In 2002, the Oakland Athletics baseball team changed the world of professional baseball forever.

The team had a terrible start to the season, however, it was to go onto punch well above its weight after a series of changes made by general manager Billy Beane.

Despite trading some of its major players for relatively unknown ones, the team’s performance grew better and better as the season went on.

What was the secret behind this success?

Well, the answer was revealed the following year in a book written by Michael Lewis.

The answer – Big Data Analytics 

The book was later turned into a hit movie called Moneyball, starring Brad Pitt and Jonah Hill. It was a real-life depiction of how a young statistics wizard helped a major league baseball team manager select the best players based on statistical data rather than personal opinion.

The result was a massive boost in the team’s performance, even despite it onboarding some unknown and underperforming players.

This story is proof of the enormous power of big data analytics to identify the best people for the job.

Thanks to data-driven movie companies such as Largo Films, this technology is now helping moviemakers to do the same.

Let’s take a look at how.

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What is big data analytics?

Big data analytics is the process of using pools of big data which are then analyzed by sophisticated computer algorithms in order to identify certain patterns or trends.

These trends can be literally anything but are most commonly user behavior patterns such as phone preferences, for example, or the types of adverts that a particular user is viewing.


In fact, it was these two data trends that launched the big data analytics boom.

Companies such as Netflix and developed sophisticated computer algorithms which allowed them to improve the results of their movie recommendation systems.

Meanwhile, companies such as Google on Yahoo used big data to tailor match advertisements with the most suitable users. This approach helped to dramatically boost advertising revenue for these companies and make them into some of the biggest companies in the world today.

Data-Driven Moviemaking

While we are still only at the dawn of big data analytics movie making, the technology has already brought profound changes to the industry.

Today, all of the major Hollywood studios are already using big data analytics to help them improve their film productions and to reach their target audience.

Everything from the script to casting decisions is now influenced by big data analytics.


Only this year did Sony Entertainment ask data-driven movie making company Largo films to evaluate the potential earning gross of its film Venom.

LargoAI, the sophisticated data analytics algorithm produced by Largo Films, accurately predicted the movies potential gross by a margin of just a few percent.

This is but one example which highlights how the industry is increasingly taking up data-driven movie making to help it manage its productions.

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Another example is targeted marketing.

In the past movie, companies spent huge sums of money on TV and magazine advertisements. Companies were forced to blanket market their movie products by simply saturating every possible media outlet with adverts.

This meant huge amounts of investment were required to get these adverts on up on all these TV channels and magazines, something which was incredibly wasteful and only insured a minimum return on investment.

Big data analytics is able to offer far more precise target marketing.

By using data sets obtain from social media movie preferences and the kind of websites individuals visit, data-driven movie making is able to accurately predict the kinds of people who will want to watch a specific type of movie.

This means that these people can be specifically targeted with adverts via their social media and other outlets including the TV channels that they regularly watch.

This makes big data analytics an incredibly powerful tool for the movie industry to not only make more suitable films but also to massively increase that return on investment.

Data-driven Actor Casting

One of the other key areas where big data analytics is going to change the conventional approach is with casting.

Today, casting is one of the most drawn out processes in movie production.

For some of the major Hollywood films, where there can potentially be hundreds if not thousands of parts, casting can take months and require a huge budget to complete.

Often mistakes are made or individuals are cast purely on the basis of their perceived box office potential rather than their actual suitability. History has proven that humans are not great at predicting the box office potential of an actor in a particular role.

One example was Paramount Pictures insistent actor Al Pacino be replaced in the role of Michael Corleone, a role for which he is critically acclaimed to this day.

Only director Francis Ford Coppola prevented Pacino from being removed, something which the studio would have forever regretted.

Had the studio had access to big data analytics software, it would have discovered that Pacino was in fact highly suitable for the role and was going to be a box office success.

In the future, big data analytics will prevent such mistakes from being made.

Simply by analyzing a script and cross-referencing it with the huge pools of data regarding actors and actresses past performance and suitability, as well as their box office potential, big data analytics will choose the best person for each role.

Entire movie casts will be selected simply by entering a film script and other movie data into a big data software solution. This solution will then troll thousands of actors and actresses’ profiles and select the most suitable ones.

This way, big data analytics will completely revolutionize the way that movies are cast.

If you wish to learn more about data-driven moviemaking and how it can benefit your movie production then head to Largo Film’s website and request a free demo.


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