Netflix In The Borderline Between The Internet And Storytelling
Today all of us can observe the changes made with the help of the internet and especially an impact that it has on the storytelling. With the appearance of internet TV it has got possible to make own decisions concerning consumed content. We can decide what to watch, when to watch, and where to watch, in comparison to linear television and cable systems that do not provide the audience with a choice and suggest watching whatever is being broadcast right now on 10 to 20 favorable channels.
Netflix takes its seat in the borderline between the internet and storytelling. With the help of subscription service, it makes a revenue and allows users to stream videos at any time on various devices. According to Netflix technology blog, in the first quarter of 2018 American streaming service provider had more than 125 million members who streamed more than 100 million hours of shows every day (Amatriain & Basilico, 2012). The key point or one of the value propositions of the streaming service is its recommendation system that helps to find the right videos for its members in no time.
Let’s have a look at Netflix development during the last decade. Historically, the Netflix recommendation problem was the difficulty in predicting the number of stars that a person would give for a video after watching, using a scale from 1 to 5. Netflix has trusted that system a lot and considered it as a main business value at the times when DVDs shipping by mail was its main business. It was the only and the main feedback from members that Netflix could get and thereafter understand that the member has watched the video. A competition was even organized with the goal to improve the accuracy of the rating prediction, resulting in algorithms that Netflix uses in its business to predict ratings even to this day. But stars and DVDs are no longer the Netflix key points. Now American entertainment company streams the content and specializes in analyzing a huge amount of data that describes what each member prefers to watch, how, with the help of what device, when and where.
Another significant step of Netflix development was the change from a single website to plenty of different devices that allow to stream the same content. The collaboration with the Roku player and the Xbox was announced in 2008, one year before the Netflix competition. Thereafter Netflix developed an app and occupied the most famous mobile device – iPhone. Nowadays Netflix can be seen on hundreds of devices: from Androids to the newest Apple TV.
Netflix years of experience and all of the collected data and researches helped to improve the product and help people find videos to enjoy, rather than focusing only on those with a highly predicted star rating. Nowadays, Netflix recommender system includes not only one algorithm but consists of various types of algorithms that together create the Netflix experience and business value of the company. Netflix collection of algorithms will be presented in the next section of the research.
According to Netflix technology blog, the recommendation system helps to win moments of truth by helping its users to find the most engaging content and prevent abandonment of its services and subscription to another OTT service provider (Amatriain & Basilico, 2012). With the help of personalization, it is even possible to find an audience for relatively niche videos that would not be shown on TV because their audience is too small to get a time slot on broadcast TV or to support advertising revenue.
Over the last decade Netflix optimized its recommendation algorithms to the point when 75% of what people stream is coming from some sort of recommendations. Netflix states: “we reached this point by continuously optimizing the member experience and have measured significant gains in member satisfaction whenever we improved the personalization for our members”.
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