The Secret to Making Blockbuster Movies Will Amaze You!
By Aditi Bhat
The secret lies with a bunch of analysts poring over statistics about when people stopped watching something, what they fast-forwarded, what they replayed, which actors were in the most successful movies, and when those movies were released.
Before you get creeped out, they are not watching your individual actions, and no, they do not know that you watched your favourite feel-good movie seven times last month. They are looking at aggregate data amassed by online streaming services like Netflix and Hulu.
With this kind of information readily available, user interaction data has gone beyond basic web design into the practical details of how people respond to TV shows and movies. Netflix is already known for its recommendation algorithm that is based on giving each movie or TV show hundreds of tags to predict what specific combination each user will like best.
With this staggering amount of data that can be collected from online viewing, streaming, and downloads, analysts can crunch the numbers not only to figure out why a movie failed, but more importantly, to predict what will do well in the future. Based on big data analytics, movie producers can pick a winning formula with greater accuracy better than ever before. They no longer need to rely solely on intuition, safe story lines, and big movie stars to pull a movie through to a profit.
Even after the movie is made, data analytics can help predict the best time for release based on certain metrics like the actors in the movie, their previous work and releases, the time of year, and previous responses to a similar type of film. For example, if an Anushka Sharma movie is released right after two other movies that she starred in, the movie might not do too well. The audience might get fatigued, especially if it is her third romantic comedy in a row.
Analysts can also look at aggregate data across social media to see the engagement levels that the target audience had across different platforms for similar movies in the past. Based on this prior experience and knowledge, analysts can put together a marketing strategy to maximize buzz and create curiosity ahead of the release. For example, Netflix put together different types of previews of House of Cards that were tailored to different target audiences, to maximize its appeal.
While this is great for filmmakers and investors, it might not be the same for audiences. The caveat to this kind of predictive analysis could be that the formulas may become more and more generic over time. As we have already seen with our Facebook newsfeed, when we get more of what we already like, we rarely see anything new. Hopefully, audiences will stay diverse and fickle so we can keep our movie options open and interesting.