What began as a meagre DVD rental service in 1998 is now one of the world’s most powerful and renowned media streaming services. Having garnered subscribers up to almost 158.3 million, an estimate of nearly 37% of the world’s internet users are using Netflix.
Having Netflix has become the “in thing” among today’s populace and “binge-watching”, a formerly alien term has now become almost interchangeable with this service. Be it cartoons, movies, original web series, TV series and documentaries available in multiple languages as well as subtitles and in varied genres and categories, Netflix has something to offer to all generations and majority nationalities.
In the early 2000s, Netflix had initiated an open competition offering 1 million dollars prize for the best collective filtering algorithm to predict the ratings of users for films, based on their previous ratings. This approach resulted in becoming the turning point for the service.
Now, Netflix uses an opulence of technological algorithms to boost and enhance its customer experience.
Below are a few approaches using Data Science which are adopted by Netflix to improve the customer experience :
In a service like Netflix, every action the user takes is recorded. The shows watched, the time of day when they are viewed, what was watched before and after that show, how quickly a series is binge-watched, when and where a user gets bored and stops watching, how long does a user take to scroll and every single click of the pause and play button. Using a detailed tagging system Netflix is able to recommend it’s users the content which it knows will be their cup of tea.
Recommendation Systems are mainly of two main types :
Content-based Recommendation Systems: In this system, the background knowledge regarding the products as well as the customer information is taken into account. Similar suggestions are provided based on the content the user has viewed on Netflix. For example, if the user has watched a film that has a “thriller” genre, similar films, having the same genre will be suggested.
Collaborative filtering Recommendation Systems: This system provides suggestions based on the similar profiles of its users and is independent of knowledge of the product. This system is based solely on the assumption that what the users prefer in the past they will also prefer in the future.
If you are interested in a more detailed take on the Recommendation Systems you can find it here.
Nick Nelson, Netflix’s global manager of creative services, stated that the company conducted research in early 2014, found that artwork was “not only the biggest influencer in a user’s decision of what to watch but also added up to over 82 percent of their focus while browsing Netflix".
One thing which can be noticed upon opening Netflix is that the thumbnail you will see for a particular movie or show may not be similar to the thumbnail another user will get. Netflix elucidates the thumbnail images and then ranks every image in an attempt to gauge which thumbnail will have the maximum possibility of getting clicked by a particular user. These calculations are mainly based on what users similar to that particular user have clicked on.
One discovery can be that users who like a certain actor or movie genre have a greater likelihood of clicking images with that certain actor or image depicting a certain scenario.
Netflix makes use of past viewed data for predicting bandwidth usage in order to help the service decide when it should cache regional servers to ensure prompt load times during high demand. Thus the service predicts which show is to be streamed in a certain location and caches the content in the nearby server when the internet traffic is minimal. This is done to ensure that the content is streamed without any buffering to maximise customer satisfaction.
Netflix undergoes a rigorous process of A/B testing for their adaptive streaming as well as content delivery network algorithms before it becomes the default customer experience.
Netflix spent around 100 million on 26 episodes of House of Cards by analysing the data of its viewers, i.e on the basis of their customer behaviour as well as feedback. Netflix knew through its use of Big Data that its customers liked Kevin Spacy and through correlation, they derived that their users also liked David Fincher.
Hence with this data, Netflix was able to bring together the ideal cast as well as director for the show. Even the show’s poster was generated through machine learning.
Netflix is well known and respected not only in reference to the content it provides but also in relation to it’s impeccable and praiseworthy marketing strategies. Knowing that the majority of its target audience are rigorous users of digital media, Netflix has been making maximum use of this knowledge. Here are some ways through which Netflix intrigues its viewers using Digital Marketing.
Instead of focusing on only one network, Netflix takes advantage of various different channels in order to engage the maximum audience.
For instance, Netflix made excellent use of Snapchat, Twitter and Word of mouth strategy to promote it’s popular “Stranger things” series.
In the case of Snapchat Netflix teamed up with the service to provide an augmented reality experience. With reference to twitter, the feature of fictional character Barb’s “Magic 8 Ball” was a profound way of generating interest. Simultaneously Netflix also used Instagram, Facebook as well as email marketing to promote the series.
Courageously fighting off the belief that emails are now a dead medium, Netflix has made excellent use of email marketing to entice new users to join their platform and then provide them personalized recommendations based on their preferences.
An interesting example of Netflix’s use of email marketing is during the promotion of its new show “The Punisher”. The service sent out emails which looked like spam and had the threat of being rejected, yet once opened, a GIF was played with “The Punisher” logo flashing upon the screen thus, prominently advertising the show.
From “watched” to “experienced” overtime, Netflix has been making attempts to make its content more interactive. This can be observed in Netflix’s shows like “Black Mirror” which focuses on the twisted possibilities of technology. The series released an interactive film “Bandersnatch” around December 2018 which actually lets the viewers take part in the story by allowing them to make key choices on behalf of the protagonist.
This interactive attempt has opened yet another door for the future of Television. Guessing the possible outcomes and actions of the character helps the users to stay engaged in advanced and also sparks the interest of potential users who get tempted to try the series.
Towards the end of June 2018, Netflix took a bold step which could have had many dire consequences. Inspired by the photo, “A Great Day in Harlem” which showcased a collection of black jazz musicians, Netflix released a video that constituted the lead black characters from its various series, movies and documentaries.
With a collection of 47 stars being involved the organisation exhibited the diversity it held in its age, race, nationality and gender, thus also simultaneously exposing the severe lack of ethnic diversity present in Hollywood. With this action, the company massively elevated its brand image by portraying itself as a progressive, revolutionary brand in the liberal and perceptive new world.
Predominantly the reason for Netflix’s growing popularity is its programmed intuitive nature. From knowing what content to offer to which user, creating a users profile to provide personalized services to each of them as well as enhancing their streaming experience and offering a variety of such convenient features, this article highlights the way the service has made exceptional use of Data handling and analytics to reach where it is today and to maximise it’s customer’s experience as well as to promote its brand.
At the same time, Netflix has employed many intriguing and admirable methods to bolster its brand through various digital marketing strategies which have also been briefly highlighted in this article. For more updates and blogs on Analytics, Do read Analytics Steps.
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