Unlike most colleges, the MBA program in Business Analytics and Data Science at Bengal Institute of Business Studies has a more practical approach when it comes to teaching students. At BIBS, we focus on granting the students a more hands-on approach to learning and with the help of case studies and corporate sessions.
The students of our MBA in Data Science program do a real-time case study and one such case study was done on Netflix’s use of data in order to better and improve their recommendation system for consumers. Based out of America, Netflix is a media service provider that provides consumers with streaming television shows, films and in-house productions with the means of a subscription model.
Initially a DVD rental service, Netflix was started in 1998 which mostly relied on third party postal services to relay their DVDs to the consumers. Even though they stopped selling DVDs after one year since its inception, they continued their rental services. In 2010, they started their online streaming service which became a huge success and required the implementation of many algorithms.
One of these algorithms is their recommendation system that Netflix uses to provide suggestions to their consumers. The ways in which a recommendation system works is that it takes data and information about the user and uses it as the input.
There are two ways in which Netflix's recommendation system works and they are as follows -
1. Content based recommendation systems - With a content based recommendation system, it takes into account the background knowledge about the product and the information about the consumer. Based on this data that is collected, Netflix offers similar suggestions based on the content that the consumer has previously viewed.
For example, as a consumer, if you have watched a film from the genre of “comedy” then you will be recommended similar films belonging to the same genre.
2. Collaborative filtering Recommendation Systems - In this system, suggestions are provided based on the homogeneity of consumer profiles, and does not depend on their knowledge of the product. It relies on the assumption that what users have liked in the past, they will also like in the present. It is quite different from a content based recommendation system. For instance, if one person enjoys the genres of crime, horror and thriller, and another person enjoys horror, thriller and action films, then the assumption is made that the first person will also enjoy action films and the latter will enjoy crime films.
Netflix popularly uses a combination of both these recommendation systems known as a Hybrid Recommendation System, in order to suggest content to its consumers all across the globe. These recommendation systems implement the data that is collected from consumers, which is then processed and analysed before suggesting content for its customers.
For students who wish to work in the ever-evolving field of data, ideally pursue an MBA in Data Science. Through the medium of this case study, the students of the Business Analytics and Data Science MBA program at BIBS were able to understand the data received by Netflix and how it offers content suggestions with the use of their recommendation systems. At BIBS, students have a more practical approach to learning which allows them to gain industry experience which prepares them for their future. The college also conducts corporate sessions with prominent business leaders which gives them an edge over other students in other colleges.