marketing_ops · workflow
Netflix uses transfer learning and knowledge graphs to support content commissioning decisions at global scale
Content decision makers at Netflix needed to identify comparable titles and predict audience sizes for new content at global scale, but conventional methods relied on a limited set of comparables and performance metrics that could not handle Netflix's increasingly vast and diverse catalog.
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 · Executive requests comparables
Content, marketing, and studio production executives need comparable title references and audience size estimates when evaluating a new title.
Tools used
MetaflowBERTLSTMGRUTransE
Outcome
Machine learning embeddings enabled scalable surfacing of comparable titles and significantly improved audience size prediction accuracy, supporting content, marketing, and studio executives in commissioning decisions globally.
Results
Cost replacedsignificantly improve prediction accuracy
Source
https://netflixtechblog.com/supporting-content-decision-makers-with-machine-learning-995b7b76006f
Grounding & classification
Source type: technical build writeup
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forecastingpredictive analyticsrecommendation systemknowledge baseproduct catalognamed customerproduction runtime claimedsource backedtools describedworkflow describedmediaaccuracy improvementemployee productivitytechnical build writeupmarketing opsdata sync enrichment