Challenge
Editorial teams across a major local news network were making daily content distribution decisions based largely on instinct and experience. Without a systematic way to analyze historical performance, monitor competitor coverage, or match stories to the right platforms, digital leads spent significant time in editorial meetings without the data needed to confidently prioritize. Coverage gaps went undetected, platform-specific format decisions were inconsistent, and there was no feedback loop to learn what actually drove audience engagement over time.
As content demands scaled across Connected TV, Mobile, Social, and Web, the manual approach became increasingly difficult to sustain across 8 geographically distributed stations.
"With AI-driven recommendations, we walk into every morning meeting already knowing what the data is telling us - and our teams trust it."
Solution
Unframe enabled the broadcaster's editorial teams to move from instinct-driven publishing to a continuous, data-backed distribution strategy. To support this, Unframe designed and deployed an AI editorial recommendation platform tailored to the network's daily editorial workflows — giving digital leads real-time guidance on which stories to cover, which platforms to prioritize, and which formats will maximize audience engagement.
The solution connects Chartbeat performance history and CMS story inventory to surface a daily "Top 10" recommendation list before each editorial meeting. It monitors competitor coverage to identify content gaps and suggests platform-specific formats in real time. Editors approve or adjust recommendations directly in the tool, creating a feedback loop that continuously sharpens the model - keeping editors in control while eliminating the manual pre-work.
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