seo-keyword-research-intent-mapping
Installation
SKILL.md
Why this matters
- 58.5% of US searches and 77.2% of mobile searches end without a click (third-party search studies, 2025). Keyword value now depends on intent + SERP context, not just volume.
- AI Overviews and AI assistants split questions into sub-queries before retrieval. A single target keyword rarely captures the full opportunity — the sub-query fragments do.
- AI Overviews reduce position #1 CTR by 34.5%+ (industry research, large keyword samples). Informational volume no longer converts to traffic the way it used to — intent weighting matters more than ever.
Process
- Seed discovery. Pull seeds from: business goals, product/service names, customer support tickets, sales call transcripts, Reddit/forum threads, internal search logs, and review-site language. Customer wording beats marketer wording.
- Expand the universe. For each seed: related terms, long-tail variations, question formats (who/what/why/how/when), modifiers (best/top/vs/alternatives/cheap/free/enterprise), and conversational phrasings ("how do I…", "what's the best way to…"). Tools: GSC Performance report (free, your own data), keyword research suites, Google autocomplete, and People Also Ask.
- Classify intent. Map each keyword to one of four intent types (matrix below). If intent is ambiguous, the SERP tells you — check what's actually ranking.
- Analyze SERP landscape. For each cluster, log: dominant content type (article/listicle/product/video), SERP features triggered (AI Overview, Featured Snippet, PAA, Local Pack, Shopping, Video carousel), and whether the top 10 is dominated by brands you can realistically beat. If the SERP is all DR 85+ enterprise tools and you're a startup, keyword difficulty scores understate reality.
- Score and prioritize. Apply the prioritization formula (below). Volume alone is misleading — a 50-volume transactional term can beat a 10K informational one for revenue.
- Map to content. Each cluster gets a target page, content type, pillar/cluster role, and URL status (exists/refresh/create).
- Fan-out sub-query analysis for AEO. For each target keyword that matters in AI search, list the 3-5 sub-queries an AI would generate and check whether your content answers each fragment directly.