Semantic core for SEO promotion. How to make a semantic core from scratch? Why is it done

AT this moment for search promotion factors such as content and structure play the most important role. However, how to understand what to write the text about, what sections and pages to create on the site? In addition to this, you need to find out exactly what the target visitor of your resource is interested in. To answer all these questions, you need to assemble a semantic core.

Semantic core- a list of words or phrases that fully reflect the theme of your site.

In the article I will tell you how to pick it up, clean it and break it into structure. The result will be a complete structure with requests clustered by pages.

Here is an example of a query engine broken down into a structure:

By clustering, I mean splitting your search queries into separate pages. This method will be relevant both for promotion in the PS of Yandex and Google. In the article I will describe completely free way creation semantic core, however, I will also show options with various paid services.

By reading this article, you will learn

• Choose the right queries for your topic
• Collect the most complete core of phrases
• Clean from uninteresting requests
• Group and create structure

Having collected the semantic core, you can

• Create a meaningful structure on the site
• Create layered menu
• Fill pages with texts and write meta descriptions and titles on them
• Collect positions of your site for queries from search engines

Collection and clustering of the semantic core

Correct compilation for Google and Yandex begins with the definition of the main key phrases your subject. For example, I will demonstrate its compilation on a fictitious online clothing store. There are three ways to collect the semantic core:

1. Manual. Using the Yandex Wordstat service, you enter your keywords and manually select the phrases you need. This method is fast enough if you need to collect keys for one page, however, there are two drawbacks.
   • The accuracy of the method is lame. You can always miss some important words if you use this method.
   • You will not be able to assemble a semantic core for a large online store, although you can use the Yandex Wordstat Assistant plugin to simplify it - this will not solve the problem.
2. Semi-automatic. In this method, I'm going to use a program to build the kernel and then manually break it down into sections, subsections, pages, and so on. This method compilation and clustering of the semantic core, in my opinion, the most effective. has a number of advantages:
   • Maximum coverage of all topics.
   • Quality breakdown
3. Auto. Nowadays, there are several services that offer fully automatic kernel collection or clustering of your requests. Fully automatic option - I do not recommend for use, because. the quality of collection and clustering of the semantic core is currently quite low. Automatic query clustering - is gaining popularity and has a place to be, but you still need to combine some pages by hand, because. the system does not provide a perfect off-the-shelf solution. And in my opinion, you will just get confused and will not be able to dive into the project.

To compile and cluster a full-fledged correct semantic core for any project, in 90% of cases I use a semi-automatic method.

So, in order for us to do the following steps:

1. Selection of queries for topics
2. Collecting the kernel by request
3. Purging of non-targeted requests
4. Clustering (we break phrases into structure)

I showed an example of selecting a semantic core and grouping for a structure above. I remind you that we have an online clothing store, let's start to disassemble 1 point.

1. Selection of phrases for your subject

At this stage, we need the Yandex Wordstat tool, your competitors and logic. In this step, it is important to collect a list of phrases that are thematic high-frequency queries.

How to select queries to collect semantics from Yandex Wordstat

Go to the service, select the city (s) / region (s) you need, drive in the most “fat” requests in your opinion and look at the right column. There you will find the thematic words you need, both for other sections, and frequency synonyms for the entered phrase.

How to select queries before compiling a semantic core with the help of competitors

Enter the most popular queries in the search engine and select one of the most popular sites, many of which you most likely already know.

Pay attention to the main sections and save yourself the phrases you need.

At this stage, it is important to do the right thing: to cover all kinds of words from your subject as much as possible and not miss anything, then your semantic core will be as complete as possible.

Applicable to our example, we need to make a list of the following phrases / keywords:

• clothing
• Shoes
• Boots
• Dresses
• T-shirts
• Underwear
• Shorts

What phrases to enter is meaningless: women's clothing, buy shoes, prom dresses, etc. Why?— These phrases are “tails” of the queries “clothes”, “shoes”, “dresses” and will be added to the semantic core automatically at the 2nd stage of collection. Those. you can add them, but that would be pointless double work.

What keys do you need to enter?"half boots", "boots" are not the same as "boots". It is the word form that is important, and not whether these words have the same root or not.

Someone will have a long list of key phrases, and for someone it consists of one word - do not be alarmed. For example, for an online store of doors, the word “doors” is quite possibly enough to compose the semantic core.

And so, at the end of this step, we should have a similar list.

2. Collection of queries for the semantic core

For the correct full-fledged collection, we need a program. I will show an example simultaneously on two programs:

• On a paid one - KeyCollector. For those who have or want to buy.
• On the free - Slovoeb. Free program for those who are not ready to spend money.

Opening the program

Create a new project and name it, for example, Mysite

Now, to further collect the semantic core, we need to do a few things:

Create new account on Yandex mail (the old one is not recommended to be used due to the fact that it can be banned for many requests). So, you have created an account, for example [email protected] with password super2018. Now you need to specify this account in the settings as ivan.ivanov:super2018 and click the "save changes" button below. More details in the screenshots.

We select a region for compiling a semantic core. You need to select only those regions in which you are going to advance and click save. The frequency of requests and whether they will fall into the collection in principle will depend on this.

All settings are completed, it remains to add our list of key phrases prepared at the first step and click the "start collecting" button of the semantic core.

The process is fully automatic and quite long. You can make coffee for now, and if the topic is wide, for example, like the one we collect, then it’s for a few hours 😉

As soon as all the phrases are collected, you will see something like this:

And at this stage is over - proceed to the next step

3. Cleaning the semantic core

First, we need to remove requests that are not of interest to us (non-targeted):

• Associated with another brand, such as "gloria jeans", "ekko"
• Information queries, e.g. "I wear boots", "Jean size"
• Similar topics, but not related to your business, for example, “used clothes”, “wholesale clothes”
• Requests that are not related to the topic in any way, for example, “Sims dresses”, “Puss in Boots” (there are quite a lot of such requests after selection in the semantic core)
• Requests from other regions, metro, districts, streets (it doesn’t matter which region you collected requests for - another region still comes across)

Cleaning must be done manually as follows:

We enter the word, press "Enter", if in our created semantic core it finds exactly the phrases that we need, select the found one and press delete.

I recommend that you enter the word not in its entirety, but using a construction without prepositions and endings, i.e. if we write the word "glory", it will find the phrases "buy jeans at gloria" and "buy jeans at gloria". When writing "gloria" - "gloria" would not be found.

Thus, you need to go through all the points and remove queries that you do not need from the semantic core. This can take a significant amount of time, and you may end up removing most of the collected requests, but the result will be a full clean and correct list all sorts of promoted queries for your site.

Upload now all your queries to excel

You can also bulk remove non-target queries from the semantics, provided you have a list. You can do this with stop words, and it's easy to do for a typical group of words with cities, subways, streets. You can download a list of such words that I use at the bottom of the page.

4. Clustering the semantic core

This is the most important and interesting part - we need to divide our requests into pages and sections, which together will create the structure of your site. A bit of theory - how to guide when splitting requests:

• Competitors. You can pay attention to how the semantic core of your competitors from the TOP is clustered and do the same, at least with the main sections. And also see which pages are in the search results for low-frequency queries. For example, if you're not sure "to do or not" a separate section for the query "red leather skirts", then type the phrase into search engine and see the output. If the search results contain resources where there are such sections, then it makes sense to make a separate page.
• Logics. Do the whole grouping of the semantic core using logic: the structure should be understandable and represent in your head a structured tree of pages with categories and subcategories.

And a couple more tips:

• It is not recommended to put less than 3 queries per page.
• Do not make too many levels of nesting, try to make sure that there are 3-4 of them (site.ru/category/subcategory/sub-subcategory)
• Do not make long URLs, if you have many levels of nesting when clustering the semantic core, try to shorten the url of categories high in the hierarchy, i.e. instead of "your-site.ru/zhenskaya-odezhda/palto-dlya-zhenshin/krasnoe-palto" do "your-site.ru/zhenshinam/palto/krasnoe"

Now to practice

Kernel Clustering by Example

To begin with, we will divide all requests into main categories. Looking at the logic of competitors, the main categories for a clothing store will be: men's clothing, women's clothing, children's clothing, as well as a bunch of other categories that are not tied to gender / age, such as just “shoes”, “outerwear”.

We group the semantic core with the help of Excel. Open our file and act:

1. Divided into main sections
2. We take one section and break it into subsections

I will show on the example of one section - men's clothing and its subsections. In order to separate some keys from others, you need to select the entire sheet and click conditional formatting-> cell selection rules-> text contains

Now in the window that opens, write "husband" and press enter.

Now all of our menswear keys are highlighted. It is enough to use the filter to separate the selected keys from the rest of our assembled semantic core.

So let's turn on the filter: you need to select the column with queries and click sort and filter-> filter

And now let's sort

Create a separate sheet. Cut the highlighted lines and paste them there. In this way, you will need to further break the kernel.

Change the name of this sheet to "Men's Clothing", a sheet where the rest of the semantic core is called "All Queries". Then create another sheet, name it "Structure" and put it as the very first one. On the page with the structure, create a tree. You should end up like this:

Now we need to divide the large menswear section into sub-sections and sub-subsections.

For ease of use and navigation through your clustered semantic core, put links from the structure to the corresponding sheets. To do this, right-click on the desired item in the structure and do as in the screenshot.

And now you need to methodically separate requests with your hands, simultaneously deleting what you might not have been able to notice and remove at the stage of cleaning the kernel. Ultimately, thanks to semantic core clustering, you should end up with a structure similar to this one:

So. What we have learned to do:

• Select the queries we need to collect the semantic core
• Collect all-possible phrases for these queries
• Clean up "garbage"
• Cluster and create structure

What you can do next by creating such a clustered semantic core:

• Create a website structure
• Create a menu
• Write texts, meta descriptions, titles
• Collect positions to track the dynamics of requests

Now a little about programs and services

Programs for collecting the semantic core

Here I will describe not only programs, but also plug-ins and online services that I use

• Yandex Wordstat Assistant is a plugin that makes it convenient to select queries from wordstat. Great for quickly compiling a core for a small site or 1 page.
• Keycollector (word - free version) is a complete program for clustering and creating a semantic core. Enjoys great popularity. A huge amount of functionality in addition to the main direction: Selection of keys from a bunch of other systems, the possibility of autoclustering, collecting positions in Yandex and Google, and much more.
• Just-magic is a multifunctional online service for compiling the core, auto splitting, checking the quality of texts and other functions. The service is shareware, for full-fledged work you need to pay a monthly fee.

Thanks for reading the article. Thanks to this step-by-step manual, you will be able to compose the semantic core of your site for promotion in Yandex and Google. If you have any questions - ask in the comments. Below are bonuses.

The semantic core is a set of search phrases and words that are used to promote the site. These search words and phrases help the robots to determine the subject of the page or the entire service, that is, to find out what the company does.

In Russian, semantics is a branch of the science of language that studies the semantic content of the lexical units of a language. Applied to search engine optimization this means that the semantic core is the semantic content of the resource. It helps to decide what information to convey to users and in what way. Therefore, semantics is the foundation, the basis of all SEO.

What is the semantic core of the site for and how to use it?

• The correct semantic core is necessary to accurately calculate the cost of promotion.
• Semantics is a vector for building internal seo-optimization: the most relevant queries for each service or product are selected so that users and search robots find them better.
• Based on it, the site structure and texts for thematic pages are created.
• Keys from semantics are used to write snippets ( short descriptions pages).

Here is the semantic core - an example of its compilation in a company website for a construction company website:

The optimizer collects semantics, parses it into logical blocks, finds out the number of their impressions, and based on the cost of requests in the top of Yandex and Google, calculates the total cost of promotion.

Of course, when selecting a semantic core, the specifics of the company's work are taken into account: for example, if the company did not design and build houses from glued beams, then we would delete the corresponding queries and not use them in the future. Therefore, an obligatory stage of working with semantics is its coordination with the customer: no one knows the specifics of the company's work better than him.

Types of keywords

There are several parameters by which keywords are classified.

1. By frequency
   • high-frequency - words and phrases with a frequency of 1000 impressions per month;
   • mid-frequency - up to 1000 impressions per month;
   • low-frequency - up to 100 impressions.

Collecting frequency by keywords helps you find out what users most often request. But a high-frequency query is not necessarily a highly concurrent query, and compiling semantics with high frequency and low competitiveness is one of the main aspects in working with the semantic core.

2. Type:
   • geo-dependent and non-geo-dependent - promotions tied to the region and not tied;
   • informational - according to them, the user receives some information. Keys of this type are usually used in articles - for example, reviews or useful tips;
   • branded - contain the name of the promoted brand;
   • transactional - implying an action from the user (buy, download, order) and so on.
3. Other types are those that are difficult to attribute to any type: for example, the key is “profiled timber”. By entering such a request into a search engine, the user can mean anything: the purchase of timber, properties, comparisons with other materials, and so on.

   From the experience of our company, we can say that it is very difficult to promote any site for such requests - as a rule, these are high-frequency and highly competitive, and this is not only difficult to optimize, but also expensive for the client.

How to build a semantic core for a website?

• By analyzing competitor sites (in SEMrush, SerpStat, you can see the semantic core of competitors):

The process of compiling a semantic core

The collected requests are not yet the semantic core, here it is still necessary to separate the wheat from the chaff so that all requests are relevant to the client's services.

To make up the semantic core, requests must be clustered (divided into blocks according to the logic of the service). You can do this with the help of programs (for example, KeyAssort or TopSite) - especially if the semantics is voluminous. Or manually evaluate and sort through the entire list, remove inappropriate requests.

Then send to the client and check if there are any errors.

The finished semantic core is a yellow brick path to the content plan, blog articles, texts for product cards, company news, and so on. This is a table with the needs of the audience that you can satisfy using your site.

• Distribute the keys across pages.
• Use keywords in meta tags