What was the challenge?
Journalists were spending time tagging stories by hand and searching the archive for related coverage. Existing tags were too broad to show which kinds of stories resonated with which audiences, communities and regions, so analytics couldn't guide editorial decisions at that level.
What did Druid Learning do?
- 1
Ran a workshop with the editorial team to align the tagging framework with the newsroom's content strategy.
- 2
Relabelled historical content, and labelled new content as it is published, across eight dimensions: user needs, article type, location, community, topic, tone, call to action and platform.
- 3
Integrated with the CMS so journalists see suggested tags and related stories while they write.
- 4
Connected the labels to Chartbeat and Google Analytics through an automated data pipeline.
- 5
Handled all development, integration and support, with no development work needed from the newsroom's team.
What were the results?
- 2M+ assets processed.
- Less time spent on manual tagging and on searching for related coverage.
- Archive content searchable by the new labels, giving editors faster access to background and context.
- Story performance and audience engagement measurable by user need, article type, topic, location and community.
Want the full details?
This is a summary of our work with NPR. Talk to us for the full story, including the approach, the integrations and how it could apply to your content.
