home Internet In what cases is the semantic core used. Compiling a top semantic core: a selection of articles on semantics. Is it necessary to select key phrases or can you do without it?

In what cases is the semantic core used. Compiling a top semantic core: a selection of articles on semantics. Is it necessary to select key phrases or can you do without it?

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From this article you will learn:

How to make the semantic core of the site
What programs to use for this
How to analyze the semantic core of a competitor's website
What mistakes are most often made in the assembly of the semantic core
How much does it cost to order a ready-made semantic core of the site

The semantic core is the basis of any Internet resource, the key to its successful promotion and attracting the target audience. How to create the semantic core of the site and what mistakes to avoid, you will learn from this article.

What is the semantic core of the site

The easiest and at the same time effective way to attract visitors to your site is to make them show interest in it by clicking on a link from a Yandex or Google search engine. To do this, you need to find out what your target audience is interested in, how, by what words, users are looking for the necessary information. The semantic core will help you with this.

The semantic core is a collection of individual words or phrases that characterize the subject and structure of your site. Semantics - originally - the field of philology, dealing with the meaning of words. Nowadays it is more often understood as the study of meaning in general.

Based on this, we can conclude that the concepts of "semantic core" and "semantic core" are synonyms.

The purpose of creating the semantic core of the site is to fill it with content that is attractive to users. To do this, you need to find out what keywords they will use to search for information posted on your site.

Submit your application

The selection of the semantic core of the site involves the distribution of search queries or groups of queries across pages in such a way that they satisfy the target audience as much as possible.

This can be achieved in two ways. The first is to analyze the search phrases of users and, based on them, create a site structure. The second way is to first come up with a framework for the future site, and then, after analysis, distribute keywords over it.

Each method has a right to exist, but the second one is more logical: first you create the structure of the site, and then fill it with search queries, by which potential customers can find the content they need through search engines.

This is how you show the quality of proactivity - you independently determine what information to convey to site visitors. Otherwise, creating a site structure based on keywords, you only adapt to the surrounding reality.

There is a fundamental difference between the approach to creating the semantic core of the site of an SEO specialist and a marketer.

A classic optimizer will tell you: to create a website, you need to select phrases and words from search queries for which you can get to the TOP of search results. Then, on their basis, form the structure of the future site and distribute the keywords across the pages. Page content is created for the selected keywords.

A marketer or entrepreneur will approach the issue of creating a website differently. First, he will think about what the site is for, what information it will carry to users. Then he will come up with an approximate structure of the site and a list of pages. At the next stage, he will create the semantic core of the site in order to understand what search queries potential customers are looking for information on.

What are the disadvantages of working with the semantic core from the position of an SEO specialist? First of all, with this approach, the quality of information on the site is significantly deteriorating.

The company should decide for itself what to say to customers, and not give out content in response to the most popular search queries. Such blind optimization can lead to the fact that some of the promising queries with low frequency rates are eliminated.

The result of creating a semantic core is a list of keywords that are distributed across the pages of the site. This list indicates the URL of the pages, keywords and the level of frequency of their requests.

An example of the semantic core of the site

How to compose the semantic core of the site: step by step instructions

Step 1. Compile the initial list of requests

First you need to select the most popular search queries on the subject of your site. There are two options for how to do this:

1. Brainstorming method- when for a short period of time you yourself or with colleagues write down all the words and phrases by which, in your opinion, users will search for information posted on your site.

Write down all possible options, including:

variations in the spelling of the name of a product or service, synonymous words, ways of writing the name in Latin and Cyrillic letters;
full names and abbreviations;
slang words;
references to the constituent elements of a product or service, for example, building materials - sand, brick, corrugated board, putty, etc.;
adjectives that reflect significant characteristics of a product or service (quality repairs, fast delivery, painless dental treatment).

2. Analyze the sites of your competitors. Open an incognito browser for your region. Look at the websites of competitors that will be shown to you by the search results for your topic. Find all potential keywords. You can determine the semantic core of a competitor's website using the com and bukvarix.com services.

Analyze contextual advertisements. On your own or with the help of specialized services (for example, spywords.ru or advodka.com), study the semantic core of someone else's site and find out which keywords competitors use.

By applying all three approaches, you will get a fairly large list of keywords. But it will still not be enough to create an effective semantic core.

Step 2. Expanding the resulting list

At this stage, Yandex.Wordstat and Google AdWords services will help you. If he takes turns entering words from your list of keys generated at the first stage into the search string of any of these services, then at the output you will get a list of refined and associative search queries.

Refined queries can include other words or phrases in addition to your word. For example, if you enter the keyword “dog”, then the service will give you 11,115,538 queries with this word, which include such queries for the last month as “photos of dogs”, “treatment of dogs”, “breeds of dogs”, etc.

Association queries are the words or phrases that users searched for along with your query. For example, along with the keyword “dog”, users entered: “dry food”, “royal canin”, “Tibetan mastiff”, etc. These search queries can also be useful to you.

In addition, there are special programs for creating the semantic core of the site, for example: KeyCollector, SlovoEB and online services - Topvisor, serpstat.com, etc. They allow not only selecting keywords, but also analyzing them and grouping search queries.

To expand the list of keys as much as possible, see what the service's search suggestions show. There you will find the most popular search terms that start with the same letters or words as yours.

Step 3. Remove unnecessary requests

Search queries can be classified in different ways. Depending on the frequency, requests are:

high-frequency (more than 1500 requests per month);
mid-frequency (600-1500 requests per month);
low-frequency (100-200 requests per month).

This classification is highly arbitrary. Assigning a request to one category or another will depend on the subject of a particular site.

In recent years, there has been an upward trend in low-frequency queries. Therefore, to promote the site, the semantic core should include mid- and low-frequency queries.

There is less competition among them, so it will be much easier to raise the site to the first page of search results than when working with high-frequency queries. In addition, many search engines welcome when sites use low-frequency keywords.

Another classification of search queries is by search objectives:

Informational- Key words that users enter in search of specific information. For example: “how to glue the tiles in the bathroom yourself”, “how to connect the dishwasher”.
Transactional- keywords that users enter when planning to perform some kind of action. For example: “watch a movie online for free”, “download a game”, “buy building materials”.
vital- queries that users enter in search of a specific site. For example: "Sberbank online", "buy a refrigerator on Yandex.Market", "vacancies on Head hunters".
Other (general)- all other search queries by which you can understand what the user is looking for. For example, the query "car" the user can enter if he wants to sell, buy or repair a car.

Now it's time to remove from the list of keywords all unnecessary ones that:

do not correspond to the theme of your site;
include competitor brand names;
include the names of other regions (for example, buy an iPhone in Moscow if your site works only for Western Siberia);
contain typos or errors (if you write “dog” instead of “dog” in the search engine, it will consider this as a separate search query).

Step 4. Define competitive requests

To effectively distribute keywords on the pages of the site, you need to filter them by importance. To do this, use the Keyword Effectiveness Index - KEI (Keyword Effectiveness Index). Calculation formula:

KEI = P2/C,

where P is the frequency of impressions of the keyword in the last month; C - the number of sites that are optimized for this search query.

The formula shows that the more popular the keyword, the higher the KEI, the more targeted traffic you will attract to your site. High competition for a search query makes it difficult to promote a site on it, which is reflected in the KEI value.

Thus, the higher the KEI, the more popular the search query, and vice versa: the lower the keyword performance index, the higher the competition for it.

There is a simplified version of this formula:

KEI \u003d P 2 /U,

where instead of C, the indicator U is used - the number of pages optimized for this keyword.

Let's look at an example of how to use the Keyword Effectiveness Index (KEI). Let's determine the frequency of requests using the Yandex Wordstat service:

At the next step, let's see how many pages are in the search results for the search query we are interested in for the last month.

Substitute the found values of the variables into the formula and calculate the keyword effectiveness index KEI:

KEI = (206 146 * 206 146) / 70 000 000 = 607

How to evaluate KEI values:

if KEI is less than 10, then search queries are ineffective;
if KEI is from 10 to 100, then search queries are effective, they will attract the target audience to the site;
if KEI is from 100 to 400, then search queries are very effective, they will attract a significant share of traffic;
with a KEI of more than 400, search queries have maximum efficiency and will attract a huge number of users.

Keep in mind that the gradation of KEI keyword performance index values is determined by the theme of the site. Therefore, the above scale of values cannot be applied to all Internet resources, since for some the value of KEI > 400 may be insufficient, and for highly specialized sites this classification is not applicable at all.

Step 5. Group keywords on the site

Clustering the semantic core of the site is a process of grouping search queries for logical reasons and based on the results of search engines. Before proceeding with the grouping, it is important to make sure that the specialist who will carry it out understands all the intricacies of the company and product, knows their specifics.

This work is expensive, especially when it comes to filling a multi-page Internet resource. But it doesn't have to be done by hand. You can cluster the semantic core of the site automatically using special services, such as Topvisor, Seranking.ru, etc.

But it is better to double-check the results obtained, since the logic of separating keys into groups for programs may not coincide with yours. In the end, you will get the final structure of the site. Now you will clearly understand which pages you need to create and which ones to eliminate.

When is it necessary to analyze the semantic core of a competitor's website?

When starting a new project.

You are working on a new project and are building the semantic core of the site from scratch. To do this, you decided to analyze the keywords that competitors use to promote their sites.

Many are suitable for you, so you use them to replenish the semantic core. It is worth considering the niche in which competitors operate. If you plan to occupy a small market share, and competitors operate at the federal level, then you cannot just take and completely copy their semantic core.

When expanding the semantic core of a working site.

Do you have a website that needs to be promoted? The semantic core was formed a long time ago, but it works inefficiently. Requires site optimization, restructuring, updating content in order to increase traffic. Where to start?

First of all, you can analyze the semantic core on competing sites using specialized services.

How to use keywords from competitor sites in the most effective way?

Here are some easy rules. First, take into account the percentage of matches for keys from your site and from other people's Internet resources. If your site is still under development, then choose any competing site, analyze it and use keywords as the basis for creating your semantic core.

In the future, you will simply compare how much your reference keys intersect with keys from competitor sites. The easiest way is to use the service to download a list of all competing sites and filter them by the percentage of intersections.

Then you need to download the semantic cores of the first few sites into Excel or Key Collector and add new keywords to the semantic core of your site.

Secondly, before copying the keys from the donor site, be sure to visually check it.

When buying a ready-made site for the purpose of subsequent development or resale.

Consider an example: you want to buy a certain site, but before making a final decision, you need to evaluate its potential. The easiest way to do this is to study the semantic core, so you can compare the current coverage of the site with competitors' sites.

Take the strongest competitor as a benchmark and compare its visibility with the results of the Internet resource that you plan to purchase. If the gap from the reference site is significant, this is a good sign: it means that your site has the potential to expand the semantic core and attract new traffic.

Pros and cons of analyzing the semantic core of competitors through special services

The principle of operation of many services for determining keywords on other people's sites is as follows:

a list of the most popular search queries is formed;
for each key, 1-10 search results pages (SERPs) are selected;
such collection of key phrases is repeated with a certain frequency (weekly, monthly or every year).

Disadvantages of this approach:

services issue only the visible part of search queries on the websites of competing organizations;
services retain a kind of "cast" of the issuance created during the collection of keywords;
services can determine the visibility of only those search queries that are in their databases;
services show only those keywords that they know.
to get reliable data about keywords on a competing site, you need to know when search queries were collected (visibility analysis);
not all requests are reflected in the search results, so the service does not see them. The reasons may be different: the pages of the site have not yet been indexed, the search engine does not rank the pages due to the fact that they take a long time to load, contain viruses, etc.;
usually there is no information about which keys are included in the base of the service used to collect search results.

Thus, the service does not form a real semantic core that underlies the site, but only a small visible part of it.

Based on the foregoing, the following conclusions can be drawn:

The semantic core of the competitor's website, formed with the help of special services, does not give a complete up-to-date picture.
Checking the semantic core of a competitor's site helps to complement the semantics of your Internet resource or analyze the marketing policy of competing companies.
The larger the keyword base of the service, the slower the process of processing the issuance and the lower the level of relevance of semantics. While the service collects search results at the beginning of the database, the data at the end of the database becomes obsolete.
Services do not disclose information about the degree of relevance of their databases and the date of the last update. Therefore, you cannot know to what extent the keywords selected by the service from a competitor's site reflect its real semantic core.
A significant advantage of this approach is that you get access to a large list of competitor keywords, many of which you can use to expand the semantic core of your own site.

TOP 3 paid services where you can find out the semantic core of competitors

Megaindex Premium Analytics

This service has a rich arsenal for analyzing the semantic core of competing sites. Using the module Site Visibility you can find and download a list of keywords, identify sites with a similar theme that can be used to expand the semantic core of your site.

One of the disadvantages of Megaindex Premium Analytics is the inability to filter the lists of keys in the program itself, you first need to download them in Excel.

Brief description of the service:

Keys.so

In order to analyze the semantic core using the keys.so service, you need to insert the url of a competitor site, select suitable sites based on the number of matching key phrases, analyze them and download a list of search queries for which they are promoted. The service makes it easy and simple. A nice bonus is the modern interface of the program.

Cons: small size of the database of search phrases, insufficient frequency of visibility updates.

Brief summary of the service:

Spywords

This service not only analyzes visibility, but also provides statistics on advertisements in Yandex.Direct. At first, it is difficult to deal with the spywords.ru interface, it is overloaded with functionality, but in general it does its job well.

With the help of the service, you can analyze competing sites, identify intersections in key phrases, and upload a list of competitor keys. The main disadvantage is the insufficient base of the service (about 23 million search phrases).

Brief summary of the service:

Thanks to special programs, websites and their semantic cores are no longer a mystery to you. You can easily analyze any Internet resources of competitors you are interested in. Here are some tips for using the information you receive:

Use keywords only from sites with similar topics(the more intersections with yours, the better).
Do not analyze portals, they have too large semantic cores. As a result, you will not supplement your own core, but only expand it. And this, as you already know, can be done endlessly.
When buying a site, be guided by the indicators of its current visibility in the search engine, compare them with the sites included in the TOP to assess the development potential.
Take keywords from competitor sites to complement the semantic core of your site, rather than building it from scratch.
The larger the base of the service you use, the more complete your semantic core will be. But pay attention to the frequency of updating search phrase databases.

7 services that will help you create the semantic core of the site from scratch online

Google Keyword Planner

If you are thinking about how to create a semantic core of a site, pay attention to this service. It can be used not only in Runet, but also in other segments where AdWords works.

Open Google AdWords. In the top bar in the section "Tools" click on option Keyword Planner. A new menu will appear in which you need to select a section "Search for new keywords by phrase, site or category." Here you can configure the following settings:

the keyword or phrase to search for;
subject matter of the product or service;
region of search queries;
the language in which users enter search queries;
keyword search engine;
negative keywords (should not be present in keywords).

Next, click on the button "Get Options" after which Google AdWords will give you possible synonyms for your keyword or phrase. The received data can be uploaded to Google Docs or CSV.

Benefits of using Google AdWords service:

the ability to select synonyms for the key phrase;
use of negative keywords to refine the search query;
access to a huge database of search queries of the Google search engine.

The main disadvantage of the service: if you have a free account, then Google AdWords will provide inaccurate data on the frequency of search queries. The error is so significant that it is impossible to rely on these indicators when promoting the site. The way out is to buy access to a paid account or use another service.

Serpstat

This service allows you to comprehensively collect user search queries by keywords and site domains. Serplast is constantly expanding the number of region bases.

The service allows you to identify your site's key competitors, determine the search phrases for which they are promoted, and form a list of them for subsequent use in the semantic core of your Internet resource.

Benefits of the Serplast service:

a large selection of tools for analyzing the semantic core of competitor sites;
informative reporting forms reflecting the frequency indicators for the selected region;
option to upload search queries for specific pages of the site.

Cons of the Serplast service:

despite the fact that the service database data is constantly updated, there is no guarantee that realistic data on the frequency of search queries will be provided between the latest updates;
not all search phrases with low frequency are displayed by the service;
limited languages and countries with which the service works.

Key Collector

This service will help you deal not only with the question of how to assemble the semantic core of the site, but also solve the problem of its expansion, cleaning and clustering. Key Collector is able to collect search queries, provide data on the level of their frequency in selected regions, and process semantics.

The program searches for key phrases in the start lists. It can be used to work with databases of various formats.

Key Collector can show the frequency of keywords from data downloaded from Serpstat, Yandex Wordstat and other services.

Semrush

Compiling the semantic core of the site in the Semrush program will cost you absolutely free. But you will receive no more than 10 key queries with data on their frequency in the selected region. In addition, using the service, you can find out what other search queries users in other regions enter for your keyword.

Advantages of the Semrush service:

works all over the world, it is possible to collect data on the frequency of search queries in the western region;
for each key phrase gives the TOP sites in the search results. You can be guided by them in the future, when forming the semantic core of your own site.

Cons of the Semrush service:

if you want to get more than 10 keywords, you need to purchase a paid version for $100;
it is not possible to download the complete list of key phrases.

keyword tool

This service allows you to collect key phrases for the semantic core of the site from foreign Internet resources in broad correspondence. Keywordtool also allows you to select search suggestions and phrases that contain the base key.

If you use the free version of the program, then in one session you can get no more than 1000 search phrases without data on their frequency level.

Advantages of the Keywordtool service:

works with different languages and in many countries of the world;
shows search queries not only from search engines, but also from popular online stores (Amazon, eBay, App Store) and the largest video hosting service YouTube;
the breadth of coverage of search phrases exceeds that of Google AdWords;
the generated list of search queries can be easily copied into a table of any format.

Disadvantages of the Keywordtool service:

the free version does not provide data on the frequency of search queries;
there is no way to load keywords at once as a list;
searches for keywords only by phrases in which they can be included, does not take into account possible synonyms

Ubersuggest

The semantic core of the site in the Ubersuggest service can be created based on the search queries of users from almost any country in the world in any language. If you use the free version, you can get up to 750 search phrases per query.

The advantage of the service is the ability to sort the list of keywords in alphabetical order, taking into account the prefix. All search queries are automatically grouped, which makes it easier to work with them when forming the semantic core of the site.

As a disadvantage of Ubersuggest, one can single out incorrect data on the frequency of search queries in the free version of the program and the inability to search by keyword synonyms.

Ahrefs Keywords Explorer

This service can collect keywords for your semantic core in broad, phrase and exact matches in the selected region, taking into account the frequency level.

There is an option to select negative keywords and view the TOP search results in Google for your main keywords.

The main disadvantages of Ahrefs Keywords Explorer are only the paid version of the program and the dependence of data accuracy on the degree of relevance of the databases.

Frequently asked questions on compiling the semantic core of the site

How many keys are enough to create the semantic core of the site (100, 1000, 100,000)?

This question cannot be answered unambiguously. It all depends on the specifics of the site, its structure, and the actions of competitors. The optimal number of keys is determined individually.

Is it worth using ready-made databases of key phrases to form the semantic core of the site?

On the Internet you can find many different resources with thematic databases of keys. For example, Base Pastukhov, UP Base, Mutagen, KeyBooster, etc. It cannot be said that you should not use such sources. Such databases contain significant archives of search queries that will be useful to you for website promotion.

But remember about such indicators as competitiveness and relevance of keys. Also keep in mind that your competitors can also use ready-made bases. Another disadvantage of such sources is the likelihood of missing key phrases that are meaningful to you.

How to use the semantic core of the site?

Key phrases selected to create a semantic core are used to compile a relevance map. It includes title, description tags and h1-h6 headings that are needed to promote the site. Also, the keys are taken as the basis for writing SEO texts for the site pages.

Is it worth taking requests with zero frequency for the semantic core of the site?

This is useful in the following cases:

If you spend a minimum of resources and time to create pages with such keys. For example, automatic generation of SEO filter pages in online stores.
Zero frequency is not absolute, that is, at the time of collecting information, the frequency level is zero, but the history of the search engine shows requests for this word or phrase.
Zero frequency only in the selected region, in other regions the frequency level for this key is higher.

5 typical mistakes when collecting a semantic core for a website

Avoid keyword phrases with high competition. After all, this does not oblige you to bring the site to the TOP by this key at all costs. You can use such a search phrase as an addition to the semantic core, as a content idea.
Refusal to use keys with low frequency. You can also use similar search terms as content ideas.
Creation of web pages for individual search queries. Surely you have seen sites where similar queries (for example, “buy a wedding cake” and “make a wedding cake to order”) have their own page. But the user who enters these requests actually wants the same thing. There is no point in making multiple pages.
Create the semantic core of the site exclusively with the help of services. Of course, collecting keys automatically makes life easier. But their value will be minimal if you do not analyze the result. After all, only you understand the features of the industry, the level of competition and know everything about the events of your company.
Over-focus on collecting keys. If you have a small site, then start by collecting semantics using Yandex or Google services. You should not immediately engage in the analysis of the semantic core on competitor sites or collect keys from different search engines. All of these methods will come in handy when you realize that it's time to expand the kernel.

Or maybe it is better to order the compilation of the semantic core of the site?

You can try to create a semantic core on your own using the free services that we have talked about. For example, "Keyword Planner by Google" can give you a good result. But if you are interested in creating a large, high-quality semantic core, plan this item in your budget.

On average, the development of the semantic core of the site will cost from 30 to 70 thousand rubles. As you remember, the final price depends on the subject of the business and the optimal number of search queries.

Not to buy a pig in a poke

A high-quality semantic core will not be cheap. To make sure that the performer understands this work and will do everything at a high level, ask him to collect trial semantics for one request. This is usually done free of charge.

To check the results, run the list of keys through Mutagen and analyze how many of them are high-frequency and low-competitive. It is not uncommon for performers to provide lists with a large number of key phrases, many of which are completely unsuitable for further use.

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The semantic core of the site is the keywords (queries) that users use on the Internet to search for services, goods and any other information that this site offers. For webmasters, this is an action plan for promoting a resource. Ideally the semantic core of the site is created once, before you start optimization and promotion.

The semantic core of the site is usually compiled in several stages:

All kinds of words (phrases) suitable for the topic of the site are selected. Initially, you can limit yourself to 100 - 200 search queries. In order to know which queries are right for you, answer yourself the question “What do I want to dedicate my site to?”
Extension of the semantic core through associative queries
Weed out inappropriate words. Here you filter out those phrases for which you will not promote your site. There are usually more than half of such words.
Highly competitive queries are eliminated, for which it makes no sense to promote the site. As a rule, three words out of five or more are removed.
And lastly, this is the correct distribution of the list of search queries on the resource pages. It is recommended to leave highly competitive queries on the main page of the resource, less competitive queries should be grouped by meaning and placed on other pages. To do this, you need to create a document in Excel and break the keywords into pages.

Selection of search queries and frequency check

The first thing to do is to collect as many possible queries on your topic that are of interest to users on the Web. There are two methods for this:

Free, which include: Wordstat Yandex, Slovoeb, the old-fashioned way, tips from Google (External Keyword Tool), analysis of the semantics of competitors and search tips.
Paid ones include Key Collector, Semrush, Pastukhov bases and some other services.

These tools are suitable for various purposes (for example, Semrush is better to use for bourgeois). Of course, all this can be entrusted to optimizers, but there is a possibility that you will have an incomplete semantic core.

Many people use the Pastukhov database to collect key phrases, but with Key Collector it is much more convenient to collect queries from Yandex and Google statistics services.

At the initial stage, it is better to collect requests in Excel, it looks like this:

If Google is more important for your resource, then focus on it, but also take into account and analyze keywords from Yandex. It is also very important to collect a long tail of low-frequency queries, for them you will get traffic much faster.

You can use another option, this is to learn key phrases (words) from competitors and use them. At this stage, you simply collect as many key phrases (words) as possible that are relevant to the topic of your resource, and then proceed to the next stage - filtering.

Analysis of requests, removal of "dummy"

This stage is already easier, here you need to filter out empty words and those that are not related to the subject of the site. For example, you have lunch delivery in Kyiv, and other cities are on the list.

How to define empty requests? Go to Yandex Wordstat and enter the keyword:

You see 881 impressions per month, but to be more specific:

Now a completely different picture emerges. Maybe this is not the best example, but the main thing is that you understand the essence. There are a large number of keywords for which sufficient traffic is visible, although in reality everything is absent there. That's why you need to weed out such phrases.

For example, if a person before (or after) typing the query “lunch delivery” wrote some other phrase in the search line (one search session is called), then Yandex makes the assumption that these search phrases are somehow interconnected. If such a relationship is observed in several people, then such associative queries are shown in the right column of wordstat.

Such search queries are sorted in the wordstat window in descending order of the frequency of their input in conjunction with the main query this month (the frequency of their use in the Yandex search engine is shown). You need to use this information to expand the semantic core of your resource.

Distribution of requests across pages

After that, you need to distribute the keywords (phrases) you have collected on the pages of your site. Distribution is much easier when you don't have the pages themselves yet.

Focus primarily on keywords in search queries and their frequency. With competition, you should do this: select the main page for one or two highly competitive queries.

For medium or low competition queries, optimize section and article pages accordingly.

If there is semantic similarity in search queries, simply collect the same phrases and classify them into one group. When compiling keywords to promote a resource, always use not only standard tools, but also a creative approach.

Combining non-standard and classic methods, you can easily and quickly create the semantic core of the site, choose the most optimal promotion strategy and achieve success much faster!

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In this post, we will describe the complete algorithm for collecting the semantic core, mainly for an informational site, but this approach can also be applied to commercial sites.

Initial semantics and site structure creation

Preparation of words for parsing and the initial structure of the site

Before we start parsing words, we need to know them. Therefore, we need to create the initial structure of our site and the initial words for parsing (they are also called markers).

You can see the original structure and words:

1. Using logic, words from the head (if you understand the topic).
2. From your competitors, which you analyzed when choosing niches or entering your main query.
3. From Wikipedia. It usually looks like this:

4. We look at wordstat for your main queries and the right column.
5. Other thematic books and reference books.

For example, the topic of our site is heart disease. It is clear that we must have all heart diseases in our structure.

You can't do without a medical handbook. I would not look at competitors, because they may not have all the diseases, most likely they did not have time to cover them.

And your initial words for parsing will be exactly all heart diseases, and based on the keys that we parse, you will build the structure of the site when you start grouping them.

In addition, you can take all the drugs for the treatment of the heart, as an extension of the topic, etc. You look at Wikipedia, headings from competitors on the site, wordstat, think logically and in this way find more marker words that you will parse.

Site structure

You can look at competitors for a general overview, but you don't always have to make a structure like theirs. You should proceed more from the logic of your target audience, they also enter queries that you parse from search engines.

For example, how to proceed? List all heart diseases, and from them already conduct symptoms, treatment. Or, nevertheless, make headings for symptoms, treatment, and from them already lead to diseases. These issues are usually resolved by grouping keywords based on search engine data. But not always, sometimes you will have to make your own choices and decide how to make the structure the best, because requests can overlap.

You must always remember that the structure is created throughout the collection of semantics and sometimes in its original form it consists of several headings, and with further grouping and collection it expands as you begin to see queries and logic. And sometimes you can compose it and not immediately worry about keywords, because you know the topic well or it is well represented by competitors. There is no system for compiling the structure of the site, you can say this is your personal creativity.

The structure can be your individual (different from competitors), but it must be convenient for people, meet their logic, and therefore the logic of search engines, and such that you can cover all thematic words in your niche. It should be the best and comfortable!

Think ahead. It happens that you take a niche, and then you want to expand it, and you start changing the structure of the entire site. And the created structure on the site is very difficult and dreary to change. Ideally, you will need to change the attachment urls and paste it all on the site itself. In short, what a tedious and very responsible job it is, so immediately decide completely like a man what and how you should have!

If you are very new to the subject of the site being created and do not know how the structure will be built, do not know what initial words to take for parsing, then you can swap the 1st and 2nd stages of the collection. That is, first parse competitors (we will analyze how to parse them below), look at their keys, based on this, create a structure and initial words for parsing, and then parse wordstat, tips, etc.

To compile the structure, I use the mind manager - Xmind. It's free and has everything you need.

A simple structure looks like this:

This is the structure of a commercial site. Usually in information sites there are no intersections and any filters of product cards. But this structure is not complicated either, it was compiled for the client to understand. Usually my structures consist of many arrows and intersections, comments - only I myself can figure out such a structure.

Is it possible to create semantics in the process of filling the site?

If the semantics is easy, you are confident in the topic and know it, then you can do the semantics in parallel with the content of the site. But the initial structure must be thrown in necessarily. I myself sometimes practice this in very narrow niches or in very wide ones, so as not to spend a lot of time collecting semantics, but to immediately launch the site, but still I would not advise doing this. The probability of mistakes is very high if you have no experience. Still, it's easier when all the semantics are ready, the whole structure is ready, and everything is ungrouped and understandable. In addition, in the finished semantics, you can see which keys should be given priority, which have no competition and will bring more visitors.

Here you also need to push away from the size of the site, if the niche is wide, then there is no point in collecting semantics, it is better to do it along the way, because it can take a month or more to collect semantics.

So we initially threw in the structure or didn’t throw it in, we decided to go with the second stage. We have a list of initial words or phrases for our topic that we can start parsing.

Parsing and working in keycollector

For parsing, of course, I use keycollector . I will not dwell on setting up keycollectora, you can read the help of this program or find configuration articles on the Internet, there are a lot of them and everything is described in detail there.

When choosing scraping sources, you should calculate your labor costs and their effectiveness. For example, if you parse Pastukhov's database or MOAB, then you will dig into a bunch of junk requests that will need to be filtered out, and this is time. And in my opinion, it's not worth it to find a couple of requests. There is a very interesting study on the topic of bases from RushAnalytics, of course they praise themselves there, but if you don’t pay attention to this, very interesting data on the percentage of bad keywords http://www.rush-analytics.ru/blog/analytica-istochnikov -semantics

At the first stage, I parse wordstat, adwords, their tips and use the Bookvarix keyword database (desktop version is free). I also used to look through the tips from Youtube manually. But recently keycollector has added the ability to parse them, which is lovely. If you are a complete pervert, you can add other keyword bases here.

Start parsing and away we go.

Cleaning the semantic core for an information site

We parsed the queries and we got a list of different words. Of course, it contains the necessary words, as well as junk ones - empty, not thematic, not relevant, etc. Therefore, they need to be cleaned.

I do not delete unnecessary words, but move them to groups, because:

In the future, they can become food for thought and acquire relevance.
We exclude the possibility of accidental deletion of words.
When parsing or adding new phrases, they will not be added if you check the box.

I sometimes forgot to set it, so I set up parsing in one group and parse the keys only in it so that the collection is not duplicated:

You can work this way or that way, whichever is convenient for you.

Collection of frequencies

We collect from all words through direct, base frequency [W] and exact [“!W”].

Everything that did not come together, we collect through wordstat.

Single-word cleaning and non-format

We filter by single words, look at them and remove unnecessary ones. There are such one-word queries that it makes no sense to move on, they are not unambiguous or duplicate another one-word query.

For example, we have a topic - heart disease. By the word “heart” there is no point in moving forward, it is not clear what a person means - this is too broad and ambiguous request.

We also look at which words the frequency did not gather - it either contains special characters in the words, or there are more than 7 words in the query. We transfer them to the non-format. It is unlikely that such requests are entered by people.

Brushing by overall and exact frequency

We remove all words with a common frequency [W] from 0 to 1.

I also remove everything from 0 to 1 according to the exact frequency [”!W”].

I break them down into different groups.

In what follows, normal logical keywords can be found in these words. If the kernel is small, then you can immediately manually review all the words with zero frequency and leave, which, as you think, people enter. This will help to cover the topic completely and it is possible that people will follow such words. But of course, these words should be used last, because there will definitely not be much traffic for them.

The value from 0 to 1 is also taken based on the topic, if there are a lot of keywords, then you can filter from 0 to 10. That is, it all depends on the breadth of your subject and your preferences.

Shoe by coverage

The theory here is as follows: for example, there is a word - "forum", its base frequency is 8,136,416, and the exact frequency is 24,377, as we see the difference is more than 300 times. Therefore, we can assume that this request is empty, it includes a lot of tails.

Therefore, by all words, I calculate, such KEI:

Fine frequency / Base frequency * 100% = coverage coverage

The lower the percentage, the more likely it is that the word is empty.

In KeyCollector this formula looks like this:

YandexWordstatQuotePointFreq / (YandexWordstatBaseFreq+0.01) * 100

Here, too, everything depends on the subject and the number of phrases in the core, so you can remove the completeness of coverage less than 5%. And where the core is large, then you can not take even 10-30%.

Purge by implicit duplicates

To clean implicit duplicates, we need to collect Adwords frequency from them and navigate by it, because it takes into account word order. We save resources, so we will collect this indicator not from the entire core, but only from duplicates.

In this way, we found and marked all non-obvious duplicates. Close the tab - Analysis of implicit duplicates. They have marked us in the working group. Now we will display only them, because the parameters are taken only from those phrases that we have shown in the group at the moment. And only then we start parsing.

We are waiting for Adwords to take the indicators and go into the analysis of implicit duplicates.

We set these parameters of a smart group mark and click - perform a smart check. In this way, only the highest-frequency Adwords queries will not be marked in our group of duplicates.

Of course, it’s better to go over all the duplicates and look manually, suddenly something was set up wrong there. Pay special attention to groups where there are no frequency indicators, where duplicates are marked by chance.

Everything that you mark in the analysis of implicit groups, it is also put down in the working group. So after the analysis is completed, just close the tab and transfer all the marked implicit duplicates to the appropriate folder.

Cleaning by stop words

Stop words I also divide into groups. Separately I will bring the cities. They may come in handy in the future if we decide to make a directory of organizations.

Separately, I enter the words containing the words photo, video. Someday they will come in handy.

And also, “vital requests”, for example, Wikipedia, I include the forum here, as well as in the medical topic, this may include - Malyshev, mosquitoes, etc.

It all depends on the subject too. You can also make separate commercial requests - price, buy, store.

It turns out here is a list of groups by stop words:

Cleaning up twisted words

This applies to competitive topics, they are often cheated by competitors in order to mislead you. Therefore, it is necessary to collect seasonality and weed out all words with a median equal to 0.

And also, you can look at the ratio of the base frequency to the average, a large difference can also indicate a request cheat.

But you need to understand that these indicators can also indicate that these are new words for which statistics have only recently appeared or they are just seasonal.

Cleaning by geo

Usually checking by geo for informational sites is not required, but just in case, I will sign this moment.

If there is any doubt that some requests are geo-dependent, then it is better to check this through the Rookee collection, although it sometimes makes mistakes, but much less often than checking this parameter by Yandex. Then, after collecting Rookee, it is worth checking all the words manually, which are indicated as geo-dependent.

Manual cleaning

Now our core has become several times smaller. We review it manually and remove unnecessary phrases.

As a result, we get the following groups of our kernel:

Yellow - worth digging, you can find words for the future.

Orange - can be useful if we expand the site with new services.

Red - not useful.

Analysis of request competition for informational sites

Having collected requests and cleaned them up, now we need to check their competition in order to understand in the future what requests should be dealt with in the first place.

Competition in the number of documents, titles, main pages

This is all easily removed through KEI in the KeyCollector.

We get data for each request, how many documents were found in the search engine, in our example in Yandex. How many main pages are in the results for this request and the request's occurrences in the title.

On the Internet, you can find various formulas for calculating these indicators, even it seems that in the freshly installed KeyCollector, according to the standard, some kind of formula for calculating KEI is built in. But I do not follow them, because you need to understand that each of these factors has a different weight. For example, the most important is the presence of the main pages in the search results, then the headings and the number of documents. It is unlikely that this importance of factors can somehow be taken into account in the formula, and if it is still possible, then you can’t do without a mathematician, but then this formula will not be able to fit into the capabilities of KeyCollector.

Link market competition

It's more interesting here. Each exchange has its own algorithms for calculating competition, and it can be assumed that they take into account not only the presence of main pages in the search results, but also the age of the pages, link mass and other parameters. Basically, these exchanges are, of course, designed for commercial requests, but still, more or less some conclusions can be drawn from information requests.

We collect data on exchanges and display averages and already focus on them.

I usually collect on 2-3 exchanges. The main thing is that all requests are collected for the same exchanges and the average number is derived only for them. And not so that some requests were collected by some exchanges, and others by others, and they deduced the average.

For a more visual view, you can apply the KEI formula, which will show the cost of one visitor based on the parameters of the exchanges:

KEI = AverageBudget / (AverageTraffic +0.01)

Divide the average budget for the exchanges by the average traffic forecast for the exchanges, we get the cost per visitor based on the data of the exchanges.

mutagen competition

It's not in keycollector, but that's no problem. Without problems, all words can be uploaded to Excel, and then run through the KeyCollector.

Why is Keyso better? It has a larger base than its competitors. It is clean, there are no phrases that are duplicated and written in a different order. For example, you will not find such repeated keys “type 1 diabetes”, “type 1 diabetes” there.

Keyso also knows how to fire sites with one counter Adsense, Analytics, Leadia, etc. You can see what other sites there are from the owner of the analyzed site. Yes, and in general, when it comes to finding competitor sites, I think this is the best solution.

How to work with Keyso?

We take any one site of our competitor, it is better of course more, but not particularly critical. Because we will work in two iterations. Enter it in the field. Zhmakaem - analyze.

We get information on the site, we are interested in competitors here, click open all.

We open all competitors.

These are all sites that somehow overlap keywords with our analyzed site. There will be youtube.com, otvet.mail.ru, etc., that is, large portals that write about everything. We do not need them, we need sites purely only on our subject. Therefore, we filter them according to the following criteria.

Similarity is the percentage of common keys in the total number of this domain.

Thematicity - the number of keys of our analyzed site in the keys of the competitor's domain.

Therefore, the intersection of these parameters will remove the shared sites.

We put the theme 10, similarity 4 and see what we get.

It turned out 37 competitors. But all the same, we will still check them manually, upload them to Excel, and if necessary, remove unnecessary ones.

Now go to the group report tab and enter all of our competitors that we found above. Click to analyze.

We get a list of keywords for all these sites. But we have not fully disclosed the topic yet. Therefore, we are moving into the group's competitors.

And now we get all the competitors, all the sites that we have entered. There are several times more of them and there are also many general thematic ones. We filter them by similarity, let's say 30.

We get 841 competitors.

Here we can see how many pages this site has, traffic and draw conclusions about which competitor is the most effective.

We export all of them to Excel. We sort through our hands and leave only the competitors of our niche, we can mark the most effective comrades in order to evaluate them later and see what chips they have on the site, queries that give a lot of traffic.

Now we again go to the group report and add all the competitors we have already found and get a list of keywords.

Here we can immediately filter the list by “!wordstat” More than 10.

Here they are our requests, now we can add them to the KeyCollector and specify that phrases that are already in any other KeyCollector group are not added.

Now we clean up our keys, and expand, group our semantic core.

Semantic core collection services

In this industry, you can find quite a few organizations that are ready to offer you clustering services. For example, if you're not willing to take the time to learn the intricacies of clustering and do it yourself, there are plenty of people out there willing to do the job.

Yadrex

One of the first on the market who started using artificial intelligence to create a sematic core. The head of the company is himself a professional webmaster and specialist in SEO technologies, so he guarantees the quality of work of his employees.

In addition, you can call the indicated numbers to get answers to all your questions regarding the work.

When ordering services, you will receive a file containing the content groups of the kernel and its structure. Additionally, you get the structure in the mindmup.

The cost of work varies depending on the volume, the larger the volume of work, the cheaper the cost of one key. The maximum cost for an information project will be 2.9 rubles per key. For the seller 4.9 rubles per key. Discounts and bonuses are provided for large orders.

Conclusion

This completes the creation of the semantic core for the information site.

I advise you to monitor the change history of the KeyCollector program, because it is constantly supplemented with new tools, for example, youtube for parsing was recently added. With the help of new tools, you can further expand your semantic core.

Semantic core- this is a set of keywords that users of search engines enter into the search box to find the answer to their query.

The collection of the semantic core is necessary in order to find all the keywords and phrases for which the company or site is ready to give an exhaustive answer, satisfy the needs of customers and for which users are looking for (formulating a query) the answer to their question. If we have a keyword, then the user will get to our site, if not, he won't.

The volume of keywords in the semantic core depends on the goals, objectives, and characteristics of the business. The scope and depth of the semantic core determine the coverage of the target audience, its conversion and cost. Full semantics allows you to increase coverage and reduce competition.

Goals of collecting the semantic core

The search and selection of keywords is one of the stages of electronic marketing. And strongly influencing further success. Based on the compiled semantic core, the following will be developed:

Website:
- The “ideal” structure of a website, online store, blog.There are 2 approaches to this issue: SEO (search engine optimization) and PR (public relations). SEO approach consists in the initial collection of all key queries. Having covered the maximum number of niche keywords, we develop the site structure, taking into account the real needs of users and their needs. With the PR method, the site structure is first developed based on the information that we want to convey to users. After that, keywords are collected and distributed throughout our structure. Which strategy to choose depends on the goals: if you need to convince something, convey some position, etc., then the PR method is chosen. If you need to get as much traffic as possible, for example, if you are making an information site or an online store, then you can choose the first method. But in general, this is the foundation for future promotion: a well-designed site structure allows you to conveniently sort information for users (positive user experience) and the ability to index search engines. The criteria for accepting the future structure of the site are the goals and expectations of users and the results of the analysis of successful competitors.
Lead generation strategy:
- SEO strategy. Having determined the search queries with the least competition and the greatest potential traffic that they can bring, a content strategy is developed for filling and optimizing the site.
- contextual advertising. When conducting advertising contextual campaigns in Yandex Direct, Google Ads, etc. we collect the maximum number of relevant keywords for which we can and are ready to satisfy the demand.
- map of information needs (content plan).Having grouped keywords according to intents (intentions) of users, a technical task is compiled and issued to copywriters for writing articles.

Study of the search process in search engines

The psychology of searching on the Internet

People don't think in words. Words are conventions through which we convey our thoughts. Everyone has their own mechanism for transforming thoughts into words, each person has his own peculiarity in formulating questions. Each query entered into the search bar of a search engine is accompanied by a person with certain thoughts and expectations.

By understanding how people search online, you can tie your marketing efforts to their interests. Knowing how the search process is going on, we select the appropriate keywords and optimize the site, set up contextual advertising.

After the user of the PS clicked on the “Search” button, the search results that appeared should meet his expectations. In other words, search results (search results and contextual advertising ads) should help solve the user's question. Therefore, it is the job of the marketer to tailor the ad and search snippet to be relevant to the search query.

reflect the search query;
take into account the stage of the buying cycle.

Those. those words that will be indicated in snippets and ads will lay the foundation for the user's expectations from your site. Therefore, the landing page that he will land on by clicking on the link must meet his expectations. By meeting these expectations, we increase the likelihood of a positive outcome. Advertising should lead the user to where he will immediately receive an answer.

Categories of search queries:

directly formulated (metal lathe, dentist);
description of the problem (how to carve a shaft, a tooth hurts);
symptoms of the problem (the lathe feed box does not work, the tooth has crumbled);
description of the incident (crunching during turning on a TV-16 lathe);
product name, article, brand, manufacturer.

If you carefully study the keywords, you can get to the bottom of the problem: while turning on a lathe, a gear broke in the feed box, so we can offer to make it or offer a new machine. Since a person did not treat a bad tooth and it crumbled due to caries, we, as dentistry, will offer to place an implant.

Classification and types of search queries

By type of search:

informational - requests to search for information, for example, "the speed of light", "how to make a fishing rod with your own hands", "why the earth is round", etc.;
navigational - queries by which users search for an organization, brand, person, etc. For example, "Coca-cola", "restaurant "Pyatkin", "Leo Tolstoy";
transactional - requests entered by users with the intention to perform some target action. For example, “buy a Samsung Galaxy S6 phone”, “download the online book “Web Analytics in Practice”;
fuzzy queries - all queries that cannot be unambiguously attributed to one of the above types, i.e. define exactly what the user is looking for. For example, "Maine Coon" - it's not clear what the user wants: to find out what kind of cat it is or looking for where to buy it, or maybe something else.

According to geo-dependence:

geo-dependent - requests that depend on the user's location. For example, "grocery stores", "tire service in the center."
geo-independent - do not depend on the location of a person. For example, "meatball recipe", "how to set the alarm".

Naturally:

natural - queries entered by users in natural human language: "prices for Samsung laptops", "characteristics of lever scissors";
telegraphic - requests entered in the "telegraphic language": "samsung laptops prices", "lever scissors characteristics".

By seasonality:

seasonal – keywords are time sensitive. Such requests are "winter tires", "New Year fireworks", "Easter eggs", etc.
off-season - they are not time sensitive, they are popular at any time of the year. Examples of such requests are: “wristwatch”, “how to cook pizza”, “install Windows”.

By frequency:

HF - high-frequency requests.
MF - mid-frequency requests.
LF - low-frequency requests.
"Long tail" (long tail) - micro-frequency search queries, usually consisting of 4 or more words and having a frequency of 1-3 per month. The total volume of such queries adds up to tangible traffic with the least competition in the issuance and with little or no effort in promotion.

It is impossible to say specifically that a certain number of queries correspond to a high-frequency query, and which to a low-frequency one, since these values vary greatly from niche to niche. Somewhere around 1000 queries per month may correspond to a low-frequency query, and in another niche it will be high-frequency.

Keyword frequency values are conditional and are intended to be ranked by popularity.

By competitiveness:

VK - highly competitive requests.
SC - average competitive requests.
NK - low competitive requests.

This classification allows you to compile a list of top-priority key queries for which search promotion will be carried out. In addition, reduce the cost per click in contextual advertising companies.

Common goals of the user, webmaster and search engine

In the process of searching for information through a search engine, 3 parties are involved: the search engine, the user and the web resource. And each of the parties has its own goals: the user needs to find the answer to his request, and the search engine and the web resource need to make money on it.

If webmasters begin to somehow manipulate the work of the PS, while not giving the required answers to users, then everyone loses: the user does not receive an answer to his request and goes to look for another search engine on another site.

Therefore, the needs of users are primary, because. without them, neither the PS nor the web resource will work. First of all, by satisfying the interests of PS users, we contribute to the overall earnings. The search engine will earn on contextual advertising, a web resource - on sales of goods or services to users or advertisers themselves. Everyone wins. Link your goals to user goals. Then the probability of a positive outcome increases dramatically.

Keyword Research

As we have already found out, keywords are thoughts expressed in verbal form. Our goal is to choose keywords that reflect the thoughts of consumers and the demand for which we can satisfy. If we have a keyword, then the user will see our message, if not, he will not see it.

Some keywords generate a lot of traffic, others a little. Some give high conversions, others generate low-quality traffic.

Each keyword constitutes a separate sub-market with its own clientele. Behind each key phrase there is some need, desire, question or suggestion that a person may not be aware of.

Having determined which stage of the buying cycle the keyword belongs to, we will understand when and why the user is looking for it, therefore, we will provide relevant information for him that meets his expectations.

Before you begin your research, ask yourself the following questions:

What keywords do we need to use to reach our target audience?
What keywords do the customer segments we are interested in use when searching for our products?
What happens in the user's head when writing this request?
What buying cycle are they in using this keyword?

Keyword Research Goals

Get insights into the existing “ecosystem” and strategize for natural and paid search.
Identify the needs of potential customers and develop an appropriate response to them.

Anatomy of queries

Key phrases consist of 3 elements:

[body]+[specifier]+[tail],

where the body (also called the "mask") is the basis of the request, only by which it is impossible to understand the intentions of users; specifiers determine the intentions of users and classify the request as transactional, informational, or navigational; the tail only details the intention or need.

For example, buy a lathe, a milling machine 6P12 characteristics, buy a bimetallic band saw for metal in Moscow time.

Knowing the anatomy of search queries allows you to collect all the masks when working out semantics, as well as correctly distribute the collected keywords according to the buying cycle when working out a paid and natural search strategy.

Keyword segmentation

When searching for masks and working out the already assembled semantic core, it becomes necessary to segment keywords for more convenient subsequent work. Having segmented keys, we understand how people are searching, therefore, we expand them with additional key queries, evaluate the likelihood of sales and work according to the strategy. There are no specific segmentation rules, because the semantics can be very different from niche to niche.

Here I will only give some examples on what grounds semanticists segment the cores:

by keyword type:
- direct demand - they are looking for what we sell, for example, a milling machine;
- indirect demand - they are looking for a milling machine, and we sell milling cutters for them;
- situational - neighbors flooded, we made repairs;
- other - navigational, vital queries.
by search objects:
- company, object (for example, a repair team);
- product (repair of milling machines);
- production, sales (wholesale/retail) (production of spare parts for repairs according to drawings);
- action on the object (commissioning);
- specialist (design engineer);
- part of the object, subservice (development of design documentation for spare parts for the milling machine).
on expected receipts.

Long tail strategy

Long-tail or the concept of a “long tail” was popularized in 2004 by Wired magazine editor Chris Anderson. The essence of the concept is that the company sells rare goods through a wide range of products worth more than bestsellers.

The concept can be seen on the example of a bookshelf. The store owner, due to limited space, will try to keep only the goods that are most popular. If the fashion for the product has already ended, then the place of the book is taken by another one that is gaining popularity.

In online bookstores, the shelf is not limited, all available books are placed in the catalog. Studies have shown that due to the wide range of books, sales of "unpopular" books exceed sales of bestsellers. This concept works in the sales of music, films, drugs, etc., and of course in the compilation of the semantic core.

As in the book example, long tail keywords can generate more traffic than high-volume keywords.

From the practice of long tail phrases have the highest conversion, i.e. people are most likely to be in the buying decision stage.

New keywords

If you are an opinion leader, have your own audience and can influence it, try creating new key search phrases around which your content will be built. If the audience picks them up, then you will be the first to stand out in the search results.

Segmentation and sales funnel

Customer segmentation and role principle

Before collecting keywords, companies need to figure out their target audience, segments, and avatars of their customers. To make it clearer, I’ll immediately give an example: a company sells vibrating plates. Therefore, its target audience will be construction companies, and the main segments will be companies that carry out road works, laying something underground, etc. Avatars are individuals who make purchase decisions and search for goods and services.

We will not dwell on this in detail here.

The role principle is that you need to pay attention to the type of people who may be looking for your product, for example, it can be an individual, a supplier, an engineer, or a general manager. People in different roles may use different keywords. Therefore, knowing the avatar of your client, his behavioral features are taken into account, keywords are selected taking into account the required roles.

For example, if your company's ordering person is an engineer, then his search queries may contain specialized technical terms.

Before we get started, it should be noted that every business has its own specific sales funnel. Here is the general concept. Consists of 2 parts: propaganda and loyalty.

Sales funnel stages:

Awareness — everywhere to inform about our product so that people know about it. This stage includes keywords of a generalized nature.
Interest- to encourage the consumer to think about how our product will make his life better. At this stage, the benefits and benefits of the product are broadcast. The main goal is to create a desire to receive the product.
The study- the consumer is looking for information in order to make an informed decision: to get acquainted with the professional jargon of the industry, brands, the name of specialized services, etc. appear in search queries. The main goal is to convey the benefits and capabilities of the product in as much detail as possible.
Comparison of analogues — the consumer compares similar products. Keywords acquire a specific character, indicating that the consumer has a certain level of knowledge.
Purchase– before making a purchase decision, the buyer studies information about prices, guarantees, shipping costs, terms of service, returns, etc. Keywords - low-frequency queries, queries with selling additives.

Keyword Research Tools

Kernel extension algorithm, nested query collection

After all the masks are collected, we proceed to the collection of key queries in depth.

You need to collect nested queries for:

writing relevant ads under the CS;
setting the required rate for a particular CA;
installation of a relevant link in the ad leading to the required page.

Automated tools for collecting nested requests are software installed on a PC, online services, browser extensions. There are a lot of them, but we use the most popular - Key Collector - a program for parsing keywords and their frequencies, installed on a computer, and also allows you to perform all the necessary activities to collect the semantic core.

It is desirable to parse each semantic group separately.

The expansion algorithm will be as follows:

mask parsing in Yandex Wordstat;
mask parsing in Google AdWords;
parsing masks in the Bookvarix database;
parsing masks in the Keys.so database;
unloading keywords from Yandex Metrics and Google Analytics;
cleaning and collecting keyword frequencies;
batch collection of search hints;
batch collection of similar search queries from search results;
cleaning and collection of frequencies.

With the help of Yandex Wordstat and Google AdWords tools, we will get the main key search phrases that have frequency and popularity in search engines. Bookvarix, Keys.so, downloading keywords from Yandex Metrics and Google Analytics, search suggestions and similar search queries will also give users tailed words.

Adaptation of the semantic core for contextual advertising

The preparation algorithm looks like this:

choose selling keywords;
segment the CS;
work out negative keywords and negative phrases;
put operators.

Keywords for YAN and GMS are selected according to a slightly different principle, unlike search keywords.

Selecting selling keywords

From the available list of key phrases, we need to understand what a person wants (his needs), what answer he wants to hear to his question. Our task is to answer, in the context of the search, those questions of a person that are of interest to us, i.e. choose those keywords that are most likely to lead to conversions.

In addition, with the help of competent CV selection, we will reduce non-target impressions, which will increase CTR and reduce the cost per click.

There are situations when the meaning of the request is not clear. In order for us to understand the meaning of what most people want in such cases, it is necessary to type this query into a search engine and look at the search results. Thanks to machine learning and other search tuning technologies, Yandex and Google already know what people want for each specific request. It remains only to analyze the results of the issuance and make the right decision. The second way is to view the attachments of the word form in Yandex Wordstat, the third way is to think out the meaning, but mark it for further development.

The completeness of the CS is one of the important factors affecting the success of an advertising campaign. Therefore, the future result will depend on the quality of keyword research. In contextual advertising, one should strive not for the volume of SL, but for its qualitative study.

Depending on the goals, in the future you can use a strategy: determine the most conversion requests, test them, and then scale up the advertising campaign.

CS segmentation

It is impossible to single out any clear segments, because everything is different from niche to niche. Most commercial websites can be segmented based on the stages of the buying cycle. Or you can select some segments yourself by studying your core.

The main task of segmentation is the ability to easily manage the company in the future: set bids and budgets, quickly find an ad and turn it on / off, etc.

Development of negative keywords and phrases

Even at the stage of collecting the semantic core, you had collected negative keywords and phrases. It remains to adapt them to your advertising campaign and conduct a cross-backing track.

Statement of operators

Operators are used for high-frequency queries to avoid black competition, as well as to save budget in highly competitive topics and more accurately formulate the phrase. Operators can be combined with each other.

Operators Yandex Direct

+ word- fixation of stop words, auxiliary parts of speech: prepositions, conjunctions, particles, pronouns, numerals.

!word- fixation of the word form.

[word1 word2]- fixing the order of words.

-word- word exception.

negative keywords- exclusion of the phrase, .

Google AdWords Operators: Keyword Match Types

Wide match type — is used by default, the ad will be shown by synonym, with a typo, by similar phrases and the same intents, for example, for the query “offices in Moscow” it may appear for the keyword “real estate Moscow”.

Broad match modifier — ads will appear on queries containing the “+” sign and their close variants (but not synonyms) in any order. For example,+ car + hyundai + tucsan.

Phrase match - the ad will be shown for phrases that exactly match the keywords or contain close words. Sensitive to word order. For example, a query like “price monitor Benq” might display an ad for keyword " Benq monitor " .

Exact match - the ad will appear on queries exactly matching the keyword or its close variants. For example, a search for “truck tire service” might show an ad for the keyword[ truck tire fitting] .

Negative keywords— ads will be shown for queries that do not contain negative keywords.

Adaptation of the semantic core for search engine promotion (SEO)

We will need the core to develop a clear logical structure of the site and the completeness of the coverage of the topic (we will describe our topic with certain keywords that are characteristic of it).

The algorithm for preparing CS for SEO is as follows:

remove informational requests from the SA (leave only commercial ones);

Semantic core clustering

Clustering- combining requests into groups based on user intentions, in other words, it is necessary to combine different requests into one group for which a person is looking for the same thing. Requests are grouped in such a way that they can be promoted on the same page (combined by user intent).

As an example, you cannot promote informational and commercial queries on the same page. Moreover, it is recommended to promote these requests on different sites.

For example, spec. clothes - work clothes, zig machine - zigovka - zigovochny machine, circular saw - circular saw - sawing machine.

Clustering can be:

manual - grouping occurs manually in any specialized program or Excel. The person carrying out the grouping is simply obliged to be well versed in the topic, otherwise nothing sensible will work;
automatic - grouping occurs automatically based on search results. This method allows you to accelerate the ungrouping of the semantic core, which consists of a huge number of key phrases. The grouping has high accuracy (much more accurate if a person who does not understand the topic was manually involved). The main advantage of this method is the grouping of requests of only the appropriate type, i.e. commercial and informational ones will not be combined into one group (the situation is well illustrated by the queries “smartphone” and “smartphones”: 1st - informational and geo-independent, 2nd - commercial and geo-dependent, but "laptop" and "laptops" are both commercial and geo-dependent);
semi-automatic - first, clusters are created automatically, and then manually before grouping. This type of clustering combines both the pros and cons of the first 2.

By type, the semantic core clustering can be:

For commercial sites, hard clustering is used in most cases. In special cases, middle can be used.

Relevance Map

The relevance map is necessary for planning pages and working out the structure of the site. The main elements are:

tree element name (category, tag, page, etc.);
cluster name;
cluster keywords;
exact frequency (“!key!word”);
title;
description;
previous Title;
previous H1;
previous Description.

Mind maps are often used to visualize the site structure.

Adaptation of the semantic core for information sites

Information requests, when viewed from the commercial side, are more likely to be related to the following stages of the awareness sales funnel, interest, study, comparison of analogues. Those. keywords do not directly drive conversions to sales. But based on them, we can inform and influence the decision of the buyer.

If we are talking about creating sites for making money on advertising, then you need to specialize in a certain topic and open it completely. The site should answer all questions on the topic due to the competent study of all semantics.

Algorithm for preparing CS for information sites:

remove commercial requests from the SA (leave only informational ones);
to cluster the remaining SA;
prepare a relevance map based on the resulting clusters.

As you can see, the algorithm is fundamentally no different from the work of adapting to SEO. The main nuance is the type of clustering. For informational sites, soft- or middle-clustering is chosen.

Semantic core on order

The cost of the semantic core is determined at the rate of 3-7 rubles. for the keyword. So, a clustered semantic core for SEO or an infosite of 10,000 keywords will cost an average of 50,000 rubles. Plus, the price will increase if you need to segment keywords for contextual advertising. The price is highly dependent on the quality of the work. If you are offered cheaper than the quoted rates, then you should at least think about why. After all, a good study of only masks sometimes takes up to 16 hours of work. Having saved on the collection of the semantic core (do not cover the entire completeness and depth of the topic), then you will lose on contextual advertising (you will be shown on the most competitive topics) and receive less customers from the search results.

Here is the simplest example of the quality of the study of the semantic core: when you query "quilting machine" you will compete in the search results between 36 competitors, when querying "creasing machines" - 27 competitors, and "quilting" - only 8 competitors.

Request "Beading machine"

Request "Screeding machine"

Hello, dear readers of the blog site. I want to make another call on the topic of "collecting the seed." First, as expected, and then a lot of practice, maybe a little clumsy in my performance. So, lyrics. I got tired of walking blindfolded in search of good luck a year after starting this blog. Yes, there were “lucky hits” (intuitive guessing of queries frequently asked to search engines) and there was some traffic from search engines, but I wanted to hit the target every time (at least to see it).

Then I wanted more - to automate the process of collecting requests and screening out “dummies”. For this reason, there was an experience with Keycollector (and his dissonant younger brother) and another article on the topic. Everything was great and even just great, until I realized that there is one very important point that remained essentially behind the scenes - scattering requests for articles.

Writing a separate article for a separate request is justified either in highly competitive topics or in highly profitable ones. For information sites, this is complete nonsense, and therefore queries have to be combined on one page. How? Intuitively, i.e. again blindly. But not all requests get along on one page and have at least a hypothetical chance to reach the Top.

Actually, today we will talk about automatic clustering of the semantic core using KeyAssort (breaking requests into pages, and for new sites also building a structure based on them, i.e. sections, categories). Well, we will once again go through the process of collecting requests for every fireman (including with new tools).

Which of the stages of collecting the semantic core is the most important?

In itself, the collection of queries (the basis of the semantic core) for a future or existing site is a rather interesting process (as anyone, of course) and can be implemented in several ways, the results of which can then be combined into one large list (by cleaning up duplicates, deleting pacifiers by stop words).

For example, you can manually start tormenting Wordstat, and in addition to this, connect the Keycollector (or its dissonant free version). However, it's all great when you are more or less familiar with the topic and know the keys you can rely on (collecting their derivatives and similar queries from the right column of Wordstat).

Otherwise (yes, and in any case it will not hurt), you can start with the “coarse grinding” tools. For example, Serpstat(nee Prodvigator), which allows you to literally "rob" your competitors for the keywords they use (see). There are other similar "robbing competitors" services (spywords, keys.so), but I "got stuck" with the former Prodvigator.

In the end, there is also a free Bukvaris, which allows you to start collecting requests very quickly. You can also order a private download from the monstrous Ahrefs database and again get the keys of your competitors. In general, it is worth considering everything that can bring at least a fraction of requests that are useful for future promotion, which then will not be so difficult to clean up and combine into one large (often even a huge list).

We will consider all this (in general terms, of course) a little lower, but at the end the main question always arises - what to do next. In fact, it’s scary even just to approach what we got as a result (having robbed a dozen or two competitors and scraping the bottom of the barrel with a Keycollector). The head may burst from trying to break all these queries (keywords) into separate pages of a future or existing site.

Which queries will successfully coexist on one page, and which ones should not even be tried to combine? A really difficult question, which I previously solved purely intuitively, because manually analyzing the issuance of Yandex (or Google) on the subject of “what about competitors” is manually poor, and automation options did not come across at hand. Well, for the time being. Nevertheless, such a tool “surfaced” and today it will be discussed in the final part of the article.

This is not an online service, but a software solution, the distribution of which can be downloaded on the main page of the official website (demo version).

Therefore, there are no restrictions on the number of processed requests - process as much as you need (there are, however, nuances in collecting data). The paid version costs less than two thousand, which, for the tasks to be solved, can be said for nothing (IMHO).

But we’ll talk about the technical side of KeyAssort a little lower, but here I would like to say about myself principle that allows you to break up a list of keywords(practically of any length) into clusters, i.e. a set of keywords that can be successfully used on one page of the site (optimize text, headings and link mass for them - apply SEO magic).

Where can you get information from? Who will tell you what will “burn out” and what will not reliably work? Obviously, the search engine itself will be the best adviser (in our case, Yandex, as a storehouse of commercial queries). It is enough to look at the results of a large amount of data (for example, analyze the TOP 10) for all these queries (from the collected list of the future seed) and understand what your competitors managed to successfully combine on one page. If this trend repeats several times, then we can talk about a pattern, and on the basis of it, it is already possible to beat the keys into clusters.

KeyAssort allows you to set the "strictness" with which clusters will be formed in the settings (select keys that can be used on one page). For example, for commerce, it makes sense to tighten the selection requirements, because it is important to get a guaranteed result, albeit at the expense of slightly higher costs for writing texts for a larger number of clusters. For informational sites, on the contrary, you can make some concessions in order to get potentially more traffic with less effort (with a slightly higher risk of “non-burnout”). Let's talk about how to do it again.

But what if you already have a site with a bunch of articles, but you want to expand an existing seed and optimize existing articles for a larger number of keywords in order to get more traffic for a minimum of effort (slightly shift the emphasis of the keywords)? This program also gives an answer to this question - you can make those queries for which existing pages are already optimized, made marker ones, and around them KeyAssort will assemble a cluster with additional queries that are quite successfully promoted (on one page) by your competitors in the issuance. It's interesting how it goes...

How to collect a pool of requests on the topic you need?

Any semantic core begins, in fact, with the collection of a huge number of requests, most of which will be discarded. But the main thing is that at the initial stage, those “pearls” get into it, under which individual pages of your future or existing site will then be created and promoted. At this stage, probably, the most important thing is to collect as many more or less suitable requests as possible and not miss anything, and then it is easy to weed out the empty ones.

There is a fair question, what tools to use? There is one unequivocal and very correct answer - different. The bigger, the better. However, these very methods of collecting the semantic core should probably be listed and given general assessments and recommendations for their use.

Yandex Wordstat and its counterparts in other search engines - initially these tools were intended for those who place contextual advertising so that they can understand how popular certain phrases are with search engine users. Well, it is clear that SEOs also use these tools and very successfully. I can recommend taking a look at the article, as well as the article mentioned at the very beginning of this publication (it will be useful for beginners).
Among the shortcomings of Vodstat, one can note:
1. A monstrous amount of manual work (definitely requires automation and it will be discussed a little later), both in punching phrases based on the key, and in punching associative queries from the right column.
2. Limiting the issuance of Wordstat (2000 queries and not a line more) can be a problem, because for some phrases (for example, “work”) this is extremely small and we lose sight of low-frequency, and sometimes even mid-frequency queries that can bring good traffic and income ( many people miss them). You have to "stretch your head a lot", or use alternative methods (for example, keyword databases, one of which we will consider below - and it's free!).
KayCollector(and his free little brother Slovoeb) - a few years ago, the appearance of this program was simply a "salvation" for many network workers (and even now it is rather difficult to imagine working on a seed without a QC). Lyrics. I bought KK two or three years ago, but I used it for several months at the most, because the program is tied to hardware (computer stuffing), and I change it several times a year. In general, having a license for KK, I use SE - so that's what laziness brings to.
You can read the details in the article "". Both programs will help you collect queries from both the right and left columns of Wordstat, as well as search suggestions for the key phrases you need. Hints are what drop out of the search bar when you start typing a query. Users often do not finish the set simply choose the most suitable option from this list. Seoshniks have figured this out and use such queries in optimization and even.
QC and SE allow you to immediately type a very large pool of requests (although it may take a long time, or buying XML limits, but more on that below) and easily weed out dummies, for example, by checking the frequency of phrases in quotation marks (learn the materiel if you don’t understand about than speech - links at the beginning of the publication) or by setting a list of stop words (especially relevant for commerce). After that, the entire query pool can be easily exported to Excel for further work or for loading into KeyAssort (clusterer), which will be discussed below.
SerpStat(and other similar services) - allows you to enter the URL of your site to get a list of your competitors for the issuance of Yandex and Google. And for each of these competitors, you can get a complete list of keywords for which they managed to break through and reach certain heights (get traffic from search engines). The pivot table will contain the frequency of the phrase, the place of the site on it in the Top and a bunch of other different useful and not very information.
Not so long ago, I used almost the most expensive Serpstat tariff plan (but only for one month) and managed to save almost a gigabyte of various useful things in Excel during this time. I collected not only the keys of competitors, but also just query pools for the key phrases I was interested in, and also collected the seedlings of the most successful pages of my competitors, which, it seems to me, is also very important. One thing is bad - now I can’t find the time to come to grips with the processing of all this invaluable information. But it is possible that KeyAssort will still take the numbness before the monstrous colossus of data that needs to be processed.
bukvariks is a free database of keywords in its own software shell. The selection of keywords takes a fraction of a second (uploading to Excel minutes). I don’t remember how many million words there are, but the reviews about it (including mine) are just excellent, and most importantly, all this wealth is free! True, the distribution kit of the program weighs 28 Gigabytes, and when unpacked, the database occupies more than 100 GB on the hard disk, but these are all trifles compared to the simplicity and speed of collecting the query pool.
But not only the speed of collecting the seed is the main plus compared to Wordstat and KeyCollector. The main thing is that there are no restrictions on 2000 lines for each request, which means that no low frequencies and beyond low frequencies will escape us. Of course, the frequency can be clarified once again through the same QC and screening out using stop words, but Bukvariks performs the main task remarkably. True, sorting by columns does not work for him, but by saving the query pool in Excel, it will be possible to sort it as you please.

Probably, at least a few more “serious” request pool cathedral tools will be provided by you in the comments, and I will successfully borrow them ...

How to clear the collected search queries from "dummy" and "garbage"?

The list obtained as a result of the manipulations described above is likely to be very large (if not huge). Therefore, before loading it into the clusterer (for us it will be KeyAssort) it makes sense to clean it up a bit. To do this, the query pool, for example, can be unloaded to the keycollector and removed:

Requests with too low frequency (I personally break through the frequency in quotes, but without exclamation marks). It is up to you to decide which threshold to choose, and in many respects it depends on the subject, competition and the type of resource for which the seed is going.
For commercial queries, it makes sense to use a list of stop words (such as "free", "download", "abstract", as well as, for example, the names of cities, years, etc.) in order to remove from the seed in advance what is known will not bring target buyers to the site (weed out freeloaders looking for information, not goods, well, and residents of other regions, for example).
Sometimes it makes sense to be guided by the indicator of competition for a given query in the issue when screening out. For example, at the request of “plastic windows” or “air conditioners”, you don’t even have to rock the boat - failure is guaranteed in advance and with a 100% guarantee.

Say that it is too simple in words, but difficult in practice. And here it is not. Why? But because one person I respect (Mikhail Shakin) did not spare the time and recorded a video with a detailed a description of how to clean up search queries in the Key Collector:

Thanks to him for this, because these questions are much easier and clearer to show than to describe in the article. In general, you can do it, because I believe in you ...

Setting up the KeyAssort seed clusterer for your site

Actually, the most interesting begins. Now all this huge list of keys will need to be somehow broken (scattered) on separate pages of your future or existing site (which you want to significantly improve in terms of traffic brought from search engines). I will not repeat myself and talk about the principles and complexity of this process, because then why did I write the first part of this article.

So our method is quite simple. We go to the official website of KeyAssort and download demo version to try the program for a tooth (the difference between the demo and the full version is the inability to unload, that is, to export the collected seed), and after that it will be possible to pay (1900 rubles is not enough, not enough according to modern realities). If you want to immediately start working on the kernel, which is called "on a clean copy", then it is better to choose the full version with the ability to export.

The KeyAssort program itself cannot collect keys (this, in fact, is not its prerogative), and therefore they will need to be loaded into it. This can be done in four ways - manually (it probably makes sense to resort to this method to add some keys already found after the main collection of keys), as well as three batch ways to import keys:

in txt format - when you just need to import a list of keys (each on a separate line of the txt file and ).
as well as two variants of the Excel format: with the parameters you need in the future, or with collected sites from the TOP10 for each key. The latter can speed up the clustering process, because the KeyAssort program does not have to parse the output itself to collect this data. However, URLs from the TOP10 must be fresh and accurate (such a version of the list can be obtained, for example, in the Keycollector).

Yes, what I'm telling you - it's better to see once:

In any case, first remember to create a new project in the same "File" menu, and only then will the import function become available:

Let's take a look at the program settings (there are very few of them), because for different types of sites, a different set of settings may be optimal. Open the "Service" tab - "Program settings" and you can immediately go to the tab "Clustering":

The most important thing here is, perhaps, choosing the type of clustering you need. The program can use two principles by which requests are combined into groups (clusters) - hard and soft.

Hard - all requests that fall into one group (suitable for promotion on one page) must be combined on one page for the required number of competitors from the Top (this number is set in the "group strength" line).
Soft - all requests that fall into the same group will partially occur on the same page for the required number of competitors and the Top (this number is also set in the "grouping strength" line).

There is a good picture that clearly illustrates all this:

If it is not clear, then never mind, because this is just an explanation of the principle, and what matters to us is not theory, but practice, which says that:

Hard clustering is best used for commercial sites. This method gives high accuracy, due to which the probability of hitting the Top of queries combined on one page of the site will be higher (with the proper approach to optimizing the text and its promotion), although there will be fewer queries themselves in the cluster, which means there are more clusters themselves (more pages will have to be created and promote).
Soft clustering makes sense to use for information sites, because the articles will be obtained with a high indicator of completeness (they will be able to answer a number of user requests that are similar in meaning), which is also taken into account in the ranking. And the pages themselves will be smaller.

Another important, in my opinion, setting is a checkmark in the box "Use Marker Phrases". Why might this be needed? Let's see.

Let's say that you already have a website, but the pages on it were optimized not for a query pool, but for one, or you consider this pool to be insufficiently large. At the same time, you wholeheartedly want to expand the seed not only by adding new pages, but also by improving existing ones (this is still easier in terms of implementation). So it is necessary for each such page to get the seed "to the full".

That's what this setting is for. After activating it, it will be possible to put a tick next to each phrase in your list of requests. You just have to find those main queries for which you have already optimized the existing pages of your site (one per page) and the KeyAssort program will build clusters around them. Actually, everything. More in this video:

Another important (for the correct operation of the program) setting lives on the tab "Data collection from Yandex XML". you can read in the article below. In short, SEOs constantly parse Yandex and Wordstat results, creating an excessive load on its capacity. For protection, captcha was introduced, and special access was developed via XML, where captcha will no longer come out and data will not be distorted by the keys being checked. True, the number of such checks per day will be strictly limited.

What determines the number of allocated limits? How Yandex evaluates your . You can follow this link (being in the same browser where you are authorized in Ya.Webmaster). For example, it looks like this for me:

There is also a graph of the distribution of limits by time of day below, which is also important. If you need to break through a lot of requests, and there are few limits, then this is not a problem. They can be purchased. Not Yandex, of course, directly, but those who have these limits, but they do not need them.

The Yandex XML mechanism allows the transfer of limits, and exchanges that have become intermediaries help automate all this. For example, on XMLProxy you can buy limits for only 5 rubles per 1000 requests, which, you see, is not at all expensive.

But it doesn’t matter, because the limits you bought will still flow to your “account”, but in order to use them in KeyAssort, you will need to go to the " Setting" and copy the long link into the "URL for requests" field (do not forget to click on "Your current IP" and click on the "Save" button to bind the key to your computer):

After that, all that remains is to insert this URL into the window with the KeyAssort settings in the "Url for requests" field:

Actually, everything is finished with the KeyAssort settings - you can start clustering the semantic core.

Keyword clustering in KeyAssort

So, I hope that you have set everything up (selected the desired type of clustering, connected your own or purchased limits from Yandex XML), figured out how to import a list with queries, and successfully transferred the whole thing to KeyAssort. What's next? And then, of course, the most interesting thing is the launch of data collection (Urls of sites from the Top 10 for each request) and the subsequent clustering of the entire list based on this data and the settings you made.

So, to get started, click on the button "Collect Data" and wait from several minutes to several hours while the program scans the Tops for all requests from the list (the more there are, the longer the wait):

It took me about a minute to make three hundred requests (this is a small core for a series of articles about working on the Internet). After which you can already proceed directly to clustering, the button of the same name on the KeyAssort toolbar becomes available. This process is very fast, and literally in a few seconds I got a whole set of calsters (groups), designed as nested lists:

Learn more about using the program interface, as well as about creating clusters for existing site pages look better in the video, because it's much clearer:

Everything we wanted, we got, and mind you - on full automatic. Lepota.

Although, if you are creating a new site, then in addition to clustering, it is very important outline the future structure of the site(define sections/categories and distribute clusters among them for future pages). Oddly enough, but it is quite convenient to do this in KeyAssort, but the truth is no longer in automatic mode, but in manual mode. How?

Again, it will be easier to see once again - everything is set up literally before our eyes by simply dragging clusters from the left window of the program to the right one:

If you did buy the program, you can export the resulting semantic core (and in fact the structure of the future site) to Excel. Moreover, on the first tab it will be possible to work with requests in the form of a single list, and on the second tab the structure that you configured in KeyAssort will already be saved. Very, very convenient.

Well, whatever. I am ready to discuss and hear your opinion about the collection of seedlings for the site.

Good luck to you! See you soon on the blog pages site

Just about the complex. Programs. Iron. Internet. Windows