Case Study - Semantic Query Clustering Engine

Group related queries so one page can rank for multiple closely related intents and lift search-driven revenue.

What it does

Clusters keywords whose Google SERPs share many of the same top results. High SERP overlap implies shared user intent, so the cluster can be targeted with one high-quality page.

Method - SERP-overlap clustering

• Define cluster membership by shared URLs in the top results
• Higher precision means tighter, more similar phrases
• Default precision - 5 shared URLs

Quick primer

• SERP = Search Engine Results Page
• Positions are ranks 1, 2, 3, and so on
• High SERP overlap implies shared intent

Data and filters

• Semrush - Organic Results, Keyword Overview
• DataForSEO - live SERP
• Webshrinker - site category
• Exclude navigational terms
• Exclude geo and brand terms
• Exclude misspellings
• Exclude adult terms

Domain vocabulary

• Maintain an industry term catalog with estimated volumes
• Use it to prioritize clusters and on-page work

Impact

• Fewer duplicate pages
• Faster rankings
• Stronger topical authority
• Typical lift - +8 to +15% search revenue
• Typical lift - +4 to +9% AOV

Role

Owned design, APIs, pipeline, and rollout with content and SEO teams.