Без кластеризації семантичного ядра досягти високих позицій у пошуковій видачі надзвичайно складно. Примітка: якщо семантичне ядро ще не зібране, вивчіть, як його скласти, у нашій попередній статті.
What is semantic core clustering?
Clustering queries is the grouping of keywords by user intent, similarity, and meaning into clusters to optimize website landing pages. The goal is to match relevant pages to refined search phrases. Example 1: Queries like "buy a laptop," "laptop price," and "cheap laptop" are grouped into one cluster if 8 out of 10 URLs in the search results overlap (hard clustering). Example 2: Queries such as "how to choose a laptop" and "laptop reviews" have an informational intent and can be grouped together for a blog article.
Why is clustering necessary?
Clustering helps turn SEO into a strategy rather than a random placement of phrases.
What it provides:
➤ Distribution of key phrases across pages. Each landing page receives its own unique set of relevant queries, improving the accuracy and depth of optimization.
➤ Improved search rankings. Search engines favor websites with clear structure — relevance and thematic focus of pages increase the chances of reaching the top.
➤ Systematic SEO promotion. Clustering provides a clear understanding of which query groups are already covered, which need refinement, and which should be merged into a single page. This saves resources and speeds up results.
It’s a navigation map for the semantic core. It helps avoid keyword cannibalization, increase page relevance, and build SEO on a strong, logical foundation. Without it, promotion loses effectiveness; with it, it becomes a precise and manageable system.
Clustering methods
To make promotion effective, it is important to choose the right grouping method. Depending on the goals and the semantic structure, different approaches are used — ranging from technical logic to user behavior models. Let’s understand their differences and when to apply each:
➤ Soft — grouping by base keyword with 3–7 matching URLs in the top search results.
➤ Hard — strict grouping with 7–10 matching URLs.
➤ By search intent — grouping based on user intent type:
- informational: “how to choose a laptop”;
- commercial: “buy laptop cheap”;
- navigational: “official Dell website”;
- transactional: “order laptop online”.
Example: The queries "buy a laptop" and "laptop price" (commercial intent) with an 8 out of 10 URL match are optimized on the "Laptops" category page.
Clustering methods
Clustering can be manual, automated, and AI-based.
1. Manual clustering
Tables are used: MS Excel, Google Sheets, LibreOffice, OpenOffice. How it works:
1.Collecting URLs: use special bookmarklets to copy search results. They can be found on the internet.
➤ Instruction: save the bookmarklet, open the search results, click on the bookmark — the URLs will be copied to the clipboard. Paste them into the spreadsheet.
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As an option, the SERP can be copied using the Chrome extension Grabbit or its analogs.
➤ Analysis: compare URLs for queries (e.g., “buy laptop” and “laptop price”). In Excel: “Conditional Formatting” → “Duplicate Values.” A match of 9 out of 10 URLs = hard clustering.
➤ Conclusion: optimize the queries on a single page.
Note:
➤ Check the search results, as the clustering logic may vary.
➤ Exception: in low-competition niches, create new pages even if competitors do not use them.
Advantages: independence from tools, high accuracy.
Disadvantages: labor-intensive, low efficiency for large datasets, risk of missing queries.
Conclusion: manual clustering is accurate but time-consuming.
2. Automated Clustering
SEO tools used:
➤ Serpstat (keyword analysis tool)
➤ Ubersuggest (keyword and SERP analysis)
➤ Mangools (SEO analysis and clustering)
➤ SpyFu (competitor analysis)
Example of working with Serpstat:
➤ Create a project in the clustering section.
➤ Enter the name and keyword phrases.
➤ Select the search engine and region.
➤ Track the rankings.
➤ Review the results.
Advantages: fast processing of large data sets, time-saving. Disadvantages: dependence on services, requires refinement. Conclusion: automated clustering speeds up work but needs verification.
Practical examples of clustering
Example 1: Commercial Cluster
➤ Queries: “buy smartphone”, “smartphone price”, “cheap smartphone”.
➤ Analysis: 9 out of 10 URL matches (Hard).
➤ Action: optimize the “Smartphones” catalog page with price filters.
Example 2: Informational Cluster
➤ Queries: “how to choose a smartphone”, “smartphone review 2025”, “best smartphones”.
➤ Analysis: 4 out of 10 URL matches (Soft), informational intent.
➤ Action: create a blog article titled “How to Choose a Smartphone in 2025”.
Example 3: Mixed Cluster
➤ Queries: “laptops for work”, “buy office laptop”.
➤ Analysis: 6 out of 10 URL matches (Soft), commercial and informational intent.
➤ Action: create a landing page with a review and a “Buy” button.
Recommendations
➤ Combine methods: manual, automated, and AI clustering for accuracy.
➤ Analyze intent: group queries by intent for relevance.
➤ Create new pages: if the analysis requires it, implement pages even if competitors do not use them.
➤ Multilingual websites: cluster queries separately for each region.
➤ Competitor analysis: study the top 10 search results to identify opportunities.
Conclusion
Semantic core clustering is the foundation of SEO, improving site ranking and structure. Use manual, automated, and AI approaches together with visualization for maximum efficiency.
Have questions or want to order clustering? Write in the comments!
