Netflix: Leveraging Big Data to Predict Entertainment Hits

 
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This case study was compiled from published sources, and is intended to be used as a basis for class discussion. It is not intended to illustrate either effective or ineffective handling of a management situation. Nor is it a primary information source.



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Case Details:

Price:

Case Code : ITSY075 For delivery in electronic format: Rs. 500;
For delivery through courier (within India): Rs. 500 + Rs. 25 for Shipping & Handling Charges

Themes

Information Technology/ Big Data/ Market Research/ Business Intelligence
Case Length : 18 Pages
Period : 2009-2013
Pub Date : 2013
Teaching Note : Not Available
Organization : Netflix
Industry : Service/ DVD rental/ Online video streaming services
Countries : US; Global

Abstract:

This case is about the big data capabilities developed by Netflix, the largest player in USA in video streaming services with a global streaming subscriber base of around 33 million. Netflix had complete details of the viewing patterns of each of its subscribers, including aspects such as when they hit the pause button and whether they switched off before the credits rolled. It had deployed this data to come up with recommendations for each of its subscribers. According to experts, Netflix's data mining competencies got a boost when the company shifted its information technology infrastructure to the cloud - this gave Netflix the flexibility to scale up - and had opted for the Hadoop architecture.

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Netflix also employed the huge dataset it had about the viewing patterns of its subscribers to get into original programming. It was so confident about the popularity of the original version of the television show “House of Cards” and of the director and the lead actor of the show's remake among its subscribers, that it bought the exclusive licensing rights of the show's remake for US$100 million. The show, as predicted by Netflix's executives, proved to be a success. Netflix was to come up with more original content shows by relying on its assessments of the viewing patterns of each of its subscribers. The case also discusses some of the concerns that experts had about Netflix's big data technologies infringing on the privacy of its subscribers. Experts also raised concerns about the outages faced by Amazon Web Services, the vendor of cloud computing services to Netflix which had resulted in the latter's site being down three times. Industry observers were apprehensive that players such as Netflix would constrain artistic creativity by employing big data to come up with predictable content merely on the basis of the past viewing patterns of their audiences. This case is meant for MBA/MS students as a part of the Information Technology and Systems curriculum. It can also be used in a Marketing Research/ Strategy curriculum.

Issues:

Understand the advantages that organizations can derive from employing big data capabilities.
Understand the kind of information technology infrastructure that companies need to put in place to create intricate databases of their customers’ consumption patterns.
Appreciate the processes/ mechanisms that go into developing big data capabilities.
Analyze the challenges that organizations have to confront when they deploy big data tools.
Discuss and debate the ways in which Netflix can avert privacy issues in assessing its subscribers’ viewing patterns.
Discuss and debate whether deploying big data to create original programming actually results in restricting the horizons of creativity.

Contents:

  Page No.
Introduction 1
Background Note 2
Big Data at Neteflix 2
Shift From Own Data Centers to Amazon Web Services 4
The Need for Original Content at Neteflix 5
Enter the 'House of Cards' 6
How Neteflix Benefited from Big Data 8
Looking Ahead 9
Exhibits 10

Keywords:

Big data; Predicting; Big data capabilities; Data mining; Business metrics; Performance; Viewing patterns; Data inventory; Algorithm; Product-attribute datasets; Netflix’s basic ranking model; A/B testing; Software architecture; Cloud computing; Hadoop architecture; Original programming; Privacy; Netflix; Amazon Web Services

Introduction - Next Page>>


 

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