Personalization is a key feature of online platforms like Netflix, influencing user discovery and engagement. However, measuring the true value of personalization presents a significant challenge, as recommendations are targeted based on predicted user preferences, making it difficult to disentangle recommendation effects from user appeal. A naive approach of counting recommended views is insufficient because it doesn't establish causality. Running experiments to turn off personalization entirely would negatively impact user experience and is therefore not a viable measurement strategy.Instead, researchers developed a hybrid approach combining a behavioral model with small-scale A/B tests. This model estimates how recommendations influence both viewing decisions and title selection by separating underlying user preferences from the incremental impact of a recommendation. The model represents member preferences dynamically, capturing how past viewing history shapes current tastes. It explicitly models the "bonus" effect of recommendations on title selection, reflecting increased awareness and information.To validate the model's accuracy in predicting substitution patterns, it was compared against results from randomized experiments. These experiments introduced exogenous variation in title visibility, mimicking how recommendation algorithms change. The model demonstrated strong predictive power, with its simulated diversion ratios showing high correlation with experimental estimates.Using the validated model, researchers conducted counterfactual analyses to compare the current Netflix recommender system against baselines like random, popularity-based, and basic matrix factorization recommendations. The current system significantly outperformed these baselines in terms of aggregate engagement, reducing it by 16% for random recommendations and 12% for popularity-based ones. Even compared to matrix factorization, the current system showed a 4% gain in engagement.Beyond just increasing viewing, the model also assessed consumption diversity. The current system maintained a broader distribution of viewership across titles, unlike popularity-based and matrix factorization recommendations which led to increased viewership concentration. Random recommendations offered more diversity but at a considerable cost to engagement. Therefore, personalization not only drives more viewing but also matches users to a more diverse set of titles, reflecting varied tastes.