How NetFlix Uses Big Data Analytics to Improve Customer Experience

Netflix is one of the leading Internet Service Providers today when it comes to streaming videos. As a result they have developed an advanced recommendation engine that uses real-time data from their massive consumer base to make recommendations of new movies and television shows. The movies themselves, the TV shows, the specials, and other content they have chosen are all being analyzed to provide the best viewing experience possible for their users. Here we will take a look at how Netflix uses big data to get better results.

how netflix uses big data

Netflix has more than 100 million subscribers and using that figure makes them have lots of data to analyze. Big data directly affects Netflix’s recommendation system and how they select the best programs for you. Netflix uses a complex approach to deciding what their subscribers want by taking into consideration not only where they live, their age range, their income level, and the types of shows they enjoy, but also factors like what sports or cities they live in or what times of day they prefer to watch their shows. This type of detailed analysis requires massive amounts of data and is the reason why Netflix uses big data to help make their decisions.

Netflix utilizes several different kinds of big data analytics in order to come up with a personalized recommendation engine. When I say personalized, I mean that each individual subscriber is given an entirely unique statistical makeup based on the data they provided when they signed up for Netflix. Every time a subscriber logs in to Netflix, the estimated duration of their stay is logged along with the IP address of the person. It is then crunched to determine how well each member of the subscriber’s demographic fit into the Netflix pool of subscribers.

In order to fully utilize the Netflix recommendation engine, Netflix only allows a single cookie to exist for each user, which means that each user will get a completely unique set of personalized recommendations for their television viewing habits. Netflix’s big data analytics system takes this one step further by allowing each user to see the “pre-watch” statistics so that they can make informed decisions about what they will watch. The statistics also tell the user what their best watching shows are at any point during the week. All of this is done by means of a custom JavaScript application which acts as a filter on Netflix’s streaming servers.

While both Hulu and Netflix employ big data analytics, they do it in very different ways. Hulu has chosen to leverage heavily on what is known as its “live viewing” data in order to build its own recommendation engine. This recommendation engine makes use of a combination of information about the progress of each episode and track point information like whether or not the frame rate hit a high, low, or average. Hulu’s recommendation engine makes full use of real time data to generate and build highly personalized recommendations for its users.

Netflix on the other hand, has chosen to leverage a different form of big data analytics called “channel activity” in order to make better business decisions. Channel activity allows Netflix to analyze how subscribers have been spending their time over the past three months. The information from this activity includes such things as how long people spent watching their favorite shows, the demographics of the viewers, and even what genre they were watching most of. Data like this is particularly useful because it helps Netflix derive a much more accurate picture of what types of ads are drawing in subscribers, and where the advertising opportunities lie.

As you can see, there are a number of distinct advantages that Netflix and Hulu can draw from when making their streaming video choices. These advantages are particularly important in light of the fierce competition, which is being felt in the online television market. Both of these streaming television companies are making large strides in becoming successful and in building huge user bases. Their respective databases of subscribers and their decision making processes regarding what to show their subscribers are two of the key reasons for their success.

In short, Netflix uses big data analytics in order to provide customers with the kind of personalized experience that truly makes watching a film or watching television as enjoyable an experience as possible. Hulu’s decision to use Hulu recommendation algorithms and to add social media functionality into their site are also designed with the aim of making their service more interactive and user friendly. Neither of these services is new; both companies have made these moves in the past few years. However, by combining these two powerful services into one, Netflix is able to take their subscribers along on the journey of viewing entertainment to the next level.