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Category Page Suggester

Use cases

Expanding category structure based on actual product inventory Aligning taxonomy with search demand using real keyword data Identifying gaps between what you sell and how users search Prioritising new categories by search volume and product coverage

Extracts 2-7 word n-grams from product H1s, matches against existing categories using PolyFuzz TF-IDF similarity scoring, and validates search demand via Keywords Everywhere API.

Filters by minimum product matches, search volume, and similarity threshold.

Originally presented at Brighton SEO.

Streamlit App

Platform

Browser-based (no installation required)

Input

Screaming Frog inlinks and crawl exports

Keywords Everywhere API key (optional)

Output

CSV with category suggestions, volumes, and similarity scores

Launch App View Source

Features

  • N-gram extraction (2-7 words) from product H1s
  • PolyFuzz TF-IDF fuzzy matching to existing categories (0-100% similarity)
  • Keywords Everywhere API integration (search volume, CPC)
  • Configurable filters: min volume, CPC, product matches, similarity threshold
  • Pluralisation handling and multi-stage deduplication

How to use

  1. 1 Crawl site with Screaming Frog, set up custom extraction for product vs category pages
  2. 2 Export inlinks.csv and internal_html.csv
  3. 3 Upload both files and map the product/category identification columns
  4. 4 Configure similarity threshold, min product matches, min volume/CPC
  5. 5 Run n-gram extraction and fuzzy matching
  6. 6 Download CSV of suggestions sorted by parent category and keyword

Frequently asked questions

What must my Screaming Frog exports contain?
internal_html.csv needs Address, Indexability, H1-1 and Title 1 columns plus your two custom extraction columns; any non-empty cell in an extraction column marks that URL as a product or category page. inlinks.csv needs the standard From and To columns. Non-indexable URLs are discarded, and H1s are stripped to ASCII, so accented characters vanish from generated keywords. Encoding is auto-detected, including Screaming Frog's UTF-16 exports.
How are Keywords Everywhere credits consumed?
One credit per deduplicated keyword suggestion, fetched in batches of 100. The app checks your remaining balance first and tells you how many credits the run needs before spending them. Volume data comes from the Google Keyword Planner source only, not clickstream.
Why do keywords with search volume disappear from the results?
Both post-API filters are strictly greater-than: volume must exceed the minimum (default 100) and CPC must exceed the minimum CPC slider. Because the CPC default is 0, keywords with a 0.00 CPC are dropped even at default settings, so suggestions with volume but no advertiser data are silently removed whenever an API key is used.
How does the minimum product match work?
It is a substring check: an n-gram counts as matching a product if it appears anywhere inside the product's H1 (default minimum 3 products). The optional fuzzy mode instead requires every word of the keyword to appear in the H1 in any order, which catches reworded titles but is much slower.
How does it avoid suggesting categories I already have?
Three checks: PolyFuzz TF-IDF similarity against existing category H1s (suggestions above the max similarity slider, default 96 percent, are dropped), a word-set comparison against the matched category's page title in any word order, and the same comparison with a naive plural formed by adding s. Irregular plurals and synonyms can still slip through, so expect a manual review pass.
Is the Keywords Everywhere key required?
No. Without it you still get every n-gram suggestion with product match counts and similarity scores, but no Search Volume or CPC columns, which means many suggestions nobody actually searches for. The key is what filters the list down to keywords with demonstrated demand.

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This tool started as bespoke client work. I build custom scripts, data pipelines, and full apps for SEO and product data problems that off-the-shelf tools don't solve.

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