Use Case: Driving 80% of watch time through intelligent content recommendations

Problem:
With thousands of shows and limited attention span, helping users discover content that fits their preferences is critical to retention.

Math-Based Solution:

• Matrix factorization using Singular Value Decomposition (SVD) to uncover latent factors that relate users and shows.

• Collaborative filtering for personalized recommendations.

• Reinforcement learning models test which thumbnails or previews maximize engagement.

• A/B testing + statistical significance testing ensure that any UI or model change delivers real improvement.

Impact:

• 80% of watch time is driven by recommendations.

• Estimated to save over $1 billion annually in churn prevention.

• Personalized experience feels like “content curated just for me.”