{"id":28743,"date":"2018-11-12T15:14:44","date_gmt":"2018-11-12T20:14:44","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/promise-and-peril-for-machine-learning-at-netflix\/"},"modified":"2018-11-12T23:31:15","modified_gmt":"2018-11-13T04:31:15","slug":"promise-and-peril-for-machine-learning-at-netflix","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/promise-and-peril-for-machine-learning-at-netflix\/","title":{"rendered":"Promise and Peril for Machine Learning at Netflix"},"content":{"rendered":"

Business Importance of Machine Learning at Netflix:<\/u><\/strong><\/p>\n

Since its founding as a mail-based DVD rental business, Netflix has offered subscribers an extensive array of movies and television shows to choose from. Now with 117 million streaming memberships that watch a combined 140 million hours daily, Netflix has increasingly been branching into content creation, including hit series such as Stranger Things<\/em> and Orange Is The New Black <\/em>[1]. <\/em>With growing scale across more and more diverse customer segments and geographies, the challenge for Netflix of offering highly engaging entertainment to each customer has become increasingly difficult \u2013 this is where Netflix has turned to machine learning to ensure that customers are finding the best content on their platform, both in terms of Netflix originals and licensed third-party content.<\/p>\n

Nearly 40% of respondents view Netflix as the service with the best original programming<\/strong><\/span><\/p>\n

\"\"<\/a>
Source: Business Insider [2]<\/figcaption><\/figure>In 2016, Netflix\u2019s Chief Product Officer Neil Hunt and VP of Product Innovation Carlos Gomez-Uribe co-authored a technical academic paper on the Netflix Recommender System, explaining the fundamentals of recommendation algorithms used by Netflix as well as the value that these algorithms provide to their business. To sum up the latter point, the authors estimate that \u201cthe combined effect of personalization and recommendations save us more than $1B per year\u201d by reducing monthly customer membership churn by a few percentage points [3]. When competing against the likes of Hulu, Amazon, and other giants, the algorithm\u2019s benefits in churn reduction create tremendous value.<\/p>\n

 <\/p>\n

Management Action Plan:<\/u><\/strong><\/p>\n

As Hunt and Gomez-Uribe remarked, the content recommendation feature is an incredibly powerful tool in retaining customers. Back in 2013, Netflix saw their recommendations driving over 75% of all content viewing, compared against consumer search [4].<\/p>\n

\"\"<\/a>
Source: InfoQ<\/figcaption><\/figure>\n

As far as aligning corporate investment with the platform, Netflix in 2014 dedicated a team of 300 employees and spent $150M (around 3% of 2014 revenues) to improve their recommendation engine \u2013 this dollar figure has likely continued to grow as the amount of content and subscribers has increased [5].<\/p>\n

The newest investment that it seems Netflix is making to create a more customized and tailored platform experience is in connection with the artwork, or the thumbnails that subscribers see when browsing TV show and movie titles [6]. For example, there are nine different artworks for the Stranger Things <\/em>show that users may see when browsing, depending on whether they have historically shown more interest in comedy, horror, or suspense in their viewing histories. However, this is in particular an area where Netflix should treat with caution, since there are downside risks of profiling and offending customers in a visually obvious way.<\/p>\n

 <\/p>\n

Several Recommendations for Netflix:<\/u><\/strong><\/p>\n

In 2009, Netflix issued the Netflix Prize challenge to \u201csubstantially improve the accuracy of predictions about how much someone is going to enjoy a movie based on their movie preferences,\u201d with a grand prize of $1M [7]. However, it seems that Netflix has not held any similar competitions since 2009. While Netflix\u2019s internal technical talent has grown tremendously over the past decade, the company should continue this legacy of open innovation to tap into the creativity and ingenuity that resides externally.<\/p>\n

Additionally, while Netflix does have a large budget and team focused on the applications of machine learning to their product offering, I worry about the relatively narrow view of online activity that Netflix currently has for its customers. Without violating data privacy restrictions, I would encourage Netflix to partner with other non-competitive platforms (e.g., Facebook, Yelp, etc.) to allow their machine learning content recommendation engine to draw from a more complete view of their subscribers when recommending content.<\/p>\n

 <\/p>\n

Open questions:<\/u><\/strong><\/p>\n

    \n
  1. Positive feedback loops:<\/strong> Any tool trained through machine learning will tend to exhibit a positive feedback loop. For example, videos with which many subscribers engage will become highly recommended to other members, and then the cycle perpetuates. Is this a bias that should try to be adjusted for, or is this actually a beneficial bias that leads to shared viewing experiences within the subscriber community?<\/li>\n
  2. Risk of racial bias:<\/strong> Just as Facebook has shown the ability to diagnose a user\u2019s age, race, and income from their activity, Netflix has enough of a glimpse into their subscriber\u2019s viewing history and other activity to do the same [8]. With this information, Netflix has appeared to target subscribers with content based on race, socioeconomic status, etc. \u2013 is this something that should be criticized or encouraged? (730 words)<\/li>\n<\/ol>\n

     <\/p>\n

    Sources:<\/strong><\/span><\/p>\n

    [1] Netflix, \u201c2018 Amended Annual Report\u201d, https:\/\/www.netflixinvestor.com\/financials\/sec-filings\/sec-filings-details\/default.aspx?FilingId=12516727<\/a>, February 5, 2018. Accessed November 2018.<\/p>\n

    [2] \u201cNew research shows Netflix’s big bet on the future is working, as it continues to outpace its competition\u201d, https:\/\/www.businessinsider.com\/netflix-original-tv-programming-analysis-from-morgan-stanley-2018-5<\/a>, May 8, 2018. Accessed November 2018.<\/p>\n

    [3] Gomez-Uribe, Carlos A., and Neil Hunt, \u201cThe Netflix Recommender System: Algorithms, Business Value, and Innovation\u201d, ACM Transactions on Management Information Systems (TMIS)<\/em>,\u00a0 https:\/\/dl.acm.org\/citation.cfm?id=2843948<\/a>, January 2016. Accessed November 2018.<\/p>\n

    [4] Amatriain, Xavier, \u201cMachine Learning & Recommender Systems at Netflix Scale\u201d, https:\/\/www.infoq.com\/presentations\/machine-learning-netflix<\/a>. November 2013. Accessed November 2018.<\/p>\n

    [5] Roettgers, Janko, \u201cNetflix spends $150 million on content recommendations every year, Gigaom<\/em>. https:\/\/gigaom.com\/2014\/10\/09\/netflix-spends-150-million-on-content-recommendations-every-year\/<\/a>. October 2014. Accessed November 2018.<\/p>\n

    [6] Wilson, Mark, \u201cNetflix Is Even Personalizing Its Graphic Design To You Now\u201d, Fast Company<\/em>. https:\/\/www.fastcompany.com\/90154608\/netflix-is-even-personalizing-its-graphic-design-to-you-now<\/a>, December 18, 2017. Accessed November 2018.<\/p>\n

    [7] Netflix, \u201cNetflix Prize\u201d, https:\/\/www.netflixprize.com\/<\/a>, Accessed November 2018.<\/p>\n

    [8] Tiku, Natasha, \u201cWhy Netflix Features Black Actors in Promos to Black Users\u201d, Wired. <\/em>https:\/\/www.wired.com\/story\/why-netflix-features-black-actors-promos-to-black-users\/<\/a>, October 24, 2018. Accessed November 2018.<\/p>\n","protected":false},"excerpt":{"rendered":"

    As the amount of content, competition, and subscribers in the online media space grows, Netflix is turning to machine learning to provide a more entertaining experience for its customers. While they are investing heavily in their proprietary recommendation engine and enjoying the benefits of increased customer retention, questions are emerging around how far into customer profiling a machine learning algorithm should ideally go.<\/p>\n","protected":false},"author":11225,"featured_media":28766,"comment_status":"open","ping_status":"closed","template":"","categories":[1014,346,143,4252],"class_list":["post-28743","hck-submission","type-hck-submission","status-publish","has-post-thumbnail","hentry","category-algorithm","category-machine-learning","category-netflix","category-recommendation","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":"\nPromise and Peril for Machine Learning at Netflix - 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\/promise-and-peril-for-machine-learning-at-netflix\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Promise and Peril for Machine Learning at Netflix - Technology and Operations Management\" \/>\n<meta property=\"og:description\" content=\"As the amount of content, competition, and subscribers in the online media space grows, Netflix is turning to machine learning to provide a more entertaining experience for its customers. 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