netflix case study 2018 pdf

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How Netflix Expanded to 190 Countries in 7 Years

By: Louis Brennan

The majority of its revenue now comes from outside the U.S.

• Length: 1391 word count
• Publication Date: Oct 12, 2018
• Discipline: Strategy
• Product #: H04LEY-PDF-ENG

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Netflix’s global growth is a big factor in the company’s success. It operates in over 190 countries, and its international streaming revenues now exceed its domestic revenues. But only eight years ago Netflix was only in the U.S. How did it expand so quickly? First, it didn’t enter all markets at once. It started slowly, in countries that were similar to its U.S. home market. Using what it learned in these markets, it expanded to a few dozen countries by 2015, and then continued learning and growing from there. Second, it adapted to local cultures and preferences, using that knowledge to appeal to customers all over the world, both with its content offerings and with the partnerships it formed with local stakeholders. Netflix’s strategy constitutes a new approach to growth that the author calls exponential globalization, and it’s one that other companies can use too.

Oct 12, 2018

Discipline:

Industries:

Broadcasting and streaming media industry

Harvard Business Review Digital Article

H04LEY-PDF-ENG

1391 word count

Deep Learning for Recommender Systems: A Netflix Case Study

• Harald Steck Netflix
• Linas Baltrunas Netflix
• Ehtsham Elahi Netflix
• Dawen Liang Netflix
• Yves Raimond Netflix
• Justin Basilico Netflix

Deep learning has profoundly impacted many areas of machine learning. However, it took a while for its impact to be felt in the field of recommender systems. In this article, we outline some of the challenges encountered and lessons learned in using deep learning for recommender systems at Netflix. We first provide an overview of the various recommendation tasks on the Netflix service. We found that different model architectures excel at different tasks. Even though many deep-learning models can be understood as extensions of existing (simple) recommendation algorithms, we initially did not observe significant improvements in performance over well-tuned non-deep-learning approaches. Only when we added numerous features of heterogeneous types to the input data, deep-learning models did start to shine in our setting. We also observed that deep-learning methods can exacerbate the problem of offline–online metric (mis-)alignment. After addressing these challenges, deep learning has ultimately resulted in large improvements to our recommendations as measured by both offline and online metrics. On the practical side, integrating deep-learning toolboxes in our system has made it faster and easier to implement and experiment with both deep-learning and non-deep-learning approaches for various recommendation tasks. We conclude this article by summarizing our take-aways that may generalize to other applications beyond Netflix.

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Netflix Case Study

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Netflix Case Analysis

Netflix, Inc. is an American media-services provider headquartered in Los Gatos, California, founded in 1997 by Reed Hastings and Marc Randolph in Scotts Valley, California. The company’s primary business is its subscription-based streaming OTT service which offers online streaming of a library of films and television programs, including those produced in-house. As of October 2018, Netflix has 137 million total subscribers worldwide, including 58.46 million in the United States. It is available worldwide except in Mainland China, Syria, North Korea, and Crimea. The company also has offices in the Netherlands, Brazil, India, Japan, and South Korea.

Netflix’s initial business model included DVD sales and rental by mail, but Hastings jettisoned the sales about a year after the company’s founding to focus on the DVD rental business. Netflix expanded its business in 2007 with the introduction of streaming media while retaining the DVD and Blu-ray rental service. The company expanded internationally in 2010 with streaming available in Canada, followed by Latin America and the Caribbean. Netflix entered the content-production industry in 2012, debuting its first series Lilyhammer.

Netflix has greatly expanded the production and distribution of both film and television series since 2012, and offers a variety of “Netflix Original” content through its online library. By January 2016, Netflix services operated in more than 190 countries. Netflix released an estimated 126 original series and films in 2016, more than any other network or cable channel. Their efforts to produce new content, secure the rights for additional content, and diversity through 190 countries have resulted in the company racking up billions in debt: $21.9 billion as of September 2017, up from $16.8 billion from the previous year.[16] $6.5 billion of this is long-term debt, while the remaining is in long-term obligations. In October 2018, Netflix announced it would raise another $2B in debt to help fund new content.

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