The Data Scientists Quietly Powering Hollywood’s Greenlight Machine

This episode is presented by AMC Network. A new chapter in Anne Rice's Immortal Universe begins with AMC's The Vampire Lestat. Get a backstage pass to the iconic frontman who pace magazine calls a boi-inspired rocker that will have fans screaming. Don't miss the legendary vampire Lestat De Leon Corp in his own electrifying rock saga. Watch The Vampire Lestat, Sundays only on AMC and AMC+. Learn more at AMCplus.com It is Thursday, July 16th. It's so secret that Hollywood is increasingly dominated by large global streaming services and that those streaming platforms are governed by data. In fact, Netflix often likes to describe itself as having one foot in Hollywood and one in Silicon Valley. But people inside Hollywood tend to know a lot about how the Hollywood side of the company works. They know a lot less about the text side, the data side that influences so many decisions and when it's working, it can help predict how a project will perform. Data has always informed what films and TV shows get made from the box office track record of stars, maybe their Q rating, to the Nielsen viewership and demographic information. But since the 2010s and the rise of director and consumer digital platforms like Prime Video, Apple TV Plus, data has become much more powerful. It used to define the content strategies and influence production choices and identify promising genres or even storylines, evaluate talent and effectively price projects for the platforms. And along with that, it has come an army of statisticians, data scientists, machine learning specialists, thousands of them across the industry, occupying key roles at the streamers. They're not at the movie premieres or thanked in Emmy speeches, but these data scientists are the quiet powers at the streamers. And few people in town know much about them or more importantly what they actually do. That's why a new book caught my eye. It's called "Data Driven Hollywood," the new data professionals in the age of streaming by VLN Russel, a professor of sociology at the University of Paris 8th. I read it and it's really about how the "creative choices" have been reinvented over the past 15 years thanks to data for better and worse. VLN talked to about 75 data scientists at the streamers and studios with titles like VP and head of science and algorithms at Netflix. And she chronicles the rise of the data nerds at the streamers, the tension with traditional producers and creative execs. So of course I had to get her on the town. Today it's the data that 8 Hollywood and how streamers decide what to make and predict what viewers will love. From The Ringer and Puck, I'm Matt Bellany and this is the town. Okay we are here with VLN Russel who is professor of sociology at the University of Paris 8th and an author of a very interesting new book. Welcome. Thank you. Thank you for having me. Everyone in Hollywood knows that data is informing most decisions at the streamers and increasingly the studios. Data data data. We hear it all the time. We see the engagement report. In fact, Netflix's engagement report is coming out today and we'll get to see the result of their efforts over the last six months. But we don't know exactly how this works. And you wrote a whole book about how data and data scientists are being used at the streamers. So first of all, how much is data informing every decision at these companies from what shows to develop to which stars to hire? So what I discovered, conducting investigation in Los Angeles and in Hollywood regarding data scientists who work for the streamers. And now, as you also mentioned it for the studios and really the distinction between the two is not as relevant as it used to be because the studios have also their own streaming platforms and the streamers are also studios. But the difference is really how much data is used and it's happening, in fact, increasingly. As we speak, it continues to develop, in fact, and now also with AI, which is a new development that I need to do a new book about, right, a new book about and you're reserved. But from the mid-20 tense on, it's been a process of having data and data specialists more and more involved in not only producing numbers that can help for marketing or that can help producers to justify their choices in retrospect, but really involved at the very early stage of deciding what should be made. And that means both the general content strategy of the streamer of the platform, as well as more specific decisions on categories of projects or on one specific project that would be expensive, for instance. So I want to get into the kind of nitty gritty of how this works because there are, from my experience, just talking to these companies, there are content strategists, there are content finance analysts that look at the economics behind each project and try to leverage data to say, "Okay, we should be placing more bets here because this genre with this filmmaker actually makes more sense for us as a platform." There are content planners that look out into the future and say, "Okay, this is in the ballpark of where we should be placing our bets. We're getting our lunch eaten by Netflix in the podcast and daytime consumption space." So all of a sudden, a year after they noticed that data, Netflix is going full speed into podcasts. So explain a little bit to us about the interaction between these content strategists, analysts, and the creative teams. Yes. So, first of all, you mentioned different categories, in fact, of data specialists. And that's very true. And in particular, what I describe in my book and what I realized doing the fieldwork is that there are two big categories of data, especially that play a different role. Some of them are data scientists, sometimes engineers, sometimes people with a PhD in computer science, and there are more on the tech side of things creating the models that are going to be used for the algorithmic measurements of things later on. And then there are the people who work in the data strategy content data analysis, content planning departments, the names vary a little bit depending on each organization in streamer. But basically, these people act as middlemen, middlewoman between the kind of hard science, data scientist, and technicians on one hand. And on the other end, the traditional type of producers that work with the creators, with the artist, to produce, to make the content, who have been traditionally the ones that were really monopolizing the power to make the decision before, depending if they were high enough studio heads and studio executives that had enough power, they were indeed very powerful in deciding what should be made. And now they have to negotiate in a way with those data scientists. And in particular, they do it through the content strategy teams and the content strategist, content analyst, that have a business background and are more able in a way to translate the language of data in a way that is understandable by the traditional producers, but also the language of the traditional producers to the data team or the data science team, rather, so that they can make the tools in a way that is more efficient for the goal that is. Right, so it's a back and forth. It's not just we should be making more comedies with Will Farrell. It's the production team coming to them and say, listen, we've been pitched a comedy golf series with Will Farrell. Give us your assessment. And these teams perform analysis based on who the audience is, who is potentially going to watch this, what the cost of the show would be, and then they come up with a price that they are willing to pay for this project if they decided something that's worthwhile. Yes, I would say it's a negotiation, but the terms of the negotiation have changed over time. An internal negotiation. Yes. And I would say at the start of this process at ISTUDE, which is in the mid-2010s and late-2010s, the traditional producers, the content, the content people, add a lot of power in those negotiations still. And the data specialists were trying to push their own agenda or their own forms of legitimate to establish their own forms of legitimate. And they're all, I call this new way of defining how content should be selection a new production narrative. So they are pushing their new production narrative that says that basically it's important to look at the data. And a lot of pushback you describe in the book to that process and it was not just you know the traditional I'm you know the creative executive I'm the producer I know better than what the data says but also you describe a lot of demographic differences you know these are PhDs these are scientists you say they are much more female than traditional Hollywood producers they're non-white they are largely immigrants they are people that are not the traditional Hollywood gatekeepers and this was a challenge but I think those days are over I think these days everyone accepts it even the top producers want to see the data they may not listen but they do want to see it. Yes and they have to if not listen at least they have to take these other key players that are the data specialists into account now at the beginning of when I started doing this field work it was in the mid 2010s and I could hear a lot of producers studio heads people with power in Hollywood at the time tell me oh I don't believe in data I don't believe in data I don't whatever they say I don't care I know I know the artist I have the relationship I have the connection I have the experience and I have an eye for quality all things that the data specialists don't consider to be a skill it's not their skill and they come from like you said they come from a very different background and they also they don't come from a film school and they don't come from the background in which they would value making art or working in entertainment well I think there's still some Taylor Sheridan would probably still take a beam of data and whip out a cigar lighted on fire and throw it out the window but I think there are more and more that will precisely there is more and more you said that the key the keeping which is that over time these two groups that are the traditional producers and the data specialists have come to share more common ground to learn the way of the other to build these negotiated order in which they can work together because it's it's absolutely true what you said there is not as much as much friction as there used to be I want to get into how these platforms assign comparative values to projects or even to individual actors and it's a mix of the internal data that they have about their customers and what people are watching but it's also third party data they are getting outside information from Google from Facebook Amazon uses IMDB to see what people are searching for and what kind of stars are getting engagement and they use that data to inform prime video so take us into how this actually works internally at the streamers to figure out what to make so at the time they use a lot of external data now all of the streaming platforms have their own internal data and they use I think the external data mostly for information that you cannot get from your own data which is what will work in the future that we don't yet have the internal data only give them information about what they already have so if they want to try to guess and that's this I call that the Oracle function their their role of predicting the physical basically well that's what this is all about trying to predict what's going to be a hit predicting hits and you know there's a long history in Hollywood of charlatans coming forward and claiming they have the magic secret to predict hits I mean Ryan Cavanaugh was the famous one at relativity media and he ultimately was proved to be not that but for as much as the data can inform hit after hit it Netflix seems to butt up against that theory adolescents squid game stranger things are these projects that the data would have said are going to be hits that's the interesting fact these hits that are the most famous of the big platforms such as Netflix the one that you just mentioned they are not necessarily projects that were backed by data or or suggested created following what what the algorithm was saying every producer is now cheering in their car that I ask that question because they hate this stuff but you know yes but that doesn't mean that the economic model of Netflix that made Netflix as successful as it is is not at least party based on their use of data but it's mostly to detect what is the most successful in what they already have and make more of the same because this with the categorization of content the big databases that they are that they are built based on the titles that they already have in their library of content and creating micro tags micro jars micro categories to to classify those those titles and then trying to compare based on that the new projects that they are offered so basically with this system there is a feedback loop that you can imagine like the system is telling you is suggesting that you are making more of the same that worked in the past and that's where we get to the the taste clusters right because Netflix uses this system of taste clusters where you say it's about 2000 separate little micro communities that they see in their data that tells them what would appeal to certain types of people and explain how that works so the taste clusters are basically made by combining the sub categories of content that have been created following the process that I mentioned before this micro categories of content are connected to micro categories of behavior that the people who are on the platform the subscribers what they are doing on the platform basically what they watch when how in how many segments everything that defines sub categories of behavior in how you consume the content. Let's use Craig as an example he watches a lot of NBA documentaries and high school comedies from the early 2000s is that a taste cluster like what like give us the categories they're using. The categories of contents can be what you just mentioned you know a movie with strong female lead in the set in the in France in the 60s for instance. So it gets that granular. That could be a sub category of content yes the categories of content can be extremely granular even even smaller than this and then you combine that with information about the behavior of people on the platform so the these sub categories of behavior it's classes of behaviors it's not groups that's why I can't tell you it's like white people that age that level of education. Oh yeah I've talked to Ted Serrana about this he's like I don't know who you are I only know what you do on the platform. Yes exactly so basically these sub categories of behavior that are not one person or one group either are connected algorithmically with the sub categories of content to produce these taste clusters but you understand that these taste clusters are very difficult to illustrate I could show you I'm not prepared to do it but I could show you a diagram what it looks like but it won't tell you who is in this taste cluster. So then that is used to create the algorithm that predicts what you Joe Blow is going to want to see next. No it's used to predict what the platform should make. Oh so that's on the make the production side so if we are seeing a taste cluster forming around a particular style of content we should make more. Yes yes. This episode is brought to you by Accenture. When your advertising operations fall out of sync campaigns slow down insights get buried and opportunities get missed. That's why Spotify and Accenture are working together to reinvent the rhythm of ad sales using automation analytics and smarter workflows to simplify campaign delivery and access better data across the business. The result less time spent on operations more time connecting brands with the moments and fandoms that matter most. To learn more check out Accenture.com/spotify. The example that always goes around Hollywood is the House of Cards example that you discuss in the book actually about how the legend is that when Netflix decided to get into original content the data people said we need a politics show set in order Washington DC and Kevin Spacey should start it and all the sudden this house of cards adaptation from the UK became available and Netflix saw it and said this is what our data people want us to make let's blow everybody out of the water with a double the bid that HBO is making and then House of Cards became the first big Netflix show. I've also heard that story is BS. Is it BS? Yes. It's a it's a tell. It's a tell that people tell. Right. Well, it doesn't mean I've asked Ted about this. He said yes. Kevin Spacey is was a big star in the movies that we had on the platform with Kevin Spacey were were big. So he was a good star for us. But I don't think it got that granular at that point. No, especially because at that point the data teams were very very small. Yeah. It happened in the very early day of using data to decide what to make. So producers I think want to know what are the most important data metrics that Netflix and others use to judge their shows. Is it pure audience engagement? Is it completion rates? Is it the so-called decay on the show like the drop off rates? Like what is it that they most care about? Or does it. I think it's a mix and it also depends on the moment. You know, when they feel that they have maxed out on how many new people they can get onto the platform. Do they feel this way or not? It depends on where? Yeah. In the in the US. Obviously, I think it's the case, but elsewhere they still have room for growth. So basically the metrics that matter most depends on the moment and their strategy in the location where they are looking at the metrics. But it's very interesting also something that I observed during my when I was doing fieldwork is that everyone else in the other platforms that are not Netflix wanted me to tell them something about Netflix because they believe that Netflix somehow has a secret. Secret recipe to make you successful. Well, you talked to 75 people including a lot of people at Netflix. Do they have a secret? No, I don't think so. I think they were smart and they were ahead of others and they took a lot of risks. They were in debt for a very long time. Yeah. And their strategy was successful. Well, the market supported that debt. Yes, eventually. But I don't think that they have a database secret. Yeah. But the funny thing is now everyone has data. The talent agencies have their own army of data scientists and you describe in the book this funny thing where it's like it's now data versus data. Whose data is better? So you've talked to people at the agencies who are obviously using the data to try to boost their clients and get more money for their shows and explain why their shows shouldn't be canceled. And yet they're going up against these big scaled platforms that have I would guess better data. The talent or the streamers? It breaks my heart to say it because I studied the talent agencies for a long time and I really enjoyed it. But it's really difficult to beat the platforms, the streamers, because the streamers, they also are very protective of the confidential nature of their data. They give very little information even to the people that they work with, the external producers or creators, because they know that their data is their power and their negotiating power. How dare you? Netflix says they're the most transparent platform out there and they release more data than anyone ever has. Data is so precious. I can't I tell you. Yeah. It's very global. It's not just Netflix. Sure. No, I get it. So you wrote this book. You researched this for many years. Are you convinced that the algorithm and the data knows best when it comes to what projects to green light and what shows to renew? I don't know if it knows best, but it knows or not algorithm and data, but the people, the people behind algorithms and the data because for me it's very, I'm a sociologist of work and occupation. So it's very important to study people at work. It's not data or algorithms that do things. It's the people behind them. So I don't know if they know better, but they know differently. They have a different way of grasping what what are the audiences and what they want and to contribute to defining it to making it happen. And it's very if you think of the way that producers traditional producers before were deciding what to make. It was mostly by focusing on their relationship with their artists, the people that they had a strong relationship with that they believed in. Right. And they thought that these artists had a connection with their audience and maybe can comment some box office. But it was really in this duo between the producer and the talent that what was going to be made was decided. And now the power balance is a little different and has shifted in favor of the data specialist. I don't know if it's it's not more accurate. It's just based on different skills. Yeah. Forget whining and dining the stars. These agencies and producers, they should be whining and dining the data guys. Maybe they do. I had one question. Are there any examples of hits that were primarily a data-driven decision? You talked about we talked about stranger things and adolescents and squid game and the Taylor Sheridan shows all shows that might not have worked you know because of the data. What about the opposite side? Are there shows that are huge hits that were primarily a data-driven decision that creative executives would not have thought of? I mean, there are shows that were supported. It's difficult to know exactly what is a show that is based on data versus not because nowadays most shows they have to be you know they have to be evaluated positively by the data teams to exist. Right. The Greenlight committees includes those people now. Exactly. So you can always claim that it's because of the data but a lot of shows that are unusual that are very different that you have never seen this type of show before cannot be really based on algorithms to suggest that this is the best thing to do because there is this you know with with the algorithmic logic it is based on what worked before. Okay so last question is AI going to blow this entire thing up? Are the data scientists going to be replaced by AI? And us too. And us too you and me both yeah. No but that's a separate question but let's focus on the the massive data employee base that these companies have. Is that going to go away? I can't really predict what the future is going to be. Oh but that's what this is all about. But it's their role not mine. Yeah but what I can tell you is that people who work in these data teams they are definitely worried. Oh yeah. Because it's true but it's not only the data specialists that have this kind of concern. It's in many many sectors that there is a risk that AI be used even for producing content. You don't maybe you don't need as many data as specialists but you also don't need a lot of crew people and even actors and actresses anymore. So. Some would argue that a lot of the content on Netflix is already AI generated but I assure you it is not. It's not supposed to at least. Yeah. No I don't I don't think it is. But but I know I'm working on China. I'm doing field work in China and AI is even bigger. And it's definitely changing. It's definitely a revolution and it will be a revolution in Hollywood as well. And it's an opportunity for me to do field work again and write another book. I can't really tell you more than that because I have not done this yet. I get it. Well I appreciate the insights here. It's fascinating. I think all producers and people who are selling projects to the streamers should read your book and at least understand what you're up against here and what you're dealing with when these companies are evaluating projects. So I appreciate you coming on the show. Thank you so much Matt. We're back with the call sheet Greg you and I saw the Odyssey on Monday night. We slept to Universal City. The premiere was in New York so we went to the press screening. Chris Nolan will not allow us to see this movie in any theater other than his preferred Universal City Walk. I'm max 70 millimeter film theater. First off before we get to the movie what did you think of City Walk? You'd never been there. I had never been in City Walk. Kind of like a like a beaten-down downtown Disney. Totally. It's one of the worst places in in Los Angeles and that says a lot. Yeah. I love you know more power to Chris. for Nolan. I would not complain to all that we get to go see the movie in like the only 70 millimeter 143 movie in LA. Like that's I know. I'm thrilled that we got to do that. Yes, people are paying hundreds of dollars on stuff for the privilege of doing that. I just love this juxtaposition. It's the most Hollywood thing ever about the images that we create about the industry and the filmmakers and everything. I just love that Christopher Nolan, the top filmmaker in the world, this very erudite British American who is the head of the director's guild. I love that he parks in the Jurassic Parking parking lot and walks across the worst mall in Los Angeles and goes and sees his movies and other movies too at the cineplex at the city walk. Well, you know, this could be fixed if they just built another one. I know the C.U. of IMAX was just interviewed talking about it. I don't know why there aren't more of these theaters in Los Angeles. I know. Rich Gelfine was on the red carpet and he said that basically they don't have more of these because they can't build them. There's no there's no film projectors. Now that that begs the question of why IMAX doesn't fund the creation of more film projectors for this format. I honestly think it's because they outside of Chris Nolan movies, there aren't filmmakers making movies like this. Like they would have to spend millions and millions of dollars to build out the infrastructure for these movies and like other than Nolan and maybe some other you know top filmmakers. There's just not the product to do it. I think there should be and maybe in the future that the future of movie going will be this event style, you know, see it in the biggest format possible. But there's got to be a business case for it and I'm not sure they see it right now. I guess I feel like in the metropolitan hubs, your New York L.A.s Chicago, there should be multiple in each of those cities. And I feel like that's the premium format thing is where we're headed and it will be a big factor in how the Odyssey performs. I agree and let that and I think really that the audience taste is shifting because of the marketing of films like this, people want this more and they should be investing in more of these style theaters because the audience can see through the fake IMAX in Century City, which is not real IMAX and all these other like premium format infinity vision, whatever that is that Disney's doing. I mean there are if you go online, you can see the difference between what a regular theater shows in the Odyssey, like what the aspect ratio looks like compared to what we saw. And it's almost like double the screen. What we got to see. I know it's amazing. So obviously you and I really like the film. The tracking is sort of a challenge this weekend because some have it at 80, some have it as high as 115, 120. I'm going to set the line here at 100. That would be more than Oppenheimer, which opened to 82 in 2023 and remember that was boosted by the whole Barbenheimer, Barbie phenomenon. It's an interesting one here because I worry that this focus on IMAX is actually going to depress the opening weekend because people are waiting to see it in that big format and maybe they could only get tickets for Thursday at, you know, 12 in the afternoon and they're not going to power this opening weekend. I agree, but as we just discussed, aren't there there's not even enough of these premium format options for people to, for that to make a meaningful difference, don't you think? Well, in New York and LA, but I think in some of these smaller cities, maybe they can see it at different times. I just worry that people are going to wait and that this will be like an avatar type situation where it opens okay, but it just has legs and legs and legs. The Nolan movies have legs. Word of mouth, you have the premium format thing. Oppenheimer and the reviews are great like 97% on Rontoman is like it's it's going to be huge. It is a true spectacle. It's going to have great word of mouth. I'm still going to take the over on 100. I'm not going to. Oh, you are. Yeah, you are. I think I think the IP is just way bigger than Oppenheimer. I think it's a much more global story. It has like every major movie star in this movie. Every relevant movie star is in this movie. The set pieces are way bigger than anything Oppenheimer did. I think it's going to be huge. It's got a Cyclops. How can you argue with the Cyclops? Yes. All right. I'll take the you've convinced me. I'll take the over. It's just hard. You know, the if you go by the pre sales, this thing is the biggest movie of all time, but the pre sales are people like the film nerds. Is this going to play in the multiplex and peoria that is not a great experience? That's the question because it is it is very much a dad movie. It absolutely is, but I do think that Nolan I think barbenheimer helped a lot where no one is IP unto himself and he knew Spielberg in that regard and people are going to go see the new Nolan movie because it's the new Nolan movie. Yes. And he will be parking in Jurassic Parking and going to see his movies and others at the city walk. I love when people record him just walking across the city. Walk to go to the theater. It's awesome. It's my favorite and I asked him about that at Cinemacon. I asked him if he's seen those videos and he said he has. Well, the building theater in LA, you can go to that one. I know. I know. I know. It's amazing. All right, that's the show for today. I want to make my guest, VLN Roussell, producer Craig Horlback, artist Jesse Lopez, and Stefano Sanchez. I know one of the you will see one more time this week.