Netflix has long prided itself on the quantitative approach it takes to providing consumers with a world-class experience. The results of the big data strategy are impressive – Netflix’s ~120 million subscribers watch over 140 million hours of video every day on over 450 million different devices (1, 2). The Netflix analytics software and recommendation engine are at the heart of what makes the platform so effective. In the words of Joris Evers, Netflix Director of Global Communications, “there are 33 million different versions of Netflix” (3).
Netflix’s core competency in data science enables the personalization of the streaming experience based on user behavior. Netflix classifies and tags content to get a nuanced view of consumer preferences. Netflix has developed over 1,000 tag types that classify content by genre, time period, plot conclusiveness, mood, etc. These tags help to define micro-genres, which, by 2014, had already reached 76,897 (4). Content micro-classification, combined with a proprietary recommendation engine, enables Netflix to serve customers better. About 75% – 80% of viewer activity is influenced by the recommendation algorithm (3). The true north of the Netflix big data strategy is the philosophy of best serving specific audiences rather than “being all things to all people,” a mantra of old broadcast TV shows (5). Utilizing data has enabled Netflix to execute their philosophy for each user, demonstrating to traditionalists that melding data with creative intuition can produce superior performance.
The other major advantage data affords is improved creative decisions. Metrics like completion rate, stop and start time, time of day, and viewing behavior (e.g., pause, fast forward, rewind, etc.) allow Netflix to make better content decisions. When greenlighting Arrested Development, Netflix saw how many users, of those who watched through to Season 3, completed the series. This helped producers decide not only whether or not to greenlight additional runs, but also what particularly resonated with audiences given audience behavior when watching certain episodes. The classic example of Netflix’s prowess was House of Cards, which rose out of a confluence of a) data on the popularity of the British version of the show, b) fans of British House of Cards who also watched a lot of movies that involved Kevin Spacey or were directed by David Fincher, and c) those who watched David Fincher movies over indexed on completion in a single sitting (3). The result? Success rates for Netflix shows are 80% vs. an average of 35% for traditional TV shows (6). Netflix has established the industry standard of data and creative talent. HBR researchers have shown that a data-driven approach to a creative endeavor, like producing TV shows, has led to greater product variety for audiences (5). As Netflix’s subscriber base grows, so does its defensible moat. The sheer volume of data is itself a competitive advantage. Some observers note that it took 6 years before Netflix reached the point they had enough data to help create shows from scratch (6). In greenlighting Orange is the New Black, Netflix had already determined the likelihood of success based on viewership data of women-led TV shows and Jenji Johan’s hit show Weeds – Netflix knew exactly which subscribers would be interested, reducing the creative risk of the new show (5). Of course, analytics does not solely drive creative decisions; Netflix still involves production studios and creative staff to make shows a reality. However, Netflix pioneered the art/science approach of data in entertainment. The natural question of how the entrance of Amazon and, to a lesser degree, Hulu impact Netflix’s future. But that’s a topic for another post.
- Netflix Subscribers Streamed Record-Breaking 350 Million Hours of Video on Jan. 7 [Internet].; 2018 [updated March 8,; cited 4/4/2018]. Available from: http://variety.com/2018/digital/news/netflix-350-million-hours-1202721679/.
- Netflix now has nearly 118 million streaming subscribers globally [Internet].; 2018 [updated Jan 22,; ]. Available from: https://www.recode.net/2018/1/22/16920150/netflix-q4-2017-earnings-subscribers.
- How Netflix Uses Analytics To Select Movies, Create Content, and Make Multimillion Dollar Decisions [Internet]. . Available from: https://blog.kissmetrics.com/how-netflix-uses-analytics/.
- Jenkins T. Netflix’s geek-chic: how one company leveraged its big data to change the entertainment industry. Jump Cut. 2016 Oct 1,(57):Web.
- Smith M, Telang R. Data Can Enhance Creative Projects — Just Look at Netflix. Harvard Business Review. 2018 January 23,.
- How Netflix Uses Big Data
[Internet].; 2018 [updated Jan 12; ]. Available from: https://medium.com/swlh/how-netflix-uses-big-data-20b5419c1edf.