When Analytics Go to the Movies: How data-crunching benefits film booking and scheduling


Film buyers have always had data available—but mostly from industry guidance and fragmented internal files. Now, new sources—and new tools—are expediting and expanding access to that information and enhancing the processes to aggregate, analyze and apply it. The result: Decision-making can be more accurate and effective, faster and smarter—and more profitably impactful.

Here, analytics experts and a film buyer talk about how analytics can be used to help exhibitors determine “What should play where, when, and for how long?”—and how the benefits can flow through to improve the customer experience.

Claudiu Tanasescu (Chief Executive Officer, Cinema Intelligence): Analytics-based film booking is about enabling exhibitors to use the data they have—and the learnings they can gain from it—to drive business decisions relative to what to play, how many prints they need, and where to play what they buy. Then, it’s about helping them determine the best schedule for each release so they can maximize occupancy and drive profitability.

Tearlach Hutcheson (Senior Director of Film, Studio Movie Grill): I buy for 245 screens and analytics help us control labor costs. Having better numbers regarding how large the audience will be really helps to plan the labor we need and the amount of concessions to have in stock—and that can really have an impact on our bottom line.

Matthew Liebmann (Senior VP, The Americas, Movio): Analytics are at the beginning of their journey. There is still more reliance on intuition and experience than raw data, but we’re starting to see the most progressive film buyers in exhibition use the data to supplement their own experience. 

Dr. Jonathan Collins (Chief Data Scientist, Showtime Analytics): Our products are based on augmenting existing workflows, not replacing them or the people doing them. We’re helping buyers to find the insights they wouldn’t necessarily have found without our tools, helping them with robust analysis to support their own experience, and giving them more confidence in their intuitive decisions.

Tanasescu: Film bookers and buyers bring their expertise and then the machine does the data-crunching and the analytics behind the scenes to drive the decisions—but the buyers and bookers ultimately own those decisions. What really happens is that we are empowering them to make faster, better, data-driven decisions.

Liebmann: You need somebody with experience, somebody who has movies in their DNA to interpret the numbers, to understand the context, and to use analytics as an additional resource. A computer and numbers without the experience is less than half the story.

Collins: We see data as another asset that cinemas have—like screens, seats and staff—that they can use to provide an improved experience for their customers. But when the data is trapped in different systems—in their point-of-sale system, or their loyalty-card system or in their web booking—it’s very difficult for them to utilize that asset and really get the value from it.

Liebmann: For example, often the raw data that comes from the loyalty programs resides primarily in the marketing department—but it needs to be used more broadly because it really has business-wide benefits.

Collins: We pull all their data from the various systems into our platform and make it available—it’s as simple as logging into their g-mail. With all that data in one place, they can see it, they can understand the trends, and they can use their understanding as a source of power to make better decisions.

Tanasescu: We feed every transaction, every movie, every show for the last three to five years into our algorithms and those algorithms become highly customized, highly tuned to the exhibitors’ particular theatres. So they have the best insights into their business, on an individual theatre-by-theatre basis. 

Hutcheson: Cinema Intelligence has the software to analyze massive amounts of very theatre-specific and movie-specific data and to provide recommendations quickly and in a format that’s useful.

Tanasescu: We complement that with film-specific data—reviews and promotional budgets—and external data—public holidays, weather information, other competing events. Another element we add is Google Search. Google has done a study and within one week of opening, they’ve found a 94 percent correlation between the number of people searching for a movie and the actual performance of that movie on opening weekend. 

Liebmann: Analytics can provide a deep dive into the details of the individuals who make up cinema admissions, in terms of who they are, their tastes and in their ability to see a movie at any particular time.

Collins: We can work with a great variety of data sources—including some very large data sets, analyze them using some very sophisticated techniques—and do it all in near real time. That’s new. And we’ve developed data sets using some very rich film metadata to help identify comparable films that provide guidance for film buyers.

Liebmann: Too often, there’s a proclivity to use as “comps” only those movies of similar tone, content or casts—which misses a really big slice of the audience. So, if you were to say, “Give me films that look similar to the R-rated animated comedy Sausage Party,” you’d be given R-rated gross-out comedies. But if we were to comp it up based on transactional history and use a similarity algorithm, the number-one most similar title would be Don’t Breathe—which is a horror film. But that makes sense: Both attract a younger-skewing, male-oriented audience and that audience doesn’t just see one kind of movie.

Collins: We’re able to use some pretty sophisticated techniques to piece out the factors that do—or do not—contribute to people going to the cinema and how they choose what to see when they go.

Hutcheson: For example, Movio did a report on seniors—and how they’re not necessarily coming in in week one, but in week three or four. If we’re booking a film like Going In Style, their analytics tell us we should put it in a medium-size auditorium—instead of a large one—and keep it there because the audience won’t drop off as much, based on those who comes to see that kind of movie.

Liebmann: In that white paper, we found that older cinema-goers visit the cinema 6.8 times annually—versus 6.2 times for Millennials. Another surprise:  More than one in four people who saw Star Wars: The Force Awakens were age 50-plus. And these older cinema-goers are creating altogether new genres. One we call “Mature Thrillers” and it’s been created to stay with their action stars throughout their careers—Cruise, Washington, Stallone, Costner, Neeson.

Collins: By helping exhibitors to really understand the customer base that they have, their tastes and their preferences, they get a sense of what films are resonating strongly with that customer base—and how different content performs in their particular sites at different times and under different conditions which gives them a sense of demand.

Hutcheson: There are three times when analytics can be helpful in supporting your decisions and negotiations as a film buyer. One is when you’re fighting to get the right movies into your theatres. That’s particularly true in the competitive art-house business. A second is when you’re struggling to convince a studio to let you take their movie off-screen to bring in a new one. The third is when you don’t want to put a movie in your theatre and that’s in conflict with how many screens the studio wants to open on.

Tanasescu: Regardless of what the analytics find, the film buyer often doesn’t have the freedom to not play a movie on opening weekend. But just agreeing to play all possible shows for every movie doesn’t necessarily maximize the opportunities for anyone. We have analytics to show how different movies can be more profitable for the exhibitor—and for the studio—when they play in the right theatres at the right times when the audience wants to see them. 

Hutcheson: Studio sales departments go into the negotiations with the screen counts they expect to reach and the rates they expect to charge. From the film buyers’ perspective, analytics help us define our goals so we can have a more rational, data-based plan in mind before we start to discuss booking with the studios.

Tanasescu: The exhibitor can also use forecasting software for other purposes. Say, for example, analytics show that with the movies they have to book, they’ll be under their budgeted revenue for a given week. They can at least encourage their marketing teams to do something about that—and they can alert their operations managers to plan labor accordingly. 

Liebmann: Data and analytics are just a better way of helping exhibitors understand who their guests are, what they like and how they spend their money and time through the entire lifecycle of the cinema industry. They’re really quite holistic in terms of how they can be used

Collins: Exhibitors who use our products tell us they find pockets of insights that suggest to them what they should do based on what they’re seeing. There are areas all across the exhibition value chain where we can help people make better decisions using data—content choices or scheduling, staffing, operations, or communications with customers.

Hutcheson: I’d argue that analytics are most helpful for middle-range films, those films that are going to take in $20 to $50 million. At this level, analytics can help you decide when and where they should play.

Liebmann: If you have a film that skews for older female audiences, for example, and you play it all day every day, there’s a risk that you’ll take the film off screen before your real audiences come to see it. Those audiences typically take 16 days to see movies, they tend to go on weekdays and 75 percent prefer to see a movie before it gets dark.

Collins: It’s little insights and suggestions like that across several sites or across the circuit that add up to better utilization of the assets they have and growing the revenue from those decisions.

Tanasescu: Scheduling software also has tremendous potential for saving time for the general manager. Right now, GMs spend most of Monday doing the schedule. We can help them do that in ten to fifteen minutes. And that leaves the GM time to enhance the experience of the moviegoer.

Collins: We provide products to help our customers optimize the experience they create for their customers. Our mantra is to help the industry have the tools they need to learn from their past so they can make better decisions in the future.

Tanasescu: One question is: What is the impact we are bringing to our customers? We’re seeing the overall impact to be a one-percent to four-percent increase in profitability across a circuit just by using our software to optimize their booking and scheduling. That can be quite a strong incentive, regardless of the size of the circuit.

Liebmann: But the numbers are not the final determining factor; context always plays a role. Experience to understand that context is critical, but analytics help to reset the way the conversation plays out. Decisions are based less on emotion and more on data-driven facts.

Tanasescu: We will always need the human knowledge and expertise of the film buyer, but I think what will change significantly is how they make their decisions.

Hutcheson: At the end of the day, we’re show people; we have to remember that there’s an emotional aspect to watching movies and that will have a big impact on what films do. Every film is just a little bit different and every film has its own pros and cons. And we may never be able to completely measure that—or forecast the precise numbers it will produce.