Netflix and Algorithms

Netflix, over the past decade, has quickly become the most popular tv and film streaming services on the internet (with around 139 million subscribers as of February 2020) . Almost anyone you speak with will at least know of it and in most cases use it on a regular basis, but what makes it so great and why is it still so relevant to this day?

Well firstly, with Netflix users spending around 1 billion hours watching movies weekly, it’s a great conversation starter. Since more or less everyone you speak to watching Netflix, it’s a great thing to talk about and a common ground which people can relate to. In this regard, Netflix definitely makes us more social however on the other hand, Netflix also arguably ruins social gatherings or family time. This is because since anyone can simply watch Netflix on their laptop, tablet or phone and is much easier for people to sit in bed and watch their favourite show rather than going downstairs and watching TV. In some cases, people would rather stay inside than go out with friends primarily because of Netflix and that we have all the entertainment we need indoors which is another example of Netflix making us less social.

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Netflix also provides us a lot more freedom with regards to what we want to watch than ordinary TV however this leads to the question of whether we are really in control of what we watch. This is where algorithms come into play. From the second we log onto Netflix, we are presented with several recommended shows that theoretically we should like. Another method Netflix use is the auto-play functionality seen on platforms like YouTube where Netflix will automatically play the next episode of the series you’re watching. Recommendation algorithms effectively see what we’ve spent the most time watching previously and recommend us similar shows based off this information. Therefore, anything we watch determines what we watch in the future.

Arguably, algorithms are good since they are objective and unbias on the surface however algorithms are made by people who aren’t objective. Often, we even go as far as to change our behaviour in order to trick the algorithm to give us something different. For example, with Netflix, users might just watch a variety of shows contrary to what they usually watch or even create a new user where they would watch different shows just to get a series of new recommendations. Netflix isn’t the only platform to use recommendation algorithms however. Spotify also uses an algorithm in order to recommend the user lots of songs they might like based on what they listen to regularly, the genre of a certain playlist or even what their friends are listening to. For example, if certain type of music keeps being recommended but you don’t want to keep listening/get recommended this type of music, people may sometimes listen to other artists/genres in order to trick and switch up the algorithm. This can work for almost any websites which use recommendation algorithms like Amazon, YouTube or even Facebook.

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Platforms like Netflix and many others ultimately use these algorithms in order to keep the viewers on the app for as long as possible. Using these algorithms to recommend the viewer another series they might like as soon as the series they’re currently watching finishes retains the flow of viewing, increases watch time and ultimately encourages the user to stay subscribed.