Data Driven Film-making – One of the Most Important Articles on Film/TV I’ve Read Recently…

Back in 2012 I was part of a Wellcome Trust session at the Sheffield Documentary Festival looking at feedback loops and creativity. This looked at how, in nature we see feedback loops all the time; our homeostasis system is a good example of such a system whereby the body temperature is kept in stasis by a set of feedback loops that switch on/off mechanisms that can either help cool us down (e.g, sweating) or warm us up (e.g. shivering). The session talked about how we’ve seen these loops in gaming both within games where methods like Split A/B testing are used to iterate games over and over, and as a meta-game choice where the feedback loops of app stores where user-reviews help drive user behavior.

I’ve talked a bit about how this might come to filmmaking and had given the example of how within film, via YouTube the filmmaker can get data about how people watch thier films. For example at at what point/s (if any) do viewers click off this video? But I didn’t give an example of how this works as a meta-film choice. So I was facinated to read this excellent report on how Netflix uses data to drive user engagement. If you’ve not used Netflix, it’s like LoveFilm in that its a video on demand system. I’d used LoveFilm and given up finding the UI really hard work to navigate, among other problems. However Netflix is a much, much cleverer beast…

We’ve now spent several weeks understanding, analyzing, and reverse-engineering how Netflix’s vocabulary and grammar work. We’ve broken down its most popular descriptions, and counted its most popular actors and directors. … What emerged from the work is this conclusion: Netflix has meticulously analyzed and tagged every movie and TV show imaginable. They possess a stockpile of data about Hollywood entertainment that is absolutely unprecedented. The genres that I scraped and that we caricature above are just the surface manifestation of this deeper database.

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They capture dozens of different movie attributes. They even rate the moral status of characters. When these tags are combined with millions of users viewing habits, they become Netflix’s competitive advantage. The company’s main goal as a business is to gain and retain subscribers. And the genres that it displays to people are a key part of that strategy. “Members connect with these [genre] rows so well that we measure an increase in member retention by placing the most tailored rows higher on the page instead of lower,” the company revealed in a 2012 blog post. The better Netflix shows that it knows you, the likelier you are to stick around.

One of the key quotes in this article, IMHO, is about where creativity exists in all this data. While the data does help you better position your resources, it can’t make good films/TV for you:

The data can’t tell them how to make a TV show, but it can tell them what they should be making.

I’d strongly recommend reading the whole article, it is well worth it!