Predicting Risk in Content Launches: How Data-Driven Insights can Transform Launch Planning

This post details the evolution of Netflix's content launch risk prediction system by introducing predictive models (boosted tree regression) for estimating Locked Cut and IMF media asset delivery dates. It quantifies schedule inaccuracy using Accumulated Error Days (AED) and demonstrates significant improvements in Mean Absolute Error (MAE) and coverage compared to manual scheduling through backtesting. The system leverages production-level signals, title metadata, and seasonal data, captured in a snapshotted form to enable flexible, phase-agnostic modeling. Serving logic defaults to scheduled dates where the model underperforms, and teams can view both dates side-by-side.