Algorithmic SEO, Data Intensity, & Visual Search
User-Intent Clustered Keyword Research
Keyword lists without intent clusters produce pages that rank for strings but fail tasks. Intent clustering groups queries by the job the user is trying to do, the SERP features that appear, and the stage of decision, then maps each cluster to a page or section. The result is fewer, stronger pages that match how people actually search and decide, rather than one page per keyword variation.
Points clés
- Cluster by task and SERP shape, not by string similarity: queries with the same intent and result types belong together.
- Identify the dominant intent per cluster (informational, navigational, transactional, commercial) before writing.
- Map one primary page per cluster and use supporting pages for distinct sub-intents.
- Use SERP features as intent signals: featured snippets, product listings, and local packs reveal what users want.
- Avoid splitting clusters into near-duplicate pages that compete with each other.
- Refresh clusters as language and products change; intent drifts faster than keyword volume.
- Measure success by task completion and conversion per cluster, not by keyword count.
- Keep a kill list of clusters that do not map to a real page or business outcome.
Questions fréquentes
How many keywords per cluster?
As many as share one intent and SERP shape; size varies, and forcing a number creates artificial splits.
Is keyword volume still useful?
Yes for sizing and prioritization, but intent and conversion matter more for page decisions.
Can one page target multiple intents?
Sometimes, but only when the intents are adjacent and the page structure clearly serves both.
What tools help clustering?
Any tool that exposes SERP features and query groups; the clustering logic matters more than the brand.
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