Big Data Analytics at Netflix

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Netflix services fall under the subscription video-on-demand (SVOD) services category as a so called over-the-top (OTT) provider, which streams content over the web directly to its customers’ devices (Iordache et al., 2021; Pajkovic, 2021). Netflix’s revenue is mainly generated by users’ subscription fees (Kasula, 2020), and to keep customers from cancelling their access, the company relies heavily on the Netflix Recommender System (NRS) which combines big data analytics with powerful machine learning (ML) algorithms (Jackman & Reddy, 2020; Pajkovic, 2021) to keep users engaged and satisfied with the service. 

The aim of the NRS is to offer a personalized experience and provide viewing recommendations to each customer (Pajkovic, 2021) based on preferences generated by their rating history (rating the titles they watch), Netflix’s requests for feedback, a “similarity” approach across subscribers, movies, genres, etc. (Kasula, 2020). The NRS is driven by powerful machine learning (ML) algorithms that feed from large datasets containing the titles offered in the Netflix platform along with their associated metadata (genre, actors, category, year the title was released, director, etc.), big data collected from subscribers that gets generated by their interaction with the platform (title rating, viewing history, scrolling behavior, watch length, fast-forwarding, exiting, search text, etc.) (Pajkovic, 2021), external data (box office details and reviews from critics), demographics, language, culture, and social media details (Kasula, 2020). The combination of these big data with algorithmic technology and intricate analytics, improves Netflix’s ability to deeply understand subscribers’ preferences and drives improved recommendations, ultimately resulting in satisfied customers. 


The success of the NRS relies on the data the ML algorithms learn from and big data analytics, allowing engineers to improve on the algorithms and supporting the Netflix’s decision-making process (Jackman & Reddy, 2020).

Manko (2021) states big data analytics enhances the decision-making process by enabling real-time decision-making, managing risk, identifying and predicting market trends, including those of the customer base by extracting meaningful insights and anticipating their wants and needs. Netflix leverages off the expertise of analytics engineers and visualization engineers to extract actionable insights from big data in efforts to improve the NRS, and continually address the challenge of how to improve the user experience, what titles to add on to the users’ catalog, etc. (Jackman & Reddy, 2020). 

Netflix draws enormous analytics capability by aligning its analytics staff across six different business areas, and although they each have a major focus, the staff is free to contribute in other areas to improve the NRS, including the decision-making process, and to simply make Netflix a better company. The six business areas described in Jackman and Reddy (2020) are: (1) Product (the Netflix app, streaming, content, customer service, and user recommendations), (2) Content (licensing, titles catalog, and titles buying decisions), (3) Membership (subscriber engagement and retention), (4) Studio (production of Netflix’s own movies and shows), (5) Marketing (brand awareness at regional and global levels), and (6) Platform (state-of-the-art engineering). 

Netflix’s SVOD service platform relies heavily on ML algorithms and big data analytics to enhance the performance of the NRS and maintain their customers engaged, satisfied, and from cancelling their access as the main source of revenue comes from subscription fees. Big data collected from the Netflix platform, external data, and social media information, along with the company’s unique business alignment to six core focus areas, allows analytics staff to extract actionable insights that improve the NRS and support the company’s decision-making process.   





References 

Iordache, C., Raats, T., & Afilipoaie, A. (2021). Transnationalisation revisited through the Netflix original: An analysis of investment strategies in Europe. Convergence, 1-19. https://doi.org/10.1177/13548565211047344 

Jackman, M., & Reddy, M. (2020, September 18). Analytics at Netflix: Who we are and what we do. Netflix Technology Blog. https://netflixtechblog.com/analytics-at-netflix-who-we-are-and-what-we-do-7d9c08fe6965 

Kasula, C. P. (2020, June 28). Netflix Recommender System: A big data case study. Towards Data Science. https://towardsdatascience.com/netflix-recommender-system-a-big-data-case-study-19cfa6d56ff5 

Manko, B. A. (2021). Big data: The effect of analytics on marketing and business. Journal of Information Technology Teaching Cases, 1-7. https://doi.org/10.1177/20438869211057284 

Pajkovic, N. (2021). Algorithms and taste-making: Exposing the Netflix Recommender System’s operational logics. Convergence, 1-22. https://doi.org/10.1177/13548565211014464

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