There could be certain degree of subjectivity in the choice of these companies. However, the selection of the top 25 is solely based on their investment and pedigree in the field of data science.
Data Science & AI application in Netflix
Netflix could quickly discern what kind of content people wanted due to its direct interaction with its customers and a quantity of data on how audience members interact with their content.
Netflix gained 2 million new customers in the United States and 1 million new subscribers globally in just three months after launching House of Cards.
Netflix has a big user base of over 148 million customers, which provides it a huge advantage when it comes to data collection. The focus then shifts to the metrics – Content from the current year was viewed; The device that was used to view the material; Depending on the device, the nature of the content watched changed. It does searches on its platform; Content portions that were re-watched; whether or not content was paused; Information about the user’s location. The time of day and week when content was viewed, as well as how it influenced the type of content viewed. Netflix uses data in a variety of ways once it has been collected.
Netflix’s recommendation system is set up in such a way that it concentrates on delivering each customer exactly what they want via a customised content ranker that arranges each Netflix user’s library based on personal information acquired about them. You may leverage big data to ensure that material given to each user is influenced by the user’s specific behaviour and interaction with your brand, similar to how Netflix does, ensuring that each user’s content experience is unique.
The most recently seen content is ranked based on whether or not users are expected to return. to keep viewing or re-watching, or whether users stopped watching because the content was not appealing to them. This is crucial to ensuring that Netflix users aren’t bored; it’s easy to want to keep pushing the same content because you’ve invested in it. It’s best to relegate the material and offer something more intriguing if user activity suggests a lack of interest.
A content affinity algorithm suggests content that is similar to what the user has just seen. It’s worth noting that consumers are more inclined to desire to consume information that’s comparable to what they just finished seeing.