Netflix: Leveraving Big Data for Strategic Decision Making

This paper explores Netflix’s history of Big Data for decision-making, how data is derived, how it is leveraged, and resulting business impacts.

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About Netflix

Netflix headquartered in Los Gatos, California was founded in 1997 as a DVD-renting and mailing business, competing in a marketplace dominated by Blockbuster. Pivoting in 2007, it emerged as a global leader in on-demand streaming service, ultimately leading to the rapid downfall of Blockbuster (“Annual Report”, 2020).

Company Profile

Netflix’s business model offers a low-cost monthly subscription service for streaming as well legacy DVD-by-mail service (Pumchanut, 2018). It offers an ad-free platform with a diverse selection of movies, TV shows, and exclusively produced content in several languages that can be viewed on any internet-connected device as well as select titles for offline viewing (“Annual Report”, 2020). Netflix operates in an intense marketplace with competitors like Hulu, Amazon, Disney, HBO, AppleTV, AT&T’s WarnerMedia, NBCUniversal, YouTube, Peacock, Roku, Discovery+ in addition to Hollywood producers, gaming providers, TikTok, multichannel video programming distributors, and pirated content providers. It aims to be a winner in moments when customers pick from entertainment choices amongst competitors (“Annual Report”, 2020).

Key Operating Stats (“Annual Report”, 2020)

  • In 190 countries excluding China, Syria, North Korea, Crimea.
  • $25 billion revenue in 2020, net income $2.8 billion.
  • 9.4K employees.
  • 73 million U.S. subscribers, 204 million worldwide. Tier-pricing of basic, standard, and premium.
  • 2 million subscriptions for DVD-by-mail service in the U.S.
  • 70 movies produced in 2020 as compared to 90 by the top 5 production companies.
  • Offices in U.S., France, India, Brazil, Japan, South Korea, UK, Netherlands.
  • Production facilities in U.S., New Mexico, and Spain.


Netflix’s vision statement “Becoming the best global entertainment distribution service.” ( and intensive growth strategy are powered by a customer-oriented growth approach to penetrate new global markets, nurture existing markets, and diversify develop new content. It does not stream everything but leverages data to hand-pick content that audiences will love streaming on-demand, delivered in a personalized fashion, that’s ad-free, and can be streamed on different devices. Its strategy execution centers around leveraging Big Data to offer movies, TV series, and produce originals. Netflix’s strategy to gain competitive advantage (Porter’s model) counteracts external competitive forces from Amazon, Disney, WarnerMedia, Discovery, HBO, Walmart, and others. It differentiates by offering content for diverse audiences. Their strategy focuses on operational efficiencies, cost-effectiveness enabling aggressive global expansion.

Description of Big Data Use

Purkayastha, et al. (2013) and Brennan (2018) mention that Netflix leverages Big Data in the areas of global content production, content mix, user interface, licensing, marketing, payment partnerships, and support of devices. Netflix analyzes data on individual viewing patterns including nuances of when subscribers pause, skip, stop, or switch to offer relevant, personalized recommendations and create brilliant, winning experiences. Netflix has leveraged viewing pattern insights to confidently venture into the production of original innovative content, cherry-picking directors, writers, actors, and securing licensing rights (Brennan, 2018; McCranken, 2013). The author expands that as part of its exponential globalization initiative, Netflix is adapting to local cultures/preferences, garnering local partnerships, and luring international viewers.

History of Big Data Use

Costa (2020) shares that in 1998 Netflix launched by renting and selling DVDs and later Blu-Ray discs. At that time, it had a parallel online offering of Blockbuster offering every single title. There was little thought to the titles customers would be interested in with the differentiator of an innovative delivery model that saved a trip and eliminated late fees (Sean, 2014). Purkayastha, et al. (2013) and Brennan (2018) mention that Netflix rapidly scaled with cloud technology investment in big data & analytics, deploying its huge dataset and leveraging mining competencies. In 2009, Netflix held an open-source crowdsourcing contest, giving away $1 million in prizes to refine its algorithms of predicting value based on movie ratings, rental details, and high-level customer information (Pumchanut, 2018). The author shares that this approach saved Netflix $1 billion annually via improved customer retention.

Brennan (2018) shares that data mining competencies have helped Netflix scale globally to 190 countries in a brief span of 8 years. A thoughtful three-stage expansion strategy involved choosing markets based on geography, similarities with the home market, availability of broadband, and presence of affluent customers. The author shares that insights gained about internalization helped expand the footprint from adjacent markets to distant markets. Brennan (2018) expands that this journey for Netflix came with its unique challenges like securing content licenses sometimes within a region or country, national regulatory restrictions that sometimes restrict content to certain markets, content needs in local regional languages, and potential members hesitant to pay fees and subscribe given the availability of free content. Today, with the push of a button, content can be simultaneously released to all of the 190 countries in 17 different languages — an advantage that provides a one-of-a-kind service to attract viewers to the platform (Costa, 2020).

Impact of Data Use on Business

With Big Data analytics as its core to growth strategy and a customer-centric model, Netflix has exponentially expanded globally curating and producing content for a host of devices. Brennan (2018) shares that leveraging data has helped Netflix make inroads even in markets where Amazon Prime reigned. The author shares that Netflix’s worldwide membership is now higher than all combined pure streaming services competitors and its international revenue now supersedes domestic earnings. Further, Costa (2020) shares that in 2019 Netflix signed 135 million new members and made more than $20 billion.

Netflix visual dashboards potentially take multiple metrics into account to understand viewer tastes. Given that typically viewers lose interest within 60–90 seconds of watching something and exploring 10–20 titles, Netflix aims to create engaging content. 80% of what subscribers view is influenced by the recommendation engine (Insidebigdata, 2018). It added 16 million new subscribers during the pandemic (“Annual Report”, 2020).

How data is used for decision-making

Netflix is a data-driven company that leverages business analytics, machine learning, AI, and deep learning (Costa, 2020). Its success rests on its ability to run analytics across all business functions to provide insights to varied stakeholder groups like members, executives, and partners.

Brennan (2018) shares that Netflix’s country-specific knowledge is both broad and deep and extends across politics, regulatory, cultural, competitor, and technical domains. Costa (2020) informs that the data analyzed includes volumes of metadata to attract new customers, keep existing sticky and delighted. The author shares that the recommendation engine takes into account viewer location, day of the week, time of the day, the device used, browsing and scrolling behavior being in addition to day/time viewers watch, search history, viewer ratings, pause/rewind/fast-forward events (Costa, 2020; Leonard, 2013; McCranken, 2013). Its machine learning algorithms use this rich data to constantly refine its predictive algorithms. Leonard (2013) shares that while Netflix may not be able to accurately say why an individual paused or skipped a video, it can do so accurately at scale with a larger sample size if enough viewers do the same at the same points of the show. Netflix knows which movies sell for weeknights vs weekend afternoons or Friday nights (McCranken, 2013).

Netflix identified that consumers in international markets have heavy use of mobile phones as a primary mode to access entertainment (Brennan, 2018). It has since then focussed on rich mobile user interface experiences from touchpoints of registration, authentication, and streaming efficiency of cellular networks. Brennan (2018) shares that the Netflix partnerships include cell operators; cable operators; internet service providers; remote and smart speaker device manufacturers to provide Netflix as part of the video-on-demand offering, and add “Netflix” button to remotes. Brennan (2018) shares that expansion into Turkey & Poland helped drive new features like subtitles, dubbing backed by marketing to attract early adopters.

In terms of marketing, Costa (2020) shares that artwork and imagery selection is based on facial expressions of the artist, lighting, colors, character positioning to appeal to viewer tastes. Aspects of production planning like identifying locations to shoot, shoot hours, scheduling actors and extras, technology choices (VFX) are streamlined using analytics to ensure it falls within budget constraints and distribution timeline (Netflix Technology Blog).

Netflix’s goal is to enable content creators the chance to bring their most creative ideas to fruition and entertain via innovative storytelling (Smith et al., 2019). Brennan (2018) shares that Netflix’s intent is not just to focus local-for-local but also local-for-global. It carefully chooses content and nurtures the creativity of artists. Areas where data is used for decision-making and predictive analytics includes determining titles to be streamed, content that should be licensed or produced, customer journey analytics from prospect to member and ongoing, marketing to existing customer base and attracting new members, portfolio mix effectiveness, and insights to implement different approaches in different markets.

Plans for future development of data and information systems for decision-making

Customer experience, pricing, and security are key areas where data analytics could transform the entertainment industry. Few areas of innovation include:

  • Experimentation on a borrowed concept from TikTok with an app called “Fast Laughs” that features 15–45 second snippets from its movies and sitcoms (Perez, 2021).
  • Presently 41% of Netflix users are watching content without paying the full price due to viewers sharing accounts and passwords (Costa, 2020). Collins (2021) shares that Netflix is pilot testing features to track down password sharing beyond a household via additional authentication, potentially generating additional revenue.
  • Alexander (2020), shares that Netflix is offering an opt-in service to help parents track content their kids are watching so they can understand their kids’ favorite shows and characters.
  • In an attempt to keep customers glued, Netflix is providing its top-tier members the option to download pushed content to view offline and while disconnected (Spangler, 2021). It would be interesting to see how Netflix plans to capture viewer events in offline mode and how it may forego some of the metadata in the spirit of revenue generation and sustainment.
  • Shaw (2019) shares that Netflix plans to publish its own online journal “Wide” that will feature artists who work on Netflix series and part of the Hollywood industry.

Experts have raised concerns infringing subscribers’ privacy in addition to constraining artistic creativity purely based on past viewing patterns (Purkayastha, et al., 2013; Schectman, 2012; Leonard, 2013). Netflix is balancing the need for original content and with streaming other’s content (Prince et al., 2018).

5-year Outlook

Netflix stocks outperformed (NYSE) during the pandemic as people turned to in-home entertainment technology and subscribers increased from 111M to 240M. Netflix’s business model will continue to keep evolving and catering to a wide array of genres and languages. It will hone its recommendation engine algorithms and adapting to changing consumer needs and desires. Being at scale Netflix will continue to offer budget-friendly subscription deals and aggressively gain market share, brand loyalty. It will become a leading content creator, creating millions of jobs and drawing viewers. There are some ethical concerns with Netflix’s metric of viewing hours and pushing content to audiences that may not be necessarily good or bad but more clickable.

Summary: Use of data for decision-making

We have come a long way from DVD-rentals and mail-ins. Netflix revolutionized the entertainment segment by quickly spanning 130+ markets internationally (Netflix Investors) catering to diverse people, cultures, languages across the globe.

In the past, Netflix’s growth was limited by the absence of high-speed broadband connections (Brennan, 2018). Enabled by accelerated growth of the internet and mass-scale adoption of smartphones, tablets, smart TVs, and the power of data intel, Netflix has successfully executed its international expansion strategy. Leonard (2013) shares that Netflix now knows more about our viewing preferences than what we know to selectively push content. Using this approach, it has captured new diverse market segments, tapping into local insights, and pioneering innovative ways of production (Brennan, 2018).

While Netflix is in an advanced mature stage in the data science journey it has new growth opportunities. It has a track record of disrupting itself and by generating intelligence from data, Netflix can prevail as an industry leader in the streaming business.


Annual Report. (2020). Netflix.

Alexander, J. (2020). Netflix wants to help parents connect with their kids by explaining what they’re watching. The Verge.

Brennan, L. (2018). How Netflix expanded to 190 countries in 7 years. Harvard Business Review.

Collins, T. (2021). Netflix is testing a new feature to crack down on shared passwords. USA Today.

Costa, C. (2020). How Data Science is boosting Netflix. Towards Data Science.

Data Science and the Art of Producing Entertainment at Netflix. (2018, March). Netflix Technology Blog.

How Netflix uses Big Data to drive success. (2018). Inside Big Data.

Schectman, J. (2012). Netflix uses Big Data to improve streaming video. Wall Street Journal.

Leonard, A. (2013). How Netflix is turning viewers into puppets.

McCranken, G. (2013). Will Netflix flourish where Hollywood failed. Harvard Business Review.

Prince, J. Greenstien, S. (2018). Does original content help streaming services attract more subscribers? Harvard Business Review.

Pumchanut, R. (2018). Netflix: Disruption creation with Big Data. Digital Harvard Business Review.

Purkayastha, D. Tangirala, V. (2013). Netflix: Leveraging Big Data to Predict Entertainment Hits. IBS Center for Management Research

Shaw, L. (2019). Netflix to publish magazines as it chases Hollywood awards.

Smith, M. Telang, R. (2019). Netflix and the economics of bundling. Harvard Business Review.

Smith, M. Telang, R. (2018). Data can enhance creative projects: Just look at Netflix. Harvard Business Review.

Taylor, B. (2018). To see the future of competition look at Netflix. Harvard Business Review.

Spangler, T. (2021, February). Netflix App Can Now Automatically Download Shows or Movies You May Be Interested In. Variety.

Perez, S. (2021, March). Netflix launches “Fast Laughs” a Tiktok like feed of funny videos. Techcrunch.




Exec Director StratEx - I bring to the table blend of data science, finance and strategy management skills with 20+ years of experience in insurance & fintech.

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Poonam Rao

Poonam Rao

Exec Director StratEx - I bring to the table blend of data science, finance and strategy management skills with 20+ years of experience in insurance & fintech.

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