seo-cluster

SERP overlap keyword clustering groups keywords by shared Google top-10 organic results rather than text similarity. This mechanism determines which keywords should target the same page versus requiring distinct pages, preventing keyword cannibalization.

To plan a hub-and-spoke content architecture, provide a seed keyword to expand into variants. The system computes SERP overlap, classifies keyword intent, selects a pillar keyword, and outputs spoke clusters with template and word-count targets.

SERP-based topic clustering prevents keyword cannibalization by enforcing primary keyword uniqueness across generated clusters. By grouping keywords only when they share top organic search results, it ensures distinct pages target distinct search intents.

Yes, you can generate an internal link matrix and cluster visualization. The process outputs a bidirectional internal linking structure as a JSON adjacency list and an interactive HTML cluster map for content strategy review and iteration.

Topic clustering supports DataForSEO for computing SERP overlap but fully functions using a WebSearch fallback. No dependencies are required to execute the clustering, intent classification, and content architecture generation.

The best way to execute an SEO topic cluster plan is integrating a blogging skill to generate content directly. If a blogging skill is unavailable, the system outputs detailed content briefs based on the cluster-plan JSON for manual execution.