, our philosophy is that everything is story. You're tie is a story, your haircut's a story, you're cereal's a story, your cars, everything. We make decisions based on the narratives that other other people tell us and that we tell ourselves about how to represent the world. Simply because the universe out there and the reality out there is too complex for our brains to really represent as it is, so we have to simplify, compress it into a set of a behavioral script that says, okay I'm, it's sort of an executive summary of their reality. And though that executive summary is a story. And so it's especially powerful in driving how what we buy and how we consume things. And so, I've build a platform that looks at, that extracts very very structured data from conversations about what is the narrative structure about a specific brand. You know, is it focused more on, you know,emotions? Is it focused more on ethics? Is it focused more on the, sort of the utility of the product? And trying to correlate that to look at what kind of narrative structure's around your brand? What kind of story around your brand, drives more sales? And so that's really really interesting, in sort of understanding, again, that cognitive relationship between stories and how efficient they are in driving specific behavior. That is exactly what my research is about. >> Yves, we could go on all day, but unfortunately we are out of time. So thank you for spending a few minutes and dropping by. Fascinating conversation. Alright, he's Yves Bergquist from USC, where all the film stuff's happening. I'm Jeff Frick, you're watching theCube. We'll be back NAB 2017 after this short break. Thanks for watching. (uptempo rock music)