Netflix: Leveraging Big Data to Predict Entertainment Hits
| Case Code: ITSY075 Case Length: 18 Pages Period: 2009-2013 Pub Date: 2013 Teaching Note: Not Available |
Price: Rs.500 Organization: Netflix Industry: Service, DVD Rental, Online Video Streaming Services Countries: US, Global Themes: Information Technology, Big Data, Market ResearchBusiness Intelligence |
Abstract Case Intro 1 Case Intro 2 Excerpts
Excerpts
Big Data at Neteflix
Netflix tracked each search that a viewer made, each good or bad rating attributed by a viewer to what s/he had just seen, besides the ratings data from third-party providers such as Nielsen. These were in addition to the location data, device data, remarks on the social media, etc. Netflix was also aware of what a subscriber most probably viewed on each of his/her devices like mobile or laptop or tablet and what viewers belonging to a specific ZIP code preferred viewing on their laptops on a Saturday night...
Shift From Own Data Centers to Amazon Web Services
According to observers, Netflix's big data capabilities received a boost with the gradual shift, beginning in 2009, of the company's computing infrastructure from its own data centers to the cloud , by engaging the services of Amazon Web Services (AWS), the cloud computing subsidiary of Amazon . Netflix's search and recommendation engine and its streaming servers started utilizing AWS's cloud infrastructure. The principal reason for Netflix's shift was that its pace of setting up data centers could not catch up with the increase in demand for its streaming services....
The Need for Original Content at Neteflix
Prior to March 2011, Netflix had had no plans of becoming the first avenue where a TV show or a movie was beamed. Rather, it concentrated on shows and movies having their second, third, or even fourth runs. In other words, Netflix dealt with content subsequent to its release either in theaters or on television. And occasionally, Netflix could lay its hands on content after it played in theaters and subsequently on television for quite a while...
Enter the 'House of Cards'
Netflix made viewers label films and TV shows with hundreds of metadata descriptors to comment, among others, about the actors, the screenplay, the overall appeal, and the category. Previously, these labels were employed to suggest other shows available on the service, fundamentally constructing outlines of individual viewers according to their choices. However, Netflix began gradually ordering original content as it was aware of what subscribers desired prior to their being in the know...
How Neteflix Benefited from Big Data
According to industry observers, instead of creating shows based on viewers' response to trial episodes akin to the route taken by conventional stations, the video-streaming business enabled Netflix to assess the viewing patterns and choices of its subscribers. It was aware of what the subscribers viewed, the time at which they viewed the shows, and even when they paused to take a recess. By incorporating the data into an algorithm, it could forecast their tastes and what would keep them hooked...
Looking Ahead
Netflix was to debut four more original shows in 2013 including the shows “Hemlock Grove” and “Arrested Development”. The latter would be a remake of ‘Arrested Development', a popular television show. With Netflix tasting success with House of Cards, Amazon and Microsoft were quick off-the-block in announcing their entry into the creation of original content. Amazon was also funding, on a trial basis, six comedy shows...
Exhibits
Exhibit I: Netflix's Financial Performance (2005-2012)
Exhibit II: What is Big Data?
Exhibit III: Netflix's Basic Ranking Model
Exhibit IV: Visual Representation of the Testing Conducted by Netfllix
Exhibit V: Netflix's Software Architecture
Exhibit VI: Netflix's Cloud-based Hadoop Architecture
Exhibit VII: Will Netflix Survive the Original Programming Game?
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