Snippets analysis using the Labrika service. How to shoot snippets Main assumptions and limitations of the considered approach

From Texterra

Snippets are different within the same search engine, even if the keywords are very slightly different from each other.

It is very useful to watch from time to time to review your snippets and correct something.

This is the title and description of your site in the SERPs:

Yandex most often takes the snippet from the text, Google - from the Meta Description.

But it can be other options, including combined ones - a piece from the Meta Description + a piece from the text.

The keywords found in the Meta Description do not directly affect the ranking of the page. But they influence indirectly - due to more effective and more clickable snippets, behavioral factors improve and positions grow.

All this suggests that the Meta Description should be carefully designed, be noticeable, interesting, make you want to click, include the most important keywords, toponyms, commercial markers. And also that your text should also be interesting, clear, useful, practical, and not watery “nothing about nothing” SEO text that suits absolutely any other site.

See good and bad moments in your snippets
When analyzing your snippets, you can and should write down the most successful moments that got into them - facts, figures, something else, and use them consciously more often in texts, Title and Meta Description.

And vice versa, see the most ineffective snippets and edit the texts on this page, including making them more interesting, clear, adding facts, figures, benefits, your USP.

And you can analyze snippets of competitors in the TOP10-20 in the same way - either mass-shoot snippets of specific competitors, or parse TOP snippets to some depth for specific queries. Then see what they have the best. And to do at least not worse.

Make sure the visitor realizes his intent
And another important point is that when reading the snippet, the user sees that on your page he can realize his search intent (intention). Looking through your snippets, you need to be aware of WHAT the target audience is looking for for this query, what drives them.

And does she see something in the snippets that would interest her?

I very often come across sites during audits where snippets contain some general texts. A person came to look for the best conditions for buying a spare part for a foreign car, and in the snippet and on the page they are told the story of the creation of this brand of car.

Using it is simple:

A. We select the mode: “parsing the issue” or “collection for a given site”.

b. Specify requests.

V. Select region and search engine.

d. Specify the depth for analysis: TOP-1/5/10/20 or 50.

We get the result. It can be exported to .CSV. History is saved.

Pay attention to the presence of special characters and emoji. The attractiveness of the snippet increases the click-through rate. Analyze, increase CTR and improve behavioral factors.

Example 1. Parse the entire TOP in depth:

Example 2. Parsing snippets for a specific URL:

And here below we see that some of the queries from this group have a different relevant page for me. .

2. Topvisor

You can also view snippets of competitors - any URL that you need to set in a new project.

See snippets in the "Positions" section in block display. They are removed every time you check the positions in the selected search engines for the query.

But (!) first, in the "Settings" section, you need to set "Collect snippets" (the cost of removing positions will increase by 0.01 rubles for each key request).

Briefly, the algorithm is as follows:

1. "Settings" - select "collect snippets", select search engines and region.

2. "Kernel" - set the core, cluster, make sure that all groups are turned on - with a green circle next to them (after clustering, they automatically turn off or you can accidentally click on the circle and turn off the group). .

3. "Positions". Click on the green arrow to remove or update positions. Select block display. We choose one PS or comparison of search engines. .

Section "Settings":

In the "Positions" section, remove positions. And switch to "block" mode:

Here above, we, for example, see that the old “SEO in 2015” tag clings to the snippet. And somehow it doesn’t look very good for those who are looking for relevant materials and need to change the tag to SEO in 2018 or remove it altogether.

It is very convenient that you can immediately see clearly:

— or how snippets changed over time for each request in one search engine
— what your snippets look like for a specific query in Google and Yandex side by side.

In the same place, you can immediately clearly see which relevant page you have for this request, where the snippet comes from, what words to correct, and so on.

Here, look:

1. We see the history of my snippets in one search engine - in Yandex for 2 dates. There was a change in the relevant page - you can see by a radical change in the title of the snippet (and hovering over the title, we will see at the bottom of the browser what link is behind it. By clicking on the title, we will go to the desired page). We see a change in the snippet and a jump in positions up.

Next, you need to decide which of the pages is the target (where we want to lead people) and try to either fix the current relevant page for the desired request, or transfer to another page. This is done primarily by increasing the external and internal link with the right words to the desired page and adjusting the optimization and page texts - remove the occurrences of the desired query from the non-targeted one, moderately add to the target one (but without overspam).

Here we also see that the snippet can be different in the same PS, when the word “how” just disappears from the request. And we clearly see that Google is quite stable in that it takes the description of the snippet from the Meta Description, and Yandex pulls it from the text. And we also see that the Meta Description of the article is not very interesting and not attractive for clicking.

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The analysis of implicit user preferences, expressed in terms of link clicks and page view duration, is the most important factor in ranking documents in search results or, for example, displaying ads and recommending news. Click analysis algorithms are well studied. But is it possible to learn something more about a person's individual preferences using more information about his behavior on the site? It turns out that the mouse movement trajectory allows you to find out which fragments of the document being viewed are of interest to the user.

This issue was the subject of a study conducted by me, Mikhail Ageev, together with Dmitry Lagun and Evgeny Agishtein at the Emory Intelligent Information Access Lab at Emory University.

We studied data collection methods and algorithms for analyzing user behavior based on mouse movements, as well as the possibilities of applying these methods in practice. They can significantly improve the formation of snippets (annotations) of documents in the search results. The work with the description of these algorithms was awarded the diploma "Best Paper Shortlisted Nominee" at the international conference ACM SIGIR in 2013. Later, I presented a report on the results of the work done in the framework of scientific and technical seminars in Yandex. You can find his summary under the cut.

Snippets are an essential part of any search engine. They help users search for information, and the usability of a search engine depends on their quality. A good snippet should be readable, showing parts of the document that match the user's query. Ideally, the snippet should contain a direct answer to the user's question or an indication that the answer is in the document.

The general principle is that the query text is matched against the document text, highlighting the most relevant sentences containing query words or query extensions. The formula for calculating the most relevant fragments takes into account matches with the query. The density of the text, the location of the text, the structure of the document are taken into account. However, for highly relevant documents that pop up at the top of the search results, text factors are often not enough. The text may contain multiple words from the query, and it is impossible to determine which fragments of the text answer the user's question based on textual information alone. Therefore, additional factors are required.

When viewing a page, the user's attention is distributed unevenly. The main attention is paid to those fragments that contain the required information.

We conducted experiments using equipment that tracks the movements of the eye pupil with an accuracy of several tens of pixels. Here is an example of the distribution of a heat map of the pupil trajectory of a user who was looking for an answer to the question of how many dead pixels an iPad 3 should have in order to be able to replace it under warranty. He enters a query that takes him to an Apple Community Forums page with a similar question. On the page, the words from the query occur repeatedly, but the user focuses on the fragment that actually contains the answer, which is visible on the heat map.

If we could track and analyze the movements of the pupils of more users, we could only highlight the ideal snippets for various queries based on this data. The problem is that users do not have eye-tracking tools installed, so you need to look for other ways to get the necessary information.

When viewing web documents, users usually move the mouse, scroll pages. In their 2010 paper, K. Guo and E. Agishtein note that pupil movements can be predicted from a trajectory with an accuracy of 150 pixels and a recall of 70%.

Below is a heat map of mouse movements when viewing a document found by query . It can be seen that the greatest activity can be traced precisely on the fragment containing information about the most severe droughts in the United States, it is from this fragment that an ideal snippet can be formed.

The idea behind our research is that mouse movement data can be collected using a JavaScript API that works in most browsers. Based on user behavior, we can predict which snippets contain relevant information, and then use this data to improve the quality of snippets. In order to implement and test this idea, several problems need to be solved. First, you need to understand how to collect realistic and large enough data about user behavior behind the search results page. Secondly, you need to learn how to determine the fragments that are most interesting to the user by mouse movements. Users have different habits: some like to highlight readable text or just hover over it with the mouse, while others open a document and read it from top to bottom, occasionally scrolling down. However, users may have different browsers and input devices. In addition, the amount of mouse movement data is two orders of magnitude larger than the amount of click data. There is also the task of combining behavioral factors with traditional textual ones.

How to collect data

To collect data, we used the infrastructure we developed in 2011. The main idea is to create a game similar to the Yandex Search Cup. The player is given a goal in a limited time to find the answer to the question on the Internet using a search engine. The player finds the answer and sends it to us along with the URL of the page where it was found. Participants are selected through Amazon Mechanical Turk. Each game consists of 12 questions. For participation in a game lasting approximately forty minutes, a guaranteed payment of $ 1 is expected. The top 25% of players get another dollar each. This is a fairly cheap way to collect data, which at the same time gives a wide variety of users from different parts of the world. Questions were taken from Wiki.answers.com, Yahoo! Answers and the like. The main condition was the lack of ready-made answers on these sites themselves. At the same time, the questions should not be too simple, but have a clear short answer that can be found on the Internet. To cut off robots and unscrupulous participants, it was necessary to implement several stages of checking the quality of the results. Firstly, there is a captcha at the login, secondly, the user needs to answer 1-2 trivial questions, and thirdly, the user must complete the task using our proxy server, thanks to which we can verify that he is really asked questions to the search engine and visited the page with the answer.

Using the standard modules for the Apache HTTP server mod_proxy_html and mod_sed, we implemented the proxying of all calls to search services. The user visited our page, saw the familiar search engine interface, but all the links there were replaced with ours. By clicking on such a link, the user got to the desired page, but our behavior-tracking JavaScript code was already embedded in it.

There is a small problem with logging: the position of the mouse is represented by coordinates in the browser window, and the coordinates of the text in it depend on the screen resolution, version, and settings. We also need an exact binding to the text. Accordingly, we need to calculate the coordinates of each word on the client and store this information on the server.

The results of the experiments were the following data:

In terms of statistics, the data looks like this:

The code and collected data are freely available at this link.

Predicting fragments that are of interest to users

To highlight snippets, the text is divided into fragments of five words. For each fragment, six behavioral factors are distinguished:

The duration of the cursor over the fragment;
The duration of the cursor stay next to the fragment (±100px);
Average mouse speed over the fragment;
Average mouse speed next to the fragment;
Fragment display time in the visible part of the viewport (scrollabar);
The time the fragment is shown in the middle of the viewport.

With the help of machine learning, all these six factors are folded into one number - the probability of a fragment being interesting. But first, we need to form a training set. At the same time, we do not know for sure what really interested the reader, what he read, and where he found the answer. But we can take fragments that intersect with the user's answer as positive examples, and all other fragments as negative ones. This training set is inaccurate and incomplete, but it is quite enough to train the algorithm and improve the quality of the snippets.

The first experiment is to test the adequacy of our model. We have trained the algorithm for predicting the interestingness of a fragment on one set of pages and apply it to another set. On the graph, the x-axis shows the predicted probability of the fragment being interesting, and the y-axis shows the average value of the measure of intersection of the fragment with the user's answer:

We see that if the algorithm is very confident that a fragment is good, then this fragment has a large overlap with the user's response.

When building a machine learning method, the most important factors turned out to be DispMiddleTime (the time during which a piece of text was visible on the screen) and MouseOverTime (the time during which the mouse cursor was over a piece of text).

Behavior-Based Snippets Improvement

So, we can determine which fragments are of interest to the user. How can we use this to improve snippets? As a starting point, we implemented a state-of-the-art snippet generation algorithm published by Yahoo! in 2008. For each sentence, a set of text factors is calculated and a machine learning method is built to predict the quality of a fragment in terms of snippet selection using assessor ratings on a scale (0,1). Several machine learning methods are then compared: SVM , ranking SVM and GBDT . We added more factors and expanded the rating scale to (0,1,2,3,4,5). To form a snippet, from one to four proposals from the set of the best are selected. Fragments are selected using a greedy algorithm that collects fragments with the best total weight.

We use the following set of text factors:

Exact match;
Number of query words and synonyms found (3 factors);
BM25-like (4 factors);
Distance between query words (3 factors);
Sentence length;
Position in the document;
Readability: number of punctuation marks, headwords, various words (9 factors).

Now that we have a snippet's weight in terms of textual relevance, we need to combine it with the snippet's interest factor calculated from user behavior. We use a simple linear combination of factors, and the weight λ in the fragment quality calculation formula is the behavior weight.

We need to choose the right weight λ. There are two extremes here: if the λ value is too small, then the behavior is not taken into account and the snippets differ from the baseline, if the λ value is too large, there is a risk that we will lose in the quality of the snippets. To choose λ, we conduct an experiment with a choice of five values from zero to one (0.1,0.3,0.5,0.7,0.9). To compare experiments, we recruited assessors who matched snippets in pairs according to three criteria:

representativeness: which of the snippets best reflects the relevance of the document to the request? You must read the document before answering the question.
readability: Which of the snippets is better written, easier to read?
Judgmentability: which of the snippets is better for finding a relevant answer and deciding whether to click on a link?

The graphs below show the proportions of pairs of snippets in which the behavioral algorithm showed an improvement in quality for three criteria and five λ values. For each of the λ values, the assessors gave a different number of ratings, and a different number of snippets differ in quality. Therefore, the confidence intervals for each of λ are somewhat different. We see that for λ=0.7 we get a statistically significant improvement in snippet quality for each of the criteria. Coverage for these snippets is also quite large: 40% of behavior-specific snippets differ from the baseline.

Main Assumptions and Limitations of the Considered Approach

First, the experiments were carried out on information questions, when the user searches for the text of the answer in documents. However, there are other types of custom intent, such as commercial, navigational. For such requests, behavioral factors may cause interference or require a different way of accounting. Second, by setting up the experiment, we assume that page views are grouped by information need. In our experiments, all users searched for the same thing for each document-query pair. Therefore, we aggregate the data for all users, calculating the average fragment weight value for all users. In the real world, users can make the same query and view the same document for different purposes. And we need to group users by intent for each request in order to be able to apply these methods and aggregate behavior data. And thirdly, in order to implement this technology in a real system, you need to find a way to collect data on user behavior. There are now browser plugins, ad networks, and hit counters that collect user click data. Their functionality can be extended by adding the ability to collect data on mouse movements.

Other applications of the method include the following:

Click Model improvement due to P(Examine | Click=0) prediction. If we track only clicks, then we cannot say with certainty why the user did not click on the link in the search results. He could read the snippet and decide that the document is irrelevant, or he simply did not see the document. With the use of mouse tracking, this problem is eliminated, and we can noticeably improve the prediction of document relevance.
User behavior on mobile devices.
Classification of mouse movements by intent. If you complicate the model, you can learn to distinguish between random mouse movements and intentional ones, when the user actually helps himself to read with the cursor. In addition, you can take into account the moments of inactivity as one of the additional signs of the fragment's interest.

The presentation was followed by a question and answer session, which can be viewed at

“Chief editor of the GetGoodRank blog, web analyst, blogger.
Snippet is one of the main elements of user attention control in search results. Today we are looking at 7 free ways to improve the snippet"

The snippet does not affect the ranking, however, a well-written snippet increases the conversion significantly. And although the snippet is an autonomous unit (not amenable to direct editing by the webmaster), it can be influenced. In this review, we will talk about 7 ways to get a quality snippet.

How to check a snippet?

No content management system, site analytics system shows the snippet as a separate element. The snippet is automatically generated by the search engine based on the page information and data provided by the optimizer about it in the Yandex Webmaster, Google Webmaster systems.

We invite you to evaluate the snippet in terms of its effectiveness in increasing conversions and improving behavioral factors. That is why it is necessary to “see” the snippet through the eyes of users.

The main method for checking a snippet is to analyze the search results for a key query. Recall that it is necessary to analyze the snippet not in isolation, but in relation to competitive sites.

7 ways to improve your snippet

Today we will talk about practical ways to maximize the effectiveness of the snippet.

1. Check the title and text of the snippet

The user evaluates the snippet as a small advertising text, which is designed to convey the main idea in one or two sentences. Check the snippet against the following criteria:

The length of the title is no more than 70 characters with spaces, and the length of the description is no more than 156 characters with spaces, otherwise search engines may truncate the snippet. And so, the effectiveness of the snippet is reduced. If search engines truncate snippet texts, then first of all, you need to check the Title and Description tags
The title and body of the snippet contain the keyword in a direct match. For maximum effect, it is desirable to place the key at the beginning of Title and Description
Literacy - is it worth arguing that even minor errors have a negative impact on the user, significantly reducing the CTR

2. Check the site for compliance with the UA requirements for displaying breadcrumbs

Breadcrumbs- a great opportunity to overcome misunderstandings between users, search results and your site. If, at the user's request, the search engine results in a page of the site that does not fully correspond to the request, then the navigation chain will provide an instant solution to the problem and help the user go to the desired page on your site, and not go to competitors for an answer.

In order for search engines to display the breadcrumbs in the search results, the site must meet the following criteria:

Webmasters' observations show that the site must meet the following requirements:

Site scale - more than 500 pages in the index
Pages listed in sitelinks should be placed one click away from the main page.
Internal linking should indicate that pages in sitelinks are most authoritative or interesting to users

5. Register your site in various Yandex services

Search engines welcome the registration of sites in various services, thus obtaining additional information about the sites. This is beneficial for the sites themselves. For example, registration in Yandex services will significantly expand and improve the snippet. Moreover, members of the services can enjoy additional benefits.

Yandex.Directory will send the snippet data about the company and the address, and for some companies, Yandex offers additional buttons right in the search. For example, the "Sign up" button is displayed next to the "Address on the map" button for a number of dental clinics.

The recording is available for companies that have entered into a cooperation agreement with Yandex partners.

For online stores, registration in Yandex.Market- this will allow not only to reach a large target audience by getting an additional traffic channel, but also to improve the snippet. Product data in Yandex.Market will be directly broadcast in the search results.

Yandex also offers various affiliate programs:

Yandex.Property
Yandex dictionaries
Yandex.Work and others

6. Use microdata

Markup is a key way to expand a snippet. For a clearer understanding of the meaning of microdata, we offer the following video:

7. Use the Power of Social Media to Reindex Fast

Any changes take effect only after the page is re-indexed by a search robot. If you have made changes to the Title or Description of the page, then the change in the snippet in the SERP will not happen immediately.

To speed up the reindexing process, you can use the popular social network Twitter by posting a link to the modified page in your account of this social platform. Twitter is the most quickly reindexed in RuNet, and by publishing a link to the corrected page, you speed up the process of its re-indexing and changing the snippet in the search results.

Conclusions:

For a high-quality snippet, the information contained both in the main page tags Title and Description, and on the page itself is important. Checking the adequacy of the materials is the primary task of the webmaster.

The template snippet is ineffective. It is necessary to use all channels of influence on this element as efficiently as possible.

Schema.org micro-markup is of great importance for generating the correct snippet.

This is a site card in the search results, which consists of a title (title) and a description (description) of the page. Such a card may contain the following elements and data:

Favicon - a picture that is displayed in the search results next to the site address and title, as well as in browser tabs
Website address. Can also be displayed as a breadcrumb

An example snippet from Yandex:

How does a snippet affect website promotion?

Having attractive snippets that include useful information is very important when promoting a site. This will help to significantly increase the number of transitions to your web resource. The main thing is to have something to interest users.

Here are some tips to help you make your site more attractive to search engines:

In snippets, it is important to use your competitive advantages. For example, if your price is lower than that of your competitors, you should indicate it in the snippet, this can significantly increase the CTR. You can also post information about promotions, gifts for the purchase and special offers, if we are talking about a commercial resource.
An attractive favicon will also do you good, it is a kind of logo for your site, which is remembered by users.
Using Open Graph and schema.org micro-markup to form a rich snippet. This will help you place quick links that allow users to immediately jump to the sections they are interested in or even post a video, company address, opening hours, and other useful information.

As a rule, if the site is on the second page in the SERPs, a properly configured snippet can give you a good result, and the position in the top ten will be yours.

Why is it important to analyze competitor snippets from the TOP-10?

With this analysis, you can see what techniques and advantages competitors are using to extract interesting ideas and use them in their own. Perhaps, thanks to these techniques, the competitor's site is in demand among users, has good traffic and positions. You can also find out how many occurrences and how they use the promoted keywords.

Competitor analysis with Labrika

The Labrika service has a very handy tool for such an analysis. You can find it in the subsection "Own and TOP10 snippets" in the "SEO audit" section of the left side menu:

What information is displayed in such a report, we will consider in detail in the following screenshot:

Keyword for which we analyze.
Position in the search engine for this keyword.
The ability to view snippets of competitors from the TOP-10.
Snippet of our site in the search results for the keyword.
The choice of the search engine in which we conduct the analysis.

To view in detail the data about sites in the TOP-10 search results, you must click on the "See TOP10 snippets" button. Let's see what information we can see after clicking.

It is necessary to make such a separate section - "If there are 3 hours for SEO work."

Because if your SEO plan is not detailed as much as possible, in detail, so that you can’t break it down even smaller, then when you think about how much you need to do there, procrastination begins. Hands are reaching out to social networks, an hour has flown by, then there is very little time left, and I don’t even want to start.

But when there are some understandable short actions that will take just 2-3 hours, then you can already do at least something to promote the site.

Snippet optimization is a small part of what I recommend doing there. But rather simple and fast part of it.

I'm not talking about rich snippets now (there will be a series of articles about rich snippets below).

I am writing here about 2 main elements - Title and Meta Decription.

Algorithm briefly:

1. We get from any analytics system (Yandex Metrica, Google Analytics) a list of pages from which most often come to you from the search.

In Yandex Metrica:

You can work separately with pages from where ONLY Google comes from, for example.

Because Google will more often see just such a snippet as you set it yourself using Title + Meta Decription. That is, it is more manageable. I will show this point below.

But it’s better to just work on all the traffic pages from the search. Changes in the Title + Meta Decription will bring a lot of useful things to Yandex as well. Especially the title changes.

Remember one more thing.

There is this rule: "working - do not touch."

It is best to improve the most traffic pages quite a bit - to correct the Title, but not to change it completely, from scratch, with different words.

Add additional thematic and keywords to the text, but do not change anything drastically.

Add linking and linking to this page (this is always a plus).

2. Studying what the current snippets of these pages look like for some requests, for which these pages are often visited. For example, using Topvisor (detailed below, with screenshots). We double-check whether all the important keywords are in the Titles at all.

We manually look at the TOP for our requests - how snippets look like from the highest competitors.

3. Making the Title and Meta Decription interesting, bright, attractive, with all the important keywords.

4. Speed up the reindexing of these pages. There are many options - social networks, a link from the main page. But I would use - each link 2-3 times at the maxi rate. This option still works great for me.

After some time, we look at changes in positions and traffic. They can be viewed by time segments, for example, in Yandex Metrics - here, using the link, I described how to diagnose Baden-Baden, in the same way, you can set the period a week before the changes, a week after they have already entered into force.

Where positions for important keywords are not yet high enough, we add them further pointwise to the TOP.

A little more detail:

A snippet is how your site appears in the search engine results - its title, description, favicon icon, sitelinks, something else.

I have researched snippets a lot in recent months and based on this I can clearly say:

Today, Yandex most often takes the description of a snippet from the text (although sometimes it also takes the meta description in whole or in part - a piece of the meta description plus a piece from the text).

Google most often takes the description of the snippet from the meta description, although if it does not contain the necessary keywords from the query, even partially, it will also take it from the text.

Most often, both PSs form the snippet title from the Title, if it contains keywords from the query.

Even if you rely only on Google snippets, it’s still most often 30-50% of traffic, or even more. But the optimization of Titles significantly improves the ranking situation in both PSs. And a high-quality Meta Description in Yandex will also help with promotion.

That is why it is so important:
A) use keywords - so that more often you get a managed snippet - set by you
B) use interesting words, numbers, brackets, capital letters, your USP, strong benefits, information about freebies, discounts and bonuses, etc. - everything that increases the clickability of the snippet.

You can also watch snippets of competitors.

We look at them in the "Positions" section with block display.

But (!) first, in the "Settings" section, you need to set "Collect snippets" here:

Then, in the "Positions" section, remove the positions. And switch to "block" mode. In the new interface, this is done like this:

And you can immediately see what and how - what is relevant, what the snippet looks like, where it comes from, what words to correct, and so on.

Relevant and landing pages

Another important point in the analysis of snippets is that most often you immediately see if the page is ranked that you need.

Is your landing page relevant in search engines. Is your page relevant in both Google and Yandex.

and roughly see different options for how the snippet might look.

If I put my Title and Meta description of this page into this snippet editor, I will get this option from them:

the actual snippet on google looks like this:

They, as we can see, are almost identical, so this is a good tool to help.

How to fill Meta Keywords

Or not fill in at all, or fill in 1-2 main keys, without overspam - this can attract attention and impose sanctions.

In general, this is how beautiful and effective, well-optimized snippets are formed.

AND THIS IS IMPORTANT TO DO FOR EVERY (!) important landing page.

Then improve the snippets of the most traffic generating pages and those that are close to the TOP.

But do it very carefully.

It is necessary to look, select different options, play with the title and meta description.

Also read a good guide from Igor Rudnik:

— an article in the Optimizer's blog (Elena Kamskaya)

— article by Denis Kaplunov

As always, I welcome your comments!

All my projects except this SEO blog:

SEO Topshop- SEO software with DISCOUNTS, on favorable terms, news of SEO services, databases, guides. Including Xrumer with the best deals and free training, Zennoposter, Zebroid and many others.

"Approaching.."- my blog on self-development, psychology, relationships, personal effectiveness