SEO Keyword Research & Intent Mapping

What this skill does

SEO Keyword Research and Intent Mapping extends classic keyword research with intent mapping at scale plus AI fan-out sub-query discovery—modeling how ChatGPT, Perplexity, and Google AI Overviews decompose user questions into follow-up queries and related prompts. It builds keyword universes tied to journey stages, SERP behaviors, and LLM conversation patterns so content covers not only head terms but the query graph around them.

Deliverables include intent-labeled clusters, sub-query maps, content gap matrices versus competitors, and monitoring hooks for Prompt Picker libraries.

Who it's for

Advanced SEO strategists, AEO-focused teams, and content leads building definitive category resources. Essential when single-keyword optimization fails because SERPs and AI answers aggregate many micro-intents into one result or citation.

Agencies differentiating research deliverables from basic planner exports use this for depth.

Key workflows

Start with pillar topic; expand primary keywords then fan-out sub-queries via SERP PAA, related searches, forum language, and AI prompt simulation. Map each node to intent and recommended content element (section, FAQ, tool, comparison table).

Crosswalk with existing site content; identify orphan intents and over-served duplicates. Prioritize by commercial value and AI citation opportunity. Link clusters to internal linking plans and BOFU/MOFU/TOFU labels.

Common questions

Teams ask fan-out versus traditional long-tail—fan-out explicitly models conversational and multi-turn research behavior in AI tools. Another question is infinite expansion risk; workflows define depth limits and scoring cutoffs.

International intent differences get locale-specific sub-query notes when multi-market.

How to use in Metaflow

Provide topic, domain, competitors, and customer interview snippets if available. Request intent map for one pillar or site-wide universe overview. Connect to Content Strategy calendar and Prompt Picker for AI monitoring on high-value sub-queries.

Intent maps visualize parent-child query relationships useful for internal linking anchors and FAQ expansion—each sub-query suggests a section or standalone supporting article. AI fan-out methodology documents prompt chains simulating buyer follow-up questions after initial category research, exposing content gaps traditional keyword tools miss. Prioritization scoring blends MSV proxies, SERP difficulty, commercial intent, and citation opportunity in AI answers. Handoff to content teams includes brief stubs per cluster with recommended word count ranges and SME topics to interview.

Cross-functional workshops use intent maps to align product marketing messaging with search language sales hears on calls—reducing disconnect between site copy and buyer vocabulary. Monitoring hooks flag when new AI paraphrases appear in answers, triggering map updates and content patches.

Deprecating obsolete sub-queries from maps after content consolidation prevents teams from maintaining pages for intents now satisfied by updated pillar URLs.

For broader context, see our roundup of claude skills for marketing, and read Claude skills for SEO for related setup guidance.