The Netflix Model: How Film Analytics Can Reduce Formulaic Entertainment and Generate BETTER Art: Part One

The Netflix Model: How Film Analytics Can Reduce Formulaic Entertainment and Generate BETTER Art: Part One

This is going to be a two-part look at what everyone is looking at, Netflix (and in theory all of the online studios), but we’re going to try to apply that to you: a not-online studio (an indie filmmaker, producer, or small studio).

My whole thing here at Big Film Data is to prove to you that movies can be more creative using data analytics. That’s why I started the How to Make a Profitable Award-Winning Indie Film series. The push-back from artists in the industry has been that asking a machine to tell us what formula is making the most money will cut out creativity. That the machine will just end up suggesting that we make more of what is already being made. That it will push us towards the statistical middle of the pack (which I explained further in  my very first post).

But, lets agree on a two things. First, stories in general are pretty formulaic. Rick from Silverpen.org describes the “Universal Story Structure“. Pixar’s rule number four outlines the formula for all of their movies as such:

Once upon a time there was ___. Every day, ___. One day ___. Because of that, ___. Because of that, ___. Until finally ___.

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The second thing that we should agree on is that Hollywood is pretty formulaic with or without analytics. Driven by financial results, they keep making the same tried and true sellers with little room for creative reach. In Why Hollywood Needs a Shot of Movie Metrics by Tom Thriveni states pretty clearly how business strategy for studios have “placed more value on predictable than predictive.”

If we agree that stories are formulaic in nature and that the studios already trend towards the tried and true statistical middle then I think that we can argue that analytics can’t really make the status quo much worse. I concede, that if an artist were to run some very basic analysis they may be tempted to slide into that middle zone but then they would find themselves competing against the mediocre fray trying to say something in a sea of nothingness. That’s not what an artist wants to do and its not the point of using analytics.

Tom Thriveni

Analytics can be used to direct an artist’s creative train toward a specific station. Film analytics can guide a filmmaker toward trends that can constrain art and with constraint can come something meaningful. Analytics can help filmmakers determine if they’re on the right track, if they are evoking the cathartic experience they had intended, or what kind of budget they should have to make a profitable movie. And it’s a take it or leave it game. The filmmaker decides when the analytics are wrong (after careful deliberation) and when to pivot on the plan. Film analytics can provide for the indie filmmaker a creative and competitive edge that the major studios don’t have just yet and one that they maybe never will have (’cause they just aren’t good at getting out of the middle of the road).

 What is your position on using analytics to make movies? Are you tying it in your own work? How?

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