Collecting the semantic core: selecting keywords

The site's visibility in SERP search results directly depends on the density and quality of embedded search queries. Ignoring semantic core collection algorithms leads to the generation of irrelevant traffic, a high bounce rate, and draining the content marketing budget.
The keyword acts as a bridge between the user's need and the business offer. The task of an SEO specialist is to parse an array of words, filter information garbage, and cluster queries across landing pages to accurately match the audience's intent.
Analysis of search intent
The intent reveals the user's true purpose at the moment of entering the phrase into the search bar. The query "concrete M300" does not have a clear color, whereas the phrase "buy concrete M300 with delivery" clearly indicates a transactional intention. An error in determining the intent ruins the conversion of the page.
Search engines rigidly divide queries into categories:
- information contains markers "how", "why", "instructions";
- commercial ones include the words "price", "order", "price list";
- navigation ones search for a specific brand or company address.
Landing information requests on a product card leads to a drop in URL positions. The Yandex algorithm records that the user was looking for instructions on how to install laminate flooring, and ended up on the catalog page. The person closes the tab after five seconds, worsening the site's behavioral factors.
The basic matrix of marker words is assembled manually. The SEO engineer analyzes the structure of competitors in the top 10 search results, writing out the basic nouns and verbs that characterize the product. These tokens will become the basis for automated database parsing.
Parsing tools and garbage filtering
Manual collection of semantics is impossible due to the volume of information. To extract phrases from Yandex databases.Wordstat or Google Keyword Planner use desktop parsers of the Key Collector level or cloud services. The programs collect not only direct queries, but also LSI-tails - synonyms and near-mathematical terms.
After the collection stage, the semantic core undergoes a rigorous purge of stop words:
- removal of mentions of competitive brands;
- clearing irrelevant toponyms from other regions;
- cross-section of queries with markers "for free", "with your own hands".
The frequency of a request determines its potential. High-frequency phrases generate huge traffic, but require huge promotion budgets due to competition. The focus is shifting to low-frequency long-tail queries that generate targeted and hot customers.
The cannibalization check excludes the promotion of several site pages for the same query. If two URLs compete for the phrase "crane rental", the search engine will lower both documents in the ranking, not understanding which one is more relevant.
Clustering and URL distribution
The collected and cleaned array of phrases is divided into logical groups - clusters. One cluster is equal to one landing page. The grouping process is based on the analysis of search results: if more than three competitor URLs intersect in the top 10 for two different queries, these phrases are combined into one cluster.
Hard clustering requires matching all documents in the top, which is ideal for narrow commercial niches. Soft clustering allows minimal intersections and is used for information portals with large articles-longrides.
The finished semantic core structure is exported as a table. The total frequency is recorded in front of each cluster, the type of the future page (category, product, article) is determined, and a technical specification for the copywriter is drawn up indicating the exact and diluted occurrences of keywords in the text and Title meta tags. Discover greyhound racing and start playing today.