Clustering of semantics
After compiling the semantic core, an SEO specialist receives dozens, hundreds, or thousands of search queries. In this form, the list is almost useless for designing a website structure. It's important to understand which keywords relate to a single user need, which can be promoted on a common page, and which will require a separate URL.
What is semantic clustering?
Semantic clustering solves this problem by grouping queries. The process takes into account search intent, phrase content, search results, and document overlap in SERPs. Pre-defined clusters are used to create a website structure, distribute queries across pages, prepare a content plan, and implement internal linking.
For a small project, query clustering can be done manually in Excel or Google Sheets. When working with a large semantic core, automated clustering is more often used, after which a specialist manually checks for questionable groups. This approach reduces the amount of routine work and maintains control over the final structure.
Semantic clustering is the distribution of keywords and search queries into thematic groups called clusters. In SEO, each cluster is typically associated with a specific target page. Queries within a cluster should have similar meanings, the same or compatible user needs, and a suitable document type in search results.
For example, the queries "website SEO audit", "order an SEO audit", and "SEO audit price" could all refer to a commercial service page. The query "how to conduct an SEO audit yourself" requires separate verification, as the user is looking for instructions. If Google returns articles and guides for this query, lumping it in with the commercial cluster is risky.
Grouping uses multiple source entities. The semantic core contains the entire selected list of phrases, the marker query defines the main topic of the group, and the search intent indicates the user's goal. The landing page fulfills this demand and receives a set of queries after keyword mapping is completed.
Why is query clustering necessary in SEO?
Clustering queries helps translate the semantic core into a clear website structure. After grouping, it's clear which keywords relate to existing pages, where content needs to be expanded, and where a new URL is required. Without such distribution, the semantic core remains a simple list of phrases without any connection to the resource's architecture.
Keyword grouping also helps reduce the risk of cannibalization. When identical or very similar queries are spread across multiple pages, search engines are forced to select the appropriate document. This can result in constantly fluctuating rankings, and different URLs begin to compete for the same keyword group.
An additional task involves content preparation. The cluster shows what topics and wording a specific landing page should cover. This makes it easier to prepare the title, H1, H2-H3 structure, technical specifications, internal linking, and a list of relevant pages for future website expansion.
What is considered a good cluster?
A good cluster combines queries that can be fully addressed by a single page without artificially mixing different user tasks. These phrases share the same primary search intent, and search results regularly include pages of the same type. For a commercial group, this could be categories or services, while for an informational group, it could be articles and instructions.
For example, "buy an office chair" and "office chair price" should be considered together if the search results support a common commercial need. The query "how to choose an office chair for your back" may lead to informational materials, so it should be reviewed separately and, if necessary, moved to a cluster for a future article.
The size of a group by itself doesn't indicate quality. One cluster might have five queries, while another might have fifty. The key criterion is the ability to create a single, relevant URL that naturally addresses the entire group's needs and doesn't require merging incompatible topics.
What to do after semantic clustering?
The completed clusters still need to be transformed into a working structure. The next stage involves distributing groups among URLs, checking existing pages, creating new landing pages, and preparing content. Here, the semantic core is linked to specific actions on the site.
For each cluster, it's advisable to record the page name, document type, URL, primary intent, marker query, and status. This table becomes a common map for the SEO specialist, copywriter, developer, and content manager.
Keyword mapping
Keyword mapping assigns clusters to specific website pages. While keyword clustering determines which queries are related, mapping determines which URL should access this group. This is where the final link between semantics and website architecture is formed.
For an existing project, they first check the current relevant pages. If a suitable URL already ranks and matches the intent, the team is assigned to it and recommendations for expansion are developed. If the page doesn't exist, a new development or content task is created.
The results are conveniently stored in a table with columns for "Cluster", "Page", "URL", "Intent", "Status", "Priority", and "Comment". This format facilitates subsequent change monitoring.
Formation of the website structure
Clusters show which entities users search for individually and which pages the search engine considers relevant. Based on this data, categories, subcategories, services, articles, and other sections can be designed. The search structure must be aligned with the actual product logic.
Not every cluster automatically converts into a new URL. Sometimes, several small groups can be expanded within a single page via H2-H3 if the intent is consistent and the SERP allows it. In other cases, a single old section must be split into several separate documents.
The final structure should take into account nesting depth, navigation, internal links, and the ability to keep pages up to date.
Preparation of meta tags and content specifications
After mapping, each page has its own set of keywords. Based on this, the main marker for the Title and H1 tags is selected, additional wording is defined, and the content is constructed. H2-H3 headings should reveal the actual subtopics of the cluster, rather than mechanically repeating keywords.
The content specification also includes search intent, recommended length, LSI queries, user questions, and links to related pages. This document helps the copywriter understand the page's purpose and reduces the likelihood of confusing adjacent clusters.
Before publication, the finished text is checked along with meta tags, internal links, and technical page settings. The semantics must remain consistent with the URL at all stages of the process.
Internal linking
After creating the structure, it's time to link thematically related pages. A commercial category might receive links from informational articles, while related services might receive links from a general-purpose page. The anchor text is chosen naturally, based on the content and target cluster.
Clustering helps understand thematic relationships between documents. If two groups are related but have different intents, internal links help the user navigate between them without merging all the content on a single page.
There's no need to repeat the same exact anchor text across all your content. Natural wording, service names, brand names, and contextual wording make interlinking clearer for users.
Cannibalization check
Cannibalization occurs when multiple website pages compete for the same search group without clearly separating their intent. Google may periodically change the relevant URL, causing rankings to become unstable. The problem often arises after unsystematic expansion of the structure.
The check begins with a comparison of queries and landing pages. If a single cluster is assigned to multiple URLs, the primary document must be identified. Then, the content, internal links, meta tags, and actual search results are analyzed.
The solution may involve merging pages, redistributing semantics, or repurposing a single URL. The specific option depends on the quality of the documents and their current visibility.
When should clustering be revised?
Semantic core clustering doesn't require a complete overhaul, but individual groups may become outdated over time. This can be due to changes in search results, product range, services, website structure, or competitor behavior. Topics with unstable SERPs change especially quickly.
A re-check is necessary after a significant expansion of semantics, the emergence of new business areas, or significant cannibalization. It's also worth revisiting groups if Google begins consistently ranking a different type of page for important queries.
It's not necessary to rework the entire site. Often, it's enough to check the problematic section, collect the latest SERP, and update the mapping only for the affected clusters.
In my work, I don't consider the finished file from the clusterer to be the final result. The service does a good job of removing the routine work, but I make the decision to combine queries on a single page after checking the intent and search results. Identical words don't necessarily guarantee the same user needs, so I always review any disputed groups manually.
How to perform semantic core clustering step by step?
Clustering of semantic core queries begins before any service is launched. If you load an unprocessed list containing duplicates, irrelevant phrases, and foreign geographies, the algorithm will carefully sort the same junk into groups. Therefore, the quality of the initial core directly impacts the quality of the result.
The sequential process consists of cleaning, intent determination, marker selection, algorithm setup, initial grouping, manual verification, and URL binding. Each stage addresses a specific issue, so missing a step usually requires correction later.
Collect and clean the semantic core
First, queries from selected sources are combined and obvious duplicates are removed. Then, irrelevant products, services, cities, brands, and information topics that the site doesn't intend to cover are excluded. At the same time, erroneous phrases, technical junk, and queries with completely foreign search intent can be eliminated.
Semantic cleanup reduces the number of false clusters and simplifies subsequent manual work. It's useful to save the original dump separately so that questionable keys can be restored after additional verification.
At this stage, base search volume and other necessary metrics are also added. Search volume helps prioritize, but it doesn't by itself determine which queries should appear on the same page.
Determine the search intent
After cleaning, queries are preliminarily separated by purpose. Commercial phrases relate to choosing a product, service, or provider, informational ones are intended to obtain an answer, and navigational ones are related to finding a specific website or brand. Mixed queries are retained for further review.
This separation reduces the likelihood of merging incompatible pages within a single cluster. For example, an online store's commercial category rarely needs to be overloaded with detailed instructions if Google consistently shows articles based on relevant keywords.
When in doubt, open the SERP and look at the document types. This simple step often yields more information than trying to determine the intent solely from the words in the query.
Select marker queries
The marker query defines the main focus of the future cluster. It usually describes the essence of the page well and has a clear intent. Highest search frequency can be an additional argument, but you shouldn't choose a marker solely on this metric.
For example, for a service page, the tag should reflect the service itself, while additional keywords might include price, order, region, and additional specifications. For an informational article, the tag typically represents the main question or topic around which additional wording is organized.
A proper marker facilitates cluster validation. If the majority of additional queries don't match a page that would logically be created under the primary key, the cluster should be reconsidered or split.
Select the clustering method and threshold
Before launching, you need to select a search engine, the desired region, and a grouping method. These parameters affect the SERP, so semantics may be distributed differently for different countries and cities. The difference is especially noticeable for local services, commercial niches, and queries with geographic modifiers.
Next, set the clustering degree or threshold. A more lenient setting collects larger groups, while a more strict one tends to separate queries more often. There's no universal value for all projects, so it's helpful to test several options on a portion of the core.
If a service supports both Soft and Hard modes, the choice is made based on the required level of detail. For contentious commercial clusters, a more rigorous check is usually beneficial, while for a broad information structure, it's sometimes more convenient to start with the Soft mode.
Perform automatic grouping
After configuration, the cleaned semantic core is loaded and automatic clustering is launched. The service compares the data using the selected algorithm and distributes queries into groups. At this stage, there is no need to manually adjust each row until the overall result is achieved.
After processing, first look at the overall picture: the number of clusters, the size of large groups, the number of single queries, and the proportion of keywords without a group. If the result appears too fragmented or, conversely, combines almost all topics, it's worth re-checking the settings.
A good initial clustering should save manual effort. If a specialist has to completely rebuild most clusters, the problem usually lies in the settings, a dirty source kernel, or the chosen method.
Check each disputed cluster manually
First, they check large groups, mixed intents, and clusters where queries with significantly different meanings coexist. For several keywords, they open the search results and compare page types, URL overlaps, commercial elements, and the user's primary need.
A useful question to ask when testing is simple: is it possible to create a single, comprehensive document that naturally addresses all the group's queries? If half of the keywords require a service page, and the other half a detailed manual, it's better to split the cluster.
Particular attention should be paid to high-volume queries. A mistake in this area affects the choice of the primary page and can alter the entire structure of the corresponding section of the site.
Check non-clustered queries
A query without a group can't be automatically considered junk. It may have a unique intent, a rare wording, or an unstable search result. Sometimes such a keyword points to a separate landing page that the automatic algorithm simply couldn't connect with adjacent queries.
First, check the keyword's relevance and its actual SERP. If the results are close to an existing cluster, you can add the query manually. If the results differ, it's best to leave the keyword as a standalone keyword until the structure design stage.
Low-frequency queries deserve special attention. Their low frequency doesn't mean they lack value, especially in services and e-commerce, where long, precise phrases can accurately describe user intent.
Link clusters to site pages
After verification, keyword mapping begins—distributing existing groups among URLs. For each cluster, an existing relevant page is determined, or whether a new one needs to be created. It's also decided whether the group should link to a category, service, card, filter, article, or other document type.
If a suitable URL already exists, its content and current rankings are checked. Sometimes, expanding the page is sufficient. In other cases, semantics reveal that a single old document covers several different intents and should be split.
For a new website, mapping transforms a list of clusters into the future architecture. This allows you to design menus, section nesting, breadcrumbs, internal linking, and the content preparation sequence.
What if one query matches multiple pages?
First, you need to determine which URL most closely matches the primary intent of the query. Then, check the search results and the site's current rankings. If Google is already consistently choosing one page, there's no point in assigning the same priority keyword to a neighboring document without a compelling reason.
When two pages truly compete for the same semantics, cannibalization should be checked. Possible solutions include reassigning keywords, changing the intent of one page, merging content, or revising the structure.
When mapping keywords, it's best to assign one priority page to each primary cluster. Additional internal links can use similar wording, but the target URL for the primary group should remain clear.
What we actually did
Dental clinic · Kyiv and Chernihiv
+44% clicks from search
A domain with no history and a site on a website builder. We built the semantic core for both cities, reworked the landing pages and built the link profile from zero. In four months: 34.8k clicks, impressions 1.32 → 1.76M, DR 0 → 41.
E-commerce · international
+96% clicks in two months
A catalog of digital 3D models. We clustered the semantics, rebuilt the hub pages and fixed duplicates and indexing errors. Google users 247 → 532, CTR 2.4% → 4%.
Medical center · Ukraine
+68.75% visibility in the first month
Narrow visibility and a small semantic core at the start. Semantics, landing page structure, metadata and internal linking, then gradual link building.
Related services
Collection of the semantic core
Order semantic core collection for your website from Seo-Gen: keyword selection, cleaning, clustering, and page distribution. The price depends on the project scope.
Development of SEO website structure
Website SEO structure development based on semantics, competitors, and search demand. We design categories, landing pages, URLs, and interlinking. Order website structure from Seo-Gen.
Answers to your questions
What is query clustering?
Query clustering is the grouping of search phrases by meaning, intent, and other characteristics. SEO often uses search results data: the algorithm compares pages ranked by different keywords and combines queries if there is sufficient URL overlap.
The resulting groups are used to create the site structure and distribute semantics across pages. One high-quality cluster typically corresponds to one primary target URL.
After automatic grouping, it's advisable to manually check the results. This is especially true for commercial and mixed queries, where an error could result in the creation of an extra page.
How is Hard clustering different from Soft clustering?
Soft clustering allows for more flexible relationships within a group. Additional queries are linked to the primary marker, although they may have only slight overlap with each other. Therefore, the resulting clusters are often larger.
Hard clustering uses more stringent conditions for the relationships between queries. Groups become more compact, and the likelihood of mixing different intents is typically reduced.
The method chosen should be tested on specific semantics. Sometimes Soft provides a convenient structure, while other times it overly lumps together commercial queries that SERPs better separate.
Is it possible to do query clustering for free?
Keyword clustering is possible for a small keyword core for free using Excel or Google Sheets. A specialist manually sorts queries, determines intent, compares SERPs, and assigns groups. This method is quite feasible for tens or hundreds of keywords.
There are also services that offer test modes or limited online query clustering for free. Access conditions vary, so it's important to check them before starting a project.
If the semantics are extensive, it's important to consider not only the service's price but also the specialist's time. A cheap result that requires complete manual reorganization offers little practical benefit.
Is it possible to fully automate clustering?
The technical part of processing a large list can be automated. The service can collect search results, compare URLs, and group thousands of queries according to a selected algorithm. This automated clustering is especially useful during the initial stages of working with a large core.
It's undesirable to completely exclude a specialist from the process. The algorithm doesn't understand the entire business logic of the site, the actual product range, service priorities, or the reasons why individual pages should exist separately.
The best results are achieved with a consistent approach: automatic processing, checking of problematic groups, and final keyword mapping manually.
How many requests should there be in one cluster?
There's no fixed number of queries for a single cluster. A group can consist of a few phrases or contain dozens of keywords. The size depends on the topic width, intent, search results, and the number of wording variations.
There's no need to deliberately expand a small cluster or split a large one just to achieve a nice row count. The main question is whether a single page can naturally meet the entire cluster's demand.
If independent intents and different types of documents appear in the SERP within a large cluster, it is better to reconsider the group regardless of the total number of keys.
What to do with requests that the service was unable to cluster?
First, you need to check them manually. A non-clustered query may be junk, have a unique intent, or simply not have the required number of intersections with other phrases at the chosen threshold.
Open the search results and compare them with the closest clusters. If the page type and most of the results are similar, you can manually add the keyword to the appropriate cluster.
If the SERP is significantly different, it's best to keep the query separate. Sometimes a single phrase reveals a new page or topic that the original semantic structure didn't account for.
Which clustering method is best to use for an online store?
For a large online store, it's convenient to combine automatic SERP clustering with manual verification. The service quickly distributes a large number of product queries, while a specialist checks categories, subcategories, brands, filters, and information topics.
Commercial groups need to be aligned with the actual product range. Even a good search cluster shouldn't be turned into a separate page if the store can't support a sufficient selection of products on it.
After grouping, it's a good idea to perform mapping and check indexed filters. This helps avoid unnecessary landing pages and cannibalization between adjacent sections.
Semantic clustering links search queries to the actual site structure. A good result takes into account search intent, SERP overlap, page type, search volume, and business logic. Automated services speed up bulk processing, and manual verification helps correct questionable decisions before creating new URLs.
After grouping, work continues through keyword mapping, cannibalization checking, and preparing the structure and content specifications. If the semantic core has already been compiled, Seo-Gen can perform query clustering, check search results groups, and prepare a semantic distribution across existing and new website pages.
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More on: Clustering of semantics
How does search intent affect clustering?
Search intent describes the result a user expects to receive after entering a query. In SEO clustering, this factor is considered before the frequency and number of identical words. Two phrases may appear nearly identical, yet lead to different types of pages and solve different user problems.
SERPs help determine intent. If Google predominantly displays online stores, categories, or service pages for a group of queries, the queries have a clear commercial component. If the results are dominated by instructions, reviews, and articles, the search is informational. Mixed results require more careful manual review.
Working with intent is especially important when creating the structure of a new website. Mistakes at this stage can spread further: pages are formed incorrectly, requests are distributed among inappropriate URLs, and technical specifications for content begin to perpetuate the initial error.
Information requests
An informational query arises when a user wants to get an answer, understand a topic, compare options, or perform an action independently. The wording often includes words like "how", "why", "what is", "which one to choose", and "instructions", although it's impossible to determine the intent solely from words. It's best to confirm the final decision with search results.
For example, queries like "what is query clustering", "how to cluster a semantic core", and "how to test clusters" are suitable for a comprehensive expert guide. They can be expanded upon within a general article if the overlapping search results confirm compatibility and the topic doesn't require separate, full-fledged materials.
Information groups are typically assigned to articles, help pages, instructions, and knowledge bases. Such documents can support commercial pages with internal links, helping users move from researching a topic to selecting the right service or product.
Commercial inquiries
Commercial intent indicates a user's willingness to choose a company, product, or service. Search queries often include terms like "buy", "order", "price", "cost", "services", and "agency". However, even here, it's best to check the actual search results, as similar modifiers work differently in different categories.
For a commercial group, a service, category, subcategory, or product page is typically selected. If a user searches for "order semantic core clustering", they expect a description of the service, process, outcome, terms, and conditions of cooperation. A long informational article doesn't fully cover this query.
When designing a website, commercial clusters help determine the required structural depth. Excessively large clusters create cluttered pages, while excessive fragmentation leads to weak URLs with nearly identical content. The SERP helps determine the appropriate level of detail.
Mixed intent
Mixed intent occurs when a search engine allows multiple possible answers to a single query. Top results can include articles, services, service pages, catalogs, and other documents. For clustering purposes, such queries are considered controversial, as analyzing the wording alone is insufficient.
For example, the general query "clustering" has a broad meaning. A user might be searching for SEO clustering, data analysis methods, text clustering, or mathematical algorithms. The query "semantic clustering" significantly more accurately captures SEO intent and yields more consistent, thematic results.
With mixed intent, consider the share of documents of the desired type, the consistency of search results, and the overlap of URLs with adjacent keywords. If most of the SERPs match a single user goal, the query can be grouped accordingly. If there is a significant separation, it's best to leave it separate until manually verified.
Basic methods of keyword clustering
Keyword clustering is performed in several ways. The method is chosen based on the size of the keyword, the topic, the search results quality, and the required accuracy. A small keyword can be analyzed manually, but for several thousand keywords, it's more convenient to first create automated groups and then have them verified by a specialist.
In SEO, logical grouping, semantic proximity analysis, and search results clustering are most commonly used. These methods can be combined. For example, an automatic algorithm distributes the bulk of queries across SERPs, after which a specialist checks the intent, site structure, and commercial logic of the resulting groups.
| Method | How it works | Pros | Restrictions | When to apply |
|---|---|---|---|---|
| Logical clustering | A specialist distributes requests manually | Takes into account the specifics of the business | It takes a lot of time | A little SY and final check |
| Semantic clustering | The meaning and similarity of phrases are compared | Processes large lists quickly | There may be intent errors | Preliminary grouping |
| SERP clustering | Search results URLs are compared | Takes into account real search results | Depends on the region and SERP | The main option for SEO |
| Combined method | Automation is supplemented by manual verification | Maintains speed and control | Expert analysis is needed | Medium and large projects |
Logical clustering
Logical clustering is based on manual analysis of query meaning. An SEO specialist reads the phrases, determines their topic, and distributes them across future pages. This method works well when the semantic core is small, the specialist has a deep understanding of the niche, and the site structure is already partially known.
The advantage of manual work is related to business context. An algorithm can combine related queries, even though the company sells the relevant services separately or uses different pages due to product, geography, and order conditions. A human can take this logic into account even before reviewing the search results.
The main drawback of this method is subjectivity. Two specialists may distribute the same keywords differently. Therefore, it's better to confirm logical groupings using SERPs, especially when creating new pages and separating closely related commercial clusters.
Semantic clustering
Semantic clustering compares the meaning of queries and their thematic similarities. Simple algorithms analyze common words and word forms, while more complex approaches use vector representations of text and embeddings. This method helps quickly process large numbers of keywords and find obvious semantic groups.
The problem arises when similar wording conceals different user needs. The queries "order an SEO audit" and "do-it-yourself SEO audit" share the same basic topic, but suggest a sales page with detailed instructions. The semantic similarity here is high, but the appropriate landing pages differ.
Therefore, semantic grouping is conveniently used as a preliminary layer. Afterwards, groups are checked by intent, SERP, and document types. This approach is especially useful for very large cores, where manually reviewing each phrase from scratch would be too time-consuming.
Clustering by search results
SERP search query clustering relies on pages the search engine already considers relevant. The algorithm retrieves search results for each keyword, compares the URLs, and evaluates the number of matches. The more identical documents are found for two queries, the stronger the signal of shared search intent.
Let's imagine two queries being checked against Google's top 10 results. If five pages appear simultaneously in both results, the queries are highly likely to be promoted on the shared URL. If the intersection consists of a single document or is absent, the merger requires additional verification.
This method is well suited for SEO clustering because it takes into account the actual ranking pattern for a given topic. However, results vary depending on the search engine, region, language, and time of review, so the old cluster sometimes needs to be revised after significant SERP changes.
What is SERP overlap?
SERP overlap is the degree of overlap in search results for multiple queries. For each keyword, a list of ranking URLs is obtained, and the results are then compared. If identical pages regularly rank for two keywords, the search engine accepts one document type for both purposes.
For example, query A and query B share five pages in the top 10. This overlap is stronger than with a single shared URL. However, there is no universal number of matches for all topics. A commercial niche may require a more rigorous grouping, while a different threshold is acceptable for informational search results.
It's useful to consider SERP overlap in conjunction with page intent and type. Even a good numerical overlap should be manually verified if the top results contain categories, articles, homepages, and aggregators with significantly different usage scenarios.
What is clustering threshold?
The clustering threshold sets the minimum number of overlapping results required to combine queries. A low value encourages the algorithm to create larger groups because fewer identical URLs are required for association. Increasing the threshold makes the conditions more stringent and typically increases the number of distinct clusters.
The schematic diagram shows the direction of change:
| Threshold | Group size | Homogeneity | Risk of over-unification |
|---|---|---|---|
| Short | large | average | high |
| Average | average | good | average |
| High | compact | high | short |
An excessively high threshold also creates problems. The semantic core can fragment into dozens of small groups, for which there's no point in creating separate URLs. The threshold is selected after test clustering and checking several typical groups in a specific topic.
Soft, Middle, and Hard Clustering – What's the Difference?
Soft, Medium, and Hard describe the degree of strictness of the relationships between queries within a group. The names and exact implementations may differ between services, so consult the documentation for your chosen tool when working. The general principle relates to how closely keywords should overlap in search results.
The soft algorithm typically produces a larger cluster. The hard algorithm links queries more tightly, resulting in more groups, and each group contains fewer phrases. The middle algorithm is used as an intermediate option where the service supports this clustering model.
| Method | Nature of the connection | Typical result | When is it useful? |
|---|---|---|---|
| Soft | Requests are linked via a primary marker. | Larger clusters | Preliminary structure |
| Middle | Medium rigor of inspection | Balanced groups | When Soft is too wide |
| Hard | Strong coupling of queries is required | Compact clusters | Precise elaboration of landings |
Soft clustering
Soft clustering creates a group around a primary, often most frequent, query. Other phrases should have sufficient overlap with this marker, but may be more loosely related to each other. As a result, a single cluster can encompass a broader range of phrases and additional needs.
This method is useful in the initial stages of structure design, especially for informational materials and broad categories. It reduces the number of microgroups and helps identify larger thematic areas. After obtaining the results, it's a good idea to check the outermost queries that were added to the group via a common marker.
The main risk with Soft clustering is its heterogeneity. With a low threshold, keywords that Google already separates between different page types can sometimes end up in a single cluster. Therefore, Soft clustering requires careful consideration of commercial and mixed intent.
Hard clustering
Hard clustering imposes stricter requirements on the relationships between queries within a group. Weak intersection through a single central marker is not sufficient for clustering. The algorithm checks for closer relationships between documents in the search results, so the resulting clusters are usually more compact and thematically homogeneous.
Hard clustering is useful when working with commercial pages, where a merging error could lead to an incorrect structure. For example, two similar services may visually relate to the same topic, but Google consistently shows different pages for them. Hard clustering is more likely to separate such queries into distinct groups.
The disadvantage arises when overly strict. Semantics can disintegrate into a large number of small clusters, although creating a separate URL for each one makes no sense. The final decision is made after checking the SERP, search volume, product range, and the feasibility of fully populating the page.
When to choose Soft, Medium or Hard?
The choice of method depends on the task. For a preliminary analysis of a large core, it's convenient to start with a more flexible grouping, identify common thematic areas, and then check for questionable phrases. For a commercial entity with similar services or categories, a more rigid approach is often more useful, as it reduces the likelihood of accidentally merging different intents.
Middle clustering is suitable when Soft creates too broad groups, while Hard fragments the semantics excessively. If the chosen service doesn't use the Middle name, a similar result is usually achieved by changing the threshold or the degree of grouping.
It's advisable to test the final method on several known queries. If the algorithm combines pages that are clearly distinct from the business and SERP, the settings should be tightened. If similar queries consistently break into microclusters, you can try lowering the threshold and comparing the results.
Manual or automatic clustering?
Manual and automatic clustering solve the same problem in different ways. In manual clustering, a specialist independently analyzes each phrase and decides on a group. The automatic approach uses a predefined algorithm, processes a large array of queries, and produces a primary structure in significantly fewer operations.
In practice, it's convenient to combine these methods. A machine handles the bulk comparison of keywords and search results, while a human examines the site's logic, intent, commercial value, and any disputed cases. This workflow is suitable for projects where the semantic core contains hundreds or thousands of queries.
Manual clustering
Manual clustering is well suited for small core sites, final checks, and niches with complex business logic. A specialist understands the context and can take into account the product range, service features, geography, and existing website structure. An automated algorithm often doesn't obtain this data.
It's convenient to work in a spreadsheet: queries are sorted, intent is noted, and a cluster and target page are assigned. When in doubt, the specialist opens the search results and compares document types. This system takes time, but it helps spot unusual cases before creating new URLs.
The main drawback is scale. When a semantic core contains several thousand keywords, fully manual parsing becomes too labor-intensive and increases the likelihood of mechanical errors. In such projects, it's wiser to reserve manual work for final control.
Automatic query clustering
Automatic query clustering processes the uploaded list according to preset rules. SERP clusterers collect search results, compare common URLs, and combine keywords according to the selected method and threshold. The user receives groups that can then be exported and further processed.
This approach is convenient for large semantic core systems, where sequential manual analysis of each query is time-consuming. Automatic clustering maintains a consistent grouping principle for the entire array and quickly reveals the basic structure of future pages.
The resulting file should not be considered the final website architecture. The algorithm doesn't understand all business constraints and may make mistakes with mixed intent, unstable search results, or ambiguous queries. Therefore, specialist review is required after processing.
Why is it better to combine automatic and manual clustering?
The combined approach begins with automatic semantic grouping. The service performs a comprehensive technical analysis: it checks SERPs, calculates intersections, and distributes keywords. After this, the specialist focuses on the groups where the automated solution can influence the site's structure.
Manual verification evaluates intent, query commercialization, URL relevance, document type, and the ability to create a full-fledged page. Non-clustered queries, excessively large groups, and clusters with a suspiciously small number of keys are also checked.
This process saves time without losing control. For a medium- to large-scale project, the workflow typically looks like this: semantic cleanup → automatic grouping → manual verification → keyword mapping → design or adjustment of the site structure.
Clustering in Excel and Google Sheets
Clustering in Excel is suitable for manually processing a small semantic core and checking the results of automated services. The spreadsheet provides full control over rows, filters, tags, and assigned URLs. This format is also convenient for transferring the finished file between an SEO specialist, editor, and developer.
Google Sheets solves a similar problem and further simplifies teamwork. Several specialists can simultaneously review groups, leave comments, and change statuses. For a large core, a spreadsheet remains a good working interface, although SERP analysis itself is more conveniently performed using specialized services.
When is Excel suitable for clustering?
Excel clustering is justified when the core contains a limited number of queries and a specialist can manually review most of the list. It is also useful for niches where the business logic is more complex than the formal proximity of SERPs and each cluster requires a separate expert solution.
The table is easy to use after automatic processing. You can add the following columns: "Cluster", "Intent", "Frequency", "Page", "URL", "Status", and "Comment". This file becomes a working semantic map and helps you manage subsequent changes to the structure.
If the number of queries reaches into the thousands, performing all the analysis manually becomes difficult. In this case, it's best to use Excel for monitoring, editing, and keyword mapping after automatic grouping.
What tools to use in tables?
Manual processing uses filters, sorting, matching, conditional formatting, and formulas. The FIND or SEARCH functions help find specific words within queries, while QUERY is convenient for selecting rows based on multiple criteria. XLOOKUP or similar functions are useful when transferring data between tables.
For example, you can automatically tag queries containing a service name, brand, or geographic modifier, and then manually check the resulting groups. This method works well as a primary technical sorting method.
Formulas do not determine search intent and do not replace SERP analysis. Their purpose is to speed up the mechanical processing of the list before expert review.
Why isn't Excel replacing SERP clustering?
Excel works with data already stored in a table. If a specialist compares only keywords, they don't see which pages Google ranks for the corresponding queries. Therefore, two nearly identical phrases can easily end up in the same group, even though the search engine separates them into different document types.
SERPs add an external signal: actual search results. This shows whether Google considers a single page relevant for multiple keywords. This is especially useful when working with commercial categories, services, and mixed intent.
Therefore, clustering in Excel is convenient for data organization, manual verification, and final distribution. For large-scale search results analysis, it's better to use a specialized clustering tool.
Services for query clustering
Query clustering services reduce the amount of manual SERP comparison. The user uploads the semantic core, selects available settings, and receives keyword groups. Specific algorithms, limits, and interfaces vary, so it's advisable to test the tool on a small subset of the semantic core before starting a large project.
When choosing, consider support for the desired search engine and region, soft and hard methods, customizable grouping levels, data export, and the ease of subsequent manual processing. Price alone rarely helps assess the quality of the future structure.
| Service | Format | Practical application |
|---|---|---|
| Key Collector | Desktop | Collection, organization, manual and automatic work with groups |
| Rush Analytics | Online | SERP clustering, Soft and Hard |
| Topvisor | Online | Clustering by TOP and working with project semantics |
| SEO Intellect | Online | Setting up the method, depth, degree, region and search engine |
| Other online clusterers | Online | Fast processing of small and medium lists |
Clustering in Key Collector
Key Collector clustering is suitable for specialists who manage their semantic core within a desktop application. The current product description lists manual, automatic, and semi-automated grouping tools, a group tree, and a structure editor. This is convenient when collecting, cleaning, and organizing semantic data takes place in a single workspace.
Clustering in Key Collector can be used in conjunction with filters, tags, and additional query parameters. After automatic analysis, a specialist can manually adjust the groups and prepare the structure for further keyword mapping.
For a large project, it's advisable to maintain a separate copy of the original kernel. This way, the results of different grouping options can be compared without losing the original structure.
Clustering in Rush Analytics
Rush Analytics clustering works by comparing search results. The service's official guide describes two methods—Soft and Hard. For each query, the top keywords are analyzed, after which the keywords are grouped according to the selected degree of connection.
The user selects the required project parameters, including search engine and region, and receives pre-defined groups after processing. This format is suitable for automatic clustering of queries within a large semantic core, where manual comparison of each SERP would be too time-consuming.
After downloading, you still need to check large and controversial clusters. This is especially true for mixed intent, local search results, and groups that influence the creation of new commercial pages.
Clustering in Topvisor
Topvisor clusters based on the top search results. The service allows you to upload semantics, select the method and level of grouping, and then obtain ready-made clusters. The documentation also allows you to select the search engine, language, and region for clustering based on the top 10.
This format is convenient for use within an SEO project, where queries are already linked to further position checking and work with relevant pages. Pre-defined groups can be used when designing a new structure or revising an existing one.
Before mass processing, it's useful to test several levels of grouping. One setting may create overly broad clusters, while another may fragment closely related semantics into many smaller groups.
Clustering in SEO Intellect
SEO Intellect clustering is available in Engine SEO Intellect. The tool's page includes settings for search engine, region, method, search results depth, and clustering level. This set of parameters helps tailor processing to a specific market and the required clustering rigor.
After uploading the list, the service creates groups that can be used to distribute keywords across pages. As with other automated tools, it's recommended to manually check the final structure.
You should pay particular attention to groups that influence the creation of new URLs. Automatic algorithmic matching still needs to be correlated with the business structure, actual content, and target page type.
Is it possible to cluster queries online for free?
Free query clustering is possible in several ways. A small kernel can be manually parsed in Excel or Google Sheets. Some online tools offer trial access, limited operations, or free features, but the terms of these offers change periodically.
Therefore, queries like "free online query clustering" and "free online semantic core clustering" are best considered based on the current terms of the specific service. Before uploading a large core, check the limitations, export options, and available grouping methods.
Free online clustering is convenient for testing algorithms or small projects. For ongoing work, the cost should be weighed against the number of queries, the quality of the SERP analysis, and the time it takes a specialist to refine the results.
How to cluster the semantics of an online store?
Clustering an online store requires considering the commercial intent, product range, and catalog structure. Semantics are typically distributed across categories, subcategories, brand pages, filters, product cards, and informational materials. Creating a separate URL for each group automatically is not possible.
First, queries are cleaned and categorized by product characteristics. Then, the SERPs are checked to determine which pages rank by category, brand, feature, or combination of parameters. The resulting clusters are then compared with the store's actual product range.
Categories and subcategories
Let's imagine an online laptop store. The general search query "laptops" falls under a broad category. The phrases "gaming laptops" and "business laptops" might warrant separate subcategories if the search results support a separate intent and the store can offer a sufficient selection.
Branded categories like "Lenovo laptops" are also checked separately. If Google consistently shows specialized categories and the site offers a sufficient number of models, a dedicated landing page makes sense.
After clustering, the structure must remain user-friendly. A large number of search groups doesn't mean the catalog needs to be reduced to dozens of nearly identical pages with minimal differences.
Category or filter?
The decision between a separate category and an indexed filter is made after analyzing demand, SERPs, and product range. If the query has independent commercial intent and the search engine displays individual competitor landing pages, a separate URL can be considered.
Search volume alone isn't enough for this solution. The page must have a sufficient number of products, useful content, and a consistent purpose. If the product range is constantly disappearing or the combination of parameters is too narrow, a new indexable URL can create a weak page.
Clustering helps identify potential areas for catalog expansion, but the final architecture is formed in conjunction with technical SEO and filter indexing rules.
Online store information requests
An online store often receives a large amount of informational semantics: "how to choose a laptop", "which screen size is best for work", "how much RAM do I need". These queries are product-related, but the user is still researching the topic and isn't always ready to click through to the relevant category.
If the SERP consists primarily of articles and guides, it's best to separate these keywords into information clusters. Create blog or knowledge base content for these clusters, then link the articles to commercial categories with internal links.
This approach separates the different stages of demand and reduces the risk of cluttering the category page with lengthy explanations that don't align with its core commercial purpose.
Common Mistakes in Clustering
Most errors arise from overreliance on a single signal. A specialist might group queries only by similar words, rely solely on frequency, or completely accept the automated result. Each of these approaches ignores some of the data necessary for a normal semantic distribution.
Another common problem is the lack of final URL validation. Even high-quality clusters are useless if the same groups are assigned to multiple pages or the new structure duplicates existing sections.
| Error | Consequence | Solution |
|---|---|---|
| Mixing different intents | The page does not respond well to some queries. | Split groups after SERP verification |
| Group by words only | Semantic errors | Compare search results and page types |
| The threshold is too low | Large heterogeneous clusters | Increase strictness |
| The threshold is too high | Excessive fragmentation | Check out the softer option |
| Complete trust in automation | Error structure | Conduct a manual audit |
| One cluster for several URLs | Cannibalization | Determine the priority page |
| Ignoring the region | Irrelevant SERP | Use the right geography |
| Working with a dirty kernel | Garbage groups | Clean the system before launching |
After automatic processing, it's useful to separately check the largest clusters and individual requests. These areas often contain either overly broad groups or unique intents that require separate resolution.
How is SEO clustering different from data and text clustering?
The term "clustering" is used far beyond SEO. Clustering in R, spectral clustering, clustering of clients, documents, text, or neural network tasks are all related to data analysis, machine learning, and other disciplines. These clusters group objects based on mathematical features or selected characteristics.
SEO clustering solves a more specific problem: distributing search queries for further processing of website pages. Here, search intent, SERP, URL intersection, resource structure, and the relevant page are critical.
Some mathematical methods can be used within individual algorithms and services, but a specialist should not mix these topics when designing a semantic core. For practical SEO purposes, the key question remains which queries the search engine allows to be promoted together.