Binge-watching on Netflix is not an accident, but a result of multiple AI algorithms.
Digital content is all about relevancy, and the whole purpose of these algorithms is to tap relevancy. This is how and this is why Netflix has become the undisputed king when it comes to video streaming. The purpose of this write-up is to explain this algorithm of Netflix, and how it displays a unique version of the site to every user.
For people who may not know, Netflix customizes the shows and movies it display, based on the viewing habits of a particular user. It uses machine learning algorithms to personalize the content displayed for each user. Netflix gives you content that you actually want to watch and experience, without you knowing it.
This is how you end up binge-watching.
There are thousands and thousands of combinations, but the result is a unique or “Personalized” page for each user. That’s how refined the algorithm has become.
How does the algorithm work?
First of all, we are not talking about just one algorithm, but multiple AI algorithms. Any video streaming platform can’t provide the entire content all at once, and it has to be curated. The thing with Netflix is that it does it much more intelligently than the rest. It knows the viewing habits of its users better than the rest.
Netflix does a very interesting thing. It segregates viewers into multiple “taste groups”. Depending on the “taste group” a viewer fits into, it recommends pop-ups or artworks. On this very basis, it organizes and displays the “genre rows”.
If a viewer falls under multiple taste groups, then Netflix considers all those taste groups while providing recommendations to that viewer.
The team of Netflix doesn’t just divide content into genres but into micro-genres. For example, it categorizes TV shows and movies into micro-genres like “Intriguing, nostalgic dramas” or “Slice of life, road trip movies”.
The data collected is complex and multi-faceted in Nature. It’s not just about processing the genre of a TV series which a user is watching and recommending him or her dramas, romances or comedies.
Apart from about what people are watching currently, Netflix also considers the shows or movies which they will watch afterward and the ones which they had watched before or even a year ago. Not just that, it also considers the time of the day at which they watch a particular TV series.
This whole approach is not a random exercise but is backed by a number of experiments and tests. The purpose behind these experiments is to understand how viewers are driven by specific kind of emotions and compels them to watch a particular movie or a series.
Why Artworks are so important to Netflix?
Netflix has an algorithm to personalize even the artworks, meaning, for the same movie or TV show it shows different artworks to the different viewers.
It knows which image or artwork a user is most likely to click, and which show he/she is most likely to watch for hours. For instance, a user might have an inclination for deeply emotional faces or reactions. Netflix knows that and accordingly puts across artworks or images. Also, it constantly tries to avoid the clickbait images that misrepresent the piece of media.
Your content needs to grab people’s attention within 90 seconds, and if it fails to do so, then they will be quick to discard it, never to return again. This is the key to creating marketing content.
This very much applies to Netflix’s content as well.
Netflix knows that the best way to capture a viewer’s attention in such a short timespan is to show him or her a highly relevant artwork, that is tailor-made to his/her sensibilities.
Earlier it used to choose the best-looking artwork of a movie or a TV show and display it for the maximum number of users. But now it has greatly personalized this process, catering to a multitude of tastes and preferences. For Netflix, it’s all about relevancy now.
For example, a user who is a die-hard fan of Al Pacino and frequently binges on his movies is more likely to see Al Pacino’s face on the artwork of ‘Heat’, rather than that of Robert Deniro. Similarly, a user who is a fan of John Travolta is more likely to see his face on the artwork of Pulp Fiction, rather than that of Uma Thurman.
No matter how fascinating it sounds, personalizing data for audiences is a tough ask, as it requires relentless exploration and experimentation. You are required to factor so many factors, and so far Netflix has done a tremendous job, and it deserves all the appreciation.
Still, there is so much more that needs to be done. Some questions need to be answered at the same time as well.
Will Netflix compromise on the quality of TV shows or movies, just to remain relevant and make users binge-watch one series after another? In what way binge-watching harming our brains if it is at all? Should Netflix take some steps regarding this, like Instagram has, by hiding the “like counts”?
We will leave you with this. Do think about it?
Hope this write-up gave you an insight on how Netflix provides relevant content to users. It’s a fascinating study and then is so much more to explore. Do let us know your views on the same. The comment section is all yours.