As explained briefly in the history Netflix, the company has overcome industry hurdles. The shift in technology alongside the new demands of consumers has challenged the company to stay current and on top of the wave regarding the engine they use and content they provide to ultimately increase their profits.

One of the hurdles is against piracy. The Internet has "free" sites, and people can also access streaming videos through Youtube and many other sites that don't pay copyrights. Furthermore, the change from renting and delivering DVD and Blue-Rays to streaming and media production has involved high investment but also high and short term returns. While it is appealing to the arts and famous actors, directors and producers have made the brand unique and have top of mind recognition. Some of its competitors include Amazon that streams both movies and television shows through Amazon Video and has become a production house as well.

Concerning data crunching, the challenge is in improving the recommender systems they use and make correct usage of collaborative filtering to access niche communities of particular preferences. Collaborative filters let sellers access what Chris Anderson calls the "long tail" of the preference distribution (Ayres, 2007, p. 21). For example, Netflix has recently changed the way it recommends content from rating stars to similar matching with based on the previously watched material.

In the book "Super Crunchers", Ian Ayres (2007) briefly introduces the topic of recommender systems and their use of collaborative filtering as a method to predict user preferences. He mentions some critiques where he points out that personalized recommendations based on past behavior and similar users may create negative outcomes. For instance, he describes that customers of Amazon got offended when they input the search term "abortion" and the recommender system asked "Did you mean adoption?", given that previous customers that had searched for abortion also searched for adoption. This serves as an example of potential consequences brought by solely generating predictions of user actions given their behavioral data.

Behavioral data is the most commonly used for online recommendations because it is more plentiful than user explicit feedback, and it can be collected overtime through users’ regular actions, rather than requiring them to continually express their preferences. Such implicit feedback from user interactions has indeed served as an effective measure for predicting future user preferences which in turn generates short-term revenue, like purchases or, in the case of Netflix, monthly subscriptions. However, how do these recommendations affect users’ choice satisfaction in the short term and long term? Ekstrand and Willemsen (2016) postulate that recommender systems should base their predictions beyond behavioral data by also taking into account users’ explicit preferences and ambitions as to support their desire of becoming better people. An important issue that emerges from personalization algorithms is the filter bubble, in which the information consumed by a user may direct the algorithm to feed only one-sided content, causing him isolation and self-detriment. The authors argue that recommendations based on past user interactions could be reinforcing behavior a user actually wishes to change. More often than not, people tend to say one thing and do another; thus, it is important for recommender systems to collect explicit feedback about a user’s desires and compare them with their actual behavior, allowing for better recommendations that help the user be more satisfied with his choices and behavior in the long term. For instance, Netflix could conduct an A/B test, in conjunction with a survey-based user study, to see whether by changing the “My List” option to say “My Wishlist” in their interface could act as a subtle way to elicit explicit feedback about the content users wish to enjoy and be exposed to on their quest to pursue a better self. Ultimately, it is essential that recommender systems adapt their data models with the aim to gain a deeper understanding of the effect of personalized recommendations on users’ behavior and ensure that they support their ambitions, helping them expand out of their filter bubbles.

As the use of internet grows, Netflix provides a large video library of TV shows, movies, and series so that people can watch them on-demand through the internet. The impact of Netflix particularly in western cultures is undeniable. Their core business is attractive to people because it offers entertainment with a large variety of selections, appealing to different people with different interests and tastes.

From a social network perspective, many former and current influential Hollywood stars and producers stand behind Netflix encouraging its consumption and development as a leading streaming company in the industry. It is a famous brand and has attractive content, which have become part of daily conversations and news between people and news outlets around the world.

With the emergence and widespread Netflix adoption, the bold new era of content distribution and technological efficiency has influenced our behavior in consuming content. The concept of ‘binge watching’ became popular because of Netflix. Streaming services replaced the traditional way of enjoying a digital product by giving us the power to control how much we consume of it. Netflix focuses on more opportunities to give a unique user experience through their homepage. One of the reasons people tend to watch another episode is because Netflix will automatically play the next video to make the watching marathon continue effortlessly. It changes the traditional way of storytelling method where you have to read one chapter of a book each week (in this case, videos) into watching several videos in one sitting. Upcoming generations will now want to only watch on-demand videos, or at least have the option to do so. And, if Netflix is the future, entertainment and politics will continue to grow apart. As there is considerable evidence, news and entertainment will become more distinct since Netflix has no news division. People love to enjoy the entertainment Netflix offers. Another point of binge watching is the feeling of withdrawal when running out of new episodes. After watching all the episodes of a series in one time, people have to wait for certain times until it’s released again. When a series is finished, people may get the feeling of addiction.

If a culture was built on a central point of mainstream entertainment, how people shaped by shows like ‘Friends’ will be shifted towards what people claim as personalization. When they are in a group, a common topic will also be shifted into a more personalized movie. If people meet others who have a similar personalized taste, they will have a deeper connection.

In addition, in the energy area, Netflix says people should watch their popular series of House of Cards instead of eating a hamburger or reading a book. They said that sitting still while watching Netflix saves more CO2 than Netflix burns, or in other words, streaming is greener than reading. However, Netflix outlined only the energy needed to deliver all of their contents to the consumers but not the energy needed to produce it.

The super crunching method behind the Netflix case is recommender systems based on collaborative filtering. It is common practice, especially in E-Commerce engines seeking to predict the rating or preference that a user would give a particular item (Ricci, Rokach and Shapira et. al 2011), in this case, movies, series or documentaries. These methods are in fact, widely used and improved year over year, researched in various prestigious Universities including TU/e Eindhoven. Hence, a vast literature exists in the field. Recommender systems and collaborative filtering, as the discussed methods, have gotten more attention and light the last years as they have become the main gate of communication and data source between users businesses and institutions. In recent years, the popularity has increased and are utilized in a variety of areas not only movies but also music (Spotify, Pandora, Deezer), news, books (Amazon), research articles, search queries, social tags, and products in general. These engines extend to other areas such as expert recommended literature, restaurants, travel, financial services, life insurance, online dating, and social media; Facebook, Twitter, Instagram, Youtube & LinkedIn.