With nearly 74 million US and Canada-based subscribers and 200 million global subscribers, Netflix is the leader in the streaming arena.
Netflix was founded in 1997 as a movie rental service. They used to ship DVDs to customers by mail, and in 2007, they launched their online streaming service. The rest is history. Currently, the company’s market cap is well beyond $200 billion and has come a long way.
What’s the secret behind their phenomenal success?
Some might say they can innovate, while others might say they are successful only because they were the first. However, not many know that the biggest reason behind Netflix’s success is that it started leveraging ML before its competitors did.
Get Best Machine Learning Certifications online from the World’s top Universities – Masters, Executive Post Graduate Programs, and Advanced Certificate Program in ML & AI to fast-track your career.
But before we talk about how Netflix has been using machine learning to get ahead in the industry, let’s first get ourselves familiar with machine learning:
What Is Machine Learning?
Machine learning refers to the study of computer algorithms that improve automatically through data and experience. They execute tasks and learn from their execution by themselves without requiring human intervention.
Machine learning has numerous applications in our daily lives, such as image recognition, speech recognition, spell-checks, and spam filtering.
Apart from Netflix, there are plenty of other companies and organisations that use machine learning to enhance their operations. These include Amazon, Apple, Google, Facebook, Walmart, etc.
What Things Does Machine Learning Affect In Netflix?
You’d be surprised to know how deep machine learning runs through Netflix’s infrastructure. From user experience to content creation, machine learning has a role to play in nearly every Netflix aspect.
You can find the impact of machine learning in the following areas of Netflix:
When you open Netflix, you are first greeted with your homepage, filled with shows you watched and shows Netflix recommends you to watch.
Do you know how Netflix determines what shows it should recommend to you?
You guessed it – they use machine learning.
Netflix uses an ML technology called a “recommendation engine” to suggest shows and movies to you and other users. As the name suggests, a recommendation system recommends products and services to users based on available data.
Netflix has one of the world’s most sophisticated recommendation systems. Some of the things their recommendation systems consider to suggest a show to you are:
- Your chosen genres (the genres you choose while setting up the account).
- The genre of the shows and movies you have watched
- The actors and directors you have watched.
- The shows and movies people with a similar taste to yours watch.
There are probably a ton of other factors Netflix uses to determine which shows to recommend. Their goal: to keep you stuck to the screen as long as possible.
The thumbnails you see for a show or movie aren’t necessarily the ones your best friend sees when they scroll through their homepage.
Netflix uses machine learning to determine which thumbnails you have the highest chance to click on. They have different thumbnails for every show and movie, and their ML algorithms constantly test them with the users.
The thumbnails that get the most clicks and generate the most interest get preference over those that don’t get clicks.
Machine learning enables Netflix to give personalised auto-generated thumbnails for every show and movie. Their chosen thumbnail depends on your preferences and watches history to ensure they have the highest chance of getting clicked on.
For example, Riverdale can have two thumbnails, a serious mystery one and a romantic one. The one you’ll see would depend on which genre you prefer the most. Clicking on a thumbnail increases your chances of watching the show or movie. This is why Netflix focuses heavily on showing you the thumbnail you’d like the most.
The Streaming Quality
When you’re watching a show, what’s the worst thing that can happen? Buffering.
Buffering can be a huge issue no matter what streaming service you use. People tend to immediately exit the platform after waiting for a few seconds because of buffering. Netflix is well aware of this issue.
Buffering can ruin a customer’s experience and make it difficult for Netflix to get their valuable time back. Moreover, the customer might switch platforms and start watching something on their competitors’ platforms, such as Hulu, Amazon Prime, HBO MAX or Disney+.
They have implemented many solutions to counter this problem, one of which is machine learning.
Machine learning enables them to keep a close eye on their subscribers’ usage of their services. These algorithms predict their users’ viewing patterns to determine when most people use their service and when this number is the lowest.
Then, they use this information to cache regional servers closest to the viewers, ensuring that no buffering (or minimal buffering) occurs when those users use the service.
The Location of a Show (or movie)
Netflix isn’t just a streaming platform for showing movies and shows. They are also a production company. Producing unique content helps to increase their revenue and profitability.
So far, this strategy has worked amazingly well because, over the years, the amount of Netflix-original content has increased substantially. In 2019, they produced 2,769 hours of original content, 80% more than the previous year.
Every show requires a shooting location. Netflix uses machine learning to determine which shooting location would be perfect for a particular show or movie.
They employ machine learning algorithms to check the cost & schedules of the crew & cast, shooting requirements (city, desert, village, etc.), weather, the possibility of getting a permit, and many other relevant factors. Machine learning enables them to quickly check and analyse these numerous factors, ensuring they quickly find a suitable shooting location.
Probably the biggest application of machine learning in Netflix is in content creation. Unlike most production companies, Netflix behaves as a tech enterprise. They don’t create content solely based on the creativity of a few writers or content creators. Instead, they use machine learning algorithms to conduct market research and find which type of content would be the most suited for a particular market segment.
ML algorithms help them stay ahead of market trends and create shows and movies for everyone. Their approach has helped them substantially as eight out of the top 10 most popular original video series from streaming providers in the US are by Netflix.
Their research helps them penetrate different market segments. For example, the content preference of teenagers would differ drastically from that of married couples. Through thorough market research and ML implementation, Netflix can successfully satisfy a diverse audience base’s content requirements.
The Secret Is Out
Now you know the secret behind Netflix’s phenomenal success. They use the latest technologies like machine learning and data science in almost every avenue of their business.
This helps them stay ahead of their competition and offer a better user experience. It’s a prominent reason why they are the biggest streaming service provider in the US.
What do you think about Netflix and its use of machine learning? Which machine learning application did you find the most intriguing?
With all the learnt skills you can get active on other competitive platforms as well to test your skills and get even more hands-on. If you are interested to learn more about the course, check out the page of Master of Science in Machine Learning & AI and talk to our career counsellor for more information.
What machine learning algorithm does Netflix use?
Netflix uses their most valued and successful algorithm NRE - Netflix Recommendation Engine to show user content based on their likes and what they watch.
How does Netflix use deep learning?
Netflix uses a deep learning algorithm to understand the users likes and dislikes and then use this data and evaluate what content the user may like and recommend it to them.