The Year 2007 was a terrible year for many big movie stars. One major exception was Will Smith, whose film “I Am Legend” set a box-office record for a movie opening in December, taking in $77 million. In 2008, Smith’s star vehicle “Hancock” grossed more than $625 million worldwide despite poor critical reviews. Smith’s success was not all that surprising, however: 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.1
Does Smith know something that Jim Carrey and others do not? Quite possibly: When Smith went to Hollywood to start his film career, he and his business manager studied a list of the 10 top-grossing movies of all time. “We looked at them and said, OK, what are the patterns?” Smith recalls. “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.”2
The leading question
Methods for predicting what consumers want have been around for decades. But how good are the newest tools?
- Science-based ways to predict success will keep transforming any industry in which customers lack the time and attention to differentiate among increasing offerings.
- A wide variety of tools have emerged, which need to be matched to the right application.
- Though potent, these systems don’t replace decision making.
Smith calls himself a “student of universal patterns” and studies box-office results after every weekend, looking for patterns of success. Given his track record of choosing films that reliably deliver $120 million or more, he is clearly an astute observer.
Smith’s ability to analyze and predict which movies are likely to succeed belies conventional wisdom on predicting consumer taste. Such predictions are viewed as an art, not a science. The reasons for success or failure are inscrutable. Producers of movies, music, books and apparel pursue their artistic visions and offer them to the public, which may or may not recognize genius when it sees it.
It’s easy to see why most people view the prediction of taste as an art. 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.
1. R. Grover, “Box Office Brawn,”