Abstract
This research study investigates the complex processes by which Netflix uses data analytics to personalize suggestions for each user. Knowing the nuances of recommendation algorithms becomes critical as streaming platforms continue to rule the entertainment industry. This study tries to clarify the basic ideas behind personalized content suggestions by thoroughly examining Netflix's approach, including algorithms, data collection techniques, and user engagement measures. It also explores the moral ramifications and privacy issues related to these data-driven behaviors. This study provides insights into how personalized recommendation systems will develop in the digital age by examining the effects on user satisfaction, retention, and overall business success. KEY WORDS Data analytics, recommendation algorithm, user engagement, personalized content.
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