SERP similarity
Compare ranking overlap
Compare search results between keywords to identify when Google treats two queries as sharing the same or a very similar intent.
Intent clustering
Group related queries
Automatically cluster keywords that share enough SERP overlap to be targeted within the same page or content group.
SEO architecture
Reduce cannibalization
Use SERP-based clustering to decide when to merge keywords and when separate pages are justified.
What it does
SERP Intent Clustering uses search-result similarity as a practical signal for understanding whether multiple keywords belong to the same search intent and should be grouped together.
SERP overlap analysis
Compare ranking URLs between keywords to measure how similar their
search result sets are.
Keyword clustering
Group queries based on configurable SERP similarity thresholds.
Search intent validation
Use actual SERP behavior instead of relying only on semantic keyword
similarity.
Content architecture support
Help decide which keywords belong on one page and which require
distinct landing pages.
How the workflow works
The automation turns a keyword list into actionable intent clusters by comparing search-result overlap.
1
Import your keywords
Start from a keyword list that needs to be grouped by intent.
2
Collect SERP results
Retrieve ranking URLs for each query.
3
Measure similarity
Compare SERP overlap and calculate which keywords share similar
result patterns.
4
Create clusters
Group keywords into intent-based clusters for SEO planning and
content architecture.
Typical output
SERP Intent Clustering
Keyword set
├── Keyword A
├── Keyword B
├── Keyword C
└── Keyword D
SERP comparison
├── Shared ranking URLs
├── SERP overlap score
└── Similarity threshold
Intent clusters
├── Cluster 1
│ ├── Keyword A
│ └── Keyword B
│
├── Cluster 2
│ └── Keyword C
│
└── Cluster 3
└── Keyword D
Why SERP similarity matters
Two keywords can look semantically similar but still trigger different search results. SERP overlap provides an additional signal to determine whether Google interprets queries as satisfying the same user need.
Avoid unnecessary pages
Merge keywords when search results indicate a shared intent.
Avoid over-clustering
Keep separate pages when SERPs show materially different intents.
Improve keyword mapping
Assign keyword groups to the most appropriate landing pages.
Support scalable SEO
Apply the same methodology to large keyword sets and site
architectures.
Technical notes
SERP data is collected using the ScrapingDog API. The workflow runs in Google Apps Script and uses Google Sheets as its working interface.
Codex was used during development as a coding agent, while the SEO methodology and clustering logic were manually reviewed.
Open source
Source code
The project source code is publicly available on GitHub and can be adapted to SERP clustering and search-intent workflows.