Worked SEO example · illustrative dataset
Keyword clustering example: 16 queries grouped into four page intents
Imagine a website helping people choose and use coffee grinders. All queries below share a product topic, but readers expect different answers: an explanation, a comparison, a shopping category or care instructions. Grouping purely by the term “coffee grinder” would create a confusing page.
SEO Clusterizer editorial worked example · verify important groups independently
Download the 16-query clustering dataset (CSV)
A reviewable training dataset: 16 illustrative queries across four groups, with intent, proposed target URL and editorial reasoning. Open it in a spreadsheet and compare your own map. These are not live SERP results, GSC queries or measured rankings.
CSV columns: data type, language, cluster, query, intent, proposed target URL, editorial reason. All example target URLs are hypothetical.
Download example CSV · 16 queriesRepeat the process with your own keyword list
1. Fix the market and source
Use queries from one language and country, and note their source and collection period. Similar wording across countries does not guarantee equivalent intent.
2. Classify the job behind each query
Distinguish “how it works”, “which one to choose”, “buy”, and “clean”. Repeated nouns do not make search intent identical.
3. Review ambiguous pairs
Compare results for “best grinder” and “espresso grinder”. Check the types of pages ranking in the target market before merging or splitting.
4. Map each task to an existing or proposed URL
Check current pages in Search Console first. Consolidate overlapping content if one page already serves the intent. Create a new URL only if it solves a different task.
5. Measure, do not assume
After publishing, review query coverage, clicks, impressions and useful onward actions. A model score is not a ranking or a traffic prediction.
16 queries and four distinct user jobs
Cluster 1: How coffee grinders work
An explainer for beginners
Illustrative target URL: /guide/how-coffee-grinders-work
| Search query | Intent | Why this URL? |
|---|---|---|
| how does a coffee grinder work | Informational | Explain grinder mechanisms |
| burr grinder versus blade basics | Informational | An explanation of mechanism differences |
| grind size for French press | Informational | May need its own guide after SERP review |
| how to adjust coffee grind size | Informational | Beginner setup instructions |
Cluster 2: Compare and choose
Feature-based comparisons with transparent criteria
Illustrative target URL: /reviews/coffee-grinders
| Search query | Intent | Why this URL? |
|---|---|---|
| how to choose a coffee grinder | Commercial research | Compare features by use case |
| best grinder for espresso | Commercial research | Specialized audience: verify a different SERP |
| burr vs blade coffee grinder | Commercial research | Compare product types before buying |
| manual vs electric coffee grinder | Commercial research | Category comparison |
Cluster 3: Buy a grinder
Product category with availability and filters
Illustrative target URL: /shop/coffee-grinders
| Search query | Intent | Why this URL? |
|---|---|---|
| buy coffee grinder online | Transactional | A shopper expects products |
| coffee grinder prices | Transactional | Current prices, not an old blog post |
| order electric coffee grinder | Transactional | Likely a category filter |
| coffee grinder delivery | Transactional | Shipping details alongside real products |
Cluster 4: Cleaning and care
After-purchase care instructions
Illustrative target URL: /guide/clean-coffee-grinder
| Search query | Intent | Why this URL? |
|---|---|---|
| how to clean a coffee grinder | Post-purchase informational | A care procedure |
| how to clean burr grinder parts | Post-purchase informational | A subsection of the same care task |
| how often to clean a coffee grinder | Post-purchase informational | Maintenance interval discussion |
| coffee grinder jammed with beans | Post-purchase informational | A troubleshooting task that may need a separate guide |
What these clusters cannot prove
- Keyword similarity does not prove that Google prefers the same result for every query.
- This example contains no search volume, live SERP overlap, verified ranking data or traffic forecasts.
- Some tasks overlap: split or merge after checking the actual search results and your own pages.
- For YMYL topics, primary-source validation and domain-specialist review remain essential. A cluster is not expert advice.
Questions about the worked example
Do 16 keywords require exactly four pages?
No. Four is the proposed mapping in this training example. The correct number depends on the actual SERPs, existing URLs, and distinct audience tasks.
Should every keyword have its own page?
Not unless it solves a demonstrably separate task. Near-duplicate pages with overlapping intent can make a site harder to navigate and maintain.
How is semantic clustering different from SERP clustering?
Semantic techniques compare text properties; SERP clustering compares result overlap for queries. Both need manual validation when decisions affect important pages.
What if I already have a page for the cluster?
Check its query coverage, content, canonical and performance in Search Console. Improve or consolidate before creating a competing URL.
Will clustering guarantee rankings or AI citations?
No. Clustering organizes content decisions; crawling, indexing, ranking and AI citations depend on search engines and many other factors.
Try this approach on real queries
Run your own keyword list through a clustering tool, assign candidate URL groups with a keyword map, and inspect overlap in Search Console. Each solves a distinct step in the workflow.