How Netflix Uses Data

Netflix has come incredibly far since its humble start as a mail order DVD rental company in 1997 (and still rents DVDs!). Few companies have adapted and changed as quickly and gracefully as Netflix.

Their secret? Data science.

Netflix began experimenting with data in 2006 when they held a competition to create an algorithm that would “substantially improve the accuracy of predictions about how much someone is going to enjoy a movie based on their movie preferences.” Since then, Netflix has taken data beyond rating prediction and into personalized ranking, page generation, search, image selection, messaging, marketing, and more.

The Netflix Recommendation Engine

Their most successful algorithm, Netflix Recommendation Engine (NRE), is made up of algorithms which filter content based on each individual user profile. The engine filters over 3,000 titles at a time using 1,300 recommendation clusters based on user preferences. It’s so accurate that 80% of Netflix viewer activity is driven by personalised recommendations from the engine. It’s estimated that the NRE saves Netflix over $1 billion per year.

It’s so accurate that 80% of Netflix viewer activity is driven by personalised recommendations.

Netflix isn’t the only company using a recommendation engine. Amazon, LinkedIn, Spotify, Instagram, Youtube, and many other web platforms all use recommendation engines to predict their users’ preferences and boost their business. But Netflix clearly has the most successful engine. 47% of North Americans prefer to use Netflix with a 93% retention rate. Amazon Prime comes in second at only 14% and every other subscription streaming service lingers in the single digits.

Netflix tracks data points like:

  • Time and date a user watched a title
  • User profile information such as age, gender, location, and selected favorite content upon sign up
  • The device used to stream
  • If the show was paused, rewound, or fast-forwarded
  • If the viewer resumed watching after pausing
  • Whether an entire TV series or movie was completed
  • How long it took a viewer to watch an entire TV series
  • Whether the viewer gave the show or movie a thumbs up
  • Scenes users have viewed repeatedly
  • The number of searches and what is searched for
  • Where a user watched the show (by postal code)
  • Browsing and scrolling behavior
  • Screen shots when the show was paused, when the user left the show, and when the user watches a scene more than once

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Original Content and Marketing

The streaming giant’s original content is successful 93% of the time.

The typical television show has only a 35% chance of succeeding. Netflix’s choices about greenlighting original content aren’t random. They’re based on data too – unlike television which relies on tradition, opinion, and sometimes luck.

Netflix also uses data to create targeted marketing campaigns for that original content. They cut over ten different versions of trailers for content that they expect to be popular.

Take House of Cards, for example. If your user profile indicated you liked “strong female leads,” you would see the previews featuring Robin Wright who played Claire Underwood. They created trailers focusing on the director, Kevin Spacey and his character Francis Underwood, the political aspects, and more. Each one chosen by an algorithm to show you with a nearly 90% guarantee you would enjoy it or at least be interested in watching the first episode.

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Data Science and Research at Netflix

Netflix has gone beyond using data to boost their business and developed an entire research department that is integrated into their business and engineering teams. They’ve released open-source machine learning algorithms and Python frameworks aimed at boosting the productivity of Data Scientists and business.

From the Recommendations Engine to choosing which original shows and movies to make, Netflix knows exactly how to capture their audience and continue growing because they have dialed in on their data. They’re more than a streaming company, they’re a data giant.

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