{"id":35178,"date":"2018-11-13T19:27:34","date_gmt":"2018-11-14T00:27:34","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/running-on-netflix-how-machine-learning-is-fueling-your-netflix-binge-watching-problem\/"},"modified":"2018-11-13T19:27:34","modified_gmt":"2018-11-14T00:27:34","slug":"running-on-netflix-how-machine-learning-is-fueling-your-netflix-binge-watching-problem","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/running-on-netflix-how-machine-learning-is-fueling-your-netflix-binge-watching-problem\/","title":{"rendered":"Running on Netflix: How Machine Learning is Fueling Your Netflix Binge-Watching Problem"},"content":{"rendered":"

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Netflix\u2019s combats its main threat, subscriber churn, by creating a personalized experience that customers are willing to pay for. This strategy has allowed the company to become the largest streaming company in the world with 137 million subscribers throughout 190 countries providing them with a library of 14,835 worldwide[1] (5,579 in the US)[2] original and licensed titles. But how can Netflix provide a personalized streaming experience to its millions of subscribers with its vast library? The answer lies on the introduction of machine learning to the company\u2019s innovation and improvement processes.<\/p>\n

Machine learning has been instrumental at creating a competitive edge for Netflix on two main avenues: (1) content suggestion and (2) content acquisition and creation.<\/p>\n

(1) Content Suggestion<\/u><\/p>\n

Two years ago, Netflix started experimenting with sophisticated supervised (e.g. classification and regression) and unsupervised (e.g. clustering and compression) machine learning algorithms[3] that aimed at aggregating large sets of data and identifying patterns in consumer behavior to improve user experience while decreasing monthly churn rate (lowest in the streaming industry at 9%\/year in 2016).[4]<\/p>\n

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Netflix has estimated that users spend 60 to 90 seconds browsing on its interface for new shows to watch before they lose interest.[5] These machine learning algorithms help users navigate through Netflix\u2019s vast library, translating into 80% of watched content coming from algorithmic recommendations[6] and annual savings of well over US$1 billion from decreasing churn rates[7].<\/p>\n

Historically, Netflix relied on customer reviews (ranking from 1 to 5 stars) to predict consumer preferences. After the introduction of machine learning algorithms on their streaming content, Netflix started collecting and analyzing a wide arrange of data on many metrics considered to be better predictors of subscriber behavior including:<\/p>\n