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LIGHTS, CAMERA, ANALYTICS!

Every Friday, as a big budget Bollywood film with its fair share of masala, thumkas, oomphs and dhishums releases, behind the curtains producers can be seen biting their nails for the movies fate. Will this movie set the cash registers ringing or turn out to be a forgettable dud?

Can’t we pull out our crystal balls, rub that magic lamp and read the tarot cards out of this? Is there a way to save our producers from these weekly heart attacks?

Let’s try and pull the rabbit out of this hat.

Lights, Camera, Analytics
Source: http://brisbanepowerhouse.org/

FROM THE GUT TO THE GUTTER

Combining the antics of Jack Sparrow, mystery of the Black pearl, demons and ships, Pirates of the Caribbean was a huge box office hit. Buoyed by this success, Disney predicted that John Carter with a similar dose of special effects and action would kill at the box office.

John Carter however turned out to be arguably the biggest flop of all time.

How could Disney get it so wrong? The culprit lies within…the gut! Our film industry for long has relied on tried and tested formulas, a film combining the usual dose of what has historically clicked with the audiences and their own assumption of what the audience wants is usually considered a safe bet, but in such an unpredictable industry where films like Queen made on a shoe string budget can prove to be a hit, and a big budget movie like Besharam can flop, time has come to re-evaluate and optimize the thinking process of what the audience wants…it’s time movie studios based their decisions on data rather than relying on gut checks.

Bollywood has recently leaned on analytics, predicting with accuracy which movies will succeed on the big screen and which movies will bomb. They collected data from various social platforms like Facebook, YouTube, Twitter, blogs to predict Ramleela’s success with 73% confidence, and they were ultimately proven to be correct.

WIKI HAS THE ANSWER… LIKE ALWAYS!

With the present techniques and approaches, big data can only tell you so much, it will never be a sure bet and has much scope for improvement. To illustrate my point, consider your own case. After watching a great movie you may sometimes feel the need to express your opinion on Facebook or Twitter, but this is after you have watched it, good or bad, plus or minus, the studio has already released the movie by this point… they have already crossed the Rubicon. For data to be useful, we need to forecast results before they happen.

A paper published by the Budapest university of Technology and economics, advocates the case of Wikipedia as a better long-term predictive indicator than twitter. Searching for and reading a Wikipedia article about an upcoming movie makes sense a month or two before the movie’s actual release, this sense of curiosity is incompatible with the concept of Twitter. A movie’s Wikipedia page, as a source of information about the cast, updates and synopsis is a fly trap for all those flies interested in watching the movie, the buzz (get it?) created, gauged by the amount of hits on that particular wiki page.

Twitter is a repository of social information, however, in almost all cases some form of text analytics has to be applied. Anyone mentioning “Harry Potter” in a tweet might be referring to the books, their cast, the theme park or even Hedwig! As a result, only a small percentage of that information will be useful. However someone going to the wiki page “Harry potter and the prisoner of Azkaban (film)” is more likely to see the movie. And, it is relatively easy to track the amount of page views and edits a certain Wikipedia entry has.

Movies Box office

ANALYTICS PLAYS HERO

Now let’s go back… let’s say Disney wanted to remake John Carter, keeping predictive analysis in mind.

To determine the cast, the studio can start by looking at all action-adventure films released in the past years that featured a particular star, sort them by date of release, location and other ancillary factors like other major events scheduled at that time, economic factors and see how successful the movie was. They could also pull data from social media commentary to determine whether audiences have developed fatigue with that star in recent times. Of Denzel Washington’s 29 movies only 19 were profitable, this translates to a success rate of 65.5% which seems low for a big star like him; however the thrillers and dramas he tends to star in are movies with a high-risk to begin with.

Are pirate movies still bankable? To determine the theme of the movie, previous movies can be sorted based on their themes, and their box office collections can be analysed to determine whether a certain subject matter may or may not gain interest. Recent sentiment analysis done for the Hindi movie industry indicates with a 69% confidence that a comedy movie will be hugely popular, or predicts a political drama will be successful with 75% confidence.

Movie success prediction also helps companies to plan their resources. A studio that expects its newest movie to be immensely popular will rent more theatre rooms in advance.

CURTAINS

In predicting a movie’s success, data alone can show only a small, narrow window of history. Today when most people have thousands of data that influence their decisions each day, and these data points in turn, are moulded by many other data points in an elaborate, perplexing web of action and reaction, with intangibles like emotions and social influences that play a huge part in our decisions also coming into play, we have an undertaking that is as difficult as combining Voldemorts broken soul.  Predictive analysis is the magic wand that can create order out of this chaos and answer the enigmatic question… Are vampires still a hit?

This blog is written by Moumita Sarkar, Analytics Consultant at BRIDGEi2i

About BRIDGEi2i: BRIDGEi2i provides Business Analytics Solutions to enterprises globally, enabling them to achieve accelerated business impact harnessing the power of data. Our analytics services and technology solutions enable business managers to consume more meaningful information from big data, generate actionable insights from complex business problems and make data driven decisions across pan-enterprise processes to create sustainable business impact. To know more visit www.bridgei2i.com

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The views and opinions expressed in this article are those of the author and do not necessarily reflect the official position or viewpoint of BRIDGEi2i.

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