How Netflix Quantifies the ROI of Its Personalized Recommendations
Netflix blends large‑scale A/B tests with causal uplift models to isolate how its recommendation engine drives watch time and reduces churn. The post walks through the statistical pipeline—from randomization to bias‑corrected metrics—showing a measurable lift of several percentage points. This blueprint lets any product team prove the business value of personalization.