AI & Research
What Is Content Intelligence?
Treating a content library as a dataset, so decisions about what to write, merge or retire come from evidence rather than opinion.
Definition
Content intelligence is the practice of analysing a body of content as structured data in order to guide editorial decisions. It combines inventory, performance data, topical classification and gap analysis to answer questions such as which pages compete with each other, which topics are uncovered, and which pages should be consolidated.
The inputs
- A complete inventory of URLs and their primary content
- Performance data per URL, typically from Search Console and analytics
- Topical labels, increasingly produced by embedding and clustering rather than by hand
- Ranking and citation data showing where each page currently appears
The questions it answers
The useful output is not a score but a decision. Which two pages are targeting the same intent and splitting their own signals. Which page earns impressions but no clicks, suggesting a mismatch between title and content. Which topic in the cluster has no page at all. Which pages are cited by AI answers and what those pages have in common.
Where clustering helps
Grouping pages or queries by meaning rather than by exact string is what makes this tractable at scale. Embedding the text and clustering the vectors surfaces near duplicate intent that keyword matching misses, which is usually where cannibalisation is hiding.
The useful output of a content audit is not a score. It is a list of pages to merge, rewrite, or retire, with a reason attached to each one.