Semantic Query Clustering Engine: SERP-Overlap Method to Consolidate Pages and Lift Search Revenue
Alexander Granovskiy - E-commerce Manager (Cleveland, Ohio, United States).
If you manage ecommerce SEO at scale, you eventually hit the same wall: too many near-duplicate pages chasing slightly different keywords, slow rankings, and diluted topical authority.
This case study summarizes a semantic query clustering engine that groups related queries by shared intent, so one strong page can rank for multiple closely related phrases and lift search-driven revenue.
Why this matters
When multiple keywords map to the same underlying intent, publishing separate pages often creates:
duplicate content risk
internal competition (pages cannibalize each other)
slower ranking and weaker authority
Clustering lets you consolidate content and focus effort where it compounds.
What the engine does
The core idea is simple and production-friendly: cluster 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
Cluster membership is defined by shared URLs in the top results.
Higher precision means tighter, more similar phrases.
Default precision is 5 shared URLs.
Quick primer (plain terms)
SERP = Search Engine Results Page.
Positions are ranks 1, 2, 3, and so on.
If two queries return many of the same top-ranking pages, they are usually the same intent.
Data sources and filters
To make the clustering useful for ecommerce execution (and not a messy keyword dump), the pipeline uses a few data sources plus strict filtering.
Data sources:
Semrush (Organic Results, Keyword Overview)
DataForSEO (live SERP)
Webshrinker (site category)
Filters (to keep clusters actionable):
Exclude navigational terms
Exclude geo and brand terms
Exclude misspellings
Exclude adult terms
Domain vocabulary (the underrated piece)
A practical addition is maintaining an industry term catalog with estimated volumes, then using it to prioritize clusters and on-page work. This keeps the output aligned to the language customers actually use and helps you pick the clusters that will move revenue first.
Impact
This approach produces operator-friendly outcomes:
Fewer duplicate pages
Faster rankings
Stronger topical authority
Typical directional results reported in the case study:
+8 to +15% search revenue
+4 to +9% AOV
Links
Full case study:
https://www.alexgranovskiy.com/case-study-semantic-query-clustering-engine/
More ecommerce case studies and playbooks:
https://www.alexgranovskiy.com/tag/case-studies/