Netflix Data Analytics
Netflix is one of the biggest tech companies in the world. They have created a very complex, widespread and popular platform which allows users to watch whatever they want, when they want. The most successful companies are those that aim to provide the best experience for their customers. Netflix has redefined entertainment with their fast-growing online streaming platform, making themselves one of the most loved smart streaming services in the market.
Have you ever wondered how data analytics can benefit your business? Do you agree that a predictive approach for decision-making is necessary for the success of the business in modern conditions? Do you want to get more information on how Netflix has implemented a successful data analytics approach to improve the quality of movies? If yes, read this insightful case study example to learn more.
Advantages of Data Analytics in Netflix
Netflix started its video streaming from the year 2007, they had understood the power of data and invested on data analytics. Their innovation was mainly focused on 2 things:
A) Data Analytics
By implementing data analytics into their video streaming, they had been able to increase the viewership of the business by 31%, which in turn had led to producing more original content. This provides us with a thought that, it is where we are going that matters, but not every step is right and helpful. Netflix had experienced complete failure as well at the beginning, but they had understood the need of data analytics and made their way through it and emerged successful.
Eg - Netflix - House of Cards which started on Feb 1, 2013 was licensed by Netflix based on the data analytics done by Netflix. They have observed that viewers who liked house of cards where fan of Kevin films and liked the movies directed by David Finche.
B) Logistics
Netflix's growth and continued expansion have come from a comprehensive approach to solving their logistics problems. They've looked at a variety of factors, from reducing fixed costs to streamlining the way video is delivered, in order to reduce the amount of time it takes to ship DVDs out. Within the past year Netflix has made some impressive progress with this, which demonstrates that they're dedicated to solving these problems. With these efforts, I don't see Netflix's logistical issues slowing them down anytime soon.
Ultimately, logistics are largely a numbers game. With more and more subscribers joining Netflix, it's going to take some careful planning on their part to ensure that they're ready to support the streaming-service load. Netflix is a company that is known for working closely with data and we can only hope that their analytics are able to help them gain an edge.
Netflix is a data analytics pioneer. They have based their product decisions on the data they collected and analyzed. The company is constantly evolving and making sure they are listening to customers and making the right product decisions. This data driven strategy has led Netflix to 100 million subscribers by 2017.
In order to know their consumers, the ideology of the company to provide satisfaction revolves around the questions such as :
- What their customer watches ?
- Whats the average watch time of their consumers ?
- What content do they watch?
- Which country/ region has the most users?
- What time consumer watch their favourite shows/movies ?
- What are the top shows/movies consumers love to watch.
- What are the various age of people subscribed to Netflix?
Although it may seem difficult to make sense of all of the data in a standard Netflix environment, with the right data analytics software, you can find patterns in your customer data that you never even knew existed. This helps your company predict what customers will want next, and if you are providing that product early enough, your company will stand a greater chance of increasing its market share.
Recommendation system
Netflix spends mainly on the Ai to make content spending decisions It shows that Ai Saves $ 1 billion per year that improves its algorithm and uses them to reduce the human intervention in programming decision. Netflix operates its own CDN, which helps to save bandwidth cost content being accessed from the same region across multiple consumer groups. Netflix have data backup of content on google cloud storage in case of any mishandling of data.
Netflix employs a two-phase recommendation system. "User-based collaborative filtering" is employed on the front end to rank the current breadth of Netflix's inventory (approximately 12,700 titles as of 2014), where "user-based collaborative filtering" relies largely on past viewing behaviors to predict what a specific user may enjoy. If titles predicted to be enjoyed fall outside a user's typical profile of preferences, the second phase kicks in: "item-based collaborative filtering."
Problem resolved using data science
Back in 2006, The Netflix Prize Competition was a friendly contest among data scientists to improve the accuracy of the company's movie recommendation algorithm. The challenge was announced in October 2006 at the NIPS Conference on Machine Learning, and the grand prize of $1 million was paid out in November 2009.
The competition was based on a dataset of 100 million ratings made by 480,000 users on 17,770 titles. The goal was to predict how many stars (on a scale from one to five) a user will give to a movie she hasn't seen yet. For example, if you've rated one hundred movies but haven't seen "Pulp Fiction," then Netflix wants to know whether you'd give it four stars or three stars or something else. The winner was determined by how accurately his or her predictions matched those made by humans who have already seen those movies.
At the end of competition, the Belkor team presented their solution & increased the accuracy of prediction by 10.06%. For this solution, they made use of "K Nearest neighbor algorithm" for post processing of the data. Then they implemented the factorization model which is popularly known as "[Singular Value Decomposition (geeksforgeeks.org/singular-value-decomposit..)" for providing an optimal dimensional embedding to its users. They also made use of "Restricted Boltzmann machine" for enhancing the capability of the collaborative filtering model. A linear combination of these 2 algorithms reduced the RMSE to 0.88.
After Netflix overcoming the challenges, Netflix got their winning algorithm, that was part of its recommendation system.
Improving Personalization
The Netflix improves the personalization experience. Netflix has always been known as a leader in personalization. They know their customers better than anyone else does, which allows them to provide them with exactly what they want. This also means that when something goes wrong or when there are changes in the market or technology, Netflix can quickly adapt and respond accordingly.
Netflix has recently announced that it will be making changes to its algorithm so that it can show users more relevant content based on what they are watching now or what they watched recently. This new feature will allow Netflix users to see more shows from different genres and countries, thus allowing them to discover new things and explore new worlds with ease.
However, with the presence of various ranking algorithms, it is often difficult to accommodate all of them and test their performance simultaneously and decided to innovate its algorithmic process.
Netflix implemented the[interleaving technique (psychology.ucsd.edu/undergraduate-program/u..) in order to speed up the experimental process to identify the best ranking algorithm and it is based on the idea that when a user views a movie, he or she may not watch it from beginning to end but rather jumps from one scene to another.
Netflix provides its users with personalized recommendations based on their preferences. This technique is applied in 2 stages:
To provide the best page ranking algorithm, and to provide personalized recommendation to its users
Unlike the traditional A/B testing, where the 2 groups of viewers are exposed to the 2 ranking algorithm
Netflix, makes use of interleaving to blend the ranking of algorithm A and B.
Thus, Netflix users are provided with enriched content using interleaving technique that is highly sensitive towards ranking the algorithm quality.
Conclusion
Users are ultimately the creators of data that Netflix is using to improve its service. Each user has his or her own unique taste, and big data helps companies understand this better. Data from social media, word-of-mouth, and a number of different sources enables companies like Netflix to generate more finely-tuned content for users. The future of streaming entertainment is likely to lean heavily on big data analysis as well.
This shows that Netflix has lots of data on its users, and it allows the company to optimize their experience. It also shows how user information allows a business to tailor products and services to meet the needs and wants of their consumers, a tactic which will continue to be used by online companies moving forward. Furthermore, with Netflix's ability to use consumer data to create content which meets consumer demands, it gives the company an incredible competitive advantage over its competitors.