{"id":29559,"date":"2018-11-12T20:25:20","date_gmt":"2018-11-13T01:25:20","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/netflix-machine-learning-to-what-extent-should-machine-learning-influence-a-strategic-decision-making-process\/"},"modified":"2018-11-12T20:26:44","modified_gmt":"2018-11-13T01:26:44","slug":"netflix-machine-learning-to-what-extent-should-machine-learning-influence-a-strategic-decision-making-process","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/netflix-machine-learning-to-what-extent-should-machine-learning-influence-a-strategic-decision-making-process\/","title":{"rendered":"Netflix & Machine Learning: To what extent should Machine Learning influence a strategic decision-making process?"},"content":{"rendered":"

Netflix\u2019s affection with machine learning and its current dilemma<\/strong><\/p>\n

Machine learning has been at the heart of Netflix\u2019s business strategy and catalyst for growth since its inception in 1997. From the earliest form of its business model as a physical DVD-by-mail rental service, Netflix has started analyzing vast amount of user rating and behavior data to identify the type of movies its customers enjoyed watching in the past and developing proprietary algorithm to predict what movies the same customers will likely enjoy watching in the future. Netflix then utilized the result to recommend each individual customer a personalized playlist of movies & TV shows [1]. In addition, it leveraged the aggregate result to make an important business decision: determining which are the most popular contents it will need to buy from the DVD distribution companies and place in the rental library to anticipate future customers demand [2].<\/p>\n

Riding the tailwind of internet proliferation phenomenon, Netflix began to convert its core business model into an online streaming service in 2007 [3]. The transformation allowed Netflix to deliver movies and TV shows instantaneously, reach new customers all around the globe, and significantly boost the number of subscribers. This new business model is a boon for its movie recommendation engine \u2013 the vast amount of fresh data helps to refine the algorithm and improve the prediction accuracy [4].<\/p>\n

The increased confidence on the ability to predict customer\u2019s taste led Netflix to a new venture \u2013 acquiring and creating its own original contents (movies and TV shows) [5]. While past decision on which DVD titles to be bought for its library is purely transactional, selecting and dropping casts have more risk and complex relationship effects. In one example, Netflix\u2019s machine learning engine indicated that more users clicked on \u201cGrace and Frankie\u201d show when the image does not contain Jane Fonda. Netflix is struggling on whether to drop her from the marketing image at the risk of irritating the actress [6]. As experts think that similar tensions will occur more often, it\u2019s decision today may set an important precedent on how the company approach similar types of problem going forward [7].<\/p>\n

\u00a0<\/strong><\/p>\n

Current solution to balance the machine learning insight <\/strong><\/p>\n

The issue was stirring up internal debate within Netflix\u2019s production and technology team. Production team \u201cdon\u2019t believe numbers as much as people in Silicon Valley\u201d; while technology team argued the data should not be ignored [7].<\/p>\n

To resolve this impasse, the management team took on two-steps action plan. First, it cultivated the culture of open communication across different teams in voicing out the benefits and concerns of each options. Secondly, it encouraged the teams to also consider the \u201cintangible impacts\u201d that are not yet captured in the machine learning\u2019s algorithm, such as the potential risk of alienating the actress, inability to partner with the actress for any future production titles featuring her, and repercussion of violating the contract agreement [7].<\/p>\n

Under this framework, the two teams were able to \u201creach common ground\u201d. Human judgement is introduced into the decision-making process \u2013 Netflix decided to keep the promotional image that also featured Jane Fonda [7].<\/p>\n

\u00a0<\/strong><\/p>\n

Additional step to consider<\/strong><\/p>\n

In high level, I agree with the management\u2019s approach to incorporate human judgement to overcome machine learning\u2019s limitation in considering crucial \u201cintangible impacts\u201d that are out of scope for the algorithm. Augmented intelligence should serve as an input in the decision-making process, helping to suggest rather than dictate the final decision.<\/p>\n

However, I think Netflix still can generate deeper insight to drive its business decision by conducting more rigorous analysis to separate the signal from the noise [8]. Currently the algorithm captures in one specific instance, there is negative correlation between Jane Fonda\u2019s appearance in the image and the number of click. It does not tell convincingly whether it is truly because of Jane Fonda or other factors in the images such as color tone, background settings, etc. Having more result from other tests in a controlled environment will help to separate consistent relationship from the random correlation \u2013 it might even predict causality whether Jane Fonda is actually less desirable than her co-star, Lily Tomlin and drive the business decision in the future.<\/p>\n

 <\/p>\n

Open Questions<\/strong><\/p>\n

Netflix has been good at providing its customers new contents based on machine learning result of what they like in the past. However, in media and entertainment industry there is a saturation point where people are bored and weary watching the similar things again and again. What should Netflix do to avoid this situation from happening? Also, how should Netflix orient its business model to ensure it can capitalize on new trends \u2013 something that did not exist in the past hence not showing up in the machine learning result, but once introduced the customers will like in the future?<\/p>\n

Word Count: 793<\/p>\n

 <\/p>\n

References:<\/strong><\/p>\n

[1] Netflix, Inc., \u201cCompany Timeline,\u201d https:\/\/signup.netflix.com\/MediaCenter\/Timeline, accessed November 2018.<\/p>\n

[2] Willy Shih, Stephen Kaufman, and David Spinola, \u201cNetflix,\u201d HBS No. 9-607-138 (Boston: 性视界 Business School Publishing, 2007).<\/p>\n

[3] Miguel Helft. \u201cNetflix to Deliver Movies to the PC.\u201d The New York Times, January 16, 2017, https:\/\/www.nytimes.com\/2007\/01\/16\/technology\/16netflix.html<\/a>, accessed November 2018<\/p>\n

[4] Francois Brochet, Suraj Srinivasan, Michael Norris, \u201cNetflix: Valuing a New Business Model\u201d, HBS No. 9-113-018 (Boston: 性视界 Business School Publishing, 2017)<\/p>\n

[5]\u00a0Gina Keating (2012).\u00a0Netflixed: The Epic Battle for America’s Eyeballs. Portfolio\/ Penguin.<\/p>\n

[6] Travis Clark, \u201cNetflix reportedly had an internal debate after its testing favored ‘Grace and Frankie’ promos without Jane Fonda.\u201d Business Insider, November 11, 2018, https:\/\/nordic.businessinsider.com\/netflix-tests-liked-grace-and-frankie-without-jane-fonda-wsj-2018-11, accessed November 2018<\/p>\n

[7] Shalini Ramachandran and Joe Flint, \u201cAt Netflix, Who Wins When It\u2019s Hollywood vs. the Algorithm?\u201d The Wall Street Journal, November 10, 2018, https:\/\/www.wsj.com\/articles\/at-netflix-who-wins-when-its-hollywood-vs-the-algorithm-1541826015<\/a>, accessed November 2018<\/p>\n

[8] Mike Yeomans (2015) \u2018What Every Manager Should Know About Machine Learning\u2019, 性视界 Business Review Digital Articles, pp. 2\u20136. Available at: http:\/\/ezp-prod1.hul.harvard.edu\/login?url=http:\/\/search.ebscohost.com\/login.aspx?direct=true&db=bth&AN=118667151&site=ehost-live&scope=site (Accessed: 12 November 2018).<\/p>\n","protected":false},"excerpt":{"rendered":"

Netflix has been relying on its state-of-the-art movies recommendation engine to propel it to various new heights. Should it keep utmost faith on the engine that took the company to where it is today or is this the time to hit the break and bring back human judgement?<\/p>\n","protected":false},"author":11576,"featured_media":29604,"comment_status":"open","ping_status":"closed","template":"","categories":[346,143,2373,4367,3673],"class_list":["post-29559","hck-submission","type-hck-submission","status-publish","has-post-thumbnail","hentry","category-machine-learning","category-netflix","category-process-improvement","category-recommendation-engine","category-streaming-movies","hck-taxonomy-organization-netflix","hck-taxonomy-industry-media-and-broadcasting","hck-taxonomy-country-united-states"],"connected_submission_link":"https:\/\/d3.harvard.edu\/platform-rctom\/assignment\/rc-tom-challenge-2018\/","yoast_head":"\nNetflix & Machine Learning: To what extent should Machine Learning influence a strategic decision-making process? - 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\/netflix-machine-learning-to-what-extent-should-machine-learning-influence-a-strategic-decision-making-process\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Netflix & Machine Learning: To what extent should Machine Learning influence a strategic decision-making process? - Technology and Operations Management\" \/>\n<meta property=\"og:description\" content=\"Netflix has been relying on its state-of-the-art movies recommendation engine to propel it to various new heights. 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