{"id":2180,"date":"2015-11-17T00:43:46","date_gmt":"2015-11-17T05:43:46","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-digit\/submission\/netflix-bringing-data-analytics-to-a-creative-industry\/"},"modified":"2015-11-17T00:43:46","modified_gmt":"2015-11-17T05:43:46","slug":"netflix-bringing-data-analytics-to-a-creative-industry","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-digit\/submission\/netflix-bringing-data-analytics-to-a-creative-industry\/","title":{"rendered":"Netflix: Bringing Data Analytics to a Creative Industry"},"content":{"rendered":"

 <\/p>\n

The film and TV business has historically been considered hit-driven and relatively unpredictable. More than half of new broadcast series are cancelled after just one season, and nearly every year we see at least one highly anticipated film flop at the box office.[1]<\/a> Netflix, however, is revolutionizing the media industry, bringing data-driven decisions to a business historically driven by creativity. As a direct-to-consumer platform, Netflix has access to data traditional media players lack. While traditional media players receive \u201cratings\u201d for their content indicating viewership among key demographics (broken down by gender and age), Netflix knows who its customers are, what they are watching, where and on what device they are watching, how much of a movie or TV show they watch, the sequence in which they watch, and a plethora of other attributes. Although this post could not possibly not cover all of the ways in which Netflix uses data (in fact, that is likely not public information), it will attempt to highlight a few of the key ways the company uses data to create and capture value.<\/p>\n

Creating Value<\/strong><\/p>\n

One of the most readily apparent ways Netflix creates value for customers is through its recommendation system. In 2012, Netflix estimated that 75% of the content its subscribers watch stems from some sort of recommendation.[2]<\/a> Essentially, the goal of the recommendation engine is to \u201crecommend titles that each member is likely to play and enjoy.\u201d[3]<\/a> Netflix\u2019s recommendation engine uses a combination of popularity and predicted rating to rank its recommendations, employing machine learning to determine the weights of each independent variable. Over time, the company has added additional variables to further improve recommendations, including member viewing queues, social media, search terms, and demographics.<\/p>\n

Netflix is also a proponent of A\/B testing as a means to improve the user experience. The company outlines its basic approach to hypothesis testing in its tech blog. Netflix first uses offline testing to as an indication of whether it should go on to pursue online A\/B testing. If a hypothesis is validated offline, Netflix then tests the concept online over thousands of users, with A\/B tests running in parallel. The process is illustrated below.<\/p>\n

\"Amatriain,<\/a>
Amatriain, Xavier and Justin Basilico. \u201cNetflix Recommendations: Beyond the 5 Stars (Part 2).\u201d June 20, 2012. http:\/\/techblog.netflix.com\/2012\/06\/netflix-recommendations-beyond-5-stars.html<\/figcaption><\/figure>\n

Value Capture<\/strong><\/p>\n

The recommendation engine and constant focus on improvement of the user experience not only create enormous value for customers, but also help Netflix to capture value by increasing customer retention. Customers\u2019 preferences are saved even when they unsubscribe from the service, and this personalization often draws customers back.<\/p>\n

Netflix also uses data as a source of value capture in content acquisition. Detailed viewership data not only tells Netflix what type of content its subscribers enjoy, but also gives the company an indication of how much it should be willing to pay for that content. Regarding Netflix\u2019s content acquisition strategy, the company\u2019s Director of Global Media Relations says, \u201cWe look for those titles that deliver the biggest viewership relative to the licensing cost.\u201d[4]<\/a> Netflix has also used this detailed data on viewer preferences in deciding which content to create itself. Before investing nearly $100m to order two full seasons of House of Cards<\/em>, Netflix reportedly considered the share of its subscribers who had streamed director David Fincher\u2019s previous work, the performance of Kevin Spacey films on the streaming service, and the popularity of the British version of House of Cards<\/em>. According to Chief Communications Officer Jonathan Friedland, this data \u201cgave [Netflix] some confidence that [they] could find an audience for a show like House of Cards.\u201d<\/em>[5]<\/a> This data seems to allow Netflix to place bigger bets with a higher degree of confidence, although only time will tell whether this is a true, sustainable advantage.<\/p>\n

Finally, Netflix uses data analytics to better market its products, capturing even more value from its content. As an illustration, Netflix created ten different trailers for House of Cards<\/em>, each created for a different audience segment based on viewing history.[6]<\/a><\/p>\n

Upcoming Challenges<\/strong><\/p>\n

Netflix\u2019s use of data analytics has benefitted the company enormously, propelling the streaming service to 69m subscribers globally today; however, success is not guaranteed. Netflix faces a proliferation of competitors (Hulu and Amazon Prime, among others). The company also faces a potential threat from its reliance on traditional media players\u2019 studios to supply its content. These competitive dynamics in part explain Netflix\u2019s more recent push into creating its own content, a more expensive and risky endeavor given unpredictable consumer tastes. Although the company has had success in much of its original content thus far, only time will tell if data-driven content creation will create a sustainable advantage for the company.<\/p>\n

 <\/p>\n

[1]<\/a> Ocasio, Anthony. \u201cTV Success Rate.\u201d May 17, 2012. http:\/\/screenrant.com\/tv-success-rate-canceled-shows-aco-172162\/<\/a><\/p>\n

[2]<\/a> Amatriain, Xavier and Justin Basilico. \u201cNetflix Recommendations: Beyond the 5 Stars (Part 1).\u201d April 6, 2012. http:\/\/techblog.netflix.com\/2012\/04\/netflix-recommendations-beyond-5-stars.html<\/a><\/p>\n

[3]<\/a> Ibid.<\/p>\n

[4]<\/a> Stenovec, Timothy. \u201cThis Is Why You Won\u2019t See Oscar Blockbusters Streaming on Netflix.\u201d The Huffington Post.<\/em> March 3, 2014. http:\/\/www.huffingtonpost.com\/2014\/03\/03\/netflix-oscar-movies_n_4892961.html<\/a><\/p>\n

[5]<\/a> Carr, David. \u201cGiving Viewers What They Want.\u201d The New York Times. <\/em>February 24, 2013. http:\/\/www.nytimes.com\/2013\/02\/25\/business\/media\/for-house-of-cards-using-big-data-to-guarantee-its-popularity.html?_r=0<\/p>\n

[6]<\/a> http:\/\/gigaom.com\/2013\/02\/12\/netflix-ratings-big-data-original-content\/<\/p>\n","protected":false},"excerpt":{"rendered":"

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