Netflix is a company that used a lotbig data to drive business success. The company accounts more than third peaktime Internet traffic in the US and has more than 109 millions subscribers allaround the world. The great success that the company achieved is mainly due todata collection from its large customer database. By collecting data easilyfrom all subscribers and analyzing them, the company could understand thecustomer viewing habits, which allowed Netflix to develop a better content thatfit with every customer. Today, Netflix is considered a Big Data companybecause it could process the huge amount of data that it receives every daywith the help of cutting edge analytical techniques and tools.
In Netflix, allemployees are highly skilled and competent in implementing data analytics totheir business areas such as personalization analytics, content delivery,messaging analytics, location analytics and so on. Therefore, Big data is used across every single aspect of Netflix business andthis is how they could predict what content their customers will enjoy andaccordingly tailor it (Fernández-Manzano, E., Neira, E., & Clares-Gavilán,J., 2016).
The main purpose of analytics is to help marketers to gain insightsabout their customers so that organizations may optimize their marketingefforts and deliver better offerings. Before analytics advent, organizationswere in the dark about their customers’ preferences. Today, analytics allowcompanies to obtain the quantitative data that they need to make rationaldecisions and improve their products because they can know what their customerswant. Lately, Netflix has 109.25 million worldwide streaming subscribers, whichis a huge base that permits the company to collect a large amount of data (Statista,2017). The big opportunity that Netflix has was using this data to make betterdecisions in order to retain and engage its users. It wisely used thisopportunity that traditional television does not have which explains itsexponential growth. Traditional television networks do not have thisopportunity in their broadcasting since they cannot know exactly who watchedwhat and at what time.
Their ratings are simple approximations that are basedon intuitions. Netflix could not only predict a persona or idea of customers’preferences or average as traditional television networks do. But it could knoweverything about its customers because it is an Internet company and this isgreat advantage in the streaming industry (Ivan, 2008). For instance, when a useris watching a series, Netflix can see the completion rate of every user.
Further, the Netflix employees would ask “how many users started that seriesfrom the first season until the last episode in the last season?” Let is assumethey get 70% as completion rate. After they would ask “What was the common cutoff point for subscribers? Why the other 30% did not completely finish theseries? What is the time frame between watching one episode and the next one?”They are asking many questions and finding the answers while analyzing theusers data in order to measure the overall engagement of a particular series ata deep level. Therefore, when Netflix sees that 70% or more of users watchedall available seasons of a series, they might restart the series by investingin an upcoming season because they are sure that their customers are going towatch it and enjoy it. In addition to that, Netflix track many events on itsmobile app and website that provide even deeper data such as when users pauseor fast forward, what day users watch which content (Netflix finds out thatduring week days, users watch series and movies on the weekend), which deviceis used (Netflix finds out that users watch on their laptop along the day butat night time they watch on their mobile phones, plus kids programs are mainlywatched on Ipad) when users come back to watch a content that they have pause,etc. All these events and many others provide meaningful insights to Netflix tomake better decisions in regard with the content, the delivery, the customerexperience and journey.
Netflix even knows when the credits roll in order tooffer just after other movies recommendations. They have personalizationalgorithms that are able to predict what users will watch next. For thisreason, just after the credits you can see the personal recommendations and forTV shows, the next episode is automatically played. The aim here is to not let theuser leave the app after watching a show or a movie and retain him to watchmore. Moreover, by using a data-driven approach, Netflix knows the amount of contentthat a user needs to watch in order to not cancel his subscription. Obviouslythe goal of Netflix is to make all its users watch their maximum of content sothat they will not cancel their subscriptions.
Netflix assumes that if they canget each user to watch at least 15 hours of content per month, then the usersare 75% less likely to cancel their subscriptions. On the other hand, if theywatch less than 5 hours of content per month, they are 95% more likely tocancel their subscriptions. Now thatNetflix has this valuable data, the challenge is to make users watch at least15 hours a month. For this reason, they added the feature of post play that payautomatically the next episode and which incredibly increased the amount ofwatched TV shows. Regarding movies, they are displaying suggestions based onthe rating of the movie that was just watched (Shekhar, 2016). Netflix is a company that couldrealize that retaining users relies mainly on understanding them well.
Understanding their preferences and their moods to incite them always to watchmore. Therefore, Netflix could engage its users and retain them through beinghighly data-driven. Using analytics and turning customer data into insightshelped enormously Netflix with completing big achievements.