How to choose the right semantic core. Creation of the semantic core of a web resource. Video - compiling a semantic core for competitors

In our article, we explained what a semantic core is and gave general recommendations on how to compose it.

It's time to look at this process in detail, creating a semantic core for your site step by step. Stock up on pencils and paper, and most importantly, time. And join...

We create a semantic core for the site

As an example, let's take the site http://promo.economsklad.ru/.

The company's field of activity: warehouse services in Moscow.

The site was developed by specialists of our website service, and the semantic core of the site was developed in stages in 6 steps:

Step 1. Compile a primary list of keywords.

After conducting a survey of several potential clients, studying three sites close to our topic and using our own brains, we compiled a simple list of keywords that, in our opinion, reflect the content of our site: warehouse complex, warehouse rental, storage services, logistics, storage space rental, warm and cold warehouses.

Task 1: Review competitors' websites, consult with colleagues, brainstorm and write down all the words that, in your opinion, describe YOUR site.

Step 2. Expanding the list.

Let's use the service http://wordstat.yandex.ru/. In the search line, enter each of the words from the primary list one by one:

We copy the refined queries from the left column into an Excel table, look through the associative queries from the right column, select among them those that are relevant to our site, and also enter them into the table.

After analyzing the phrase “Warehouse rental,” we received a list of 474 refined and 2 associative queries.

Having carried out a similar analysis of the remaining words from the primary list, we received a total of 4,698 refined and associative queries that were entered by real users in the past month.

Task 2: Collect a complete list of queries on your site by running each of the words in your primary list through Yandex.Wordstat query statistics.

Step 3. Stripping

First, we remove all phrases with a frequency of impressions below 50: “ how much does it cost to rent a warehouse?" - 45 views, " Warehouse rental 200 m" - 35 impressions, etc.

Secondly, we remove phrases that are not related to our site, for example, “ Warehouse rental in St. Petersburg" or " Warehouse rental in Yekaterinburg", since our warehouse is located in Moscow.

Also, the phrase “ warehouse lease agreement download“- this sample may be present on our website, but there is no point in actively promoting this request, since the person who is looking for a sample contract is unlikely to become a client. Most likely, he has already found a warehouse or is the owner of the warehouse himself.

Once you remove all unnecessary queries, the list will be significantly reduced. In our case with “warehouse rental,” out of 474 refined queries, only 46 relevant to the site remained.

And when we cleaned the full list of refined queries (4,698 phrases), we received the Semantic Core of the site, consisting of 174 key queries.

Task 3: Clean up the previously created list of refined queries, excluding from it low-frequency keywords with less than 50 impressions and phrases that are not related to your site.

Step 4. Revision

Since you can use 3-5 different keywords on each page, we won’t need all 174 queries.

Considering that the site itself is small (maximum 4 pages), we select 20 from the full list, which, in our opinion, most accurately describe the company’s services.

Here they are: warehouse rental in Moscow, warehouse space rental, warehouse and logistics, customs services, safekeeping warehouse, warehouse logistics, logistics services, office and warehouse rental, safekeeping of goods and so on….

These keywords include low-frequency, mid-frequency, and high-frequency queries.

Please note that this list is significantly different from the primary one taken from your head. And it is definitely more accurate and efficient.

Task 4: Reduce the list of remaining words to 50, leaving only those that, in your experience and opinion, are most optimal for your site. Don't forget that the final list should contain queries of varying frequency.

Conclusion

Your semantic core is ready, now is the time to put it into practice:

• review the texts on your site, maybe they should be rewritten.
• write several articles on your topic using selected key phrases, post the articles on the site, and after search engines index them, register in article directories. Read “One unusual approach to article promotion.”
• pay attention to search advertising. Now that you have a semantic core, the effect of advertising will be much higher.

Good afternoon friends.

Surely you have already forgotten the taste of my articles. The previous material was quite a long time ago, although I promised to publish articles more often than usual.

Recently the amount of work has increased. I created a new project (an information site), worked on its layout and design, collected a semantic core and began publishing material.

Today there will be very voluminous and important material for those who have been running their website for more than 6-7 months (in some topics for more than 1 year), have a large number of articles (on average 100) and have not yet reached the bar of at least 500-1000 visits per day . The numbers are taken to a minimum.

The importance of the semantic core

In some cases, poor website growth is caused by improper technical optimization of the website. More cases where the content is of poor quality. But there are even more cases when texts are not written according to requests at all - no one needs the materials. But there is also a very huge part of people who create a website, optimize everything correctly, write high-quality texts, but after 5-6 months the site only begins to gain the first 20-30 visitors from search. At a slow pace, after a year there are already 100-200 visitors and the income figure is zero.

And although everything was done correctly, there are no errors, and the texts are sometimes even many times higher quality than competitors, but somehow it doesn’t work, for the life of me. We begin to attribute this problem to the lack of links. Of course, links give a boost in development, but this is not the most important thing. And without them, you can have 1000 visits to the site in 3-4 months.

Many will say that this is all idle chatter and you won’t get such numbers so quickly. But if we look, such numbers are not achieved precisely on blogs. Information sites (not blogs), created for quick earnings and return on investment, after about 3-4 months it is quite possible to achieve a daily traffic of 1000 people, and after a year - 5000-10000. The numbers, of course, depend on the competitiveness of the niche, its volume and the volume of the site itself for the specified period. But, if you take a niche with fairly little competition and a volume of 300-500 materials, then such figures within the specified time frame are quite achievable.

Why exactly do blogs not achieve such quick results? The main reason is the lack of a semantic core. Because of this, articles are written for just one specific request and almost always for a very competitive one, which prevents the page from reaching the TOP in a short time.

On blogs, as a rule, articles are written in the likeness of competitors. We have 2 readable blogs. We see that they have decent traffic, we begin to analyze their site map and publish texts for the same requests, which have already been rewritten hundreds of times and are very competitive. As a result, we get very high-quality content on the site, but it performs poorly in searches, because... requires a lot of age. We are scratching our heads, why is my content the best, but doesn’t make it to the TOP?

That is why I decided to write detailed material about the semantic core of the site, so that you could collect a list of queries and, what is very important, write texts for such groups of keywords that, without buying links and in just 2-3 months, reached the TOP (of course, if quality content).

The material will be difficult for most if you have never encountered this issue in its correct way. But the main thing here is to start. As soon as you start acting, everything immediately becomes clear.

Let me make a very important remark. It concerns those who are not ready to invest in quality with their hard-earned coins and always try to find free loopholes. You can’t compile semantics at high quality for free, and this is a known fact. Therefore, in this article I describe the process of collecting semantics of maximum quality. There will be no free methods or loopholes in this post! There will definitely be a new post where I’ll tell you about free and other tools with which you can collect semantics, but not in full and without the proper quality. Therefore, if you are not ready to invest in the basics of your website, then this material is of no use to you!

Despite the fact that almost every blogger writes an article about this. kernel, I can say with confidence that there are no normal free tutorials on the Internet on this topic. And if there is, then there is no one that would give a complete picture of what should be the output.

Most often, the situation ends with some newbie writing material and talking about collecting the semantic core, as well as using a service for collecting search query statistics from Yandex (wordstat.yandex.ru). Ultimately, you need to go to this site, enter queries on your topic, the service will display a list of phrases included in your entered key - this is the whole technique.

But in fact, this is not how the semantic core is assembled. In the case described above, you simply will not have a semantic core. You will receive some disconnected requests and they will all be about the same thing. For example, let’s take my niche “website building”.

What are the main queries that can be named without hesitation? Here are just a few:

• How to create a website;
• Website promotion;
• Website creation;
• Website promotion, etc.

The requests are about the same thing. Their meaning comes down to only two concepts: creation and promotion of a website.

After such a check, the wordstat service will display a lot of search queries that are included in the main queries and they will also be about the same thing. Their only difference will be in the changed word forms (adding some words and changing the arrangement of words in the query with changing endings).

Of course, it will be possible to write a certain number of texts, since requests can be different even in this option. For example:

• How to create a wordpress website;
• How to create a joomla website;
• How to create a website on free hosting;
• How to promote a website for free;
• How to promote a service website, etc.

Obviously, separate material can be allocated for each request. But such compilation of the semantic core of the site will not be successful, because there will be no complete disclosure of information on the site in the selected niche. All content will be about only 2 topics.

Sometimes newbie bloggers describe the process of compiling a semantic core as analyzing individual queries in the Yandex Wordstat query analysis service. We enter some separate query that does not relate to the topic as a whole, but only to a specific article (for example, how to optimize an article), we get the frequency for it, and here it is - the semantic core is assembled. It turns out that in this way we must mentally identify all possible topics of articles and analyze them.

Both of the above options are incorrect, because... do not provide a complete semantic core and force you to constantly return to its compilation (second option). In addition, you will not have the site development vector in your hands and will not be able to publish materials in the first place, which should be published among the first.

Regarding the first option, when I once bought courses on website promotion, I constantly saw exactly this explanation for collecting the core of queries for a website (enter the main keys and copy all queries from the service into a text document). As a result, I was constantly tormented by the question “What to do with such requests?” The following came to mind:

• Write many articles about the same thing, but using different keywords;
• Enter all these keys into the description field for each material;
• Enter all the keys from the family. kernels in the general description field for the entire resource.

None of these assumptions were correct, nor, in general, was the semantic core of the site itself.

In the final version of collecting the semantic core, we should receive not just a list of queries in the amount of 10,000, for example, but have on hand a list of groups of queries, each of which is used for a separate article.

A group can contain from 1 to 20-30 requests (sometimes 50 or even more). In this case, we use all these queries in the text and the page in the future will bring traffic to all queries every day if it gets to 1-3 positions in the search. In addition, each group must have its own competition in order to know whether it makes sense to publish text on it now or not. If there is a lot of competition, then we can expect the effect of the page only after 1-1.5 years and with regular work to promote it (links, linking, etc.). Therefore, it is better to focus on such texts as a last resort, even if they have the most traffic.

Answers to possible questions

Question No. 1. It is clear that the output is a group of queries for writing text, and not just one key. In this case, wouldn’t the keys be similar to each other and why not write a separate text for each request?

Question No. 2. It is known that each page should be tailored to only one keyword, but here we get a whole group, and in some cases with a fairly large content of queries. How, in this case, does the optimization of the text itself occur, because if there are, for example, 20 keys, then the use of each at least once in the text (even a large one) already looks like text for a search engine, and not for people.

Answer. If we take the example of requests from the previous question, then the first thing to sharpen the material will be precisely for the most frequent (1st) request, since we are most interested in its reaching the top positions. We consider this keyword to be the main one in this group.

Optimization for the main key phrase occurs in the same way as it would be done when writing text for only one key (the key in the title heading, using the required number of characters in the text and the required number of times the key itself, if required).

Regarding other keys, we also enter them, but not blindly, but based on an analysis of competitors, which can show the average number of these keys in texts from the TOP. It may happen that for most keywords you will receive zero values, which means that they do not require use in the exact occurrence.

Thus, the text is written using only the main query in the text directly. Of course, other queries can also be used if the analysis of competitors shows their presence in the texts from the TOP. But this is not 20 keywords in the text in their exact occurrence.

Most recently I published material for a group of 11 keys. It seems like there are a lot of queries, but in the screenshot below you can see that only the main most frequent key has an exact occurrence - 6 times. The remaining key phrases do not have exact occurrences, but also diluted ones (not visible in the screenshot, but this is shown during the analysis of competitors). Those. they are not used at all.

(1st column – frequency, 2nd – competitiveness, 3rd – number of impressions)

In most cases, there will be a similar situation, when only a couple of keys need to be used in an exact occurrence, and all the rest will either be greatly diluted or not used at all, even in a diluted occurrence. The article turns out to be readable and there are no hints of focusing only on search.

Question No. 3. Follows from the answer to Question No. 2. If the remaining keys in the group do not need to be used at all, then how will they receive traffic?

Answer. The fact is that by the presence of certain words in the text, a search engine can determine what the text is about. Since keywords contain certain individual words that relate only to this key, they must be used in the text a certain number of times based on the same competitor analysis.

Thus, the key will not be used in the exact entry, but the words from the key will be individually present in the text and will also take part in the ranking. As a result, for these queries the text will also be found in the search. But in this case, the number of individual words should ideally be observed. Competitors will help.

I answered the main questions that could drive you into a stupor. I will write more about how to optimize text for groups of requests in one of the following materials. There will be information about analyzing competitors and about writing the text itself for a group of keys.

Well, if you still have questions, then ask your comments. I will answer everything.

Now let's start compiling the semantic core of the site.

Very important. I won’t be able to describe the whole process as it actually is in this article (I’ll have to do a whole webinar for 2-3 hours or a mini-course), so I’ll try to be brief, but at the same time informative and touch on as many points as possible . For example, I will not describe in detail the configuration of the KeyCollector software. Everything will be cut down, but as clear as possible.

Now let's go through each point. Let's begin. First, preparation.

What is needed to collect a semantic core?

Before creating the semantic core of the site, we will set up a key collector to correctly collect statistics and parse queries.

Setting up KeyCollector

You can enter the settings by clicking on the icon in the top menu of the software.

First, what concerns parsing.

I would like to note the “Number of streams” and “Use primary IP address” settings. The number of threads to assemble one small core does not require a large number. 2-4 threads are enough. The more threads, the more proxy servers are needed. Ideally, 1 proxy per 1 thread. But you can also have 1 proxy for 2 streams (that’s how I parsed it).

Regarding the second setting, in case of parsing in 1-2 threads, you can use your main IP address. But only if it is dynamic, because... If a static individual entrepreneur is banned, you will lose access to the Yandex search engine. But still, priority is always given to using a proxy server, as it is better to protect yourself.

On the Yandex.Direct parsing settings tab, it is important to add your Yandex accounts. The more there are, the better. You can register them yourself or buy them, as I wrote earlier. I bought them because it’s easier for me to spend 100 rubles for 30 accounts.

You can add it from the buffer by copying the list of accounts in the required format in advance, or load it from a file.

Accounts must be specified in the “login:password” format, without specifying the host itself in the login (without @yandex.ru). For example, “artem_konovalov:jk8ergvgkhtf”.

Also, if we use several proxy servers, it is better to assign them to specific accounts. It would be suspicious if at first the request comes from one server and from one account, and the next time a request is made from the same Yandex account, the proxy server is different.

Next to the accounts there is a column “IP proxy”. Next to each account we enter a specific proxy. If there are 20 accounts and 2 proxy servers, then there will be 10 accounts with one proxy and 10 with another. If there are 30 accounts, then 15 with one server and 15 with another. I think you understand the logic.

If we use only one proxy, then there is no point in adding it to each account.

I talked a little earlier about the number of threads and the use of the main IP address.

The next tab is “Network”, where you need to enter proxy servers that will be used for parsing.

You need to enter a proxy at the very bottom of the tab. You can load them from the buffer in the desired format. I added it in a simple way. In each column of the line I entered information about the server that is given to you when you purchase it.

Next, we configure the export parameters. Since we need to receive all requests with their frequencies in a file on the computer, we need to set some export parameters so that there is nothing superfluous in the table.

At the very bottom of the tab (highlighted in red), you need to select the data that you want to export to the table:

• Phrase;
• Source;
• Frequency "!" ;
• The best form of the phrase (you don’t have to put it).

All that remains is to configure the anti-captcha solution. The tab is called “Antikapcha”. Select the service you are using and enter the special key that is located in the service account.

A special key for working with the service is provided in a letter after registration, but it can also be taken from the account itself in the “Settings - account settings” item.

This completes the KeyCollector settings. After making the changes, do not forget to save the settings by clicking on the large button at the bottom “Save changes”.

When everything is done and we are ready for parsing, we can begin to consider the stages of collecting the semantic core, and then go through each stage in order.

Stages of collecting the semantic core

It is impossible to obtain a high-quality and complete semantic core using only basic queries. You also need to analyze competitors’ requests and materials. Therefore, the entire process of compiling the core consists of several stages, which in turn are further divided into substages.

1. The basis;
2. Competitor analysis;
3. Expansion of the finished list from stages 1-2;
4. Collection of the best word forms for queries from stages 1-3.

Stage 1 – foundation

When collecting the kernel at this stage you need to:

• Generate a main list of requests in the niche;
• Expanding these queries;
• Cleaning.

Stage 2 – competitors

In principle, stage 1 already provides a certain volume of the core, but not fully, because we may be missing something. And competitors in our niche will help us find missing holes. Here are the steps to follow:

• Collecting competitors based on requests from stage 1;
• Parsing competitors' queries (site map analysis, open liveinternet statistics, analysis of domains and competitors in SpyWords);
• Cleaning.

Stage 3 – expansion

Many people stop already at the first stage. Someone gets to the 2nd, but there are a number of additional queries that can also complement the semantic core.

• We combine requests from stages 1-2;
• We leave 10% of the most frequent words from the entire list, which contain at least 2 words. It is important that these 10% are no more than 100 phrases, because a large number will force you to dig deep into the process of collecting, cleaning and grouping. We need to assemble the kernel in a speed/quality ratio (minimal quality loss at maximum speed);
• We expand these queries using the Rookee service (everything is in KeyCollector);
• Cleaning.

Stage 4 – collecting the best word forms

The Rookee service can determine the best (correct) word form for most queries. This should also be used. The goal is not to determine the word that is more correct, but to find some more queries and their forms. In this way, you can pull up another pool of queries and use them when writing texts.

• Combining requests from the first 3 stages;
• Collection of the best word forms based on them;
• Adding the best word forms to the list for all queries combined from stages 1-3;
• Cleaning;
• Export the finished list to a file.

As you can see, everything is not so fast, and especially not so simple. I outlined only a normal plan for compiling a kernel in order to get a high-quality list of keys at the output and not lose anything or lose as little as possible.

Now I propose to go through each point separately and study everything from A to Z. There is a lot of information, but it’s worth it if you need a really high-quality semantic core of the site.

Stage 1 – foundation

First, we create a list of basic niche queries. Typically, these are 1-3 word phrases that describe a specific niche issue. As an example, I propose to take the “Medicine” niche, and more specifically, the sub-niche of heart diseases.

What main requests can we identify? Of course, I won’t write everything, but I will give a couple.

• Heart diseases
• Heart attack;
• Cardiac ischemia;
• Arrhythmia;
• Hypertension;
• Heart disease;
• Angina, etc.

In simple words, these are common names for diseases. There can be quite a lot of such requests. The more you can do, the better. But you shouldn’t enter it for show. It makes no sense to write out more specific phrases from the general ones, for example:

• Arrhythmia;
• Arrhythmia causes;
• Treatment of arrhythmia;
• Arrhythmia symptoms.

The main thing is only the first phrase. There is no point in indicating the rest, because... they will appear in the list during expansion using parsing from the left column of Yandex Wordstat.

To search for common phrases, you can use both competitor sites (site map, section names...) and the experience of a specialist in this niche.

Parsing will take some time, depending on the number of requests in the niche. All requests are by default placed in a new group called "New Group 1", if memory serves. I usually rename groups to understand which one is responsible for what. The group management menu is located to the right of the request list.

The rename function is in the context menu when you right-click. This menu will also be needed to create other groups in the second, third and fourth stages.

Therefore, you can immediately add 3 more groups by clicking on the first “+” icon so that the group is created in the list immediately after the previous one. There is no need to add anything to them yet. Let them just be.

I named the groups like this:

• Competitors - it is clear that this group contains a list of requests that I collected from competitors;
• 1-2 is a combined list of queries from the 1st (main list of queries) and 2nd (competitors’ queries) stages, in order to leave only 10% of the queries consisting of at least 2 words and collect extensions from them;
• 1-3 – combined list of requests from the first, second and third (extensions) stages. We also collect the best word forms in this group, although it would be smarter to collect them in a new group (for example, the best word forms), and then, after cleaning them, move them to group 1-3.

After completing parsing from yandex.wordstat, you receive a large list of key phrases, which, as a rule (if the niche is small) will be within several thousand. Much of this is garbage and dummy requests and will have to be cleaned up. Some things will be sifted out automatically using the KeyCollector functionality, while others will have to be shoveled by hand and sitting for a while.

When all the requests are collected, you need to collect their exact frequencies. The overall frequency is collected during parsing, but the exact frequency must be collected separately.

To collect statistics on the number of impressions, you can use two functions:

1. With the help of Yandex Direct - quickly, statistics are collected in batches, but there are limitations (for example, phrases of more than 7 words will not work, even with symbols);
2. Using analysis in Yandex Wordstat - very slowly, phrases are analyzed one by one, but there are no restrictions.

First, collect statistics using Direct, so that this is as fast as possible, and for those phrases for which it was not possible to determine statistics using Direct, we use Wordstat. As a rule, there will be few such phrases left and they will be collected quickly.

Impression statistics are collected using Yandex.Direct by clicking on the appropriate button and assigning the necessary parameters.

After clicking on the “Get data” button, there may be a warning that Yandex Direct is not enabled in the settings. You will need to agree to activate parsing using Direct in order to begin determining frequency statistics.

You will immediately see how packs in the “Frequency” column! » exact impression indicators for each phrase will begin to be recorded.

The process of completing a task can be seen on the “Statistics” tab at the very bottom of the program. When the task is completed, you will see a completion notification on the Event Log tab, and the progress bar on the Statistics tab will disappear.

After collecting the number of impressions using Yandex Direct, we check whether there are phrases for which the frequency was not collected. To do this, sort the “Frequency!” column. (click on it) so that the smallest or largest values ​​appear at the top.

If all zeros are at the top, then all frequencies are collected. If there are empty cells, then impressions for these phrases have not been determined. The same applies when sorting by kill, only then you will have to look at the result at the very bottom of the list of queries.

You can also start collecting using Yandex Wordstat by clicking on the icon and selecting the required frequency parameter.

After selecting the frequency type, you will see how the empty cells gradually begin to fill.

Important: do not be alarmed if empty cells remain after the end of the procedure. The fact is that they will be empty if their exact frequency is less than 30. We set this in the parsing settings in Keycollector. These phrases can be safely selected (just like regular files in Windows Explorer) and deleted. The phrases will be highlighted in blue, right-click and select “Delete selected lines.”

When all the statistics have been collected, you can start cleaning, which is very important to do at each stage of collecting the semantic core.

The main task is to remove phrases that are not related to the topic, remove phrases with stop words and get rid of queries that are too low in frequency.

The latter ideally does not need to be done, but if we are in the process of compiling this. cores to use phrases even with a minimum frequency of 5 impressions per month, this will increase the core by 50-60 percent (or maybe even 80%) and make us dig deep. We need to get maximum speed with minimal losses.

If we want to get the most complete semantic core of the site, but at the same time collect it for about a month (we have no experience at all), then take frequencies from 4-5 monthly impressions. But it’s better (if you’re a beginner) to leave requests that have at least 30 impressions per month. Yes, we will lose a little, but this is the price for maximum speed. And as the project grows, it will be possible to receive these requests again and use them to write new materials. And this is only on condition that all this. the core has already been written out and there are no topics for articles.

The same key collector allows you to filter requests by the number of impressions and other parameters fully automatically. Initially, I recommend doing just this, and not deleting garbage phrases and phrases with stop words, because... it will be much easier to do this when the total core volume at this stage becomes minimal. What's easier, shoveling 10,000 phrases or 2,000?

Filters are accessed from the Data tab by clicking on the Edit Filters button.

I recommend first displaying all queries with a frequency of less than 30 and moving them to a new group so as not to delete them, as they may be useful in the future. If we simply apply a filter to display phrases with a frequency of more than 30, then after the next launch of KeyCollector we will have to reapply the same filter, since everything is reset. Of course, you can save the filter, but you will still have to apply it, constantly returning to the “Data” tab.

To save ourselves from these actions, we add a condition in the filter editor so that only phrases with a frequency of less than 30 are displayed.

In the future, you can select a filter by clicking on the arrow next to the floppy disk icon.

So, after applying the filter, only phrases with a frequency of less than 30 will remain in the list of queries, i.e. 29 and below. Also, the filtered column will be highlighted in color. In the example below you will only see a frequency of 30, because... I show all this using the example of a kernel that is already ready and everything is cleaned. Don't pay any attention to this. Everything should be as I describe in the text.

To transfer, you need to select all phrases in the list. Click on the first phrase, scroll to the very bottom of the list, hold down the “Shift” key and click once on the last phrase. This way, all phrases are highlighted and marked with a blue background.

A small window will appear where you need to select the movement.

Now you can remove the filter from the frequency column so that only queries with a frequency of 30 and above remain.

We have completed a certain stage of automatic cleaning. Next you will have to tinker with deleting garbage phrases.

First, I suggest specifying stop words to remove all phrases containing them. Ideally, of course, this is done immediately at the parsing stage so that they do not end up on the list, but this is not critical, since clearing stop words occurs automatically using the KeyCollector.

The main difficulty is compiling a list of stop words, because... They are different for each topic. Therefore, whether we cleaned up stop words at the beginning or now is not so important, since we need to find all the stop words, and this is a laborious task and not so fast.

On the Internet you can find general thematic lists, which include the most common words like “abstract, free, download, p...rno, online, picture, etc.”

First, I suggest using a general topic list to further reduce the number of phrases. On the “Data Collection” tab, click on the “Stop words” button and add them in a list.

In the same window, click on the “Mark phrases in the table” button to mark all phrases containing the entered stop words. But it is necessary that the entire list of phrases in the group is unchecked, so that after clicking the button, only phrases with stop words remain marked. It's very easy to unmark all phrases.

When only marked phrases with stop words remain, we either delete them or move them to a new group. I deleted it for the first time, but it’s still a priority to create a group “With stop words” and move all unnecessary phrases to it.

After cleaning, there were even fewer phrases. But that’s not all, because... we still missed something. Both the stop words themselves and phrases that are not related to the focus of our site. These may be commercial requests for which texts cannot be written, or texts can be written, but they will not meet the user’s expectations.

Examples of such queries may be related to the word “buy”. Surely, when a user searches for something with this word, he already wants to get to the site where they sell it. We will write text for such a phrase, but the visitor will not need it. Therefore, we do not need such requests. We look for them manually.

We slowly and carefully scroll through the remaining list of queries to the very end in search of such phrases and discovering new stop words. If you find a word that is used many times, then simply add it to the existing list of stop words and click on the “Mark phrases in the table” button. At the end of the list, when we have marked all unnecessary queries during manual checking, we delete the marked phrases and the first stage of compiling the semantic core is completed.

We have obtained a certain semantic core. It is not quite complete yet, but it will already allow you to write the maximum possible part of the texts.

All that remains is to add to it a small part of the requests that we might have missed. The following steps will help with this.

Stage 2 – competitors

At the very beginning, we compiled a list of common phrases related to the niche. In our case these were:

• Heart diseases
• Heart attack;
• Cardiac ischemia;
• Arrhythmia;
• Hypertension;
• Heart disease;
• Angina, etc.

All of them belong specifically to the “Heart Disease” niche. Using these phrases, you need to search to find competitor sites on this topic.

We enter each of the phrases and look for competitors. It is important that these are not general thematic ones (in our case, medical sites with a general focus, i.e. about all diseases). What is needed is niche projects. In our case - only about the heart. Well, maybe also about the vessels, because... the heart is connected to the vascular system. I think you get the idea.

If our niche is “Recipes for salads with meat,” then in the future these are the only sites we should look for. If they are not there, then try to find sites only about recipes, and not in general about cooking, where everything is about everything.

If there is a general thematic site (general medical, women's, about all types of construction and repair, cooking, sports), then you will have to suffer a lot, both in terms of compiling the semantic core itself, because you will have to work long and tediously - collect the main list of requests, wait a long time for the parsing process, clean and group.

If on the 1st, and sometimes even on the 2nd page, you cannot find narrow thematic sites of competitors, then try using not the main queries that we generated before the parsing itself at the 1st stage, but queries from the entire list after parsing. For example:

• How to treat arrhythmia with folk remedies;
• Symptoms of arrhythmia in women and so on.

The fact is that such queries (arrhythmia, heart disease, heart disease...) are highly competitive and it is almost impossible to get to the TOP for them. Therefore, in the first positions, and maybe even on the pages, you will quite realistically find only general thematic portals about everything due to their enormous authority in the eyes of search engines, age and reference mass.

So it makes sense to use lower-frequency phrases consisting of more words to find competitors.

We need to parse their requests. You can use the SpyWords service, but its query analysis function is available on a paid plan, which is quite expensive. Therefore, for one core there is no point in upgrading the tariff on this service. If you need to collect several cores over the course of a month, for example 5-10, then you can buy an account. But again - only if you have a budget for the PRO tariff.

You can also use Liveinternet statistics if they are open for viewing. Very often, owners make it open to advertisers, but close the “search phrases” section, which is exactly what we need. But there are still sites where this section is open to everyone. Very rare, but available.

The easiest way is to simply view the sections and site map. Sometimes we may miss not only some well-known niche phrases, but also specific requests. There may not be a lot of material on them and you can’t create a separate section for them, but they can add a couple of dozen articles.

When we have found another list of new phrases to collect, we launch the same collection of search phrases from the left column of Yandex Wordstat, as in the first stage. We just launch it already, being in the second group “Competitors”, so that requests are added specifically to it.

• After parsing, we collect the exact frequencies of search phrases;
• We set a filter and move (delete) queries with a frequency of less than 30 to a separate group;
• We clean out garbage (stop words and queries that are not related to the niche).

So, we received another small list of queries and the semantic core became more complete.

Stage 3 – expansion

We already have a group called “1-2”. We copy phrases from the “Main list of requests” and “Competitors” groups into it. It is important to copy and not move, so that all phrases remain in the previous groups, just in case. It will be safer this way. To do this, in the phrase transfer window you need to select the “copy” option.

We received all requests from stages 1-2 in one group. Now you need to leave in this group only 10% of the most frequent queries of the total number and which contain at least 2 words. Also, there should be no more than 100 pieces. We reduce it so as not to get buried in the process of collecting the core for a month.

First, we apply a filter in which we set the condition so that at least 2-word phrases are shown.

We mark all the phrases in the remaining list. By clicking on the “Frequency!” column, we sort the phrases in descending order of number of impressions, so that the most frequent ones are at the top. Next, select the first 10% of the remaining number of queries, uncheck them (right mouse button - uncheck selected lines) and delete the marked phrases so that only these 10% remain. Don’t forget that if your 10% is more than 100 words, then we stop at line 100, no more is needed.

Now we carry out the expansion using the keycollector function. The Rookee service will help with this.

We indicate the collection parameters as in the screenshot below.

The service will collect all extensions. Among the new phrases there may be very long keys, as well as symbols, so it will not be possible to collect frequency through Yandex Direct for everyone. You will then have to collect statistics using the button from Wordstat.

After receiving statistics, we remove requests with monthly impressions of less than 30 and carry out cleaning (stop words, garbage, keywords that are not suitable for the niche).

The stage is over. We received another list of requests.

Stage 4 – collecting the best word forms

As I said earlier, the goal is not to determine the form of the phrase that will be more correct.

In most cases (based on my experience), collecting the best word forms will point to the same phrase that is in the search list. But, without a doubt, there will also be queries for which a new word form will be indicated, which is not yet in the semantic core. These are additional key queries. At this stage we achieve the core to maximum completeness.

When assembling its core, this stage yielded another 107 additional requests.

First, we copy the keys into the “1-3” group from the “Main Queries”, “Competitors” and “1-2” groups. The sum of all requests from all previously completed stages should be obtained. Next, we use the Rookee service using the same button as the extension. Just choose another function.

The collection will begin. Phrases will be added to a new column “Best form of phrase”.

The best form will not be determined for all phrases, since the Rookee service simply does not know all the best forms. But for the majority the result will be positive.

When the process is completed, you need to add these phrases to the entire list so that they are in the same “Phrase” column. To do this, select all the phrases in the “Best phrase form” column, copy (right mouse button - copy), then click on the big green “Add phrases” button and enter them.

It is very easy to make sure that phrases appear in the general list. Since adding phrases to the table like this happens at the very bottom, we scroll to the very bottom of the list and in the “Source” column we should see the add button icon.

Phrases added using extensions will be marked with a hand icon.

Since the frequency for the best word forms has not been determined, this needs to be done. Similar to the previous stages, we collect the number of impressions. Don’t be afraid that we are collecting in the same group where the other requests are located. The collection will simply continue for those phrases that have empty cells.

If it is more convenient for you, then initially you can add the best word forms not to the same group where they were found, but to a new one, so that only they are there. And already in it, collect statistics, clear garbage, and so on. And only then add the remaining normal phrases to the entire list.

That's all. The semantic core of the site has been assembled. But there is still a lot of work left. Let's continue.

Before the next steps, you need to download all requests with the necessary data into an Excel file on your computer. We set the export settings earlier, so you can do it right away. The export icon in the KeyCollector main menu is responsible for this.

When you open the file, you should get 4 columns:

1. Phrase;
2. Source;
3. Frequency!;
4. The best form of the phrase.

This is our final semantic core, containing the maximum pure and necessary list of queries for writing future texts. In my case (narrow niche) there were 1848 requests, which equals approximately 250-300 materials. I can’t say for sure - I haven’t completely ungrouped all the requests yet.

For immediate use, this is still a raw option, because... requests are in a chaotic order. We also need to scatter them into groups so that each contains the keys to one article. This is the ultimate goal.

Ungrouping the semantic core

This stage is completed quite quickly, although with some difficulties. The service will help us http://kg.ppc-panel.ru/. There are other options, but we will use this one in view of the fact that with it we will do everything in the quality/speed ratio. What is needed here is not speed, but first and foremost quality.

A very useful thing about the service is that it remembers all actions in your browser using cookies. Even if you close this page or the browser as a whole, everything will be saved. This way there is no need to do everything at once and be afraid that everything may be lost in one moment. You can continue at any time. The main thing is not to clear your browser cookies.

I will show you how to use the service using the example of several fictitious queries.

We go to the service and add the entire semantic core (all queries from the excel file) exported earlier. Just copy all the keys and paste them into the window as shown in the image below.

They should appear in the left column “Keywords”.

Ignore the presence of shaded groups on the right side. These are groups from my previous core.

We look at the left column. There is an added list of queries and a “Search/Filter” line. We will use the filter.

The technology is very simple. When we enter part of a word or phrase, the service in real time leaves in the list of queries only those that contain the entered word/phrase in the query itself.

See below for more clarity.

I wanted to find all queries related to arrhythmia. I enter the word “Arrhythmia” and the service automatically leaves in the list of queries only those that contain the entered word or part of it.

The phrases will move to a group, which will be called one of the key phrases of this group.

We received a group containing all the keywords for arrhythmia. To see the contents of a group, click on it. A little later we will further divide this group into smaller groups, since there are a lot of keys with arrhythmia and they are all under different articles.

Thus, at the initial stage of grouping, you need to create large groups that combine a large number of keywords from one niche question.

If we take the same topic of heart disease as an example, then first I will create a group “arrhythmia”, then “heart disease”, then “heart attack” and so on until there are groups for each disease.

As a rule, there will be almost as many such groups as the main niche phrases generated at the 1st stage of collecting the core. But in any case, there should be more of them, since there are also phrases from stages 2-4.

Some groups may contain 1-2 keys altogether. This may be due, for example, to a very rare disease and no one knows about it or no one is looking for it. That's why there are no requests.

In general, when the main groups are created, it is necessary to break them down into smaller groups, which will be used to write individual articles.

Inside the group, next to each key phrase there is a cross; by clicking on it, the phrase is removed from the group and goes back to the ungrouped list of keywords.

This is how further grouping occurs. I'll show you with an example.

In the image you can see that there are key phrases related to arrhythmia treatment. If we want to define them in a separate group for a separate article, then we remove them from the group.

They will appear in the list in the left column.

If there are still phrases in the left column, then to find deleted keys from the group, you will have to apply a filter (use the search). If the list is completely divided into groups, then only deleted requests will be there. We mark them and click on “Create group”.

Another one will appear in the “Groups” column.

Thus, we distribute all the keys by topic and, ultimately, a separate article is written for each group.

The only difficulty in this process lies in analyzing the need to ungroup some keywords. The fact is that there are keys that are inherently different, but they do not require writing separate texts, but detailed material is written on many issues.

This is clearly expressed in medical topics. If we take the example of arrhythmia, then there is no point in making the keys “arrhythmia causes” and “arrhythmia symptoms”. The keys to treating arrhythmia are still in question.

This will be found out after analyzing the search results. We go to Yandex search and enter the analyzed key. If we see that the TOP contains articles devoted only to symptoms of arrhythmia, then we separate this key into a separate group. But, if the texts in the TOP cover all the issues (treatment, causes, symptoms, diagnosis, and so on), then ungrouping in this case is not necessary. We cover all these topics in one article.

If in Yandex exactly such texts are at the top of the search results, then this is a sign that ungrouping is not worth doing.

The same can be exemplified by the key phrase “causes of hair loss.” There may be “causes of hair loss in men” and “...in women.” Obviously, you can write a separate text for each key, based on logic. But what will Yandex say?

We enter each key and see what texts are there. There are separate detailed texts for each key, then the keys are ungrouped. If in the TOP for both queries there are general materials on the key “causes of hair loss”, within which questions regarding women and men are disclosed, then we leave the keys within one group and publish one material where topics on all keys are revealed.

This is important, since it is not for nothing that the search engine identifies texts in the TOP. If the first page contains exclusively detailed texts on a specific issue, then there is a high probability that by breaking a large topic into subtopics and writing material on each, you will not get to the TOP. And so one material has every chance of getting good positions for all requests and collecting good traffic for them.

In medical topics, a lot of attention needs to be paid to this point, which significantly complicates and slows down the ungrouping process.

At the very bottom of the “Groups” column there is an “Unload” button. Click on it and we get a new column with a text field containing all the groups, separated by a line indent.

If not all keys are in groups, then in the “Upload” field there will be no spaces between them. They only appear when ungrouping is completely completed.

Select all the words (key combination Ctrl+A), copy them and paste them into a new excel file.

Under no circumstances click on the “Clear all” button, as absolutely everything you have done will be deleted.

The ungrouping phase is over. Now you can safely write the text for each group.

But, for maximum efficiency, if your budget does not allow you to write out everything. core in a couple of days, and there is only a strictly limited opportunity for regular publication of a small number of texts (10-30 per month, for example), then it is worth determining the competition of all groups. This is important because the groups with the least competition produce results in the first 2-3-4 months after writing without any links. All you need to do is write high-quality, competitive text and optimize it correctly. Then time will do everything for you.

Definition of group competition

I would like to note right away that a low-competition request or group of requests does not mean that they are very micro low-frequency. The beauty is that there is a fairly decent number of requests that have low competition, but at the same time have a high frequency, which immediately gives such an article a place in the TOP and attracts solid traffic to one document.

For example, a very real picture is when a group of queries has 2-5 competition, and the frequency is about 300 impressions per month. Having written only 10 such texts, after they reach the TOP, we will receive at least 100 visitors daily. And these are only 10 texts. 50 articles – 500 visits and these figures are taken as a ceiling, since this only takes into account traffic for exact queries in the group. But traffic will also be attracted from other tails of requests, and not just from those in groups.

This is why identifying the competition is so important. Sometimes you can see a situation where there are 20-30 texts on a site, and there are already 1000 visits. And the site is young and there are no links. Now you know what this is connected with.

Request competition can be determined through the same KeyCollector absolutely free of charge. This is very convenient, but ideally this option is not correct, since the formula for determining competition is constantly changing with changes in search engine algorithms.

Better identify competition using the service http://mutagen.ru/. It is paid, but is closer to real indicators as much as possible.

100 requests cost only 30 rubles. If you have a core for 2000 requests, then the entire check will cost 600 rubles. 20 free checks are given per day (only to those who top up their balance by any amount). You can evaluate 20 phrases every day until you determine the competitiveness of the entire core. But this is very long and stupid.

Therefore, I use the mutagen and have no complaints about it. Sometimes there are problems related to processing speed, but this is not so critical, since even after closing the service page, the check continues in the background.

The analysis itself is very simple. Register on the site. We top up the balance in any convenient way and check is available at the main address (mutagen.ru). We enter a phrase into the field and it immediately begins to be evaluated.

We see that for the query being checked, the competitiveness turned out to be more than 25. This is a very high indicator and can be equal to any number. The service does not display it as real, since this does not make sense due to the fact that such competitive requests are almost impossible to promote.

The normal level of competition is considered to be up to 5. It is precisely such requests that are easily promoted without unnecessary gestures. Slightly higher values ​​are also quite acceptable, but queries with values ​​greater than 5 (for example, 6-10) should be used after you have already written texts for minimal competition. The fastest possible text in the TOP is important.

Also during the assessment, the cost of a click in Yandex.Direct is determined. It can be used to estimate your future earnings. We take into account guaranteed impressions, the value of which we can safely divide by 3. In our case, we can say that one click on a Yandex Direct advertisement will bring us 1 ruble.

The service also determines the number of impressions, but we do not look at them, since the frequency of the “!request” type is not determined, but only the “request”. The indicator turns out to be inaccurate.

This analysis option is suitable if we want to analyze a single request. If you need a mass check of key phrases, then there is a special link on the main page at the top of the key entry field.

On the next page we create a new task and add a list of keys from the semantic core. We take them from the file where the keys are already ungrouped.

If there are enough funds on the balance for analysis, the check will begin immediately. The duration of the check depends on the number of keys and may take some time. I analyzed 2000 phrases for about 1 hour, when the second time checking only 30 keys took several hours.

Immediately after starting the scan, you will see the task in the list, where there will be a “Status” column. This will help you understand whether the task is ready or not.

In addition, after completing the task, you will immediately be able to download a file with a list of all phrases and the level of competition for each. The phrases will all be in the same order as they were ungrouped. The only thing is that the spaces between the groups will be removed, but this is not a big deal, because everything will be intuitive, because each group contains keys on a different topic.

In addition, if the task has not yet completed, you can go inside the task itself and see the competition results for already completed requests. Just click on the task name.

As an example, I will show the result of checking a group of queries for the semantic core. Actually, this is the group for which this article was written.

We see that almost all requests have a maximum competition of 25 or closer to it. This means that for these requests, I either won’t see the first positions at all or won’t see them for a very long time. I wouldn’t write such material on the new site at all.

Now I published it only to create quality content for the blog. Of course, my goal is to get to the top, but that’s later. If the manual reaches the first page, at least only for the main request, then I can already count on significant traffic only to this page.

The last step is to create the final file, which we will look at in the process of filling the site. The semantic core of the site has already been assembled, but managing it is not entirely convenient yet.

Creating a final file with all data

We will need the KeyCollector again, as well as the latest excel file that we received from the mutagen service.

We open the previously received file and see something like the following.

We only need 2 columns from the file:

1. key;
2. competition.

You can simply delete all other contents from this file so that only the necessary data remains, or you can create a completely new file, make a beautiful header line there with the names of the columns, highlighting it, for example, in green, and copy the corresponding data into each column.

Next, we copy the entire semantic core from this document and add it to the key collector again. Frequencies will need to be collected again. This is necessary so that the frequencies are collected in the same order as the key phrases are located. Previously, we collected them to weed out garbage, and now to create the final file. Of course, we add phrases to a new group.

When the frequencies are collected, we export the file from the entire frequency column and copy it into the final file, which will have 3 columns:

1. key phrase;
2. competitiveness;
3. frequency.

Now all that’s left to do is sit a little and do the math for each group:

• Average group frequency - it is not the indicator of each key that is important, but the average indicator of the group. It is calculated as the usual arithmetic mean (in Excel - the "AVERAGE" function);
• Divide the total frequency of the group by 3 to bring the possible traffic to real numbers.

So that you don’t have to worry about mastering calculations in Excel, I have prepared a file for you where you just need to enter data in the required columns and everything will be calculated fully automatically. Each group must be calculated separately.

There will be a simple calculation example inside.

As you can see, everything is very simple. From the final file, simply copy all the entire phrases of the group with all the indicators and paste them into this file in place of the previous phrases with their data. And the calculation will happen automatically.

It is important that the columns in the final file and in mine are in exactly this order. First the phrase, then the frequency, and only then the competition. Otherwise, you will count nonsense.

Next, you will have to sit a little to create another file or sheet (I recommend) inside the file with all the data about each group. This sheet will not contain all the phrases, but only the main phrase of the group (we will use it to determine what kind of group it is) and the calculated values ​​of the group from my file. This is a kind of final file with data about each group with calculated values. This is what I look at when choosing texts for publication.

You will get the same 3 columns, only without any calculations.

In the first one we insert the main phrase of the group. In principle, any one is possible. It is only needed to copy it and, through a search, find the location of all the group keys that are on another sheet.

In the second we copy the calculated frequency value from my file, and in the third we copy the average value of the group’s competition. We take these numbers from the “Result” line.

The result will be the following.

This is the 1st sheet. The second sheet contains all the contents of the final file, i.e. all phrases with indicators of their frequency and competition.

Now we look at the 1st sheet. We select the most “cost-effective” group of keys. We copy the phrase from the 1st column and find it using search (Ctrl + F) in the second sheet, where the rest of the phrases of the group will be located next to it.

That's all. The manual has come to an end. At first glance, everything is very complicated, but in reality it is quite simple. As I already said at the very beginning of the article, you just have to start doing it. In the future I plan to make a video manual using these instructions.

Well, on this note I end the instructions.

All friends. If you have any suggestions or questions, I'm waiting for you in the comments. See you later.

P.S. Record for volume of content. In Word it turned out to be more than 50 sheets of small print. It was possible not to publish an article, but to create a book.

Best regards, Konstantin Khmelev!

The semantic core (SC) is a set of keywords, queries, and search phrases that you need to use to promote your website so that targeted visitors from search engines come to it. Compiling a semantic core is the second stage after setting up your site on hosting. It is on a well-written NL that determines how much quality traffic will be on your website.

The need to compile a semantic core lies in several points.

Firstly, it makes it possible to develop a more effective search engine promotion strategy, since the webmaster, who will create the semantic core for his project, will have a clear idea of ​​what search engine promotion methods he will need to apply to his site and decide on the cost of search promotion, which greatly depends on the level of competition for key phrases in search engine results.

Secondly, the semantic core makes it possible to fill the resource with higher quality content, that is, content that will be well optimized for key queries. For example, if you want to make a website about suspended ceilings, then the semantic core of queries should be selected based on this keyword.

In addition, compiling a semantic core involves determining the frequency of use of keywords by search engine users, which makes it possible to determine the search system in which it is necessary to pay more attention to the promotion of a particular query.

It is also necessary to note some rules for compiling a semantic core that must be followed in order to be able to compose a high-quality and effective SL.

Thus, the semantic core must include all possible key queries for which the site can be promoted, except for those queries that cannot bring at least a small amount of traffic to the resource. Therefore, the semantic core should include high-frequency keywords (HF), medium-frequency (MF) and low-frequency (LF) queries.

Conventionally, these requests can be broken down as follows: LF - up to 1,000 requests per month, MF - from 1,000 to 10,000 requests per month, HF - more than 10,000 requests per month. It is also necessary to take into account the fact that in different topics these figures can vary significantly (in some particularly narrow topics the maximum frequency of requests does not exceed 100 requests per month).

Low-frequency queries simply need to be included in the structure of the semantic core, because any young resource has the opportunity to advance in search engines, first of all, using them (and without any external website promotion - you wrote an article for a low-frequency query, the search engine indexed it, and your the site can be in the TOP in just a few weeks).

It is also necessary to follow the rule of structuring the semantic core, which is that all keywords of the site must be combined into groups not only by their frequency, but also by the degree of similarity. This will make it possible to optimize content more efficiently, for example, optimize some texts for several queries.

It is also advisable to include such types of queries as synonymous queries, slang queries, abbreviated words, and so on into the structure of the semantic core of many sites.

Here is a clear example of how you can increase site traffic just by writing optimized articles that include mid-range and a large number of low-frequency queries:

1. Men's-themed site. Although it is updated very rarely (for all time, a little more than 25 articles have been published), thanks to well-chosen queries it is gaining more and more traffic. No links were purchased to the site at all!

At the moment, the growth in traffic has stopped, as only 3 articles have been published.

2. Women's themed website. At first, unoptimized articles were published on it. In the summer, a semantic core was compiled, which consisted of queries with very low competition (I will tell you below how to collect such queries). In response to these requests, relevant articles were written and several links were purchased on thematic resources. You can see the results of such promotion below:

3. Medical website. A medical niche with more or less adequate competition was chosen and more than 200 articles were published for tasty queries with good bids (on the Internet, the term “bid” refers to the cost of one user click on an advertisement link). I started buying links in February, when the site was 1.5 months old. So far, most traffic comes from Google; Yandex has not yet taken into account purchased links.

As you can see, the selection of the semantic core plays a vital role in website promotion. Even with a small budget or no budget at all, you can create a traffic website that will bring you profit.

Selection of search queries

You can select queries for the semantic core in the following ways:

Using free search query statistics services Google, Yandex or Rambler

Using special software or an online service (free or paid).

Analyzing competitors' websites.

Selection of search queries using search query statistics services Google, Yandex or Rambler

I want to warn you right away that these services do not show entirely accurate information about the number of search queries per month.

For example, if you look at 4 pages of search results for the query “plastic windows”, the statistics service will show you that this request was searched not 1, but 4 times, since not only the first page of the search results is considered as an impression, but also all subsequent ones. viewed by user. Therefore, in practice, the real numbers will be slightly lower than those shown by various services.

To determine the exact number of transitions, it is best, of course, to look at the traffic statistics of competitors who are in the TOP 10 (liveinternet or mail.ru, if it is open). This way, you can understand how much traffic the request you are interested in will bring.

You can also roughly calculate how many visitors a given request will bring you, depending on the position the site will occupy in search results. Here's how the CTR (click-through rate) of your site will change at different positions in the TOP:

Let’s take, for example, the query “!repair!apartment”, region “Moscow and region”:

In this case, if your site occupies 5th position in the search results, this request will bring you about 75 visitors per month (6%), 4th place (8%) - 100 visitors per month, 3rd place (11%) - 135 villages/month, 2nd place (18%) - 223 villages/month. and 1st position (28%) - 350 visitors per month.

You can also influence CTR using a bright snippet, thereby increasing traffic for this request. You can read how to improve a snippet and what it is.

Google Search Query Statistics

Previously, I used Google search query statistics more often, since I primarily promoted this search engine. Then it was enough to optimize the article, purchase as many different PR links for it as possible (it is very important that these links were natural and that visitors followed the links) and voila - you are in the TOP!

Now the situation in Google is such that it is not much easier to advance in it than in Yandex. Therefore, you have to pay much more attention to both writing and designing (!) articles and buying links for the site.

I would also like to draw your attention to the following fact:

In Russia, Yandex is the most popular (Yandex - 57.7%, Google - 30.6%, Mail.ru - 8.9%, Rambler -1.5%, Nigma/Ask.com - 0.4%), so if you are promoting in this country, you should first It's worth focusing on Yandex.

In Ukraine, the situation looks different: Google - 70-80%, Yandex - 20-25%. Therefore, Ukrainian webmasters should focus on promotion in Google.

To use Google query statistics, go to

Let's look at an example of selecting keywords for a culinary site.

First of all, you need to enter a basic query, on the basis of which keyword options for the future semantic core of the site will be selected. I entered the query “how to cook”.

The next step is to select the match type. There are 3 types of matches: broad, phrase and exact. I recommend selecting accurate as this option will show the most accurate information for the request. And now I will explain why.

Broad match means that impression statistics will be shown for all words that are in this query. For example, for the query “plastic windows” it will be shown for all words that include the word “plastic” and the word “windows” (plastic, window, buy windows, buy blinds for windows, PVC windows prices). In a word, there will be a lot of “garbage”.

Phrase matching means that statistics will be shown for words in the exact order in which they are listed. Along with the specified combination of words, other words may also be present in the request. For the request “plastic windows” the words “inexpensive plastic windows”, “plastic windows Moscow”, “how much do plastic windows cost”, etc. will be taken into account.

We are most interested in the indicator “Number of requests per month (target regions)” and “Approximate cost per click” (if we are going to place Adsense advertising on the site pages).

Yandex search query statistics

I use search query statistics almost every day, as they are presented in a more convenient form than their counterpart in Google.

The only disadvantage of Wordstat is that you won’t find match types in it, you won’t save selected queries to your computer, you won’t be able to find out the cost of a click, etc.

To obtain more accurate results, you need to use special operators with which you can clarify the queries that interest us. You can find a list of operators here.

If you simply enter the query “how to cook” in Wordstat, we get the following statistics:

This is the same as if we selected “broad match” in Adwords.

If you enter the same query, but in quotes, we will get more accurate statistics (analogous to phrase matching in Adwords):

Well, to get statistics only for a given request, you need to use the “!” operator: “!how!to prepare”

To get even more accurate results, you need to specify the region for which the site is being promoted:

Also in the top panel of Wordstat there are tools with which you can view the statistics of a given query by region, by month and by week. With the help of the latter, by the way, it is very convenient to analyze the statistics of seasonal requests.

For example, after analyzing the request “how to cook”, you can find out that it is most popular in the December months (this is not surprising - everyone is preparing for the New Year):

Rambler search query statistics

I would like to warn you right away that the statistics of requests from Rambler are losing their relevance every year more and more (primarily, this is due to the low popularity of this search engine). Therefore, you most likely won't even have to work with it.

There is no need to enter any operators into Adstat - it immediately displays the frequency of the request in the case in which it was entered. It also has separate query frequency statistics for the first page of search results and for all search pages, including the first one.

Selection of search queries using special software or online services

Rookee can not only promote your queries, but can also help in creating the semantic core of the site.

With Rookee, you can easily select a semantic core for your website and be able to approximately predict the number of visits for selected queries and the cost of promoting them to the top.

Selection of queries using the free Slovoeb program

If you are going to compile a semantic core (semantic core) at a more professional level, or you need query statistics for Google.Adwords, Rambler.Adstat, the VKontakte social network, various link aggregators, etc., I advise you to immediately purchase Key Collector.

If you want to create a large semantic core, but do not want to spend money on purchasing paid programs, the best option in this case would be the Slovoeb program (read information about the program here). It is the “younger brother” of Kay Collector and allows you to collect EA based on query statistics on Yandex.Wordstat.

Installing the SlovoEB program.

Download the archive with the program from .

Make sure the archive is unlocked. To do this, in the file properties (select “Properties” in the context menu), click the “Unblock” / “Unblock” button, if present.

Unzip the contents of the archive.

Run the executable file Slovoeb.exe

Let's create a new project:

Select the desired region (Yandex.Wordstat Regions button):

Save the changes.

Click on the button “Left column of Yandex.Wordstat”

If necessary, we set “Stop words” (words that should not be included in our semantic core). Stop words can be the following words: “free” (if you sell something on your website), “forum”, “Wikipedia” (if you have your own information site that does not have a forum), “porn”, “sex” (well, everything is clear here), etc.

Now you need to set the initial list of words on the basis of which the SL will be compiled. Let's form the core for a company that installs suspended ceilings (in Moscow).

When selecting any semantic core, the first step is to make a classification of the analyzed topic.

In our case, suspended ceilings can be classified according to the following criteria (I make similar convenient mind maps in the MindJet MindManager program):

Helpful advice: for some reason, many webmasters forget to include the names of small localities in the semantic core.

In our case, the names of areas of interest to us in Moscow and cities in the Moscow region could be included in the NL. Even if there are very few requests per month for these keywords (“Stretch ceilings Golitsyno”, “Stretch ceilings Aprelevka”, etc.), you still need to write at least one short article for them, the title of which would include the required key. You don’t even have to promote such articles, because most often there will be very little competition for these requests.

10-20 such articles, and your site will consistently have several additional orders from these cities.

Click the “Left Column of Yandex.Wordstat” button and enter the necessary queries:

Click on the “Parse” button. As a result, we get the following list of requests:

We filter out all unnecessary requests that do not fit the theme of the site (for example, “do-it-yourself suspended ceilings” - although this request will bring some traffic, it will not bring us clients who will order the installation of ceilings). We select these queries and delete them so as not to waste time analyzing them in the future.

Now you need to clarify the frequency for each of the keys. Click on the “Collect frequencies “!” button:

Now we have the following information: the request, its general and exact frequency.

Now, based on the received frequencies, you need to review all requests again and delete unnecessary ones.

Unnecessary queries are queries that:

The exact frequency (“!”) is very low (in the topic I have chosen, in principle, you need to value every visitor, so I will filter out requests that have a monthly frequency of less than 10). If the topic was not construction, but, say, some general thematic one, then you can safely filter out requests whose frequency is below 50-100 per month.

The ratio of total and exact frequencies exceeds very high. For example, the request “will buy suspended ceilings” (1335/5) can be immediately deleted, because it is a “dummy request”.

Requests with very high competition should also be deleted; it will be difficult to advance on them (especially if you have a young site and a small budget for promotion). Such a request, in our case, is “stretch ceilings”. In addition, most often, those queries that consist of 3.4 or more words are more effective - they bring more targeted visitors.

In addition to Slovoeb, there is another excellent program for convenient automatic collection, analysis and processing of statistics on impressions of Yandex.Direct keywords -.

News search queries

In this case, the FL is created differently than for a regular content project. For a news site, first of all, you need to select the categories under which the news will be published:

After this, you need to select queries for each section. News requests can be expected or unexpected.

For the first type of requests, the informational occasion is predictable. Most often, you can even accurately determine the date when there will be a surge in the popularity of a particular request. This could be any holiday (New Year, May 9, March 8, February 23, Independence Day, Constitution Day, church holidays), event (musical events, concerts, film premieres, sports competitions, presidential elections).

You can prepare such requests in advance and determine the approximate volume of traffic in this case using

Also, do not forget to look at the traffic statistics of your site (if you have already reviewed some event) and competitors’ sites (if their statistics are open).

The second type of request is less predictable. These include breaking news: disasters, catastrophes, some events in the families of famous people (birth/wedding/death), the release of an unannounced software product, etc. In this case, you just need to be one of the first to publish this news.

To do this, it is not enough to simply monitor news in Google and Yandex - in this case, your site will simply be one of those that simply reprinted this event. A more effective method that allows you to hit the big jackpot is to monitor foreign sites. By publishing this news as one of the first on the RuNet, you, in addition to the tons of traffic that the servers on your hosting will receive, will receive a huge number of backlinks to your site.

Movie-related queries can also be classified as the first type (expected queries). Indeed, in this case, the date of the premiere of the film is known in advance, the script of the film and its actors are approximately known. Therefore, you need to prepare in advance the page on which the film will appear, and temporarily add its trailer there. You can also publish news about the film and its actors on the site. This tactic will help you take TOP positions in search engines in advance and will bring visitors to the site even before its premiere.

Do you want to find out what queries are currently trending or predict the relevance of your topic in the future? Then use services that provide information about trends. It allows you to perform many analysis operations: comparing search trends for several queries, analyzing the geographic regions of a query, viewing the hottest trends at the moment, viewing relevant current queries, exporting results to CSV format, the ability to subscribe to an RSS feed for hot trends, etc. .

How to speed up the collection of the semantic core?

I think that everyone who came across the collection of a semantic core had the thought: “How long and tedious the parsing is, I’m tired of sorting through and grouping thousands of these queries!” This is fine. This happens to me too sometimes. Especially when you have to parse and sort through a syntax that consists of several tens of thousands of requests.

Before you start parsing, I strongly advise you to divide all requests into groups. For example, if your website theme is “Building a house,” break it down into the foundation, walls, windows, doors, roof, wiring, heating, etc. Then it will simply be much easier for you to sort through and group requests when they are located in a small group and are related to each other by a specific narrow topic. If you just parse everything in one pile, you will end up with an unrealistically huge list that will take more than one day to process. And so, by processing the entire list of keywords in small steps, you will not only process all requests more efficiently, but you will also be able to simultaneously order articles from copywriters for already collected keys.

The process of collecting a semantic core for me almost always begins with automatic parsing of queries (for this I use Key Collector). You can also collect it manually, but if we work with a large number of requests, I don’t see the point in wasting my precious time on this routine work.

If you work with other programs, then they will most likely have a function for working with proxy servers. This allows you to speed up the parsing process and protect your IP from being banned by search engines. To be honest, it’s not very pleasant when you urgently need to complete an order, and your IP is banned for a day due to frequent access to the Google/Yandex statistics service. This is where paid proxies come to the rescue.

Personally, I don’t use them at the moment for one simple reason - they are constantly banned, finding high-quality working proxies is not so easy, and I don’t want to pay money for them again. Therefore, I found an alternative way to collect CN, which speeded up this process several times.

Otherwise, you will have to look for other sites for analysis.

In most cases, sites hide these statistics, so you can use entry point statistics.

As a result, we will have statistics of the most visited pages of the site. We go to them, write out the main queries for which it was optimized, and based on them we collect the SYNOPSIS.

By the way, if the site has open statistics “By search phrases”, you can make your work easier and collect queries using Key Collector (you just need to enter the site address and click on the “Get data” button):

The second way to analyze a competitor’s website is by analyzing their website.

Some resources have a “Most Popular Articles” widget (or something like that). Sometimes the most popular articles are chosen based on the number of comments, sometimes based on the number of views.

In any case, having before your eyes a list of the most popular articles, you can figure out what requests this or that article was written for.

The third way is to use tools. It was actually created to analyze the trustworthiness of sites, but, to be honest, it considers trustworthiness very bad. But what he can do well is analyze requests from competitors’ websites.

Enter the address of any site (even with closed statistics!) and click the trust check button. At the very bottom of the page, site visibility statistics by keywords will be displayed:

Website visibility in search engines (by keywords)

The only negative is that these queries cannot be exported; everything must be copied manually.

The fourth way is to use services and .

With its help, you can determine all queries for which the site ranks in the TOP 100 in both search engines. It is possible to export positions and queries to xls format, but I was never able to open this file on any computer.

Well, the last way to find out your competitors’ keywords is with the help of

Let’s analyze the query “house made of timber” as an example. In the “Competitor Analysis” tab, enter this request (if you want to analyze a specific site, you just need to enter its url in this field).

As a result, we get information about the frequency of the request, the number of advertisers in each of the search engines (and if there are advertisers, then there is money in this niche) and the average cost of a click in Google and Yandex:

You can also view all ads in Yandex.Direct and Google Adwords:

And this is what the TOP of each PS looks like. You can click on any of the domains and see all the queries for which it is in the TOP and all its contextual ads:

There is another way to analyze competitors’ requests, which few people know about - using the Ukrainian service

I would call it the “Ukrainian version of Spywords”, since they are very similar in functionality. The only difference is that the database contains key phrases that Ukrainian users are looking for. So if you work in UA-net, this service will be very useful for you!

Request competition analysis

So, the requests have been collected. Now you need to analyze the competition for each of them in order to understand how difficult it will be to promote this or that keyword.

Personally, when I create a new website, I first of all try to write articles for queries that have low competition. This will allow you to bring your site to fairly good traffic in a short time and with minimal investment. At least in such areas as construction and medicine, it is possible to reach 500-1000 visitors in 2.5 months. I’m generally silent about women’s issues.

Let's see how you can analyze competition manually and automatically.

Manual method of competition analysis

We enter the desired keyword in the search and look at the T0P-10 (and if necessary, T0P-20) of sites that are in the search results.

The most basic parameters you need to look at are:

The number of main pages in the TOP (if you are promoting the internal page of the site, and your competitors mainly have main pages in the TOP, then most likely you will not be able to overtake them);

The number of direct occurrences of the keyword in the title of the page.

For queries such as “website promotion”, “how to lose weight”, “how to build a house” there is unrealistically high competition (there are many main pages in the TOP with a direct inclusion of the keyword in the Title), so it is not worth promoting on them. But if you search for the query “how to build a house from foam blocks with your own hands with a basement,” then you will have a better chance of getting into the TOP. Therefore, I once again focus my attention on the fact that you need to promote queries that consist of 3 or more words.

Also, if you analyze search results in Yandex, you can pay attention to the TCI of sites (the higher, the harder it will be to overtake them, because a high TCI most often indicates a large link mass of the site), whether they are in the Yandex Catalog (sites with the Yandex Catalog have greater trust), its age (search engines like age-related sites more).

If you analyze the TOP sites in Google, pay attention to the same parameters that I wrote about above, only in this case, instead of the Yandex Catalog there will be a DMOZ directory, and instead of the TCI indicator there will be a PR indicator (if in the TOP the site pages have a PR of 3 to 10, it won’t be easy to overtake them).

I recommend analyzing sites using the plugin. It shows all the information about the competitor's website:

An automatic way to analyze request competition

If there are a lot of requests, then you can use programs that will do all the work for you hundreds of times faster. In this case, you can use Sloboeb or Key Collector.

Previously, when analyzing competitors, I used the “KEI” indicator (competition indicator). This function is available in Key Collector and Slovoyobe.

In Slovoeb, the KEI indicator simply shows the total number of sites in the search results for a particular request.

In this regard, Key Collector has an advantage, since it has the ability to independently set a formula to calculate the KEI parameter. To do this, go to Program Settings – KEI:

In the “Formula for calculating KEI 1” insert:

(KEI_YandexMainPagesCount * KEI_YandexMainPagesCount * KEI_YandexMainPagesCount) + (KEI_YandexTitlesCount * KEI_YandexTitlesCount * KEI_YandexTitlesCount)

In the “Formula for calculating KEI 2” insert:

(KEI_GoogleMainPagesCount * KEI_GoogleMainPagesCount * KEI_GoogleMainPagesCount) + (KEI_GoogleTitlesCount * KEI_GoogleTitlesCount * KEI_GoogleTitlesCount)

This formula takes into account the number of main pages in the search results for a given keyword and the number of pages in the TOP 10 in which this key phrase is included in the title of the page. Thus, you can obtain more or less objective competition data for each request.

In this case, the smaller the request KEI, the better. The best keywords will be with KEI=0 (if they have at least some traffic, of course).

Click on the data collection buttons for Yandex and Google:

Then click on this button:

In the KEI 1 and KEI 2 column, data on KEI queries for Yandex and Google will appear
respectively. Let's sort the queries in ascending order of the KEI 1 column:

As you can see, among the selected queries there are some for which promotion should not pose any special problems. In less competitive topics, to bring a similar request to the TOP 10, you just need to write a good optimized article. And you won’t need to buy external links to promote it!

As I said above, I have used the KEI indicator before. Now, to assess the competition, I just need to get the number of main pages in the TOP and the number of occurrences of the keyword in the Title of the page. Key Collector has a similar function:

After that, I sort the requests by the column “Number of main pages in Yandex PS” and make sure that according to this parameter there are no more than 2 main pages in the TOP and as few occurrences in the headings as possible. After the NA has been compiled for all these requests, I reduce the filter parameters. Thus, articles under NK requests will be published first on the site, and articles under SC and VK requests will be published last.

After all the most interesting queries have been collected and grouped (I’ll talk about grouping below), click on the “Export data” button and save them to a file. I usually include the following parameters in the export file: Yandex frequency with “!” in a given region, the number of main pages of sites and the number of occurrences of the keyword in the headings.

Tip: Kay Collector sometimes does not quite correctly display the number of occurrences of requests in the headers. Therefore, it is advisable to additionally enter these queries in Yandex and look at the results manually.

You can also evaluate the competitiveness of search queries using a free

Grouping requests

After all the requests have been selected, the time comes for the rather boring and monotonous work of grouping requests. You need to select similar queries that can be combined into one small group and promoted within one page.

For example, if your list contains the following queries: “how to learn to do push-ups”, “how to learn to do push-ups at home”, “how to teach a girl to do push-ups”. Similar requests can be combined into one group and one large optimized article can be written for it.

To speed up the grouping process, parse keywords in parts (for example, if you have a site about fitness, then during parsing, break keywords into groups that will include queries related to the neck, arms, back, chest, abs, legs and etc.). This will make your work much easier!

If the received groups contain a small number of requests, then you can stop there. And when you end up with a list of several dozen, or even hundreds of queries, you can try the following methods.

Working with stop words

Key Collector has the ability to specify stop words that can be used to mark unwanted keywords in the resulting query table. Such queries are usually removed from the semantic core.

In addition to removing unwanted queries, this function can also be used to search for all the necessary word forms by a given key.

Specify the required key:

In the table with queries, all word forms of the specified key will be highlighted:

We transfer them to a new tab and there we manually work with all requests.

Filtering for the "Phrase" field

You can find all word forms of a given keyword using the filtering settings for the “Phrase” field.

Let's try to find all queries that include the word “bars”:

As a result we get:

Group Analysis Tool

This is the most convenient tool for grouping phrases and further manually filtering them.

Go to the “Data” - “Group Analysis” tab:

And this is what is displayed if you open any of these groups:

By marking any group of phrases in this window, you simultaneously mark or unmark the phrase in the main table with all queries.

In any case, you cannot do without manual work. But, thanks to Kay Collector, part of the work (and not a small one!) has already been done, and this, at least a little, simplifies the process of compiling the website’s SYNOPSIS.

Once you've manually processed all the requests, you should end up with something like this:

How to find a low-competition, profitable niche?

First of all, you must decide for yourself how you will make money on your site. Many novice webmasters make the same stupid mistake - they first create websites on a topic that they like (for example, after buying an iPhone or iPad, everyone immediately runs to make another “Apple-themed” website), and then they begin to understand that The competition in this niche is very high and it will be almost impossible for their shit site to get to the top. Or they create a culinary website because they like to cook, and then realize with horror that they do not know how to monetize such traffic.

Before creating any website, immediately decide how you will monetize the project. If you have an entertainment theme, then teaser advertising is most likely suitable for you. For commercial niches (in which something is sold), contextual and banner advertising are perfect.

I would like to immediately dissuade you from creating websites on general topics. Sites that write “everything about everything” are now not so easy to promote, because the TOPs have long been occupied by well-known trust portals. It will be more cost-effective to create a narrowly themed website that will outstrip all competitors in a short time and gain a permanent position in the TOP. I'm telling you this from personal experience.

My narrowly themed medical website already 4 months after its creation ranks first in Google and Yandex, having overtaken the largest medical general portals.

Promoting narrowly themed sites is EASIER and FASTER!

Let's move on to choosing a commercial niche for the site on which we will make money from contextual advertising. At this stage, you need to adhere to 3 criteria:

When assessing competition in a certain niche, I use a search in the Moscow region, where competition is usually higher. To do this, specify “Moscow” in the region settings in your Yandex account:

The exception is cases when the site is made for a specific region - then you need to look at competitors specifically for this region.

The main signs of low competition in search results are as follows:

Lack of complete answers to the query (irrelevant results).

There are no more than 2-3 main pages in the TOP (they are also called “faces”). A larger number of “faces” means that the entire site is purposefully promoted for this request. In this case, it will be much more difficult to promote the internal page of your site.

Inaccurate phrases in the snippet indicate that the required information is simply not available on competitors’ websites or that their pages are not optimized for this request at all.

A small number of large thematic portals. If, for many requests from your future network, there are many large portals and highly specialized sites in the TOP, you need to understand that the niche has already been formed, and the competition there is very high.

More than 10,000 requests per month based on base frequency. This criterion means that you should not “narrow” the subject of the site too much. The niche must have a sufficient amount of traffic from which you can make money in context. Therefore, the main request in the selected topic must have at least 10,000 requests per month according to Wordstat (without quotes and taking into account the region!). Otherwise, you will need to expand the topic a little.

You can also roughly estimate the amount of traffic in a niche using statistics on traffic to sites that occupy first positions in the TOP. If these statistics are closed for them, then use

Most seasoned MFA professionals look for niches for their sites not in this way, but using the Micro Niche Finder service or program.

The latter, by the way, shows the SOC parameter (0-100 - the request will reach the TOP only on internal optimization, 100-2000 - average competition). You can safely select queries with SOC less than 300.

In the screenshot below you can see not only the frequency of queries, but also the average cost per click and site position for queries:

There is also a useful feature called “Potential Ad Buyers”:

You can also simply enter the query you are interested in and analyze it and similar queries:

As you can see, the whole picture is in front of you, all that remains is to check the competition for each of the requests and select the most interesting of them.

We estimate the cost of bids for YAN sites

Now let's look at how to analyze the cost of bids for YAN sites.

Let's enter the queries that interest us:

As a result we get:

Attention! Let's look at the rate of guaranteed impressions! The actual approximate cost per click will be 4 times less than that shown by the service.

It turns out that if you display a page about the treatment of sinusitis in the TOP and visitors click on the advertisement, then the cost of 1 click on the YAN advertisement will be 12 rubles (1.55/4 = $0.39).

If you select the “Moscow and region” region, the bids will be even higher.

That is why it is so important to take first places in the TOP in this particular region.

Please note that when analyzing queries, you do not need to take into account commercial queries (for example, “buy a table”). For content projects, it is necessary to analyze and promote information queries (“how to make a table with your own hands”, “how to choose a table for the office”, “where to buy a table”, etc.).

Where to look for queries to select a niche?

1. Start brainstorming and write down all your interests.

2. Write what inspires you in life (it could be a dream, or a relationship, lifestyle, etc.).

3. Look around and write down all the things that surround you (literally put everything in a notebook: a pen, a light bulb, wallpaper, a sofa, an armchair, paintings).

4. What is your occupation? Maybe you work in a factory behind a machine or in a hospital as an ENT doctor? Write down all the things you use at work in a notebook.

5. What are you good at? Write this down too.

6. Look through advertisements in newspapers, magazines, advertisements on websites, and spam emails. Perhaps they offer something interesting that you could make a website about.

Find sites there with 1000-3000 visitors per day. Look at the topics of these sites and write down the most frequent queries that bring them traffic. Perhaps there is something interesting there.

I will give a selection of one narrowly thematic niche.

So, I became interested in such a disease as “heel spur”. This is a very narrow micro-niche, for which you can make a small website of 30 pages. Ideally, I advise you to look for broader niches for which you could make a website of 100 or more pages.

Open and check the frequency of this request (we won’t specify anything in the region settings!):

Great! The frequency of the main request is more than 10,000. There is traffic in this niche.

Now I want to check the competition for queries for which the site will be promoted (“heel spur treatment”, “heel spur treatment”, “heel spur how to treat”)

I switch to Yandex, specify the “Moscow” region in the settings and this is what I get:

In the TOP there are many internal pages of general thematic medical sites, which will not be difficult for us to overtake (although this will happen no earlier than in 3-4 months) and there is also one site that is tailored to the topic of heel spur treatment.

The wnopa website is a 10-page shit website (which, however, has PR=3) with a traffic of 500 visitors. A similar site can also be overtaken if you make a better site and promote the main page for this request.

Having analyzed the TOP for each of the requests in this way, we can come to the conclusion that this microniche has not yet been occupied.

Now there is the last stage - checking the bids.

We go to and enter our requests there (there is no need to select a region, since our site will be visited by Russian-speaking people from all over the world)

Now I find the arithmetic average of all guaranteed impressions indicators. It turns out to be $0.812. We divide this indicator by 4, which gives an average of $0.2 (6 rubles) per click. For medical topics, this is, in principle, a normal indicator, but if you wish, you can find topics with better bids.

Is it worth choosing a similar niche for a website? It's up to you to decide. I would look for broader niches that have more traffic and better cost per click.

If you really want to find a good theme, I highly recommend you follow the next step!

Make a table with the following columns (I indicated the data approximately):

In this way, you must write out at least 25 requests per day! Spend 2 to 5 days searching for a niche and you will eventually have plenty to choose from!

Choosing a niche is one of the most important steps in creating a website, and if you are careless about it, you can assume that in the future you will simply throw away your time and money.

How to write the right optimized article

When I write an article optimized for any request, I first of all think about making it useful and interesting to site visitors. Plus, I try to format the text so that it is pleasant to read. In my understanding, an ideally formatted text should contain:

1. A bright, eye-catching headline. It is the title that first attracts the user when he views the search engine results page. Please note that the title must be completely relevant to what is written in the text!

2. Lack of “water” in the text. I think that few people like it when something that could be written in 2 words is presented on several sheets of text.

3. Simplify paragraphs and sentences. To make the text easier to read, be sure to break large sentences into short ones that fit on one line. Simplify complex turns. It’s not for nothing that in all newspapers and magazines the text is published in small sentences in columns. Thus, the reader will perceive the text better, and his eyes will not get tired so quickly.

Paragraphs in the text should also not be large. A text that alternates between small and medium-sized paragraphs will be best perceived. An ideal paragraph would be 4-5 lines.

4. Use subheadings (h2, h3, h4) in the text. A person needs 2 seconds to “scan” an open page and decide whether to read it or not. During this time, he runs his eyes across the page and identifies for himself the most important elements that help him understand what the text is about. Subheadings are precisely the tool with which you can grab the visitor’s attention.

To make the text structured and easier to understand, use one subheading for every 2-3 paragraphs.

Please note that these tags must be used in order (h2 to h4). Eg:

You don't need to do this:

<И3>Subtitle<И3>

<И3>Subtitle<И3>

<И2>Subtitle<И2>

But this is how you can:

<И2>Subtitle<И2>

<И3>Subtitle<И3>

<И4>Subtitle<И4>

You can also do this as follows:

<И2>How to pump up your abs<И2>

<И3>How to pump up your abs at home<И3> <И3>How to pump up your abs on the horizontal bar<И3> <И2>How to pump up your biceps<И2> <И3>How to pump up biceps without exercise equipment<И3>

Keywords should be used to a minimum in the texts (no more than 2-3 times, depending on the size of the article). The text should be as natural as possible!

Forget about putting keywords in bold and italics. This is a clear sign of over-optimization and Yandex really doesn’t like it. In this way, highlight only important sentences, thoughts, ideas (at the same time, try not to include keywords in them).

5. Use bulleted and numbered lists, quotes in all your texts (do not confuse them with quotes and aphorisms of famous people! We only include important thoughts, ideas, tips), pictures (with the alt, title and signature attributes filled in). Insert interesting thematic videos into articles, this has a very good effect on the length of time visitors stay on the site.

6. If the article is very large, then it makes sense to navigate through the text using content and anchor links.

After you have made all the necessary changes to the text, run your eyes over it again. Isn't it hard to read? Maybe some proposals need to be made even smaller? Or do you need to change the font to a more readable one?

Only after you are satisfied with all the changes in the text, you can fill out the title, description and keywords and send it for publication.

Title requirements

Title is the title in the snippet that is displayed in search results. The title in the title and the title in the article (the one that is highlighted with the tag

) must be different from each other and at the same time, they must contain the main keyword (or several keywords).

Description requirements

The description should be vivid and interesting. It needs to briefly convey the meaning of the article and do not forget to mention the necessary keywords.

Keywords requirements

Enter the promoted keywords here separated by a space. No more than 10 pieces.

So, this is how I write optimized articles.

First of all, I select the queries that I will use in the article. For example, let’s take the main request “how to learn to do push-ups.” Enter it into wordstat.yandex.ru

Wordstat displayed 14 queries. I will try, if possible, to use most of them in the article (it is enough to mention each keyword once). Keywords can and should be declined, rearranged, replaced with synonyms, and made as varied as possible. Search engines that understand Russian (especially Yandex) perfectly recognize synonyms and take them into account when ranking.

I try to determine the size of an article by the average size of articles from the TOP.

For example, if in the TOP 10 there are 7 sites in the search results, the text size of which is mainly 4,000-7,000 characters, and 3 sites in which this indicator is very low (or high), I simply do not take into account the last 3 attention.

Here is the approximate html structure of an article optimized for the query “how to learn to do push-ups”:

……………………………

How can a girl learn to do 100 or more push-ups in 2 months?

……………………………

What should proper nutrition be like?

……………………………

Daily regime

……………………………
……………………………

Training program

Some optimizers advise that you must include the main key in the first paragraph. In principle, this is an effective method, but if you have 100 articles on your site, each of which contains a promoted query in the first paragraph, this can have unpleasant consequences.

In any case, I advise you not to get hung up on one specific scheme, experiment, experiment and experiment again. And remember, if you want to make a site that search robots will love, make sure that real people love it!

How to increase website traffic using a simple manipulation of linking?

I think that each of you has seen many different linking schemes on sites, and I am more than sure that you did not attach much importance to these schemes. But in vain. Let's look at the first linking scheme, which is found on most sites. True, it still lacks links from internal pages to the main page, but this is not so important. The important thing is that in this case, on the site the greatest weight will be given to the category pages (which most often are not promoted and are closed from indexing in robots.txt!) and the main page of the site. Pages with posts in this case have the least weight:

Now let's think logically: optimizers who promote content projects promote which pages first? That's right - pages with posts (in our case, these are 3rd level pages). So why, when promoting a site with external factors (links), do we so often forget about equally important internal factors (correct linking)?

Now let's look at a diagram in which inner pages will have more weight:

This can be achieved by simply correctly configuring the flow of weight from the top-level pages to the lower, 3rd level pages. Also, do not forget that internal pages will be additionally boosted by external links to other sites.

How to set up this kind of weight flow? It’s very simple - using nofollow noindex tags.

Let's look at a site where you need to upgrade internal pages:

On the main page (1st level): all internal links are open for indexing, external links are closed via nofollow noindex, the link to the site map is open.

On pages with categories (2nd level): links to pages with posts (3rd level) and to the site map are open for indexing; all other links (to the main page, categories, tags, external) are closed via nofollow noindex.

On pages with posts (3rd level): links to pages of the same 3rd level and to the site map are open, all other links (to the main page, categories, tags, external ones) are closed using nofollow noindex.

After such linking was done, the site map received the greatest weight, which evenly distributed it across all internal pages. The inner pages, in turn, increased significantly in weight. The main page and the category page received the least weight, which is exactly what I wanted to achieve.

I will check the weight of the pages using the program. It helps to clearly see the distribution of static weight on the site, concentrate it on promoted pages, and thereby raise your site in search engine results, significantly saving your budget on external links.

Well, now, let's move on to the most interesting thing - the results of such an experiment.

Women's themed website. Changes to the site's weight distribution were made in January of this year. A similar experiment brought +1000 to traffic:

The site is under a Google filter, so the traffic increased only in Yandex:

Do not pay attention to the decline in traffic in May, this is seasonality (May
holidays).

2. The second site dedicated to women, changes were also made in January:

This site, however, is constantly updated and links to it are purchased.

Terms of reference for copywriters for writing articles

Article title: How can greens be useful?

Uniqueness: not lower than 95% by Etxt

Article size: 4,000-6,000 characters without spaces

Keywords:

what are the benefits of greens - the main key

healthy greens

beneficial properties of greens

benefits of greens

Requirements for the article:

The main keyword (listed first) does not have to be in the first paragraph, it just needs to appear in the first half of the text!!!

PROHIBITED: grouping keywords in parts of the text; it is necessary to distribute keywords throughout the text.

Keywords should be highlighted in bold (this is to make it easier for you to check your order)

Keywords need to be entered harmoniously (therefore, you need to use not only direct occurrences of keys, but you can also inflect them, swap words, use synonyms, etc.)

Use subheadings in your text. In subheadings, try not to use direct occurrences of keywords. Make subheadings for each article as varied as possible!

Use no more than 2-3 direct occurrences of the main query in the text!

If in an article you need to use the keywords “how greens are useful” and “how greens are useful for humans,” then by writing the second keyword, you will automatically use the first one. That is, there is no particular need to write a key twice if one already contains an entry of another key.

Sergey Arsentiev

Semantic core or how to choose keywords for a website

I am unlikely to sin against the truth if I say that the basis for successful promotion is the initially correct and effective selection of keywords for which it is profitable to promote the site.

It is impossible to embrace the immensity, so each Internet project is optimized for specific search queries made by users and which exactly correspond to the company’s activities. The totality of all these requests constitutes the so-called “ semantic core of the site».

Compiling a semantic core begins with an analysis of the site’s theme and the selection of approximate expressions by which a potential audience will search for a given Internet project. That is, if a company offers PVC windows, then it is logical to make a list of approximately such queries as “PVC windows”, “plastic windows”, “double glazed windows”, etc. The broader the horizons and experience of the optimizer, the easier it is to do this.

The basic rule that I now successfully use in my work: keywords need to be selected not just for the entire site, but for its specific page.

Why?
Firstly, this is necessary in order not to get confused in hundreds and thousands of key phrases. Personally, for me, there is nothing more tedious and unnatural than pushing a lot of selected keys that correspond to the theme of the entire site onto its individual pages. It is much easier and more correct to select different keys initially for different pages of the site.

Secondly, modern SEO is closely tied to user behavior. That is, you need to strive to ensure that the selected keys correspond as much as possible to the meaning and direction of the text on the posted page. So that the visitor, who then uses this key to go to the site page, receives exactly the information that he wanted to find when entering his key.

100 key queries were found for a website selling PVC windows. When the optimizer began to shove them across the pages, it turned out that the key “PVC photo windows” ended up on the page with the price list for the products offered. It’s just that this page had a couple of pictures with windows, so the optimizer decided that the request was quite suitable for it.

But over time, when the “Portfolio” section was filled out on the site, where dozens of works were presented, it became clear that this request is much better suited for a portfolio, where a whole photo gallery is presented, and not for a regular page with a couple of photos.

In other words, the search query must correspond to the page on the site that best suits it.

Personally, for me, it’s easier to initially start not from the request and the need to push it somewhere on the site, but from the site page, its essence, meaning and purpose for the target visitor, selecting relevant requests for this page.

Of course, in order to initially select keys for specific pages, you need to have a site plan and the structure of its existing or future sections in mind. But as they say, “it’s better to lose a day, then fly in half an hour.” There are exceptions to this rule, but in most cases, selecting queries for a specific page simplifies the optimizer’s task several times.

Where to select search queries?

So, you have an approximate list of requests for certain pages of the site. Now we need to evaluate their quantitative characteristics - whether these queries are searched in principle and if so, which queries are searched more often, which ones less often and in what particular word form.

Search engines have such statistics, which methodically record and store all search queries from users. And they provide free services with which you can extract this data.

If you enter search queries of interest into these services, you can find out how many times in a particular region visitors entered them into the search form. Now let’s take a closer look at the most popular free service for selecting keys for the semantic core from Yandex.

How to select keywords in Yandex?

Compared to the Google service, the Yandex service is, of course, as simple as a rake. But it is precisely this simplicity that makes it an even more convenient tool for an SEO optimizer than the “sophisticated” Google. At least when I started my work as an optimizer, it was easier and more familiar for me to use. So I will describe it in detail.

To obtain keyword statistics, you will need an active Yandex account. Under it, you need to go to the statistics page http://wordstat.yandex.ru/, select a region and enter the search query of interest in the appropriate field. At the same time, it is important to use certain formatting of the key query in order to understand how many search queries there were for this particular phrase. To do this, the query is placed in quotation marks and an exclamation point is placed before each word. Like this: "!PVC windows".

If you enter a query without quotes, it will be a broad match. That is, the number of requests will include “PVC windows” and “PVC windows in Moscow” and “buy PVC windows” - that is, those requests for which promotion may not be planned!

Let's take a closer look at the types of keyword matches in Yandex:

Request Format	Description	What queries will be included in the statistics?	Quantity per month
PVC windows	The total number of queries for a given phrase will include all queries with these words in any word form, in any location and with any other words in the search phrase.	PVC windows pvc windows in Moscow installation of pvc windows	136 000
!PVC windows	An exclamation point before a word means that the word must appear only in the specified word form and in the same sequence relative to each other. However, other words may be present in queries.	PVC windows in Moscow pvc window installer pvc windows video	73 000
"PVC windows"	Only those queries that consist only of words in quotation marks, in the same sequence, but in any word form, will be included.	PVC windows PVC windows	10 000
"!PVC windows"	The most exact match to the search query: words in a different word form, sequence, or any other words in the search query are not included.	PVC windows	9 699

Obviously, only the last option is suitable for SEO needs: “!windows!pvc”, because promotion is not a matter in which the word form of the key is not particularly important. In a regular search for queries that differ even by more than one letter, different sites may be shown in the TOP. Therefore, all the efforts of the optimizer should be concentrated on the most specific search keys with an exact match of each letter to the most frequent queries.

One person wrote that he wanted to promote the site for the request “promo games.” Apparently, he saw that such a request occurs about 2600 times a month. But he was not convinced that this particular word form was the most suitable for him. And if you look, then:

Thus, the client wanted to invest in promoting a site on demand, which could bring such a small number of visitors that it would hardly be effective for him.

Maybe he thought that if he ranks for the query “promo games”, he will automatically rank for all queries with these words. But SEO is not advertising, it is the work of optimizing a website for specific words, in which even the ending matters. Thus, I repeat once again that the selection of key queries for the site is the most important stage that affects the effectiveness of the entire promotion. And you need to do this correctly, otherwise there may be no result from SEO at all.

Expand the semantic core.

Agree that different people may search for such a product as PVC windows in different ways. Someone will enter into the search “PVC windows”, someone else “PVC windows”, someone else “plastic windows” and so on. The optimizer’s task is to come up with as many different variants of similar queries as possible and analyze each of them. It often happens that the option that didn’t immediately come to mind is the one that is recruited the most, and the competition for it is minimal!

But what to do if your imagination is no longer enough to come up with new options for search queries? Then pay attention to the right window of Yandex statistics. It displays a list of queries that people from the client’s target audience have already asked in the search. There may be a lot of useful information in the form of different types of similar search queries.

After this, you need to again enter each of these queries into Yandex statistics and see how many times it was searched for in the same word form, that is, for example, “!plastic!windows”.

By the way, this way you will identify only high- and mid-frequency queries. And if the task is to advance through cheap low-frequency queries (read more), then you need to break each selected key into subkeys, for which you can simply click on a suitable search query and various options for this search query will appear.

If there are usually only a few high-frequency queries, dozens of mid-frequency queries, then hundreds and thousands of low-frequency queries. Then the cycle repeats: you will need to enter each low-frequency query in an unchanged word form (suddenly almost no one is looking for it!) and determine the most popular of them.

You can already imagine the amount of work, right? Therefore, I highly recommend not doing this manually, but rather automating routine operations as much as possible.

A program for compiling a semantic core.

In addition to manually selecting search queries, you can use the paid software product KeyCollector, which allows you to automate the collection process, increasing its efficiency without exaggeration hundreds of times.

If you are serious about making money by providing SEO services, you simply need to fork out the cash and purchase KeyCollector, otherwise your business will be ineffective.

How to select keywords for SEO promotion (video)

However, if you decide to try the free version of Keycollector, you can download it here: click on any icon of the social network you are using and a link will open. It's simple and free.

Hi all! When you run a blog or content site, there is always a need to compile a semantic core for the site, cluster or article. For convenience and consistency, it is better to work with the semantic core according to a well-established scheme.

In this article we'll consider:

• how the semantic core for writing an article is collected;
• what services can and should be used;
• how to correctly enter keys into an article;
• my personal experience in selecting SY.

How to collect a semantic core online

1. First of all, we need to use the service from Yandex - . Here we will make an initial selection of possible keys.

In this article, I will collect SYNOPSIS on the topic “how to lay laminate flooring.” In a similar way, you can use these instructions for compiling a semantic core for any topic.

1. If our article is on the topic “how to lay laminate flooring,” then we will enter this query to obtain information about the frequency in wordstat.yandex.ru.

As we can see, in addition to the target request, we received many similar requests containing the phrase "lay laminate", here you can eliminate all unnecessary things, all the keys that will not be discussed in our article. For example, we will not write about similar topics such as “how much does it cost to lay laminate flooring”, “The laminate was laid unevenly” and so on.

To get rid of many obviously inappropriate requests, I recommend using operator "-" (minus).

1. We substitute a minus, and after it all the words are off topic.

1. Now, select everything that remains and copy the queries into Notepad or Word.

1. Having inserted everything into the Word file, we go through it with our eyes and delete everything that will not be disclosed in our article. If there are false queries, then a keyboard shortcut will help you check for their presence in the document Ctrl+F, a window opens (sidebar on the left), where we enter the search words.

The first part of the work is done, now we can check our Yandex semantic core template for pure frequency, the quote operator will help us with this.

If there are few words, then this can be easily done directly in Wordstat by substituting the phrase in quotation marks and finding the pure frequency (quotes indicate how many requests there were with the content of this particular phrase, without additional words). And if, as in our example of the semantic core of an article or website, there are a lot of words, then it is better to automate this work using the Mutagen service.

To get rid of numbers use the following steps with a Word document.

1. Ctrl+A— to highlight the entire contents of the document.
2. Ctrl+H— calls up a window for replacing characters.
3. Substitute in the first line ^# and click “replace all” this combination will remove all numbers from our document.

Be careful with keys that contain numbers, the above steps may change the key.

Selection of semantic core for a website/article online

So, I wrote in detail about the service. Here we will continue learning how to compile a semantic core.

1. We go to the site and use this program to compile a semantic core, since I haven't seen a better alternative.

1. First, let’s parse the pure frequency; for this we go through “Wordstat parser” → “mass parsing”

1. We paste all our selected words from the document into the parser window (Ctrl+C and Ctrl+V) and select “Wordstat Frequency” in quotes.

This process automation costs only 2 kopecks per phrase! If you are selecting a semantic core for an online store, then this approach will save you time for mere pennies!

1. Click send for verification and, as a rule, after 10-40 seconds (depending on the number of words) you will be able to download words that already have a frequency in quotes.

1. The output file has a .CSV extension and is opened in Excel. We begin to filter the data to create an online semantic core.

• We add the third column, it is needed to display competition (in the next step).
• We set a filter on all three columns.
• We filter the “frequency” column “in descending order”.
• Everything that has frequency below 10 - deleted.

We received a list of keys, which we can use in the text of the article, but first it is necessary to check them for competition. After all, if this topic has been covered far and wide on the Internet, does it make sense to write an article on this key? The likelihood that our article on it will reach the TOP is extremely low!

1. To check the competition of the online semantic core, go to “competition”.

1. We begin to check each request and substitute the resulting competition value into the corresponding column in our Excel file.

Price checking one key phrase is 30 kopecks.

After the first top-up, you will have access to 10 free checks every day.

To determine phrases that are worth writing an article take the best frequency-competition ratio.

It's worth writing an article:

• frequency not less than 300;
• competition is not higher than 12 (less is better).

Compiling a semantic core online using low-competition phrases will give you traffic. If you have a new website, it will not appear immediately, you will have to wait from 4 to 8 months.

In almost any topic you can find MF and HF with low competition from 1 to 5; for such keys it is realistic to receive 50 visitors per day.

To cluster semantic core queries, use , they will help you create the correct site structure.

Where to insert the semantic core in the text

After collecting the semantic core for the site, it’s time to write key phrases into the text and here are some recommendations for beginners and those who “don’t believe” in the existence of search traffic.

Rules for inserting keywords into the text

• You only need to use the key once;
• words can be declined according to cases, changed places;
• you can dilute phrases with other words, it’s good when all the key phrases are diluted and readable;
• you can remove/replace prepositions and question words (how, what, why, etc.);
• You can insert the signs “-” and “:” into the phrase

For example:
there is a key: “How to lay laminate flooring with your own hands” in text it might look like this: “...in order to lay laminate boards with our own hands we will need...” or so, “Everyone who tried to lay laminate flooring with their own hands...”.

Some phrases already contain others, for example the phrase:
“how to lay laminate flooring in the kitchen with your own hands” already contains a key phrase “how to lay laminate flooring with your own hands”. In this case, it is allowed to omit the second one, since it is already present in the text. But if there are not enough keys, then it is better to also use it in the text either in the Title or in the Description.

• if you can’t fit a phrase into the text, then leave it, don’t do it (at least two phrases can be used in the Title and Description and not write them in the body of the article);
• Necessarily, one phrase is the title of the article (the fattest frequency is competition), in the language of webmasters, this is H1; it is enough to use this phrase once in the body of the text.

Contraindications for writing keys

• You cannot separate the key phrase with a comma (only as a last resort) or a period;
• You cannot enter the key into the text directly so that it will not look natural (not readable);

Page title and description

Title and Description- this is the title and description of the page, they are not visible in the article, they are shown by Yandex and Google when search results are displayed to the user.

Writing rules:

• the title and description should be “journalistic”, that is, attractive for clicking;
• contain thematic (relevant to the request) text, for this we enter key phrases (diluted) in the title and description.

Are common character requirements at the plugin All in one SEO pack, the following:

• Title - 60 characters (including spaces).
• Description - 160 characters (including spaces).

You can check your creation or that received from for plagiarism using.

With this, we have dealt with the topic of what to do with the semantic core after compilation. In conclusion, I will share my own experience.

After compiling the semantic core according to the instructions - my experience

You may think that I am trying to sell you something that is not believable. In order not to be unfounded, here is a screenshot of statistics for the last, (but not the only) year of this site, How I managed to rebuild my blog and start getting traffic.

This training in compiling a semantic core, although long, is effective, because in website building the main thing is the right approach and patience!

If you have any questions or criticism, write in the comments, I will be interested, and also share your experience!

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