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CLUSTERIZE

Cluster keywords by common word stems for SEO content planning and analysis.

Firma della Formula

=CLUSTERIZE(keywords, [minGroupSize])

Restituisce: string[][]

Panoramica

CLUSTERIZE takes a range of keywords and groups them together based on common word stems, producing a structured table with cluster names, associated keywords, and counts. This is an essential tool for SEO professionals and content marketers who need to organize large keyword lists into topical clusters for content planning.

Parametri

Parametro	Tipo	Obbligatorio	Descrizione
`keywords`	Range	Sì	A range containing keywords to cluster (e.g., A1:A100).
`minGroupSize`	number	No (2)	Minimum number of keywords required to form a cluster. Defaults to 2.

Esempi

Basic SEO keyword clustering

Group a list of SEO-related keywords into topical clusters with the default minimum group size of 2.

=CLUSTERIZE(A1:A10)

Input

seo tools

seo tips

seo strategies

content marketing

content writing

link building tips

link building tools

Output

Cluster	Keywords	Count
seo tools	seo tools, seo tips, seo strategies	3
content marketing	content marketing, content writing	2
link building tips	link building tips, link building tools	2

Strict clustering with minimum size 3

Only show clusters that contain at least 3 keywords, filtering out smaller groups.

=CLUSTERIZE(A1:A20, 3)

Input

best running shoes

running shoes for men

running shoes reviews

yoga mat

yoga mat thick

hiking boots

Output

Cluster	Keywords	Count
best running shoes	best running shoes, running shoes for men, running shoes reviews	3

E-commerce product keyword clusters

Cluster product-related search terms to identify main product category themes for content strategy.

=CLUSTERIZE(B2:B50, 2)

Input

wireless headphones

wireless earbuds

bluetooth headphones

bluetooth earbuds

noise cancelling headphones

Output

Cluster	Keywords	Count
wireless headphones	wireless headphones, wireless earbuds	2
bluetooth headphones	bluetooth headphones, bluetooth earbuds	2

Blog topic discovery

From a large keyword list, find only the most prominent themes with at least 4 related keywords each.

=CLUSTERIZE(A2:A200, 4)

Input

email marketing tips

email marketing strategy

email marketing tools

email marketing automation

social media tips

social media strategy

Output

Cluster	Keywords	Count
email marketing tips	email marketing tips, email marketing strategy, email marketing tools, email marketing automation	4

Casi d'Uso

SEO & Content Marketing

Content strategy planning

Group hundreds of keywords into topical clusters to plan pillar pages and content hubs that cover entire themes.

Paid Advertising

PPC ad group creation

Organize keyword lists into tightly themed clusters for creating focused ad groups with better quality scores.

E-commerce

Product catalog organization

Cluster product search terms to identify natural category and subcategory structures for site architecture.

Market Research

Competitor keyword analysis

Group competitor ranking keywords into themes to identify content gaps and opportunities in your own strategy.

Publishing

Blog topic ideation

Cluster keyword research data into blog topic clusters to plan an editorial calendar covering all relevant themes.

Customer Support

FAQ page structuring

Cluster customer search queries into topic groups to organize FAQ sections and help documentation logically.

Suggerimenti Professionali

SUGGERIMENTO

Start with minGroupSize of 2 to see all clusters, then increase to 3 or higher to focus on the most significant keyword themes.

SUGGERIMENTO

Use the Count column to prioritize clusters -- larger clusters often represent more important topics to target.

SUGGERIMENTO

Export the results and use them to structure your site's content hierarchy around the identified topic clusters.

SUGGERIMENTO

Combine with GET_SEARCH_VOLUME_FROM_GOOGLE to add volume data to each keyword cluster for prioritization.

SUGGERIMENTO

Run CLUSTERIZE on competitor keywords to discover topic areas they cover that you might be missing.

The function works by applying a simple stemming algorithm to each word in every keyword phrase. Words are reduced to their root forms by removing common suffixes like -ing, -ed, -s, -ly, -tion, and -ment. Keywords that share the same set of word stems are grouped into the same cluster. The output is a three-column table: the cluster representative keyword, all keywords in the cluster joined by commas, and the count of keywords per cluster.

The optional minGroupSize parameter (default: 2) controls the minimum number of keywords required to form a cluster. Setting it to 1 shows all keywords including singletons, while higher values like 3 or 5 filter to only the most significant groupings. This helps you focus on keyword themes that have enough volume to warrant dedicated content.

CLUSTERIZE is commonly used after pulling keyword data from tools like Google Keyword Planner, Ahrefs, or SEMrush. Instead of manually sorting through hundreds or thousands of keywords, you can quickly identify the main topical themes and plan content around them. The clusters help you create comprehensive pillar pages and content hubs that cover entire topics rather than individual keywords.

For AI-powered keyword generation, see AI_KEYWORDS. For related search query discovery, use SUGGEST_QUERIES. To check search volumes for your clustered keywords, use GET_SEARCH_VOLUME_FROM_GOOGLE.

Errori Comuni

Error: Keywords range required

Causa: The keywords parameter was not provided or the range is empty.

Soluzione: Provide a valid range reference containing keyword strings, such as A1:A100.

"No clusters found with minimum size X"

Causa: No group of keywords shares enough common word stems to meet the minGroupSize threshold.

Soluzione: Lower the minGroupSize parameter (try 2 or even 1) to see all groupings. Your keywords may be too diverse to form clusters.

Very few clusters from a large keyword list

Causa: The keywords use very different vocabulary, or the simple stemming misses semantic relationships.

Soluzione: Review whether your keywords share any common words. The algorithm groups by shared word stems, so "buy laptop" and "purchase computer" will not cluster together despite similar meaning.

Domande Frequenti

The function uses a simple suffix-removal approach, stripping common English suffixes like -ing, -ed, -s, -ly, -tion, and -ment from each word. This groups variations like "running" and "run", or "marketing" and "market" together.

The default minGroupSize is 2, meaning only clusters with at least 2 keywords are shown. Single keywords without matches are excluded from the output.

The output has three columns: Cluster (the representative keyword), Keywords (all keywords in the cluster joined by commas), and Count (the number of keywords in that cluster). The first row is always a header row.

The stemming algorithm is designed for English suffixes. It may partially work for other languages that share similar suffix patterns, but results will be less accurate for non-English keywords.

The function works with any number of keywords, but very large lists (thousands) may take longer to process due to the computation involved. For best performance, keep lists under 500 keywords per call.

The stemming algorithm is simple and does not recognize synonyms or semantic similarity. "Cheap shoes" and "affordable shoes" will be in different clusters because "cheap" and "affordable" have different stems. For semantic clustering, consider using AI functions.

The output format is fixed as a three-column table (Cluster, Keywords, Count). You can use other spreadsheet functions to reformat or extract specific columns from the output.

The function returns a single cell with the message "No clusters found with minimum size X" where X is the minGroupSize value you specified.

Funzioni Correlate

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