Keyword clustering
Paste a keyword list and get it back as clear topic clusters with total volume, ready for a content plan or a prompt set.
First row: keyword,search_volume. One keyword per line.
How it works
A keyword column and a search volume column. Use the sample to see the format.
A language model reads every keyword and groups those that share a topic and searcher intent.
Each cluster shows its keywords and combined volume. One cluster usually maps to one page or one set of prompts.
Keyword clustering groups keywords that mean the same thing to a searcher, so one page can serve all of them instead of ten thin pages competing with each other. Grouping by shared intent works better than grouping by shared words: "best running shoes" and "top-rated trainers" belong together.
People ask AI engines the same underlying questions in many phrasings. A cluster is a ready-made set of those phrasings. Pick the clusters that matter to your business and turn each into a handful of prompts to track, so you measure topics rather than single wordings.
A header row with keyword,search_volume, followed by one keyword and its monthly volume per line. Volumes can come from any SEO tool. Very large lists are best split into batches of a few hundred rows.
A language model reads the full list, understands what each keyword is asking for, and groups keywords that share a topic and intent. It then names each cluster and adds up the search volume.
A few hundred rows works well. Longer lists can time out or produce looser clusters, so split them into batches.
Yes. Treat each cluster as a topic and write several natural-language questions for it, in the languages your buyers use. GeoGen then tracks them daily across AI engines.
The CSV is sent to a GeoGen service to be clustered and is not shown to other users. Do not include confidential data.
Five a day per browser.
Keep going
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