content people
need leaves clues.*
*sometimes they're hiding in the search data.
what were people
actually using?
Maintaining a knowledge base creates a lot of possible priorities. Search and engagement data gave me another way to decide where attention could have the most value.
I analyzed article engagement across a seven-month period to identify which content was attracting the most demand, when usage was peaking, and what those patterns could tell us about maintenance and discoverability.
not every article
deserves equal attention.
A small group of support and call-flow content was driving the majority of engagement. Instead of treating the knowledge base like one giant maintenance list, the data created a clearer picture of where accuracy, discoverability and optimization mattered most.
protect high-traffic content
Prioritize accuracy and maintenance where usage shows people consistently depend on the answer.
follow the spikes
Monthly patterns can reveal launches, process changes or moments when certain guidance becomes especially important.
question low engagement
Low usage can be a reason to revisit search terms, placement, relevance or whether the content needs to exist at all.
keep measuring
Content performance turns maintenance from a periodic cleanup into an ongoing feedback loop.
analytics doesn't replace editorial instinct. it gives it somewhere better to look.