YouTube Ads Creative Analysis- Google Developers Blog

This post details a machine learning-based approach for analyzing YouTube ad creatives to understand which components affect view-through rate (VTR). It outlines a 5-step process: defining business questions, extracting raw components using Google Cloud Video Intelligence API, engineering features (manually and with LLMs), modeling VTR with engineered features, and interpreting the results. The post highlights the use of LLMs for AI-based feature engineering and discusses challenges like feature interactions and data representativeness. It also suggests activation strategies using Ads Creative Studio and Video Experiments.