{"id":31802,"date":"2018-11-13T13:51:39","date_gmt":"2018-11-13T18:51:39","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/to-all-the-shows-ive-loved-before-netflix-and-machine-learning\/"},"modified":"2018-11-13T13:51:39","modified_gmt":"2018-11-13T18:51:39","slug":"to-all-the-shows-ive-loved-before-netflix-and-machine-learning","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/to-all-the-shows-ive-loved-before-netflix-and-machine-learning\/","title":{"rendered":"To All the Shows I\u2019ve Loved Before: Netflix and Machine Learning"},"content":{"rendered":"

The State of Streaming:<\/strong> In October 2018, AT&T announced plans to launch a subscription video on demand (SVoD) platform combining HBO, a 5 million subscriber streaming powerhouse, with its other WarnerMedia assets [1]. Around the same time, Disney freed up $15 billion to invest in its direct-to-consumer, Disney-branded platform set for launch in 2019 [2]. Meanwhile, Hulu reached 12 million subscribers, Amazon invested over $5 billion in original content after winning an Emmy and multiple Oscars [3], and Apple, Youtube, Facebook, and even Walmart announced intentions to compete in the SVoD space. Only a decade after Netflix went all-in on its first-to-market streaming model, every goliath in the entertainment business was primed to steal its market share [4]. To date, Netflix\u2019s competitive advantage for its 125 million subscribers has been its commitment to using data and machine learning to differentiate the user experience and content offering, with an annual investment of over $1 billion in technology [5]. But will this be enough for Netflix to sustain its leadership position in our living rooms for the next decade?<\/p>\n

Netflix\u2019s Edge:<\/strong> For an SVoD, key differentiating factors are user interface and content offering. Netflix, which has never been bashful about collecting data [Exhibit 1]<\/strong>, uses machine learning for both to produce a seamless, customized viewing experience. Since users typically bail after ~60-90 seconds of trying to find something to watch [6], the recommendation engine is a critical component of the user interface, generating 80%+ of total tv show viewings on the Netflix, specifically [7]. Netflix focuses on both what content is being recommended and how those recommendations are presented to the user. \u201cWhat content?\u201d is addressed through a common algorithm in recommender systems called \u2018collaborative filtering\u2019, which compiles data from multiple users to produce individualized preference predictions. The teaching inputs include explicit (Chiara thumbs-upped Black Mirror) and implicit data (Chiara binge watched Black Mirror in one weekend), and the tags for each title are comprehensive, including filming location, genre, cast composition, and categorizations like \u201cshows that reveal the dark side of society\u201d. The algorithm also considers behavioral patterns, such as viewing frequency and times, along with biases similar users have exhibited [8]. After predictions are generated across various algorithms, Netflix uses linear stacking to combine all results of simultaneous predictive models to produce a final recommendation [9].<\/p>\n

But Netflix doesn\u2019t stop here: machine learning is also used to determine how best to present recommendations to the user. While the iterative nature of the home screen\u2019s rows of titles may seem like an obvious customization to frequent users, many may not realize that Netflix even customizes the image of a title based on what is most likely to convert users. To do this, Netflix decomposes each individual frame within the title to isolate most impactful stills using face detection, motion estimation, camera shot identification, and object detection, which are then pushed to the creative team as foundations for customized artwork [10]. During the viewing process, Netflix then uses machine learning to optimize streaming quality based on device and location.<\/p>\n

Sample Recommendations:\u00a0\"\"<\/a><\/strong><\/p>\n

A further benefit of all the data used to feed the recommendation engine is its ability to inform decisions on content offerings \u2013 both licensed and original. This can be observed through Netflix\u2019s recent emphasis on original romantic comedies. By analyzing viewing patterns, Netflix noticed that viewers were frequenting its library of old romantic comedies and the supply of new rom-coms were limited. The company reacted by releasing 11 titles of this genre in 2018, resulting in over 80 million accounts watching at least one globally. One of these titles \u201cTo All the Boys I\u2019ve Loved Before\u201d was one of the most viewed original films ever, with strong repeat viewings [11].<\/p>\n

Looking Ahead:<\/strong> In a now hyper-competitive SVoD landscape, Netflix\u2019s key differentiator is its massive library of viewing data informing future decisions. While this is an undeniable first-mover advantage, as companies withhold titles from Netflix by offering them vertically, Netflix will become increasingly dependent on original content. The company has begun to address this longer-term issue by applying a data-driven approach to the entire production cycle, using mathematical optimization to reduce costs and time spent on everything from location-hunting and cast-scheduling to post-production work [12]. Notably, Netflix is also spending nearly twice as much on marketing vs. technology [Exhibit 2]<\/strong>, acknowledging that recommendation engines alone are not enough to drive traffic to brand new Netflix-only titles.<\/p>\n

Key Questions:<\/strong> How differentiated is Netflix\u2019s approach to technology and is the recommendation system powerful enough to maintain an uncontested user experience? Will Netflix be able to utilize its data to produce \/ acquire enough original content, quickly enough to stave off the competition? And importantly, will competitors take advantage of any blindspots generated by Netflix\u2019s maniacal commitment to data as a key driver of decision-making? (797 words)<\/p>\n

Exhibit 1<\/strong> (Source: BBC, Twitter):
\n\"\"<\/a>Exhibit 2<\/strong> (Source: Credit Suisse Report [13]):<\/p>\n

\"\"<\/a><\/p>\n

 <\/p>\n

[1] Adam Levy, \u201cAT&T’s Plan to Take on Disney and Netflix.\u201d The Motley Fool<\/em>, The Motley Fool, 12 Oct. 2018, www.fool.com\/investing\/2018\/10\/12\/atts-plan-to-take-on-disney-and-netflix.aspx, accessed November 2018.<\/p>\n

[2] Elizabeth Winkler, \u201cDisney Gets $15 Billion to Invest in Streaming.\u201d The Wall Street Journal<\/em>, Dow Jones & Company, 26 Sept. 2018, www.wsj.com\/articles\/disney-gets-15-billion-to-invest-in-streaming-1537981191, accessed November 2018.<\/p>\n

[3] Elaine Low, \u201cNetflix, Amazon, Hulu, Apple, Disney Prepare For Streaming Video War.\u201d Investor’s Business Daily<\/em>, 27 Dec. 2017, www.investors.com\/news\/netflix-amazon-hulu-apple-disney-prepare-streaming-video-war\/, accessed November 2018.<\/p>\n

[4] Tim Carmody, \u201cNetflix Opens Up Streaming-Only for US Customers, DVD Plans Get Price Bump.\u201d Wired<\/em>, Conde Nast, 4 June 2017, www.wired.com\/2010\/11\/netflix-opens-up-streaming-only-for-us-customers-dvd-plans-get-price-bump\/, accessed November 2018.<\/p>\n

[5] Larry Dignan, \u201cNetflix to Raise Technology, Marketing, Content Spending in 2018.\u201d ZDNet<\/em>, ZDNet, 22 Jan. 2018, www.zdnet.com\/article\/netflix-to-raise-technology-marketing-content-spending-in-2018\/, accessed November 2018.<\/p>\n

[6] Chris Raphael, \u201cHow Machine Learning Fuels Your Netflix Addiction.\u201d RTInsights<\/em>, 12 Mar. 2017, www.rtinsights.com\/netflix-recommendations-machine-learning-algorithms, accessed November 2018.<\/p>\n

[7] Libby Plummer, \u201cThis Is How Netflix’s Top-Secret Recommendation System Works.\u201d WIRED<\/em>, WIRED UK, 21 Aug. 2017, www.wired.co.uk\/article\/how-do-netflixs-algorithms-work-machine-learning-helps-to-predict-what-viewers-will-like, accessed November 2018.<\/p>\n

[8] Dave Smith, \u201cNetflix and Chill: Building a Recommendation System in Excel.\u201d Towards Data Science<\/em>, Towards Data Science, 5 July 2018, towardsdatascience.com\/netflix-and-chill-building-a-recommendation-system-in-excel-c69b33c914f4, accessed November 2018.<\/p>\n

[9] The Netflix Prize and Production Machine Learning Systems: An Insider Look<\/em>. Mathworks, 2016, www.mathworks.com\/tagteam\/86975_92959v00_Netflix_Whitepaper.pdf, accessed November 2018.<\/p>\n

[10] Netflix Technology Team, \u201cAVA: The Art and Science of Image Discovery at Netflix.\u201d Medium.com<\/em>, Netflix, 7 Feb. 2018, medium.com\/netflix-techblog\/ava-the-art-and-science-of-image-discovery-at-netflix-a442f163af6, accessed November 2018.<\/p>\n

[11] \u201cNetflix Q3 2018 Shareholder Letter.\u201d Netflix Q3 2018 Shareholder Letter<\/em>, Netflix, 16 Oct. 2018, s22.q4cdn.com\/959853165\/files\/doc_financials\/quarterly_reports\/2018\/q3\/FINAL-Q3-18-Shareholder-Letter.pdf, accessed November 2018.<\/p>\n

[12] Netflix Technology Team, \u201cData Science and the Art of Producing Entertainment at Netflix.\u201d Medium.com<\/em>, Netflix, 27 Mar. 2018, medium.com\/netflix-techblog\/studio-production-data-science-646ee2cc21a1, accessed November 2018.<\/p>\n

[13] Douglas Mitchelson, et al, \u201cContent Ramp Adding Torque to the Flywheel.\u201d Content Ramp Adding Torque to the Flywheel<\/em>, Credit Suisse, 10 July 2018, research-doc.credit-suisse.com\/docView?language=ENG&format=PDF&sourceid=em&document_id=1080565301&serialid=6m3%2F3JVrXxkMkutdm%2Bc%2BO8SDHvVKuo19Ab6XSotZ844%3D&cspId=1928945512742584320, accessed November 2018.<\/p>\n

 <\/p>\n","protected":false},"excerpt":{"rendered":"

Why machine learning is at the core of Netflix’s competitive advantage<\/p>\n","protected":false},"author":11753,"featured_media":31803,"comment_status":"open","ping_status":"closed","template":"","categories":[4509,346,2484],"class_list":["post-31802","hck-submission","type-hck-submission","status-publish","has-post-thumbnail","hentry","category-data-science","category-machine-learning","category-streaming-services","hck-taxonomy-organization-netflix","hck-taxonomy-industry-motion-pictures-and-video","hck-taxonomy-country-united-states"],"connected_submission_link":"https:\/\/d3.harvard.edu\/platform-rctom\/assignment\/rc-tom-challenge-2018\/","yoast_head":"\nTo All the Shows I\u2019ve Loved Before: Netflix and Machine Learning - Technology and Operations Management<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/to-all-the-shows-ive-loved-before-netflix-and-machine-learning\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"To All the Shows I\u2019ve Loved Before: Netflix and Machine Learning - 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