Search
Close this search box.

Is Netflix Using AI To Be The Best Streamed Platform?

This article explores Netflix's numerous AI efforts, focusing on prominent executives and their contributions.

Netflix is “THE” go to streaming platform covering global sensation series like the “Squid Games” to the 94 episodes of the renowned series “Grace and Frankie,” which cover genres such as comedy, mystery, drama, and fantasy.

Choosing what to watch might be difficult for new Netflix customers who have exhausted their friends’ recommendations. Enter Netflix’s advanced approach to improving the user experience: machine learning and artificial intelligence (AI).

At the heart of Netflix’s recommendation engine is machine learning technology, which analyses user preferences and a variety of behavioural indications. The programme methodically evaluates data from 223 million paying users, including specific feedback such as thumbs up or down ratings, viewing hours, geographic location, binge-watching behaviours, and so on.

By analysing these patterns, the AI continually improves its grasp of individual preferences, allowing it to provide accurate and personalised content suggestions. This data-driven strategy guarantees that consumers are continuously given suggestions based on their specific viewing interests, thereby improving their Netflix experience. This article explores Netflix’s numerous AI efforts, focusing on prominent executives and their contributions.

Early Adoption and Evolution of AI at Netflix

Personalized Recommendations

Netflix depends heavily on recommendation and search algorithms to provide personalised entertainment options to its worldwide audience. These technologies are constantly modified to improve the user experience.

Personalisation begins on the site and continues through targeted messages and app alerts, ensuring that members are informed and engaged. Advanced search tools allow customers to effectively traverse Netflix’s huge catalogue by supporting numerous languages and input methods from a variety of devices, including TV remotes and voice commands. Netflix is also experimenting with new methods to offer and explain suggestions in order to save browsing time while increasing viewing satisfaction.

Despite tremendous advancements in recommendation algorithms, Netflix recognises that there is still potential for improvement. The firm is dedicated to improving its algorithms and investigating additional personalisation opportunities. Their research covers a wide range of topics, including machine learning, recommender systems, contextual bandits, reinforcement learning, natural language processing, fundamental models, and causal inference, with results consistently published at major conferences.

According to Reed Hastings, Former Chairman of Netflix, “Technology has been the story of human progress from as long back as we know. In 100 years people will look back on now and say: That was the Internet Age.”

Thumbnail Personalization

Netflix uses AI to create personalized thumbnails for each user by analyzing and ranking hundreds of frames from movies and TV shows. This AI-driven approach ensures that the visual representation of content matches individual preferences, increasing engagement.

Initially, Netflix sourced title images from studios but found them ineffective in a grid format. To address this, Netflix began creating its own thumbnails from video frames. For example, analyzing frames from a single episode of “Stranger Things” involves over two million static frames, making manual selection impractical.

To scale this effort, Netflix developed AVA, a suite of tools and algorithms that scan each frame in the library. AVA evaluates metadata and ranks frames based on criteria like face detection, motion estimation, camera shot identification, and object detection.

The Frame Annotation process focuses on frames representing titles and character interactions, excluding undesirable traits like blinking or blurring. Netflix trained its Convolutional Neural Network (CNN) with a dataset of twenty thousand faces, prioritizing main characters based on their prominence and interactions.
Each frame receives a score indicating its suitability as a thumbnail. AVA considers factors like actor prominence, image diversity, and maturity filters to compile the final list of thumbnails that best represent each title. This AI-driven process ensures engaging and tailored thumbnails, enhancing the overall viewing experience.

Source: Netflix TechBlog

Advanced AI Applications

Optimal Streaming Quality

With over 220 million active users, maintaining high-quality streaming is a significant challenge. Netflix uses AI to predict subscriber numbers and optimize video quality, even during peak times. Netflix utilizes data science for streaming optimization, employing encoding algorithms, bitrate adaptation, and content delivery networks to ensure users enjoy a seamless viewing experience. These efforts have notably reduced buffering time and boosted user engagement.

The company leverages a mix of supervised and unsupervised machine learning algorithms to refine streaming quality. In their study, “Optimizing Video Quality for Millions of Netflix Customers”. It describes how Netflix employs machine learning models to predict buffering likelihood and dynamically adjust video quality.

Furthermore, Netflix strategically places video assets closer to subscribers in advance, further enhancing the viewing experience.

Experimentation and Causal Inference

Netflix’s Data Science and Engineering organisation emphasises experimentation and causal inference. Data science teams work collaboratively with Product Managers, engineering teams, and other business divisions to plan, perform, and analyse experiments. Netflix’s internal experimentation platform (XP) is important to this initiative, since it enables scalable experimentation and promotes cooperation across centralised and business-unit-specific teams.

At Netflix, data scientists that specialise in experimentation and causal inference gain deep domain experience in their particular business sectors. They use rigorous scientific approaches to improve the Netflix experience for existing and prospective subscribers, actively participating in all stages of the experimentation lifecycle, from data discovery and test design to results analysis and insight synthesis. This methodology allows Netflix to quickly innovate across its service offerings, employing scientific information to improve member satisfaction.

Generative AI and Storytelling

AI in Content Creation

With Bela Bajaria, as the Chief Content Officer of Netflix has started exploring the use of AI in storytelling. This initiative aims to equip content creators with AI tools to enhance narrative techniques and engage audiences more effectively. Netflix’s CEO highlighted the transformative potential of AI in content creation during an investor meet, drawing parallels to the success of their recommendation algorithms.

In 2023, Netflix gained approximately 8.9 million new subscribers, reaching a total of about 238.4 million worldwide. With a vast catalog of over 15,000 movies and series, navigating Netflix can be overwhelming for users. This algorithm not only enhances user engagement but also informs Netflix’s content production strategies based on viewing patterns and preferences. In 2024, Netflix’s recommendation system remains sophisticated, guiding user interactions from the homepage with curated selections like “Selected for you,” “Trending,” “Similar to,” and “New,” influencing the success of Netflix originals like “Stranger Things” and “The Witcher.”

“These new content types require us to really evolve the experience that lives today. We like to joke that our current homepage experience on TV is about 10 years old. That doesn’t mean we haven’t done tremendous amounts of work to improve it over time. But at its core, it’s remained the same and it really was built and designed for a streaming video-on-demand service. Every facet of how we’ve arranged everything anticipates an on-demand video experience.,” said Eunice Kim, Chief Product Officer. “If we’re doing our jobs, we shouldn’t be talking about the product all that much, but it should be working for people.”

Innovating People Management

Netflix’s incorporation of AI into talent management demonstrates a purposeful strategy to improving company processes while prioritising customer experience. Sergio Ezama, Chief Talent Officer of Netflix, emphasises the need of using AI technologies strategically to empower staff and enhance decision-making processes. He emphasises that HR directors should choose whether to build AI skills internally or buy them externally, assuring alignment with employee demands and increasing operational efficiency.

Ezama emphasises AI’s role in democratising access to insights and data, allowing executives to gain vital information straight from their devices. This transition decreases reliance on conventional HR Business Partners (HRBPs) for mundane duties, allowing them to focus on more valuable operations. 

Controversies and Challenges

The integration of AI in storytelling has not been without controversy. The documentary “What Jennifer Did” faced criticism for using AI-generated imagery without proper disclosure, highlighting the ethical challenges of AI in creative processes. Despite these challenges, Netflix remains committed to exploring AI’s potential in content creation.

Netflix does not simply compete with rival streaming services for client attention; it also attempts to attract interest within the critical first 60 seconds of surfing. Netflix’s personalised recommendation engine, which uses AI, is critical for keeping customers and minimising churn, saving the firm an estimated $1 billion each year. By personalising video recommendations based on individual watching habits rather than popularity, Netflix not only increases user engagement but also increases exposure to a wider range of material, influencing trends in original programming and content production. As Netflix refines its algorithms, future developments promise even greater influence on consumer satisfaction and business productivity.

Abuse and Fraud Detection

Subscription abuse, which includes actions like password sharing to escape paying, is a major issue in the streaming industry, particularly for platforms such as Netflix. Netflix has introduced tough steps to counteract borrowed account logins, which are expected to cost the company $10 billion each year. These include using device-level and geolocation data to analyse user access habits, analysing IP addresses and device IDs, and doing behavioural analytics. By implementing these tactics, Netflix hopes to prevent subscription abuse, optimise income sources, and enforce service rules, all while providing a smooth user experience to paying users.

Future Prospects

Netflix’s commitment to AI and Gen AI continues to shape its future. The company is exploring new AI-driven features and improvements, aiming to enhance user experience and maintain its competitive edge. As AI technologies advance, Netflix appointed Elizabeth Stone as the Chief Technology Officer. Netflix’s AI initiatives are expected to evolve, offering even more personalized and engaging content to its global audience.

Netflix has indicated that adopting new and emerging technologies could heighten the risk of facing intellectual property claims, especially concerning AI-generated content where copyright and other protections are uncertain. As generative AI-created entertainment continues to evolve, the landscape remains fluid and subject to rapid change.

Dean Garfield, Netflix’s Vice President of Public Policy, is responsible for increasing the company’s worldwide reach through smart public policy initiatives. One major endeavour under his supervision has been the creation of Netflix’s own back-end distribution network, Open Connect. This network sends hardware to local ISPs, allowing them to store and serve Netflix streaming material locally. This technology not only reduces load on ISP servers, but it also provides consistent streaming experiences, especially during high-demand events such as the global sensation “Squid Game.” Open Connect is supplied at no cost to network operators, and is complemented by technical assistance and constant monitoring by Netflix to ensure service quality.

Conclusion

From personalised recommendations to AI-driven storytelling, Netflix’s AI integration demonstrates the company’s dedication to innovation. By integrating AI and Gen AI, Netflix has enhanced the user experience and changed streaming industry norms. The company’s further exploration of AI’s capabilities is expected to have a greater influence on the future of entertainment technology.

Picture of Anshika Mathews
Anshika Mathews
Anshika is an Associate Research Analyst working for the AIM Leaders Council. She holds a keen interest in technology and related policy-making and its impact on society. She can be reached at anshika.mathews@aimresearch.co
Subscribe to our Latest Insights
By clicking the “Continue” button, you are agreeing to the AIM Media Terms of Use and Privacy Policy.
Recognitions & Lists
Discover, Apply, and Contribute on Noteworthy Awards and Surveys from AIM
AIM Leaders Council
An invitation-only forum of senior executives in the Data Science and AI industry.
Stay Current with our In-Depth Insights
The Biggest Exclusive Gathering Of CDOs & Analytics Leaders In United States

MachineCon 2024
26 July 2024, New York

MachineCon 2024
Meet 100 Most Influential AI Leaders in USA
Our Latest Reports on AI Industry
Supercharge your top goals and objectives to reach new heights of success!

Cutting Edge Analysis and Trends for USA's AI Industry

Subscribe to our Newsletter