How Netflix Uses Data to Pick Movies and Curate Content

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Founded in 1997 as a subscription mail-order DVD company, Netflix has grown to be the top digital streaming platform, with over 160 million subscribers worldwide. The streaming giant has steadily grown over the past two decades using insights from its treasure trove of user data to personalize content recommendations and inform content curation.

To learn more, check out the infographic below created by the Ohio University Online Master of Business Administration program.

How data analytics can influence the creation of an optimized user experience.

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The Rise of a Digital Streaming Giant

A company that produces content watched by hundreds of millions of users has a story of its own – one that started with the desire to create an “Amazon.com of something,” according to Marc Rudolph, one of Netflix’s founders.

The Story of Netflix

Entrepreneurs Marc Randolph founded Netflix in 1997. Two years later, the company began offering an online subscription that allowed people to select a movie or TV show from the website, which would then be sent to the individual by mail in the form of a DVD. They launched a streaming option in 2017, one year after engaging customers in a $1 million contest. In 2010, Netflix launched a streaming-only plan. Three years later, they launched the original series “House of Cards.” The streaming-only plan option had expanded to 190 countries and territories by 2016.

In 2019, Netflix boasted 167 million subscribers, $1.87 billion in revenue, and 7,100 employees. The number of subscribers was more than the combined number of Amazon Prime, Hulu, and Disney Plus subscribers through 2019-20. This also translates to a lot of watched content: The most-watch title in Netflix history as of April 2020, Spenser Confidential, had received 85 million views. The second most-watch title, 6 Underground, received 83 million views.

How Netflix Data Is Used for Personalization

Netflix’s growth is largely due to its ability to personalize content recommendations for users across the globe. To do so, Netflix collects data and uses algorithms to curate a personal experience for each user.

What Netflix Knows

Some of Netflix’s data is built from information that users voluntarily provide, like their name, address, e-mail, payment method, and content reviews. Netflix itself automatically collects other forms of data, such as the platform used to watch Netflix, a user’s watch history, search queries, and time spent watching a show. The company also collects some bits of data from other sources, such as demographic data, interest-based data, and Internet browsing behavior.

The Netflix Approach to Personalization

The personalization of the Netflix experience is multi-faceted. For instance, the company personalizes images, text descriptions, tags, and trailers. It also considers how much content should be shown to users as they browse and adapts the size of the content’s cover art. Additionally, it offers content recommendations specific to the watch history of the device.

There are four modes Netflix uses to build recommendations. The continuation mode encourages the user to continue watching a TV show. The discovery mode helps a user find a new movie to watch. The list mode feature titles the user added to the “My List” section. Finally, the re-watch mode is set up to enable the user to view a previously watched title.

Putting Data into Action

Collecting data is only one piece of the puzzle. Figuring out how to use it to solve problems is a much more challenging task. Data teams at Netflix use specific processes, tools, and techniques to gain insights from its treasure trove of data.

How a Typical Data Project is Structured

The first step to a data project involves defining success. This is done by understanding the business and goals as well as asking key questions regarding what needs to be measured and what is the metric of success. The next step is to create a technical plan that translates the business goal into a data problem, using data tools and existing techniques to solve data problems while being mindful of the latest research on data science. The third step is to create a proof of concept by using tools like SQL and Python as well as spreadsheets to share results with stakeholders. The final step is to develop a production model that modularizes code for reproducibility and improves algorithmic efficiency.

Data-Driven Careers

Netflix has grown to the size it is today thanks to the help of individuals passionate about diving deep into data.

Computer and Information Research Scientist

Computer and information research scientists address computing issues by developing theories and models, assist scientists and engineers in solving computing problems, and develop and improve software systems. The position requires a minimum of a master’s degree and has a 2018 median annual pay of $118,370.

Data Engineer

Data engineers develop technical solutions to improve data usage and data access, develop and translate computer algorithms into prototype code, and create reports, dashboards, and tools for users. The role requires a minimum of a bachelor’s degree and features a 2018 median annual salary of around $92,000.

Securing a Career in Data Analytics

A data analytics career pays exceptionally well – in financial terms as well as in terms of overall career satisfaction. Graduates of business analytics programs can expect a competitive job market with exciting opportunities to work for successful companies, such as Netflix.