Procrastination. All of us have been subjected to this monster at some time or another. I myself am an expert procrastinator. What is my go-to activity when I have so much to do and my brain just won’t allow me to be productive? Netflix.
Millions of people log into Netflix everyday and spend hours binge watching an ever-expanding selection of movies, TV shows and documentaries covering a range of genres. There really is something available for everyone.
Netflix knows how to keep you hooked, the minute you finish watching 7 seasons of a TV show, you are immediately shown something else to watch suggesting that this is something that “you might also enjoy”, how did they know I would enjoy that?!
An article that I came across recently on my twitter feed, got me thinking about how Netflix uses data-driven marketing to learn more about their users in order to keep developing more focused recommendations, so that they can ensure that their customers keep coming back for more. This got me thinking about just how much of my usage on the site is being tracked and how I felt about it.
“Netflix has two basic methods of determining users’ preferences: by asking what they prefer or by inferring what they prefer from (patterns in) discrete interactions within the system” (Lawrence, 2015, p. 359).
The first of these two methods is quite standard, while the latter relies on specific algorithms that focus more on the information that its users reveal, rather than state when it comes to their preferences.
In other words, the company has programs in place that track your recently played and most viewed genres on the website to then generate media that you might be interested in watching.
One of the main reasons that the Company tracks their users in this way is to ensure customer satisfaction. Below is a short video that I created and tweeted out explaining this in more detail.
Although Netflix keeps their algorithms hidden from the public these days, this wasn’t always the case. In 2006, Netflix announced a competition that gave the opportunity for an “individual or team to develop a recommendation system capable of predicting movie ratings with at least 10% greater accuracy than Cinematch, the company’s existing system” (Hallinan & Striphas, 2016, p.118). This competition was called the ‘Netflix Prize’ and offered the winners $1 million in prize money.
The competition went on for three years (yes, that long) and although the company did not end up using the winners algorithm, according to Hallinan and Striphas (2016), the company’s quest to find new and more extensive ways to connect its users with movies that they love interfered with cultural foundations, and in doing so they managed to make a connection between algorithms and art.
Most companies and websites use algorithms and data mining techniques in order to keep track of their users and help better our experiences online. Social Media sites such as Facebook are well versed in such methods. Yes, the idea of this can seem quite scary and it can even be argued as an invasion of privacy. However, the fact that Netflix has adopted these methods for me, personally, doesn’t seem like such a bad thing. How else would I decide what TV show to watch next?
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- Gomez-Uribe, C, & Hunt, N 2015, ‘The Netflix recommender system: Algorithms, business value, and innovation’, ACM Transactions on Management Information Systems, vol. 6, no. 4. Available from: 10.1145/2843948. [24 August 2016].
- Hallinan, B, Striphas, T, 2016, ‘Recommended for you: The Netflix Prize and the production of algorithmic culture’, New Media & Society 2016, Vol. 18, no.1, Sage Publications ltd., p. 118-119, DOI: 10.1177/1461444814538646
- Lawrence,E, 2015, ‘Everything is a Recommendation: Netflix, Altgenres and the Construction of Taste’, Knowledge Organisation, Vol. 42, no. 5, p.359, Applied Science & Technology Source, EBSCOhost, viewed August 24 2016