Netflix: a streaming giant’s big data approach to entertainment

Netflix used data analytics in its transformation from a DVD-renting service to the world's most valuable media company

Netflix claimed 35 leading nominations at this year’s Academy Awards, beating its previous record of 24 it had set last year. [1] The movie, “Mank”, led with 10 nominations, including Best Picture. The accomplishment cements its place among other movie studio giants such as Disney, which it had surpassed in 2020 to become the most valuable media company. [2]

The streaming giant’s rise from a DVD-rental service to the world’s most valuable media company was powered by data analytics, which helped it understand its subscribers’ taste and tailor unique viewing experiences for each user. The same algorithms that helped with content curation now help Netflix with content production and marketing. In fact, its success with big data now serves as its competitive advantage over ever growing number of streaming platforms. It boasts a 93% customer retention rate, compared to Amazon Prime’s 75% and Hulu’s 64%. [3]

From the very beginning, the company collected and analyzed user data to create unique profiles. The data collected was basic at first and included information such as subscriber’s demographics, shows liked, categories browsed, or titles searched. [3] Another layer of data and complexity of the algorithm was added through the streaming mode of content delivery. Today, Netflix collections information on user interactions with presented content, such as the date and time a user watched a show, which device was used, whether the show was paused, resumed after pausing, and finished, and the time it took for a user to watch the show. These data points and many others power the “recommendation engine,” which curates the wealth of content on the website to deliver the perfect show selection to users every time they sign onto Netflix. Netflix takes the same approach to data analytics when choosing which shows to distribute on its platform. For example, its decision to green light the Jenji Kohan’s show, “Orange is the New Black,” was based on the producer’s previous success among its subscribers with the show, “Weeds.” [3]

In recent years, Netflix felt the pressure to enter content production as content producers, such as Disney and Disney+, began to enter the streaming space to claim a slice of the ever growing pie and pull its products from the platform [4]. When the company made this move, it continued to use data analytics to guide every step of the process. In 2013, the company’s announcement to produce David Finch’s remake of “House of Cards” with a $100 million budget might have shocked the industry, but for Netflix “the success was never really in doubt.” [5] The company bet on the talent, genre, and large loyal fan-base of the original show, all of which predicted it to be the “perfect show.” The success of the show was not a fluke either, as Netflix repeated it with other shows, including “Orange is the New Black,” “Stranger Things,” and most recently “Queen’s Gambit.” [6]

Finally, Netflix takes the same approach to marketing the content it produces, saving it time and money. For the same show, “House of Cards,” it cut different trailers to promote the show to its diverse subscriber base. [3] The profiles with preference for strong female characters were shown the trailer version that focused on its female characters, while those who have shown preference for Finch’s prior work were shown the trailer version that focused on his role in the show’s production. In the future, the company plans to deliver custom trailers for most of the content on its website, further increasing the value it provides to its users in finding the perfect viewing experience every time they sign on to Netflix.

References: 

[1] Lang, B. 2021. Netflix Dominates 2021 Oscar Nominations, Disney Plus and Apple Score First Nods. Variety. Available at: <https://variety.com/2021/film/awards/netflix-oscar-nominations-2021-disney-plus-hulu-1234930905/> [Accessed 23 March 2021].

 

[2] Vlastelica, R. 2020. Netflix Market Cap Surpassess Disney Amid ‘Stay at Home’ Orders. Bloomberg. Available at: <https://www.bloomberg.com/news/articles/2020-03-24/netflix-market-cap-surpasses-disney-amid-stay-at-home-orders> [Accessed 23 March 2021].

 

[3] Dixon, M. 2019. How Netflix used big data and analytics to generate billions. Selerity. Available at: <https://seleritysas.com/blog/2019/04/05/how-netflix-used-big-data-and-analytics-to-generate-billions/> [Accessed 23 March 2021].

 

[4] Franck, T. 2019. Disney’s streaming service will rival Netflix with 160 million subscribers, JP Morgan says. Available at: <https://www.cnbc.com/2019/03/06/disneys-streaming-service-will-rival-netflix-says-jp-morgan.html> [Accessed 23 March 2021].

 

[5] Markman, J. Netflix Harnesses Big Data To Profit From Your Tastes. Forbes. Available at: <https://www.forbes.com/sites/jonmarkman/2019/02/25/netflix-harnesses-big-data-to-profit-from-your-tastes/?sh=cc413d966fdc> [Accessed 23 March 2021].

 

[6] Bikker, Y. 2020. How Netflix Uses Big Data to Build Mountains of Money. The Startup. Available at: <https://medium.com/swlh/how-netflix-uses-big-data-to-build-mountains-of-money-829364caefa7> [Accessed 23 March 2021].

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Student comments on Netflix: a streaming giant’s big data approach to entertainment

  1. Super interesting. I’ve always wondered to what extent Netflix incorporates data in the production process. For example, I get that Netflix used customer segmentation to determine that House of Cards would be a good fit for their audience given its broad strokes (drama, politics) but do they ever get so specific as to use certain actors/actresses? Do they select certain film sites? Given the creative nature of show production I think this is an interesting dynamic to balance.

    1. Hi Merrill, although I cannot find concrete proof of Netflix, some articles referenced above allude to them using popularity of certain actors in casting decisions. I too think it is interesting how formulaic they can get before every production starts to feel like something that their viewers had already seen.

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