Netflix builds a causal machine learning framework to identify successful visual components in promotional artwork
Netflix's vast and diverse catalog made scalable experimentation on promotional artwork impossible using traditional A/B tests, which investigated only one visual aspect at a time and required manual image labeling or creation of new asset variants, severely limiting how many titles could be tested simultaneously.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Artwork served and engagement logged
Netflix's Artwork Personalization serves promotional artwork assets to hundreds of millions of members every day, and user engagement patterns are captured to determine whether engagements resulted in successful title selection.
Tools used
computer vision algorithmsXGBoostingcausal forestDouble ML
Outcome
Netflix developed a causal ML framework using Double ML and causal forests that reduces experimental effort to uncover causal relationships in artwork performance and delivers data-driven creative insights to strategists and creatives, enabling genre-level guidance on artwork selection.
What failed first
Previous A/B tests investigated one artwork attribute at a time, typically within a single genre, which was not scalable due to manual labeling requirements and missed the multidimensional nature of artwork performance.