Case Study: Hearst Newspapers achieves real-time, accurate content classification and higher ad revenue with Google Cloud Platform

Hearst Newspapers Engaging readers with cloud machine learning

Hearst Newspapers, the newspaper division of Hearst reaching millions across 30+ digital properties, faced a growing challenge: manually sorting and labeling roughly 3,000 new articles a day with a legacy system that was slow and often inaccurate, forcing teams to leave content unclassified and limiting personalization and ad targeting.

By adopting Google Cloud Natural Language API (with BigQuery, CDP integration, and other GCP tools), Hearst now classifies content and recognizes entities in real time across all properties. The automated workflow improved speed and accuracy, eliminated much manual tagging, enabled full-content inventory matching for ads, supports forecasting of ad performance, segments users by reading habits, and has increased ad revenue while saving editorial and ad ops hours.

Hearst Newspapers

Naveed Ahmad

Senior Director of Data