Predicting Content Launch Risk: Transforming Release Planning with Data-Driven Insights

All content at Netflix goes through multiple stages from development to launch readiness. In particular, once the final video file, the IMF (Interoperable Master Format), is delivered during the Post-Production stage, time-sensitive launch preparation work begins, including subtitle creation, compliance review, and quality control (QC).

Currently, schedules manually provided by production partners have limitations such as data gaps and low accuracy. Schedule delays caused by variability in the production process can directly lead to launch failures, and schedule discrepancies right before launch are especially critical.

To address this, Netflix introduced the Accumulated Error Days (AED) metric. AED measures the cumulative deviation between the scheduled delivery date and the actual delivery date, and analysis showed a strong correlation where higher AED increases the likelihood of content launch delays.

To solve this problem, Netflix is developing a predictive model using Boosted Tree Regression. Through this, they aim to provide expected delivery dates (ETA) for unlisted assets and improve the accuracy of existing schedules, proactively managing launch risk.