Netflix is widely seen as one of the world’s most successful streaming platforms to date. Many might accredit this success to its broad library of fantastic titles and simple, yet effective, UI. However, behind the scenes a lot more is going on, which keeps users on the platform longer, and most importantly, reduces subscriber churn.
While Netflix has 277 million paid subscribers across 190 countries, no user experience is the same for any of these users. Over time, Netflix has developed its incredibly intelligent Netflix Recommendation Algorithm (NRE) to leverage data science, and create the ultimate personalized experience for every user. I think most of us are aware of some personalization algorithms, but not the extent to which they go!
The NRE is composed of multiple algorithms that filter Netflix’s content based on a user’s profile. These algorithms filter through more than 5000 different titles, divided in clusters, all based on an individual subscriber’s preferences. The NRE works by analyzing a wealth of data, including a user’s viewing history, how long they watch specific titles, and even how often they pause or fast-forward. This, in turn, results in videos with the highest likelihood of being watched by the user, being pushed to the front. Which is, according to Netflix, essential, since the company estimates that they only have around 90 seconds to grab a consumer’s attention. I think, as consumer attention drops even further (with apps like TikTok destroying our attention span), this might become even more of a problem in the future. I mean, who has the time to sit down and watch a whole movie these days??
This also ties into the concept of the Long Tail which we discussed, which refers to offering a wide variety of niche products that can appeal to smaller audience segments. Netflix can now surface lesser-known titles to the right audiences using its recommendations algorithms. While these niche titles might have never been discovered by users in the past, Netflix can now monetize the Long Tail of its Library. You must have definitely noticed that your family or friends have titles on their Homepage that you would never see on your own, and this is the NRE at work.
While this model is largely successful, it might raise concerns around content bias. For example, Netflix’s use of different promotional images for the same content based on a user’s perceived race or preferences has sparked debate. Although the intent is to tailor recommendations more effectively, it risks reinforcing stereotypes and narrowing the scope of content that users are exposed to.
Ultimately, user data is exchanged for a super personalized experience, though this experience can sometimes be flawed. What do you think about Netflix’s NRE and its effects on users? Do you think this data exchange is fine, or would you rather just see the same Homepage as everyone else?
