there is an essay question which will require lots of referencing please kindly reference using the Harvard method
A Netflix Case Study 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 gamered 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. FanalyticSteps DVD NETFLIX in Use of Data Science and algorithms to improve customer experience 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. O The Independent Institute of Education (Pty) Lid 2031 Page S of 15 101 20, 21 Recommendation System In a service like Netflix, every action the user takes are 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 a user takes to scroll and every single click of the pause and play button. Using a detailed tagging system, Netflix can recommend its 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 considered. 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. 2. Personalised Thumbnails 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 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. 3. Optimized streaming quality 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
