How Netflix Uses Data
Case Study
Netflix is currently a leading competitor in the streaming world, but the company did not begin with this business model in mind. In 1998, Netflix debuted as a by mail DVD rental service which was not a huge success because of their heavy reliance on the US postal system. The streaming platform that we all know and love today launched in 2007 and the executives were extremely focused on providing a seamless and individualized movie watching experience. One way they attempted to provide this flawless experience was through their recommendation system which is primarily based on Big Data collected from user activity. Simply put, the recommendation system suggests movies based on what the user has watched previously, but the algorithm has been tweaked multiple times to improve the viewing experience using data and analytics.
Netflix is constantly updating and improving their streaming service by using data and analytics collected from users. “Analytics gives businesses the quantitative data they need to make better, more informed decisions and improve their services”(Bulygo 5). As of July 2018, Netflix had over 130 million streaming users and 33 million different versions of the site existed. Netflix is able to provide an individualized experience for each one of these users by analyzing where the user is (zip code), What day the user usually watching content, when the content is paused/ fast forwarded, rewound, when the user returns to paused content, searches, browsing and scrolling behavior, ratings, and more. “Traditional television networks don’t have these kinds of privileges in their broadcasting. Ratings are just approximations, green-lighting a pilot is based on tradition and intuition” (Bulygo 8). Traditional broadcasting does not have access to such specific data, which gives Netflix an advantage over traditional television. All of the data is analyzed by Netflix to gain insight on their customers and what kind of content they should acquire so they do not waste money on content that will not be well received.
Another way Netflix uses their data is to be cost efficient by analyzing the viewing behavior of their users and acquiring content that they know will have an abundant audience on the platform. “Netflix seeks the most efficient content. Efficient here meaning content that will achieve the maximum happiness per dollar spent” (Bulygo 37). If the users are happy with the content they are provided then they are less likely to cancel their subscription. Keeping users happy is a main goal of Netflix and it is central to their success which is why they focus on collecting and analyzing data. One example of how Netflix uses their data to acquire successful content is the purchase of the license to House of Cards. Netflix out bid both AMC and HBO for the rights to the American version of the show and were awarded the production of 26 episodes (2 seasons, 13 episodes per season). Netflix spent over $100 million on House of Cards, but they were able to make an informed decision to do so with the help of data and analytics.
Before Netflix made the decision to buy the rights to House of Cards they were able to analyze how well the series would perform based on how many people watched the British version, how many of those people also watched Kevin Spacey films, and how many people movies directed by David Fincher such as The Social Network. Additionally, once House of Cards was done production, Netflix created more than 10 different trailers that were distributed based off of a user’s viewing habits. “If you watched a lot of Kevin Spacey films, you saw a trailer featuring him. Those who watched a lot of movies starring females saw a trailer featuring the women in the show. And David Fincher fans saw a trailer featuring his touch” (Bulygo 24). All of these factors were tremendous elements of the success of House of Cards and they were all made possible by with use of data.
Netflix evolved from a DVD mail subscription service to an online streaming service that has been incredibly successful since 2007. The company maintains their success by using a data fueled algorithm to suggest new content for users based on their viewing habits. Additionally, Netflix utilizes data and analytics when deciding what kind of content to purchase the licensing to and what kind of content to create. Their main goal to “achieve maximum happiness per dollar spent” (Bulygo 37). Netflix wants to ensure that they are providing users with content that they are guaranteed to enjoy to reduce the risk of cancelation. While researching how Netflix uses data to provide an individualized and enjoyable experience, I was reminded of the uses and gratification theory. This theory is an approach to understanding why and how people desire specific media to satisfy certain needs. Netflix uses data, and this theory to an extent, to provide users with the content they desire and prompt them to keep watching by recommending similar content.
Theory
I am going to analyze binge-watching viewing habits on Netflix through the lens of the Uses and Gratifications Theory. Binge watching is a popular viewing method on Netflix and it has a lot to do with the way Netflix presents suggested content to the viewer. Netflix knows when the credits are about to roll on the show or movie the user is watching which is why they suggest a new show or movie before the user even has a chance to return to the browse page. This strategy encourages longer viewing habits and decreases the chances of a user cancelling their subscription.
Based on a study called Binge-watching motivates change: Uses and gratifications of streaming video viewers challenge traditional TV research completed by Emil Steiner of Rowan University and Kun Xu of Temple University they found that people have multiple motives for binge-watching online content. The researchers stated: “We found that viewers have the following motives for binge-watch: catching up, relaxation, sense of completion, cultural inclusion, and improved viewing experience. We also noted that interviewees often differentiated their binge-watching experiences by their levels of attentiveness” (Steiner and Xu 6). The objective of Netflix is to get the user to stay on the site for as long as possible by accurately suggesting titles that the user will enjoy. Additionally, Netflix strives to maximize happiness which is a large reason why the Uses and Gratifications Theory is relevant in this case study. Understanding why a user is inclined to view an excessive amount of content in a short amount of time may influence the way Netflix releases and suggests content to users.
Part of the Uses and Gratifications Theory is why people select the kind of content they do, but Netflix uses their data driven algorithm to provide the user with individualized suggested content based on their viewing habits, therefore Netflix plays an important role in the Uses and Gratification theory. Additionally, the researchers found that “Catching up through binge-watching was described as a convenient and empowering behavior. It allowed interviewees to control their entertainment by scheduling viewing at their convenience”( Steiner and Xu 23). Participants involved in this study also said they binge content they had seen before as a way to unwind and fall asleep at night, or to have playing in the background as they complete other tasks. Others attributed their binging habits to the sense of completion, or the feeling that they need to know what happens in a show. There are many components involved in why people choose to binge-watch a show, and Netflix takes all of this data into consideration when they acquire, release, and suggest content.
The Uses and Gratifications Theory can be used to analyze why people choose to bing-watch online television as well as what they choose to watch. Netflix assists in what users chose to watch by recommending titles based off of the data they have collected about a user’s viewing habits. Once the user has chosen a show to watch, the reasons they binge vary. People binge-watch content to relax, to catch up on a series before the new season is released, to feel included in discussions at work, or even just to have it on in the background as they fall asleep. Whatever the reason, Netflix uses the data they collect to ensure that the user continues to watch.
Data
Bibliography
Bulygo, Zach. “How Netflix Uses Analytics To Select Movies, Create Content, & Make Multimillion Dollar Decisions.” Neil Patel, 15 Feb. 2019, neilpatel.com/blog/how-netflix-uses-analytics/.
Steiner, Emil, and Kun Xu. “Binge-Watching Motivates Change: Uses and Gratifications of Streaming Video Viewers Challenge Traditional TV Research.” Research Gate, Research Gate , 15 Jan. 2018, www.researchgate.net/publication/322424390_Binge-watching_motivates_change_Uses_and_gratifications_of_streaming_video_viewers_challenge_traditional_TV_research.
https://www.reddit.com/r/DunderMifflin/comments/aavy4r/1_most_watched_show_on_netflix/
