Movies and Data Science! The first thing many of us must have thought of must be the movie Moneyball. The film is about how a statistician uses his technical knowledge to create a baseball team full of underrated players to have a successful tournament. But here we want to talk about how Data Science is used to predict success factors of a movie and how these factors can be engineered to make a film a hit.
William Goldman, a two-time Oscar-winning screenwriter, famously said, “Nobody knows anything… Not one person in the entire motion picture field knows for a certainty what’s going to work [at the box office].”
Netflix and Data Science
This was long back when film industry experts and marketers used coarse demographic data to analyze why a movie did well or bad at the box office. These data points and the methods used were not able to capture a lot of customer preferences. Consider the time when Netflix went from DVD rental company to online streaming giant. With everything on their platform, they enabled themselves to capture almost every minute detail of customer behavior on their platform. Consider the famous Netflix show House of Cards. In a survey, Netflix found that the subscribers were 86% less likely to cancel their subscription because of that show alone. But you know what’s more fascinating? Netflix knew the show is going to be a success even before the show was on it. How? Big Data and Analytics. Netflix committed to two seasons of the show, or 26 episodes, bidding a reported $100 million. That’s $3.8 million per episode. They found that the original version of the show, which happens to be UK based was watched by people who also watched Kevin Spacey’s movies.
Netflix Prize, a competition hosted by Netflix with the prize money of $1 million, was awarded to a team in September 2009. The competition required you to submit an algorithm which predicts movie preferences of the users. In case you are interested in taking a look at the winning entry, here’s the link to the paper published.
Movies and Data Science
Another case where Data Science is used to help the success of movies is deciding the release date of a film. It is essential as many events are happening simultaneously in the world which may or may not relate to film. For example, sports events like the World Cup, or political event like elections. A lot of film marketing requires public engagement. With such competing scenarios, it becomes hard for a film release to grab significant attention on social media. Case in point – ‘Cats & Dogs’ and ‘America’s Sweethearts’ was scheduled to release on July 04, 2001. To avoid competition, ‘America’s Sweethearts’ was moved forward by a week to July 13, 2001, but soon a new entrant, ‘Legally Blonde’ was announced to be released on July 13, 2001.
The subtitles of the movies have recently been used to understand viewer behavior. A simple approach like Bag of Words is used to analyze the phrases and words in the film dialogues to determine essential factors for the success of the film.
Bollywood and Data Science
Let’s talk about our film industry – Bollywood! Approximately 2.2 billion Bollywood movie tickets are sold every year in India. And the revenue is proliferating. But as more and more movies from outside of India are being aired in India, Bollywood has to keep up with the growing competition. Marketing of films has to be done carefully. For example, the tie period between the first glimpse of the movie and the release date is significant. If the period is too long, one may not be able to keep up with the hype. If it is too short, the marketing efforts will not have time to penetrate through a large audience. Also, the duration decides the marketing budget. The producer is charged each day for any advertisements or banner he/she puts up for the film. Once the budget is decided, it has to be divided among the channels. How much of it is going to be used in offline channels like college event? How much is to be spent on social media channels? These million dollar questions are today answered effectively with Data Science.
Conclusion
In conclusion, one can see that films are not produced in a traditional way anymore. There is a lot of reasoning and analysis that goes behind deciding what kind of a movie has to be made when to make it, who should make it and even how long it should be? If Data Science seems interesting to you, you might want to start learning more about it today. There has never been a better time to launch a career in Analytics, Machine Learning, and AI.
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