For decades, consumer-facing businesses in the entertainment industry, such as movie studios and music companies, have had an uneasy and complicated relationship with new technologies.
Hollywood, in particular, has been loud and proud of its anti-tech status. “The movie industry ignored or tried to stave off sound, color, television, home video, computer animation, and digital editing and cinematography,” notes a recent CIO.com article on Hollywood and IT, “before realizing that each revolution would help grow the business, ensure its cultural relevance and expand the creative possibilities.”
Since the days of the Silent Picture Era, however, one of a moviemaker’s most vexing and perplexing challenges has been how to predict whether a picture will be a success or flop. The process of deciding which movies to green light and which to pass over has long been considered an “art form” left to a chosen few industry titans.
“Historically, neither the creators nor the distributors of ‘cultural products’ have used analytics—data, statistics, predictive modeling—to determine the likely success of their offerings,” write authors Thomas Davenport and Jeanne Harris in a recent Sloan Management Review article. “Instead, companies relied on the brilliance of tastemakers to predict and shape what people would buy.”
But now, argue Davenport and Harris, predictive software tools have finally become too robust and necessary for movie production companies to ignore any longer.
“Today, companies have unprecedented access to data and sophisticated technology that allows even the best-known experts to weigh factors and consider evidence that was unobtainable just a few years ago,” the authors write. “Creators and distributors of cultural products are attempting to predict how successful a particular product will be before, during or after its creation.”
The article is worth the read, if you’re at all interested in how and why certain movies get made, and how new software tools can help better inform decision-making—though, not replace human input in the process. The authors give numerous examples where a little examination of Hollywood data has enabled easier, better decisions.
A notable example is the actor Will Smith, and why he and his movies have been so successful. According to the authors, Smith’s “ability to analyze and predict which movies are likely to succeed belies conventional wisdom on predicting consumer taste.” Early in his career, write Davenport and Harris, Smith and his manager analyzed the 10 top-grossing films of all time. “We looked at them and said, OK, what are the patterns?” Smith recalls, in the article. “We realized that 10 out of 10 had special effects. Nine out of 10 had special effects with creatures. Eight out of 10 had special effects with creatures and a love story.”
Smith may not have used software, but he’s as clever as a fox: With the exception of the Harry Potter movies, those in which Smith star have higher opening weekends and average box-office receipts than movies with any other male lead, notes the Sloan article.
Another example: a vendor called Epagogix has software that can predict the success of a movie based on attributes contained within a movie’s script, such as where scenes are shot. As part of a test, states the Sloan article, the software predicted that the 2007 film “Lucky You” would bomb and take in just $7 million in box-office receipts. “The film, which featured a major star (Drew Barrymore), a well-known director and screenwriter, and a plot about a popular topic, professional poker, cost $50 million to make,” write Davenport and Harris. “Epagogix, however, was on target, as the film brought in a paltry $6 million.”
Some studio executives, however, aren’t completely sold on movie-prediction software. “The primary obstacles,” note the authors, “appear to be cultural rather than analytical or technological.”
If there ever was a time to tap into new technologies, now would be that time for the movie industry: Even as movie attendance has dropped 19 percent from its 2002 peak of 1.6 billion theatergoers, the number of films released each year since then has increased by 30 percent, notes a recent New Yorker article on the difficulties of marketing movies. (“Studios now are pimples on the ass of giant conglomerates,” one studio’s president of production tells the New Yorker. “So at green-light meetings it’s a bunch of marketing and sales guys giving you educated guesses about what a property might gross.”)
So while Will Smith chooses his movies based on historical box-office data and has done quite well for himself, many decision-makers in corporate America, just like the studio heads, choose to forgo BI analytics and predictive modeling software in favor of gut-based decisions, which I wrote about the other day (“To Hell with BI: 40 Percent of Execs Trust Gut.”)
Predictive technology is no cure-all for all that ails the movie industry today. But to ignore it altogether would be an “Ishtar”-like failure.