Netflix, Hulu, Amazon Prime, and the like have become a lifesaver for many people during coronavirus. Indeed, how else are you supposed to spend time cooped up in the same place for a long time? Reading, video gaming, zooming or binge-watching another season of Stranger Things, that’s what you can do if you want to stay sane while being confined to your place.
And, not surprisingly, popular streaming services have experienced a great surge of new subscribers, TV watching, and time spent on these services during the lockdown. It seems that the popularity of Netflix, Apple TV+, and the rest will only grow. But how do they keep viewers glued to the screens and how do they satisfy their audiences with the right products? Data science is the answer. Netflix is an excellent case study to showcase the power of AI and how it can be applied to benefit your audience. Let’s see how it works for one of the largest downstream traffic companies on the internet—Netflix.
The History of Netflix Data Science and Its Recommendation System
In the late ‘90s, when Netflix just started as a DVD sales & rental company, the only things they could analyze were the titles of movies and TV shows that were ordered by their customers, the shows and films that were in their DVD queues, and movie star ratings from 1 to 5. But that was not enough. This is why in 2007-2009, Netflix had a one-million dollar-prize public competition to improve their existing recommendation system of five-star rating.
BellKor’s Pragmatic Chaos team managed to boost Netflix predicting algorithm by 10.05%. That was a huge advancement not only for the company that would give it a major boost to improve its streaming service but the world in general.
From 2012 Netflix started to produce its original content and created such amazing shows and movies as War Machine, Narcos, House of Сards, Orange is the New Black, and many others. In 2016, the company went global,meaning that now viewers could subscribe to Netflix from different countries around the globe.
Nowadays, Netflix has become the most popular streaming service in the world due to its recommendation system capacity to personalize content and satisfy users’ preferences and needs with the help of data they have accumulated within many years and various viewers. You can not only decide on the devices they are using and where they are watching a show, you can also easily figure out how much time they spend on the streaming service, what kind of content they prefer, and what they are likely to choose next. In addition, the company uses both demographic data and behavioral information.
The personalized user experience starts from the device you prefer—from a smart TV to Xbox and PlayStation, to your homepage. You might see personalized images of TV shows and movies, recommended shows on the main screen, which is just an assembly of different algorithms. For instance, you’ll see personalized rows with recommended TV series as Netflix algorithms will show the best options. Moreover, the recommendation system appeals not only to your taste, but to the taste of your entire household, which provides diverse experiences to everyone using the streaming service.
Using accumulated data, Netflix gives its members truly unique recommendations as they go beyond simple categorization of genres like thrillers or comedies that you’d prefer, but pinpoint more exact preferences like ‘90s thrillers that you may enjoy.
The company also utilizes and optimizes ranking algorithms to provide viewers with the best content that will suit their preferences and needs.
What Does Netflix Use for Big Data?
Millions of users from dozens of countries are watching Netflix right now. And it means that more and more clusters of data are coming to the company that have to be stored, analyzed, and used to yield spectacular results. The streaming service provider uses different kinds of information — ratings from users (several billions stats), social media data, search terms, metadata, video queue data, critics reviews, box office performance, demographics, locations, languages, just to name a few. And to deal with that data, Netflix has fully migrated to AWS Cloud and here are the main technologies that the company uses to deal with large chunks of data.
Amazon S3 or Amazon Simple Storage Service is a place to store data for better scalability, availability, and performance.
Apache Kafka is a distributed stream-processing system.
Apache Hive is the most used data warehouse.
Apache Spark is a big data processing analytics engine.
Netflix also uses Python, R, Tableau, Sting, Presto, Pandas, TensorFlow, and many other technologies. And this is how Netflix uses big data.
What Netflix Machine Learning Algorithms Does the Company Use?
Netflix has used a lot of models to build the recommendation and personalization engine it has today. And this is just a fraction of what is being used at the company today.
- Clustering algorithms from k-means to affinity propagation
- Logistic regression
- Linear regression
- Elastic nets
- Matrix factorization
- Singular value decomposition
- GBDTs or Gradient-boosted decision trees
- Random forests
- Restricted Boltzmann machines
- Markov chains
- Latent Dirichlet allocation
- Deep learning
All in all, Netflix uses data science in all business areas from marketing and localization to user acquisition, quality control, and streaming.
It’s hard to imagine the vastness of tastes of different people throughout the globe. Just imagine that Netflix alone has 193 million subscribers in more than 190 countries. All these members have various tastes in movies and TV shows and the Netflix recommendation algorytm’s main task is to provide each and every one of them with personalized content.
If you’re looking to implement Data Science into your business, you can make use of LITSLINK data science consulting services.