Keyword Clustering Guide
Group related queries by intent and actual result overlap.
Cluster by the answer a reader needs
A keyword cluster is a set of queries that can be satisfied by one useful destination. Start with semantic similarity, then compare search results and expected formats. Queries with the same words can still require different pages; different words can express the same task.
A concrete example
'How to clean a French press' and 'French press cleaning steps' likely fit one tutorial. 'French press vs pour over' requires a comparison. Assign each cluster a primary page and note related questions that belong as sections rather than standalone URLs.
How to make the decision
Do not accept automated grouping without reviewing ambiguous queries. Excessive splitting creates repetitive pages; excessive merging makes a page fail several distinct tasks.
Practical checklist
- Normalize obvious spelling variants
- Group by reader outcome
- Compare SERPs for ambiguous phrases
- Merge overlapping destinations
- Separate truly different formats and tasks
Keep the architecture finite
The right number of pages is the number of distinct reader tasks you can serve well and maintain. Before approving another URL, compare its promise, expected result type, and proposed outline with existing content. When they overlap, improve the stronger page and connect related material with descriptive links.
What to do next
Write down the single question this research should answer and the evidence you still need. Check the current search results, identify the page that best matches the reader's task, and make one specific improvement or publishing decision. If the answer depends on a third-party product, verify its current terms on the provider's website before acting.
Common question
Should similar words always share a page?
No. Compare the task and SERP. Similar terms can lead to a tutorial versus a product comparison, which need different destinations.
Continue reading
For a tool-assisted approach, see our LowFruits review.