Arguably the most important bit — User experience is key to the success of any software application. No matter how innovative the app is or fancy the interface looks, a poor UX (user experience) is sure to frustrate your users and impede growth.
Ever since the dot-com bubble, software applications have grown in prominence. Today, they’re spread out across several platforms, including Windows, iOS, Android, Linux, and even the web.
For software developers and product designers, the aim is to create tools that solve problems and meet user needs while outperforming the competition.
The entertainment industry, for instance, is one of the most competitive. The likes of Netflix, HBO, Disney+, Hulu, and others compete actively to deliver top-notch content to their audiences.
The core secret to Netflix’s success and remarkable churn rate has been its adoption of artificial intelligence technologies, which has resulted in greatly improved user experience across its mobile and web-based streaming platforms.
Read on to learn all about how Netflix uses AI-driven technology to fuel its growth.
What is Artificial Intelligence?
Artificial Intelligence (AI) is an advanced form of cognitive computing that makes computers adept at identifying and processing data patterns for predictive analysis.
In other words, AI makes computers reason intelligently in a near-human capacity. AI is developed by studying the cognitive patterns of human thought processes.
Today, artificial intelligence is at the heart of modern computing, helping to add value to software tools by automating complex processes and providing rich insight into Big data or large data sets.
AI has several use cases, from robot engineering to innovations in cybersecurity systems, virtual assistants, smart home devices, and much more.
It is impossible to speak of Artificial Intelligence(AI) without mentioning Data Science and Machine Learning (ML), as they’re all interlinked.
Let’s take a brief look at the intersection of the three concepts.
How do AI, ML, and Data Science intersect?
To begin with, you must recognize that the common factor between these three disciplines is data. Data science is all about managing, processing, and interpreting big data for informed decision-making.
On the other hand, ML uses special algorithms to analyze data, learn and make predictive analyses, while AI uses continuous data feeds to learn and optimize decision-making.
Here is another perspective.
Data science is focused on extracting meaning and logic from unstructured and structured data sets and is applied to solve problems via prescriptive, descriptive, and predictive analysis.
With AI, the focus is on making computers perform cognitive, human-like tasks like decision-making and perception.
Meanwhile, ML focuses on providing ways for computers to synthesize data, learn, and use the gleaned insights to make improvements over time.
We can therefore say that machine learning is a subset of artificial intelligence, while data science is the fuel that powers both disciplines.
At this point, the entire picture becomes clearer, and your quick mind may have outdistanced the pace of this article to connect the dots regarding how Netflix uses these technologies.
Well, there’s no need to speculate. We delve right into that in the next section.
How does Netflix Use Machine Learning?
From Trevor Noah’s standups to the 12-season-long Big Bang Theory series, Netflix has a massive array of titles for you to choose from across several genres, including comedy, psychological thriller, legal drama, fantasy, and many more.
If you’re new to Netflix, you’ll probably struggle to decide on one piece to watch and enjoy, and there are thousands of options to choose from. Even if you get recommendations from friends, you’ll run out of them at some point.
How, then, does Netflix improve your user experience in this regard? How is the app able to recommend relevant content to you from a catalog of 17 thousand titles? You’ve probably guessed it correctly — Machine Learning and Artificial Intelligence.
Machine learning technologies are at the heart of Netflix’s recommendation system. This Netflix AI mechanism is responsible for making recommendations based on your preferences and a host of other factors.
The Netflix algorithm curates all user pages, identifying patterns in their rating and watching history. Explicit and implicit data collected include thumbs-down or thumbs-up clicks, the time you watch content, the location you’re streaming from, whether you choose to binge or not, etc.
The AI processes all of this data from Netflix’s 223M paid subscribers, analyzing the patterns and user behavior using machine learning. Thus, the algorithm is able to provide more accurate predictions and recommendations for your next watch.
This greatly improves Netflix’s user experience and automatically draws more subscribers their way.
Use Cases of AI/ML/Data Science in Netflix
Netflix continues to grow strong after so many years, even in the face of fierce competition from the likes of HBO Max, Disney Plus, Hulu, and Amazon Prime.
Much of the reason for its success is its constant strive for product improvement. In other words, Netflix is devoted to improving its users' experience and upgrading its service. The Netflix AI recommendation engine is arguably the platform’s most important product feature.
The AI, via Machine Learning mechanisms, stores data on your hobbies and watching habits to give you accurate recommendations. All of this is possible because of Netflix AI's ability to collate and recommend video content based on each user's preferences.
Here are some of the most common applications and use cases of data science, machine learning, and artificial intelligence in Netflix:
- Content recommendation
This is the most obvious use case. As previously emphasized, one of the biggest ways that Netflix optimizes its user experience is via tailored recommendations.
If you'd like to experiment, you can try this out:
Open Netflix on your device and check out your recommended list. Now, go to your friend's Netflix dashboard and check out theirs. You'll find a different set of recommendations.
For instance, if you're apt to stream animated content, you'll most probably get recommendations along the lines of Arcane and other popular series (if you haven't watched them).
Your friend who watches legal dramas would find recommended content more suited to their viewing inclinations.
The Netflix AI recommends content based on your preferred genre and other interesting data, such as what viewers of your favorite content are watching the most.
It's all one very complex mechanism. However, the underlying factor is data and how Netflix AI learns to adapt all the data gleaned from how users interact with the platform to improve user experience.
With this machine learning algorithm, you don't have to worry about what to watch next, given that there are tons of video content available on the platform.
You can hardly go wrong with Netflix's recommendations.
The great thing about this machine learning model and algorithms is that they improve with time. The more content you stream, and the more time you spend interacting with the platform's many features, the more "intelligent" the machine gets, and the more accurate the recommendations are.
- Auto-generated thumbnails
Automatically generated thumbnails are a key part of Netflix's OTT recommendation engine, as they determine to a large extent, whether users will watch specific content or not.
While surfing through Netflix and searching for your next exciting watch, you’ll come across interesting-looking image thumbnails for movies or series you haven't watched.
Whether you like it or not, it's clickbait! Sooner or later, you'll be tempted to check it out, and you may find that it's a series or movie that you'll like (after reading the synopsis, reviews, and checking out ratings).
This is another way that the Netflix machine learning algorithm works. Here, it's via personalized auto-generated thumbnails.
Yes, you have it right there - the images are personalized.
The thumbnails have a great impact, as they're attractive and thus able to generate sufficient interest. The thumbnail selection may show an actor or genre, drawing users' interest.
These images simply don't appear randomly. The Netflix data science mechanism studies your individual tastes, culling all the data for machine learning purposes to develop a predictive algorithm.
This algorithm subsequently forms the basis for the Netflix AI engine that makes recommendations via personalized auto-generated thumbnails.
For instance, the TV series Shadow and Bone can have thumbnails linking to Barbarians and Cursed.
Whatever your tastes, you're sure to find something for you. All you have to do is to click on these thumbnails, and you'll be redirected to similar content to the previous one.
It's quite exciting to play around with these features. It works like magic (such is the accuracy of the recommendations), but it really isn't magic. It's simply the Netflix AI at work and wonderful to behold.
- Streaming quality
No matter how much time you spend streaming Netflix and enjoying its impressive repertoire of exciting video content, there's one thing that's absolutely guaranteed to ruin your viewing experience — poor video quality.
There's something awesome about watching a movie in its full visual glory, complete with high-powered graphics and thousands of rich, high-definition pixels.
Unfortunately, low video quality and buffering can reduce enjoyment and irritate the viewing experience.
Although these occurrences aren't frequent, they're a major concern for Netflix, putting them at risk of losing subscribers to rival platforms like Hulu and HBO Plus.
This one's not a case of recommendations, so you might wonder how AI, data science, and machine learning apply here.
But they all do apply here.
The Netflix machine learning algorithm predicts viewer patterns to determine periods of network traffic congestion in various regions.
Once they determine this, it's a simple matter of caching the regional servers that are closest to the viewers. This ensures that loading times are as minimal as possible and buffering is non-existent during peak viewing periods.
- Content quality checks
Another one of the most popular use cases of the Netflix data science model is in the assessment of audio, video, and subtitles in quality checks.
Certain data is fed into the system, and each content must pass the inspection. Additionally, human scrutiny is required for more quality assurance.
This systematic use of data science means that Netflix has been able to strongly influence users' decision to watch and provide them with another activity to enjoy on the platform other than watching content- searching for content.
Some of the features that Netflix uses to improve the quality of its content and attract more subscribers include
- Customized video ranker
- Trending now section
- Top-N video ranker
- Page generation
- Video-video similarity
Benefits Of Netflix AI
Netflix's technology advancements in AI, machine learning, and data science are one of the reasons why the platform is beloved by so many today.
Here are some of the major benefits of Netflix AI:
- Helps users discover the next best show
There are tons of series out there, and it can be hard to identify which is worthwhile. The Netflix AI mechanism means you never have to worry about finding the next big show.
All you have to do is to follow the recommendations and sift through the other recommendation features. This reduces bounce rates drastically.
- Cost-savings for Netflix
The Netflix OTT search engine is an excellent alternative to conventional digital marketing as it helps create awareness and boost users' interest in video content.
Netflix typically conducts data analytical research on viewed content and user patterns to promote certain shows. For instance, if your streaming activity revolves around fantasy and magical content, the Netflix machine learning algorithm displays similar popular content.
This is a promotional advantage for Netflix that saves them money on external advertising campaigns, especially for new content.
Instead of creating awareness about new shows the traditional way, they can recommend a show to those most likely to watch them — those at the bottom of the sales funnel, so to speak.
- Optimizes ad campaigns for Netflix advertisers
In the previous section, we discussed how Netflix's data science models help the platform save ad costs. The same goes for its clients’ existing ad campaigns on the platform.
The platform lets advertisers optimize their campaigns via personalization. This allows for more efficient use of ad spending and consequently leads to higher conversion rates. Bear in mind that by doing this, Netflix also gets to protect its users’ interests. They don’t have to sit through totally irrelevant ads.
The bottom line is that Netflix offers advertisers a unique and powerful way of reaching and engaging with their target audiences via AI.
Data science, machine learning, and artificial intelligence are irreversibly interlinked. With so many use cases in the modern world, it's no surprise that big names in the video subscription industry are using these technologies to improve their user experience.
Netflix technology advancements and adoption in these fields have yielded great results. Today, the 3.5% Netflix churn rate is one of the lowest in the industry, and for very good reasons.
Aside from the fact that Netflix has truly awesome content, its creators have gone the extra mile to ensure that users have reason to keep coming back.
With improved recommendations to users, it's now easier than ever to find new content. Users can also be reasonably sure that they're getting excellent recommendations. It's like getting movie suggestions from a fellow movie geek, only this time, it's a virtual friend who has an unnatural knowledge of your viewing patterns.
As discussed, the benefits aren't all for the users either. As a result of these tech advancements, the platform can optimize its marketing strategies and convert even more potential customers to paying subscribers.
As AI technologies advance, we will see Netflix's OTT recommendation engine get better.
Suppose you want something as powerful as Netflix's recommendation system for your own use. In that case, you can try out Argoid's powerful AI-driven video recommendation engine to grow your revenue, gain more users, and reduce churn.
What is Machine Learning?
Machine learning involves developing and analyzing data-based algorithms to draw logical inferences and make accurate predictions.
How does Netflix use AI?
Netflix's most significant use of AI is in recommending accurate content based on user preferences.
How can I watch the best shows on Netflix?
You can follow Netflix's AI-driven recommendations based on your usage patterns to find awesome video content to enjoy.