How Netflix Got Big with Big Data

Femi Akintoye our consultant managing the role
Posting date: 8/24/2018 7:51 AM
There’s little argument that Netflix have changed the game when it comes to how people consume entertainment. Whilst Amazon, Disney and Apple seek to replicate the success of Netflix’s model, they still lead the way with over 130 million subscribers worldwide and have just broken HBO’s 17-year streak as the most nominated ‘network’ at the Emmy’s with an astonishing 112 nominations. 

Having begun life as a subscription-based DVD rental-service created in response to founder Reed Hasting’s frustration with late rental fines, Netflix were one of the first to offer video-streaming as an option for viewing films and TV. Now filled with scores of original programming, the secret to their success lies not just in creativity and innovation, but in Big Data. 

Top Picks From Your Data


When the former CEO of the now-defunct Blockbuster claimed: “Netflix doesn’t really have or do anything that we can’t or don’t already do ourselves”, he made a vital oversight. Whilst Netflix may have offered fewer films and TV shows at the time, they were already busy collecting, and utilising, customer data in a way that hadn’t been done before. This included:

What do people search for?
When do they watch a program?
What device do they watch on?
Do genre preferences vary with device?
When do they stop watching?
What shows are the likely to ‘binge’?
Or even what are the horror films that people find too scary to watch until the end…

Netflix used, and still uses, this information to create recommendations for each user, curating an individual experience based upon personal preferences. This technique has been incredibly successful with over 75% of viewer activity based upon these recommendations.

And they continue to finesse how their collect their data, switching from a five-star rating system to a thumbs up/thumbs down model. Cameron Johnson, Netflix’s Director of Product Innovation had observed: “a difference between what [users] say, and what they do,”. For example, frequently-watched comedies were being awarded three stars, as opposed to occasionally-watched, but ‘more worthy’ documentaries being given five stars. By simplifying the system to a like/dislike set-up, Netflix can provide subscribers with recommendations “more aligned with what people actually play”. 

Stream if you want to go faster


Unlike traditional broadcast mediums, Netflix’s income doesn’t come from advertising, or a pay-per-view service, but subscribers. That means their main ambitions are to generate new subscribers and keep existing ones.

If Netflix has data that tells them users who stream over a specific number of hours of programming are more likely to stay, they can place their focus on ensuring they watch at least that many hours. It’s highly likely that the introduction of the ‘skip-credits’ feature was a result of Netflix realising that this was the time when people were most likely to turn off, when the was an opportunity to encourage them to watch more. 

Perhaps most interestingly of all, Netflix’s Big Data team are helping inform creativity. This ranges from supplying that data that helps personalise trailers for new content based on each subscriber’s preferences, to deciding which shows to commission. Netflix’s data told them that prison-based dramas, shows with strong-female ensembles, and programs with LGBT+ themes and characters were both popular, and shared a lot of audience overlap. With all this information at hand when they commissioned ‘Orange Is The New Black’ for a full series, Netflix could be sure that there was an audience for the show. 

As more and more companies add their own streaming services, including Disney’s expected behemoth, this targeted original content is going to become more and more valuable for Netflix. Fortunately, they’re long-used to changing not just how people watch, but also what they watch. 

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