Decoding Netflix: How Algorithms Steer Your Streaming Experience

Netflix has revolutionized how we consume entertainment. If choosing your next show on Netflix seems too difficult, the platform has launched a new feature where it will choose for you. With a vast library of content, the streaming giant relies heavily on sophisticated algorithms to personalize the viewing experience for each of its millions of subscribers. These algorithms, constantly learning and adapting, play a crucial role in determining what shows and movies are presented to you, influencing your viewing choices in ways you might not even realize. This article delves into the inner workings of Netflix's recommendation system, exploring how it uses data, machine learning, and a touch of human curation to guide your journey through the world of streaming entertainment.

The Rise of Predictive Algorithms

Netflix isn't alone. Predictive algorithms, the use of machine learning algorithms and big data to generate highly tailored predictions for people, are being used across sectors, including higher education. Netflix leverages a predictive algorithm to suggest viewing options it thinks you will enjoy the most. Based on viewing and ratings data, among other things,. These algorithms analyze vast amounts of data to predict user preferences and behavior. In the context of streaming, this means suggesting movies and shows that you are likely to enjoy based on your viewing history, ratings, and other factors.

The Three Pillars of Recommendation

To understand how Netflix's algorithm works, it's helpful to think of it as a three-legged stool. "The three legs of this stool would be Netflix members; taggers who understand everything about the content; and our machine learning algorithms that take all of the data and put things together," says Todd Yellin, Netflix’s vice president of product innovation.

1. User Data: The Foundation

The first leg is user data. Netflix collects a wealth of information about its subscribers, including:

What they watch: This is the most obvious and crucial piece of data. Netflix tracks every show and movie you watch, how long you watch it for, and whether you finish it.
What they watch after, what they watch before, what they watched a year ago, what they’ve watched recently and what time of day
Ratings: While Netflix has moved away from star ratings, it still uses the thumbs up/thumbs down system to gauge your explicit preferences. “Explicit data is what you literally tell us: you give a thumbs up to The Crown, we get it,” Yellin explains.
Search history: What you search for on Netflix provides valuable insights into your interests.
Device and time of day: The device you use to watch Netflix and the time of day you watch can also influence recommendations.
Implicit data: "Implicit data is really behavioural data. You didn’t explicitly tell us 'I liked Unbreakable Kimmy Schmidt', you just binged on it and watched it in two nights, so we understand that behaviourally. The majority of useful data is implicit."

With over 250 million active profiles, Netflix has a massive dataset to work with.

Read also: Movies for Student Success

2. Content Tagging: Understanding the Catalog

The second leg is content tagging. Netflix employs dozens of in-house and freelance staff who watch every minute of every show and movie on the platform and tag it with various attributes. The tags they use range massively from how cerebral the piece is, to whether it has an ensemble cast, is set in space, or stars a corrupt cop. These tags go beyond simple genres and delve into more nuanced aspects of the content, such as:

Genre and subgenre
Themes and topics
Setting and time period
Actors and directors
Mood and tone
Character traits

3. Machine Learning: The Brains of the Operation

The third leg is machine learning. Netflix uses sophisticated machine learning algorithms to analyze the user data and content tags and identify patterns and relationships. These algorithms learn what types of shows and movies you tend to enjoy based on your past behavior and then recommend similar content. "We take all of these tags and the user behaviour data and then we use very sophisticated machine learning algorithms that figure out what’s most important - what should we weigh," Yellin says. "How much should it matter if a consumer watched something yesterday? Should that count twice as much or ten times as much compared to what they watched a whole year ago? How about a month ago? How about if they watched ten minutes of content and abandoned it or they binged through it in two nights? How do we weight all that? That’s where machine learning comes in. What those three things create for us is ‘taste communities’ around the world. It’s about people who watch the same kind of things that you watch." More than 80 per cent of the TV shows people watch on Netflix are discovered through the platform’s recommendation system. That means the majority of what you decide to watch on Netflix is the result of decisions made by a mysterious, black box of an algorithm.

How the Algorithm Works in Practice

The Netflix algorithm uses a variety of techniques to personalize your viewing experience. Here are some key aspects:

Taste Communities: Viewers fit into multiple taste groups - of which there are "a couple of thousand" - and it’s these that affect what recommendations pop up to the top of your onscreen interface, which genre rows are displayed, and how each row is ordered for each individual viewer.
Personalized Recommendations: Based on your viewing history and taste communities, Netflix recommends shows and movies that you are likely to enjoy.
Genre Rows: The genres displayed on your homepage are tailored to your interests.
Row Ordering: The order in which shows and movies are displayed within each row is also personalized.
Thumbnail Personalization: Even the thumbnail images used to promote shows and movies are personalized to appeal to your tastes. The same movie might appear with different thumbnail images for different users, emphasizing romance for one viewer and action for another.
Breaking Preconceived Notions: Netflix uses machine learning and algorithms to help break viewers’ preconceived notions and find shows that they might not have initially chosen. To do this, it looks at nuanced threads within the content, rather than relying on broad genres to make its predictions. This explains how, for example, one in eight people who watch one of Netflix's Marvel shows are completely new to comic book-based stuff on Netflix.

The Algorithm's Impact

Netflix claims that this customization algorithm is responsible for the low churn rate compared with their competitors (<10% for Netflix, compared with over >15% for Disney+, and >20% for Apple + and HBO Max). Churn is one of the most sensitive inputs for the valuation of a subscriber-based business and thus this customization algorithm is a huge part of Netflix’s success.

The algorithm's influence extends beyond mere recommendations. It also impacts:

Content Creation: Based on the data collection and use of ML/AI predictive abilities, Netflix can size audiences for types off content. Netflix can size audiences for types off content [6]. The biggest challenge facing Netflix is the threat from new entrants ranging from Apple+, Disney+ and HBO Max, to Amazon Prime and Hulu. The streaming wars are a reality. This is especially a concern when considering that HBO and Disney are consistently some of the premier content creation studios. However, Netflix has entered the content creation game, and is using ML to be more efficient with content creation investments. The biggest opportunity Netflix has at present is their strong retention and brand name.
Streaming Quality: Another example is using historical streaming data to better under predict and manage streaming demand which has the effect of increasing streaming quality and improving the customer experience [4][5]. What that means in practice is that Netflix caches data in servers closest to the users who likely will want it to improve load times.

The Human Element

While algorithms play a central role, Netflix also recognizes the importance of human curation.

Content Quality: Netflix has created a supervised quality control algorithm that passes or fails the content such as audio, video, subtitle text, etc. based on the data it was trained on. If any content is failed, then it is further checked by manual quality control to ensure that only the best quality reached the users. After all, you probably wouldn't watch Stranger Things on Netflix if the subtitles were wrong or the audio was lagging behind the video.

The Billion-Dollar Algorithm

The effectiveness of Netflix's recommendation system has a significant impact on the company's bottom line. Back in 2016, when Netflix had about 80 million subscribers, company executives valued this algorithmic matchmaking at $1 billion per year in retained customers. A decade later, the streaming giant now has 325 million subscribers worldwide. While Netflix hasn't updated that figure publicly, the math suggests its recommendation system has become one of the most valuable pieces of software in entertainment. Netflix $NFLX gives itself 90 seconds. That's how long research shows the average subscriber will browse before losing interest and drifting to a competitor. In that window, the company's recommendation engine must surface something compelling from a catalog of thousands. Get it right, and the subscriber stays. Get it wrong often enough, and they cancel.

The Future of Streaming

As Netflix continues to evolve, its algorithm will likely become even more sophisticated. The company is already exploring the use of generative AI to further personalize the viewing experience and enhance content creation. Netflix frames these tools as enablers for human storytellers, not replacements. But if the Warner Bros. acquisition succeeds, the company won't just be shaping new stories - it will control a library of old ones made long before algorithms had any say. The company already uses machine learning to select which frames from a show might work best as promotional images, to generate personalized artwork, and to assist with visual effects.

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