Netflix Ad Recommendation System
The Netflix Ad Recommendation System is a product management-led initiative aimed at driving ad personalization while maintaining an engaging viewer experience. The product vision focused on optimizing ad placements using machine learning models to deliver high-relevance content, improving both user retention and advertiser ROI.
Product Overview This product leverages user behavior analytics, genre affinity, and subscription data to deliver contextual and engaging ad experiences. Designed with scalability in mind, the solution uses a real-time recommendation engine powered by collaborative filtering and natural language processing techniques. Key Features - Personalized Ad Targeting: ML models suggest ads based on viewing history, preferences, and watch patterns. - Seamless Ad Integration: Dynamic insertion points ensure non-intrusive ad delivery during natural content pauses. - Real-time Learning: Behavioral feedback loops adjust targeting accuracy in real-time. - A/B Testing Engine: Tests various ad formats (static vs. interactive), durations, and placements. Product Management Contributions - Strategic Visioning: Defined product scope, value proposition, and OKRs aligned with Netflix’s monetization roadmap. - Experimentation Framework: Designed and implemented A/B and multivariate testing strategies. - KPI Tracking: Built dashboards with Power BI to monitor CTRs, conversion rates, and user churn metrics. - Stakeholder Alignment: Collaborated with ML engineers, data scientists, UX teams, and ad sales for roadmap prioritization. Technology Stack - ML: Python (Scikit-learn), NLP, KNN algorithm - Data Processing: Pandas, NumPy, MySQL - Infrastructure: Docker, AWS for scalable deployment - Visualization: Power BI, Matplotlib - Prototyping: Figma (UX flows), Jupyter Notebooks (model analysis) Milestones & Achievements - Improved ad click-through rate by 18% over baseline - Reduced bounce rate by 22% on ad-supported tier - Delivered 3 MVP releases across 6-month Agile cycles - Captured over 10K user feedback sessions to inform feature iteration Business Impact The initiative enhanced ad delivery relevance, increasing ad revenue while preserving user satisfaction. Resulted in reduced churn for the ad-supported plan and new monetization channels based on user profiles and ad engagement.