People also ask
In practice
Every query implies a page type that satisfies it, and a link aimed at a mismatched page spends budget teaching engines nothing: an informational query answered by a product page loses to the guide that actually explains; a comparison query pointing at the homepage bounces the buyer. Intent mapping makes the match explicit: for each target cluster — definitions, comparisons, “best of”, alternatives, pricing — the plan names the page that serves that intent and the publisher context that would credibly reference it.
The link types then follow the intent: informational intents take guest posts and linkable assets (the guide, the tool, the data study); comparison intents take insertions into the roundups that serve them; navigational and brand intents take the mention layer — the third-party pages that corroborate what the brand is. The mapping also polices the anchor strategy: anchors that describe the intent (“API monitoring comparison”) work when the linked page delivers it; anchors chasing a keyword the page cannot satisfy convert nothing and eventually rank nothing.
The AI layer runs on the same logic: engines answer intent-shaped prompts by citing intent-matched sources, so a page that serves “best tools for X” intent — honestly, currently, completely — is both the ranking candidate and the citation candidate. The operational artefact is the map itself: keyword or prompt cluster → intent → target page → placement type → anchor approach, reviewed as intents migrate — because categories rename their problems, and a map built on last year’s vocabulary routes this year’s budget to yesterday’s pages.
See also: Keyword-to-page mapping, Prompt research.
