Internal optimization and promotion of the site, Stage 1. Compiling...

Internal optimization and website promotion

First, the technical condition of the Internet resource is analyzed: identifying and eliminating possible problems that hinder indexing and effective promotion. The site should be adapted for promotion in such a way that the search engines perceive it as a valuable resource for users with unique information. Value the promoted site depends on two components:

• from the content (texts) of the site;

• from the technical state of the site.

Factors that hinder site indexing can be, for example:

• The lack of a robots.txt file, which sets the search path path on the site;

Stages of external and internal search engine optimization and website promotion on the Internet

Fig. 4.7. Stages of external and internal search engine optimization and website promotion

• the absence of a site in the database of the search engine;

• Improper configuration of the site map.

Then a semantic kernel is compiled.

Compiling a list of selling requests (semantic kernel)

Semantic core is a statistically significant set of selling queries (phrases and words) that are used by a motivated target audience to search products, services, information or resources on the Internet. In other words, the semantic core is understood as search queries, for which target visitors will go to the site from search engines. The best selling inquiries are those that reflect the company's unique trade offer , as the visitor will have more loyalty to this company than to others in a comparative buyer's analysis of several sites.

If the site is promoted using graphical promotion (increase in attendance due to internal optimization and the weight of the promoted site), then the number of key phrases significantly increases due to the addition to high-frequency requests of low- and mid-frequency.

The semantic core should also be compiled on the basis of the contents of the optimized site, competitor sites and statistics on search queries. Correct compilation of the semantic core allows to significantly increase the relevance of the site.

Site relevance is the degree to which the text and subject matter of the site match the word or expression specified as the key when searching for information. Relevance indicators are used by search engines to determine the order in which search results are delivered. For example, when searching with Yandex pages containing the expression "cell phones", pages that are more relevant (more consistent with the keyword "cell phones") will be closer to the top of the list of search results.

The algorithm for compiling the semantic kernel includes several stages:

1) compiling the "skeleton (zero version) of the semantic kernel. To realize the first stage, terms are chosen that are used in the information content of the site and in other advertising materials of the company. Also, the terms that could be used in search queries by potential motivated visitors are chosen speculatively. To do this, a selection of search terms is made, divided into three groups:

• user needs;

• products/services;

• brands (brands of manufacturers).

For example, a sample of search terms for different companies can be represented as follows (Table 4.1);

Table 4.1

Compiling a selection of search terms for companies of different lines of business




User Needs



Brands (brands brands)

Search term

Internet -







Buy a phone Samsung

A company that repairs household appliances





Repair of washing machines Bosch

Higher educational institution


second highest economic



Second Higher Economic Education at the Financial University

2) to the resulting semantic "skeleton" the analyst on the basis of research of industrial and commercial activity of the concrete enterprise synonyms and related words are added, contributing to the fullest coverage of the target audience.

For example, for an online store of cell phones, the search query "buy a phone Samsung (Table 4.2) can be supplemented with the following synonyms and related words corresponding to the enterprise's TTP:

• buy a phone Samsung Galaxy,

• buy a Samsung Galaxy phone, etc .;

3) At this stage, the resulting semantic "skeleton" core is built up by really existing queries on the same topic using the online services of search engine statistics "Yandex" ( ), ( ), Google ( trends). Thus, related thematic directions of demand are determined for further research in order to search for sites of similar themes that are clearly visible in search engines, for the subsequent analysis of the competitive situation, placing their advertising or exchanging links. As a result, the first version is produced - the semantic core procurement, which is characterized by a large number of rare users of search services for words and phrases (Table 4.2);

Table 4.2

Adding to the skeleton semantic core of really existing queries from the online search engine service "Yandex"

Search term

Synonyms and related words

Definition of existing queries from the online search engine service "Yandex"


frequency per month

buy phone Samsung

1. buy phone Samsung

buy a phone Samsung


buy a phone Samsung


speakers + for the phone Samsung buy


2. buy phone Samsung Galaxy

buy a phone Samsung Galaxy


buy a phone Samsung galaxy s


buy a phone Samsung galaxy nexus


3. buy phone Samsung Galaxy

buy a Samsung Galaxy phone


phone samsung galaxy 2 to buy


buy phone samsung galaxy



4) frequency analysis. To effectively influence the target consumers, only statistically significant queries should be left from the list obtained. To do this, using the query statistics services, "Yandex.Direct" and " the frequency of mentioning by users of the network in the search engines of the words and phrases that make up the semantics core preparation is calculated. Users of search engines, as a rule, do not differ from each other but use queries, so data on the volume of demand for semantic kernel procurement, obtained with the help of "Yandex", can be extrapolated to the entire Runet with a coefficient of about 2 (it is considered that " Yandex ", currently actively used by 45-55% of users Runet).

Thus, the total frequency of all queries that are included in the semantic query kernel by Yandex.Direct "is calculated. and is reduced to 100% of all requests in RuNet, taking into account the extrapolation factor. Then low-frequency requests are excluded from the semantic kernel. As a result, the final list of search queries entering the semantic core is formed.

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