Netflix has gained so much popularity in recent years that it has managed to find itself the subject of youth colloquialisms and cultural zeitgeist. Co-founded in 1997 by Reed Hastings and Marc Randolph, Netflix saw humble beginnings as an online DVD rental and sales site. Over the course of one decade, Netflix started offering different services to appeal to its consumers – including the introduction of a subscription system, the incorporation of personalized movie recommendations for users, and finally its transition into a streaming platform in 2007. Since then, not only has Netflix provided a successful model of this up-and-coming sector in the entertainment industry, inviting competitors such as Hulu, Disney+, and HBO Max (Burroughs, 2018); the company also continues to challenge and reinvent its boundaries by expanding into other digital platforms, partnering with consumer electronic companies, and even creating original content. The result is a base of 193 million subscribers spanning 190 countries worldwide and a hefty revenue of 20.15 billion USD from 2019 alone. The United States boasts 43 million subscribers, including 52% of adults and 54% of households in the country (Statistica). This begs the question – with such a diverse pool of streaming services to choose from, what makes Netflix so popular?
 
Our team believes that the answer likely lies in the platform’s predictive algorithm, which is designed to maximize the “efficiency” of its content (Lobato; Gomez-Uribe, Carlos & Hunt). The algorithm tracks viewer statistics on Netflix to form personalized recommendations and produce original content that they know their audiences will enjoy (Maryanchyk, 2008). An “efficient show” is one that achieves the maximum happiness per dollar spent or delivers the biggest viewership relative to licensing cost, and Netflix periodically adds and removes licensed material from its catalog based on this metric. Therefore, the types of content that Netflix offers at a given time should be reflective of viewer preferences. As such, we hypothesize that Netflix's preeminence in the streaming industry is due to its ability to cater its catalog to viewer preferences, audience demographics, and trends in popular culture.
 
To test our thesis, we conducted analyses on a publicly available Kaggle dataset, which provided data and metadata on all the shows in Netflix’s U.S. catalog in January 2020. We hope this data will strengthen our understanding of the factors that drive Netflix’s popularity as well as its relevance to content availability. (More background on the data can be found in our critique.)

For further background research, we conducted a literature review that yielded mixed opinions about Netflix and its use and infrastructure. Some authors argue that the streaming services platform has been disruptive in the internet network and television market as it offers more accessibility towards a variety of media content than in the past,  as well as new business models in the relevant field (Crawford, 2013). Additionally, Netflix aims to enhance user experience in watching movies and TV shows. However, others use this point to reinforce their argument that Netflix infringes on data privacy and harms the health of Netflix users (McDonald & Smith-Rowsey, 2016; Pahayahay & Khalili-Mahani, 2020). They claim that prediction algorithms, one strategy to improve user experience, are tailored to make the users watch more through continued recommendation. As this consumption behavior then leads to binge-watching, there has been a controversial debate about the use of Netflix (Jenner, 2017; Pittman & Sheehan, 2015).

Through this project, we hope to analyze consumer statistics to understand current trends and predict the future development of the streaming industry. We chose Netflix particularly because of its pioneering presence in the field, in addition to its large user database which provides a more comprehensive understanding of the proliferation of consumer preferences among different demographics. Lastly, we aim to shed some light on the mixed opinions in the literature review from a humanistic angle. By integrating our knowledge of Netflix’s business model and prediction algorithms, we hope to educate readers on the platform’s attractiveness and examine the factors that make it appealing to the masses.