Data Analytics Can Predict the Next Blockbuster

Data analytics are changing the way movies are made.

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Ghostbusters. Pan. Fantastic Four. The Lone Ranger. John Carter. Box office flops are not just common, they’re becoming the norm.

Box office profits are at a two-decade low as generations who have grown up with streaming services and 70 inch high definition televisions in their homes aren’t heading to the cinema in droves the way their parents did.

This is not to say there isn’t still profits to be made in major motion pictures, however. Quite the contrary actually. When done right, films are still capable of reeling in massive amounts of money. According to Box Office Mojo, the top 30 films of 2016 all grossed more than $100 million in profits. But what separates a flop from a hit?

Is it genre? Star power? Direction? Budget?

In the end, it’s still a guessing game at times. But that may be changing thanks to data analytics.

“What our company does is try to get sentiment about how people feel about movies and movie concepts from the idea stage,” Josh Lynn, head of Piedmont Media Research said in an interview with the FiveThirtyEight podcast. “The whole point is try to determine from an early point in time whether or not this is an idea people are interested in. Is this going to do well and is this something studios should try to pursue?”

Using nothing more than descriptions of a film’s premise, Lynn’s company looks at a swath of the ticket buying public in demographics broken down by age, gender, ethnicity, etc. to create what becomes known as a “consumer engagement metric.” That measure yields insight into how an idea is received. The goal is to take something that would “normally be subjective and make it objective,” Lynn said.

By partnering with Piedmont, film companies can get insight into whether or not audiences even have an interest in the idea of a film. That can inform every decision from there on, from casting to marketing approaches.

Gauging Consumer Reaction

When it comes to movies, people don’t yell at or applaud the actors onstage like they do in live theater. But we all know what they do: take to the internet. Cloaked in anonymity, they feel free to offer their true opinions. There are concerns about getting bad data from paid trolls, but sites such as IMDb or Rotten Tomatoes offer a movie mogul instant access to public opinion.

These sites can also give decision-makers a more granular look at opinions. Right now, movie companies still break people down into gender and big age groups, such as over or under 25. That does little to give industry leaders a detailed look at what movies work with which demographic, IBM’s Richard Maraschi told The Atlantic.

He’s working with studios to use more advanced metrics on large data sets, such as internet feedback from fans, to find important likes and dislikes among various niche segments of the population.

This can help movie companies decide which movies to make and even who to cast in the movie, perhaps preventing major box office flops that seem almost commonplace now.

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Film Title Production Budget (millions) Worldwide Gross (millions) Estimated Loss (millions)
John Carter (2012) $306.6 m $284.1m $200m
The Lone Ranger (2013) $250m $175.6m $190m
Pan (2015) $150m $123.8m $150m
Fantastic Four (2015) $125m $168m $100m
Ghostbusters (2016) $144m $229m $70m



Movie studios often take a mass market approach. A new movie is pasted on billboards, shown in television spots, placed in ads all over the internet and, of course, played as a trailer before movies currently screening in theaters. It’s scattershot and expensive. But it’s also seen as necessary because movie companies want to generate big first weekends. A bad first weekend typically is predictive of the movie’s entire run.

Rather than spending $50 million-plus on marketing for an opening weekend, movie companies are becoming more targeted in where they place ads. Using past data, including comments about films online, companies can create optimized marketing plans that target niche segments by using specific media channels.

How targeted? Masachi told The Atlantic the goal could be something like targeting soccer Moms in Florida who love action movies.

A well-known example of data analytics driving movie marketing is the film Warcraft, now considered one of Hollywood’s most lopsided releases of all time in terms of what it did domestically compared to internationally.

In the U.S., the film was a flop, raking in a mere $46.6 million. The film debuted first in the U.S. and then in China, where it generated $220 million of its total $433 million worth of tickets sold. What was the difference?

Legendary Entertainment, an analytics firm out of Boston, MA, was able to acquire information on advanced ticket buyers in China and use data analytics to target similar demographics with trailers for the film. Advanced ticket buying is more popular in China due to price incentives, but it also gave Legendary the information it needed to cultivate a larger audience for the film.

“We pour out cash to advertise [our films] on television in the US,” Legendary’s Chief Analytics Office Matthew Marolda told the Financial Times. “In China, you can be so much more digitally-oriented and the cost is so much lower. Analytics is a broad toolset that can change outcomes. There is never a situation that it doesn’t change.”

Likewise, the release of a film early in a foreign market might give movie marketers some clue as to what is working best in the film and with which demographic, helping them to refine their marketing when the movie comes out in the U.S.

This all provides a benefit to consumers as well: not being bothered with advertising for movies you don’t want to see.



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