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Some movies are obvious hits. Like, for example, Avengers: Infinity War, which made a record-breaking $258 million at the domestic box office last weekend, filling seats and the pockets of Marvel Studios parent company Disney. But not every summer—or spring, or fall—blockbuster has the benefit of 10 years and 18 movies of built-up audience goodwill. So while the Mouse House knew they had a potentially earth-shattering hit on their hands well before opening night, other studios trying to catch up have no way of predicting whether their latest attempts to hit big will do so.
Actually, they might. Machine learning is everywhere, and artificial intelligence is no longer just a Spielberg-Kubrick collaboration. These days, Amazon can practically anticipate when you might need toilet paper and Netflix can predict your next binge, so it only seems natural that Hollywood will start using AI to predict the next big blockbuster, or at least improve its chances of becoming one. In fact, several companies are already working on algorithmic ways to predict box office results. Whether or not algorithms are better at picking winners than studio execs, however, is another matter—one that’s still far from resolved.
“Filmmakers are getting closer to understanding what moviegoers go to theaters to see thanks to neural networks fed off of data from previous box office hits,” says Landon Starr, the head of data science at Clearlink, which uses machine learning to help companies understand consumer behavior. “Although this technology isn’t spot-on quite yet, AI-powered predictions are likely stronger than the human calculations used in the past.”
And they’re advancing quickly. Vault, an Israeli startup founded in 2015, is developing a neural-network algorithm based on 30 years of box office data, nearly 400,000 story features found in scripts, and data like film budgets and audience demographics to estimate a movie’s opening weekend. The company is only a couple years in, but founder David Stiff recently 告诉 Fortune that roughly 75 percent of Vault’s predictions “come ‘pretty close’” to films’ actual opening grosses.
Scriptbook takes a similar approach, using its own AI platform to predict a movie’s success based on the screenplay only. The Antwerp startup’s AI analyzed 62 movies from 2015 and 2016, and claims it was able to successfully predict the box office failure or success of 52 of them, judging 30 movies correctly as profitable and 22 movies correctly as not profitable.
Inspired by Netflix’s “what to watch next” system, Boston-based company Pilot compares potential film projects to a database of information about widely released movies of the past 30 years or so. Taking into account variables like the cast, director, writer, budget, and plot summary, Pilot’s web app predicts the first weekend gross and full domestic box office take. The company claims its results are 70 percent accurate two years from release and 80 percent on-target after the release of the film’s first trailer—not too bad considering that app has (probably) never watched a film.
And these three outfits are just a sampling of the many young companies seeking to give a gut-punch to Hollywood, which has traditionally, well, trusted its gut when it comes to deciding what will succeed. But outsiders are not the only ones trying to find ways to use AI for bigger and better blockbusters.
All this talk of AI, analytics, deep learning, and big data, of course, leaves out one major component of the movie-making process: creativity.
While most studios still rely on the traditional methods to put butts in seats—billboards, TV commercials, press junkets for big stars—one production company is also looking to big data to improve how it markets its biggest movies, and who it markets those movies to. Legendary Entertainment, the studio behind movies like Godzilla 和 Warcraft, brought on Matthew Marolda in 2013 to be its chief analytics officer. Using his background in sports analytics and marketing, Marolda determined Legendary was not gathering the right data about its potential audience and that better information could give Legendary a leg up on studios using traditional methods.
“We have built dozens of AI tools that contribute to the goal of improving the prospects of the movie via marketing,” Marolda says. “Those tools range from developing audiences with a high probability to buy tickets to understanding the visuals that are most shared—which informs what trailers or new images to produce—to optimizing a media mix, which maximizes the value of every impression.”
Unlike what is happening at startups outside Hollywood, Legendary’s methods aren’t about predicting the potential success of a movie before it’s made. Instead they’re about optimizing the success of projects already in development, using analytics to decide when and how to release teasers and trailers, and determining how to customize impressions for different potential audiences, even scoring potential moviegoers in terms of their likelihood of attending a specific film. As of yet, this information hasn’t been used during the creative process to optimize a movie to cater to the largest possible audience—but it likely will be, very soon.
All this talk of AI, analytics, deep learning, and big data, of course, leaves out one major component of the movie-making process: creativity. Obviously studios want to make the biggest profit possible on their blockbusters, but while it’s clear movies like Infinity War will do big business, what about the unlikely successes?
It’s worth remembering that Infinity War itself is the result of one of those unlikely victories. Ten years ago, a movie about a semi-obscure comic book character starring an actor not known to be a sure thing was a bit of a gamble. It became a surprise hit, and now Marvel movies blow the competition out of the water annually and Robert Downey, Jr. is one of the highest-grossing actors of all time. Would Vault, or Pilot, or Scriptbook, have predicted that? Would Legendary Pictures’ very targeted marketing have given general audiences short shrift, thinking that only hardcore comic book fans would see Iron Man?
Phil Contrino, the head of media and research for the National Association of Theater Owners, is skeptical. “Movies are so dependent on execution. AI processes can look at things before the production process has started like scripts, actors, et cetera and make a good hypothesis if a movie will be successful from those items,” he says. “But, the movie has to be made. Just because it’s historically been successful doesn’t mean that it’s going to be. And there are so many examples of stellar work from the cast and crew during production making a film a huge box office success.”
Even the most data-driven pro sports franchises still need good athletic chemistry to win titles. Moneyball methods might’ve made the Oakland A’s a surprise regular season success, but the team that really showed what advanced stats could do when combined with unpredictable human elements was the 2004 Boston Red Sox, which leveraged players’ scorecards, how they handled pressure, and their team chemistry to win a World Series. If Hollywood wants to do something similar with AI and analytics, it should also understand that it matters who’s wielding that data, and how—and that the best movies are still by people, about people, and for people.