What is the Data Science Strategy of Netflix?

The case study of 'House of Cards' in itself is self-explanatory how Netflix uses data science to strategize its business. Netflix uses big data to conceive successful content.

Due to the direct relationship Netflix has with its subscribers, as well as an abundance of data on how audience members interact with their content, the company could easily determine what kind of content people wanted.

Within three months of introducing House of Cards, Netflix added 2 million subscribers in the US and 1 million additional subscribers internationally.

When it comes to gathering data, Netflix's huge user base of over 148 million subscribers gives it a massive advantage. It then focuses on the metrics – Date content was watched; The device on which the content was watched; How the nature of the content watched varied based on the device; Searches on its platform; Portions of content that got re-watched; Whether content was paused; User location data; Time of the day and week in which content was watched and how it influences the kind of content watched. Once data has been gathered, Netflix uses this data in a lot of ways.

This recommendation system is designed in such a way that Netflix focuses on giving each user just what the user wants through a personalized content ranker that organizes each Netflix user's collection based on the personal information collected about the user. Like Netflix, you can use big data to ensure that content delivered to each user is influenced by the user's personal activity and interaction with your brand, ensuring the content experience is unique for every user.

Recently viewed content is sorted based on an analysis of whether users are expected to continue watching or re-watching, or whether users stopped watching due to not finding the content interesting. This is key in ensuring that Netflix doesn't bore its users; it can be tempting to want to keep promoting the same content since you've invested in it. If user activity indicates a lack of interest, it is better to relegate the content and offer something more interesting.

A content affinity algorithm recommends content similar to content a user just watched. It is important to note that people are more likely to want to consume content similar to the one they just consumed.