SEO help you have more sell with sending real visitors from search engines with right keywords that describe your good with the best way fro visitors really needs to your goods and services.

SEO help you have visitors from your country, city or even street with creation of the right keywords for the visitors search for buying good or service in his/her street !

SEO helps you stay higher of the competitors. For example you have an rent car agency in small city in one tourist country! You need to optimize your website with keywords that local and international person be able to find you in search result fast and easy.

One person from other city comes to your city and want to back to his city, need to find your agency in your city and his city to !Also one tourist wants to come to your city and  need to plan for his trip and find all goods and services in your city, He/ She needs to a car to rental and then SEO can help you all tourists find you fast and easy and also higher the competitors !

Finally with a good pricing strategy with the best offer and deals you can sure the most of visitors will be your customer !

We can do all of these steps for you on low price !

Design professional website with SEO service !

Each SEO expert has the own seo methods and in this way the seo result is the best method to select a good SEO expert for doing SEO service for your website or blog !

Please visit one of our last SEO result .

SEO means Search Engine optimization

© IndexHttp.com

Here all of hte Sane’s websites : www.Sane.asia  www.golnarde.com www.صانع.com that we did SEO service for all of them and now here are the results :

www.sane.asia increased from 0 to 4 less than 2 months :

Persian : www.sane.asia/ferforje/

Arabic  www.sane.asia/ferforje/ar/

English www.sane.asia/ferforje/en/

Sane metal industry have another website too that they page ranks increased to that listed below :

www.Golnarde.com 3/10

www.صانع.com  3/10

www.SaneForj.com 1/10

Please note all of these sites has less than 2 months old ! and last page rank was 0 for all of them !

Full news in Sane page

Doing SEO for your websites will help have real visitors that searches their needs with keywords and SEO will optimize your websites for the keywords that visitors will find you with them !

Sane Metal industry is a Persian – Iranian Factory and we design the website and doing the SEO service for this site.

We started it about 3 weeks ago and the result is here for these keywords :

گل نرده is the first of the Google page

فرفورژه is the second page of the Google result page.

simple definition of back link mean your link to other websites.

Backlinks, also known as incoming links, inbound links, inlinks, and inward links, are incoming links to a website or web page. In basic link terminology, a backlink is any link received by a web node (web page, directory, website, or top level domain) from another web node.[1]

Inbound links were originally important (prior to the emergence of search engines) as a primary means of web navigation; today, their significance lies in search engine optimization (SEO). The number of backlinks is one indication of the popularity or importance of that website or page (for example, this is used by Google to determine the PageRank of a webpage). Outside of SEO, the backlinks of a webpage may be of significant personal, cultural or semantic interest: they indicate who is paying attention to that page

www.IndexHttp.com          pr 3 – All pages
www.2rism.net                  pr 3 – All pages
www.Webmasterha.com     pr 3 – All pages
www.mmd.name               pr 3 – All pages
www.GYMA-Project.com     pr 3 – First page

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www.indexhtto.com                  Rank = 3
www.indexhttp.com/?lang=fa      Rank = 3
www.indexhttp.com/2011/10/28/google-and-alexa-pagerank/           Rank = 2
www.indexhttp.com/%d8%b3%d8%a6%d9%88_seo/     Rank = 2
www.indexhttp.com/2011/07/14/seo-as-a-international-markets/    Rank = 2
www.indexhttp.com/2011/10/28/how-google-finds-your-needle-in-the-webs-haystack/      Rank = 1
www.indexhttp.com/2011/10/28/website-linking-methods/ 
Rank = 1

As you see there is just one website with one domain name but with many high page rank and your website URL will add to all of these pages !

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Search engine rankings

Search engines often use the number of backlinks that a website has as one of the most important factors for determining that website’s search engine ranking, popularity and importance. Google’s description of their PageRank system, for instance, notes that Google interprets a link from page A to page B as a vote, by page A, for page B.[2] Knowledge of this form of search engine rankings has fueled a portion of the SEO industry commonly termed linkspam, where a company attempts to place as many inbound links as possible to their site regardless of the context of the originating site.

Websites often employ various search engine optimization techniques to increase the number of backlinks pointing to their website. Some methods are free for use by everyone whereas some methods like linkbaiting requires quite a bit of planning and marketing to work. Some websites stumble upon “linkbaiting” naturally; the sites that are the first with a tidbit of ‘breaking news’ about a celebrity are good examples of that. When “linkbait” happens, many websites will link to the ‘baiting’ website because there is information there that is of extreme interest to a large number of people.

There are several factors that determine the value of a backlink. Backlinks from authoritative sites on a given topic are highly valuable.[3] If both sites have content geared toward the keyword topic, the backlink is considered relevant and believed to have strong influence on the search engine rankings of the webpage granted the backlink. A backlink represents a favorable ‘editorial vote’ for the receiving webpage from another granting webpage. Another important factor is the anchor text of the backlink. Anchor text is the descriptive labeling of the hyperlink as it appears on a webpage. Search engine bots (i.e., spiders, crawlers, etc.) examine the anchor text to evaluate how relevant it is to the content on a webpage. Anchor text and webpage content congruency are highly weighted in search engine results page (SERP) rankings of a webpage with respect to any given keyword query by a search engine user.

Increasingly, inbound links are being weighed against link popularity and originating context. This transition is reducing the notion of one link, one vote in SEO, a trend proponents[who?] hope will help curb linkspam as a whole.

It should be noted that although many people will say building too many links too quickly will get you penalized, it actually will not. If it were possible to be penalized for building too many incoming links, people would target their competition for penalties by building “too many” links.

Google page rank is so important for all of international webmasters and websites.

The websites has higher page rank has more value and can receive more advertisement and visitors.

You can check your website or weblog page rank with Google toolbar. Google toolbar is free and you can download it now !

Download Google Toolbar

linking methods : 1 Reciprocal link     2 Resource linking   3 Forum signature linking    4 Blog comments   5 Directory link building    6 See also   7 References  8 External links

Reciprocal link

A reciprocal link is a mutual link between two objects, commonly between two websites to ensure mutual traffic. For example, Alice and Bob have websites. If Bob’s website links to Alice’s website, and Alice’s website links to Bob’s website, the websites are reciprocally linked. Website owners often submit their sites to reciprocal link exchange directories in order to achieve higher rankings in the search engines. Reciprocal linking between websites is an important part of the search engine optimization process because Google uses link popularity algorithms (defined as the number of links that lead to a particular page and the anchor text of the link) to rank websites for relevancy.^{[citation needed]}

Resource linking

Resource links are a category of links, which can be either one-way or two-way, usually referenced as “Resources” or “Information” in navbars, but sometimes, especially in the early, less compartmentalized years of the Web, simply called “links”. Basically, they are hyperlinks to a website or a specific webpage containing content believed to be beneficial, useful and relevant to visitors of the site establishing the link.

In recent years, resource links have grown in importance because most major search engines have made it plain that—in Google’s words– “quantity, quality, and relevance of links count towards your rating.”^[1]

The engines’ insistence on resource links being relevant and beneficial developed because many artificial link building methods were employed solely to “spam” search-engines, i.e. to “fool” the engines’ algorithms into awarding the sites employing these unethical devices undeservedly high page ranks and/or return positions.

Despite cautioning site developers (again quoting from Google) to avoid “‘free-for-all’ links, link popularity schemes, or submitting your site to thousands of search engines (because) these are typically useless exercises that don’t affect your ranking in the results of the major search engines^[2] — at least, not in a way you would likely consider to be positive,”^[3] most major engines have deployed technology designed to “red flag” and potentially penalize sites employing such practices.

Forum signature linking

Forum signature linking is a technique used to build backlinks to a website. This is the process of using forum communities that allow outbound hyperlinks in a member’s signature. This can be a fast method to build up inbound links to a website; it can also produce some targeted traffic if the website is relevant to the forum topic. It should be stated that forums using the nofollow attribute will have no actual Search Engine Optimization value.

Blog comments

Leaving a comment on a blog can result in a relevant do-follow link to the individual’s website. Most of the time, however, leaving a comment on a blog turns into a no-follow link, which is almost useless in the eyes of search engines, such as Google and Yahoo! Search. On the other hand, most blog comments get clicked on by the readers of the blog if the comment is well-thought-out and pertains to the discussion of the other commenters and the post on the blog.^{[citation needed]}

Directory link building

Website directories are lists of links to websites, which are sorted into categories. Website owners can submit their site to many of these directories. Some directories accept payment for listing in their directory, while others are free.^[4]

Posed in this way, the problem seems impossible. Yet this description is not too different from the World Wide Web, a huge, highly-disorganized collection of documents in many different formats. Of course, we’re all familiar with search engines (perhaps you found this article using one) so we know that there is a solution. This article will describe Google’s PageRank algorithm and how it returns pages from the web’s collection of 25 billion documents that match search criteria so well that “google” has become a widely used verb.

Most search engines, including Google, continually run an army of computer programs that retrieve pages from the web, index the words in each document, and store this information in an efficient format. Each time a user asks for a web search using a search phrase, such as “search engine,” the search engine determines all the pages on the web that contains the words in the search phrase. (Perhaps additional information such as the distance between the words “search” and “engine” will be noted as well.) Here is the problem: Google now claims to index 25 billion pages. Roughly 95% of the text in web pages is composed from a mere 10,000 words. This means that, for most searches, there will be a huge number of pages containing the words in the search phrase. What is needed is a means of ranking the importance of the pages that fit the search criteria so that the pages can be sorted with the most important pages at the top of the list.

One way to determine the importance of pages is to use a human-generated ranking. For instance, you may have seen pages that consist mainly of a large number of links to other resources in a particular area of interest. Assuming the person maintaining this page is reliable, the pages referenced are likely to be useful. Of course, the list may quickly fall out of date, and the person maintaining the list may miss some important pages, either unintentionally or as a result of an unstated bias.

Google’s PageRank algorithm assesses the importance of web pages without human evaluation of the content. In fact, Google feels that the value of its service is largely in its ability to provide unbiased results to search queries; Google claims, “the heart of our software is PageRank.” As we’ll see, the trick is to ask the web itself to rank the importance of pages.

How to tell who’s important

If you’ve ever created a web page, you’ve probably included links to other pages that contain valuable, reliable information. By doing so, you are affirming the importance of the pages you link to. Google’s PageRank algorithm stages a monthly popularity contest among all pages on the web to decide which pages are most important. The fundamental idea put forth by PageRank’s creators, Sergey Brin and Lawrence Page, is this: the importance of a page is judged by the number of pages linking to it as well as their importance.

We will assign to each web page P a measure of its importance I(P), called the page’s PageRank. At various sites, you may find an approximation of a page’s PageRank. (For instance, the home page of The American Mathematical Society currently has a PageRank of 8 on a scale of 10. Can you find any pages with a PageRank of 10?) This reported value is only an approximation since Google declines to publish actual PageRanks in an effort to frustrate those would manipulate the rankings.

Here’s how the PageRank is determined. Suppose that page P_j has l_j links. If one of those links is to page P_i, then P_j will pass on 1/l_j of its importance to P_i. The importance ranking of P_i is then the sum of all the contributions made by pages linking to it. That is, if we denote the set of pages linking to P_i by B_i, then

Let’s first create a matrix, called the hyperlink matrix, in which the entry in the i^th row and j^th column is

We will also form a vector whose components are PageRanks–that is, the importance rankings–of all the pages. The condition above defining the PageRank may be expressed as

Let’s look at an example. Shown below is a representation of a small collection (eight) of web pages with links represented by arrows.

with stationary vector

This shows that page 8 wins the popularity contest. Here is the same figure with the web pages shaded in such a way that the pages with higher PageRanks are lighter.

Computing I

There are many ways to find the eigenvectors of a square matrix. However, we are in for a special challenge since the matrix H is a square matrix with one column for each web page indexed by Google. This means that H has about n = 25 billion columns and rows. However, most of the entries in H are zero; in fact, studies show that web pages have an average of about 10 links, meaning that, on average, all but 10 entries in every column are zero. We will choose a method known as the power method for finding the stationary vector I of the matrix H.

How does the power method work? We begin by choosing a vector I ⁰ as a candidate for I and then producing a sequence of vectors I ^k by

We will illustrate with the example above.

It is natural to ask what these numbers mean. Of course, there can be no absolute measure of a page’s importance, only relative measures for comparing the importance of two pages through statements such as “Page A is twice as important as Page B.” For this reason, we may multiply all the importance rankings by some fixed quantity without affecting the information they tell us. In this way, we will always assume, for reasons to be explained shortly, that the sum of all the popularities is one.

Three important questions

Three questions naturally come to mind:

• Does the sequence I ^k always converge?
• Is the vector to which it converges independent of the initial vector I ⁰?
• Do the importance rankings contain the information that we want?

Given the current method, the answer to all three questions is “No!” However, we’ll see how to modify our method so that we can answer “yes” to all three.

Let’s first look at a very simple example. Consider the following small web consisting of two web pages, one of which links to the other:

with matrix

Here is one way in which our algorithm could proceed:

In this case, the importance rating of both pages is zero, which tells us nothing about the relative importance of these pages. The problem is that P₂ has no links. Consequently, it takes some of the importance from page P₁ in each iterative step but does not pass it on to any other page. This has the effect of draining all the importance from the web. Pages with no links are called dangling nodes, and there are, of course, many of them in the real web we want to study. We’ll see how to deal with them in a minute, but first let’s consider a new way of thinking about the matrix H and stationary vector I.

A probabilitistic interpretation of H

Imagine that we surf the web at random; that is, when we find ourselves on a web page, we randomly follow one of its links to another page after one second. For instance, if we are on page P_j with l_j links, one of which takes us to page P_i, the probability that we next end up on P_i page is then .

As we surf randomly, we will denote by the fraction of time that we spend on page P_j. Then the fraction of the time that we end up on page P_i page coming from P_j is . If we end up on P_i, we must have come from a page linking to it. This means that

Notice that, given this interpretation, it is natural to require that the sum of the entries in the PageRank vector I be one.

Of course, there is a complication in this description: If we surf randomly, at some point we will surely get stuck at a dangling node, a page with no links. To keep going, we will choose the next page at random; that is, we pretend that a dangling node has a link to every other page. This has the effect of modifying the hyperlink matrix H by replacing the column of zeroes corresponding to a dangling node with a column in which each entry is 1/n. We call this new matrix S.

In our previous example, we now have

with matrix

and eigenvector

In other words, page P₂ has twice the importance of page P₁, which may feel about right to you.

The matrix S has the pleasant property that the entries are nonnegative and the sum of the entries in each column is one. In other words, it is stochastic. Stochastic matrices have several properties that will prove useful to us. For instance, stochastic matrices always have stationary vectors.

For later purposes, we will note that S is obtained from H in a simple way. If A is the matrix whose entries are all zero except for the columns corresponding to dangling nodes, in which each entry is 1/n, then S = H + A.

How does the power method work?

In general, the power method is a technique for finding an eigenvector of a square matrix corresponding to the eigenvalue with the largest magnitude. In our case, we are looking for an eigenvector of S corresponding to the eigenvalue 1. Under the best of circumstances, to be described soon, the other eigenvalues of S will have a magnitude smaller than one; that is, if is an eigenvalue of S other than 1.

We will assume that the eigenvalues of S are and that

. This assumption is not necessarily true, but with it we may more easily illustrate how the power method works. We may write our initial vector I ⁰ as

Then

Since the eigenvalues with have magnitude smaller than one, it follows that if and therefore , an eigenvector corresponding to the eigenvalue 1.

It is important to note here that the rate at which is determined by . When is relatively close to 0, then relatively quickly. For instance, consider the matrix

The eigenvalues of this matrix are and . In the figure below, we see the vectors I ^k, shown in red, converging to the stationary vector I shown in green.

Now consider the matrix

Here the eigenvalues are and . Notice how the vectors I ^k converge more slowly to the stationary vector I in this example in which the second eigenvalue has a larger magnitude.

 

When things go wrong

In our discussion above, we assumed that the matrix S had the property that and . This does not always happen, however, for the matrices S that we might find.

Suppose that our web looks like this:

In this case, the matrix S is

Then we see

 

 

I 0	I 1	I 2	I 3	I 4	I 5
1	0	0	0	0	1
0	1	0	0	0	0
0	0	1	0	0	0
0	0	0	1	0	0
0	0	0	0	1	0

In this case, the sequence of vectors I ^k fails to converge. Why is this? The second eigenvalue of the matrix S satisfies and so the argument we gave to justify the power method no longer holds.

To guarantee that , we need the matrix S to be primitive. This means that, for some m, S^m has all positive entries. In other words, if we are given two pages, it is possible to get from the first page to the second after following m links. Clearly, our most recent example does not satisfy this property. In a moment, we will see how to modify our matrix S to obtain a primitive, stochastic matrix, which therefore satisfies .

Here’s another example showing how our method can fail. Consider the web shown below.

In this case, the matrix S is

 

 

with stationary vector

Notice that the PageRanks assigned to the first four web pages are zero. However, this doesn’t feel right: each of these pages has links coming to them from other pages. Clearly, somebody likes these pages! Generally speaking, we want the importance rankings of all pages to be positive. The problem with this example is that it contains a smaller web within it, shown in the blue box below.

 

Links come into this box, but none go out. Just as in the example of the dangling node we discussed above, these pages form an “importance sink” that drains the importance out of the other four pages. This happens when the matrix S is reducible; that is, S can be written in block form as

Indeed, if the matrix S is irreducible, we can guarantee that there is a stationary vector with all positive entries.

A web is called strongly connected if, given any two pages, there is a way to follow links from the first page to the second. Clearly, our most recent example is not strongly connected. However, strongly connected webs provide irreducible matrices S.

To summarize, the matrix S is stochastic, which implies that it has a stationary vector. However, we need S to also be (a) primitive so that and (b) irreducible so that the stationary vector has all positive entries.

A final modification

To find a new matrix that is both primitive and irreducible, we will modify the way our random surfer moves through the web. As it stands now, the movement of our random surfer is determined by S: either he will follow one of the links on his current page or, if at a page with no links, randomly choose any other page to move to. To make our modification, we will first choose a parameter between 0 and 1. Now suppose that our random surfer moves in a slightly different way. With probability , he is guided by S. With probability , he chooses the next page at random.

If we denote by 1 the matrix whose entries are all one, we obtain the Google matrix:

 

Notice now that G is stochastic as it is a combination of stochastic matrices. Furthermore, all the entries of G are positive, which implies that G is both primitive and irreducible. Therefore, G has a unique stationary vector I that may be found using the power method.

The role of the parameter is an important one. Notice that if , then G = S. This means that we are working with the original hyperlink structure of the web. However, if , then . In other words, the web we are considering has a link between any two pages and we have lost the original hyperlink structure of the web. Clearly, we would like to take close to 1 so that we hyperlink structure of the web is weighted heavily into the computation.

However, there is another consideration. Remember that the rate of convergence of the power method is governed by the magnitude of the second eigenvalue . For the Google matrix, it has been proven that the magnitude of the second eigenvalue . This means that when is close to 1 the convergence of the power method will be very slow. As a compromise between these two competing interests, Serbey Brin and Larry Page, the creators of PageRank, chose .

Computing I

What we’ve described so far looks like a good theory, but remember that we need to apply it to matrices where n is about 25 billion! In fact, the power method is especially well-suited to this situation.

Remember that the stochastic matrix S may be written as

and therefore the Google matrix has the form

 

Therefore,

 

Now recall that most of the entries in H are zero; on average, only ten entries per column are nonzero. Therefore, evaluating HI ^k requires only ten nonzero terms for each entry in the resulting vector. Also, the rows of A are all identical as are the rows of 1. Therefore, evaluating AI ^k and 1I ^k amounts to adding the current importance rankings of the dangling nodes or of all web pages. This only needs to be done once.

With the value of chosen to be near 0.85, Brin and Page report that 50 – 100 iterations are required to obtain a sufficiently good approximation to I. The calculation is reported to take a few days to complete.

Of course, the web is continually changing. First, the content of web pages, especially for news organizations, may change frequently. In addition, the underlying hyperlink structure of the web changes as pages are added or removed and links are added or removed. It is rumored that Google recomputes the PageRank vector I roughly every month. Since the PageRank of pages can be observed to fluctuate considerably during this time, it is known to some as the Google Dance. (In 2002, Google held a Google Dance!)

Summary

Brin and Page introduced Google in 1998, a time when the pace at which the web was growing began to outstrip the ability of current search engines to yield useable results. At that time, most search engines had been developed by businesses who were not interested in publishing the details of how their products worked. In developing Google, Brin and Page wanted to “push more development and understanding into the academic realm.” That is, they hoped, first of all, to improve the design of search engines by moving it into a more open, academic environment. In addition, they felt that the usage statistics for their search engine would provide an interesting data set for research. It appears that the federal government, which recently tried to gain some of Google’s statistics, feels the same way.

There are other algorithms that use the hyperlink structure of the web to rank the importance of web pages. One notable example is the HITS algorithm, produced by Jon Kleinberg, which forms the basis of the Teoma search engine. In fact, it is interesting to compare the results of searches sent to different search engines as a way to understand why some complain of a Googleopoly.

Source

]]> http://indexhttp.com/2011/10/28/how-google-finds-your-needle-in-the-webs-haystack/feed/ 0 http://indexhttp.com/2011/10/28/google-and-alexa-pagerank/ http://indexhttp.com/2011/10/28/google-and-alexa-pagerank/#comments Fri, 28 Oct 2011 07:07:07 +0000 www.SEOsecrets.info http://indexhttp.com/?p=81 Continue reading →]]> We all use Google.com or any other search engine to find some info about some words , places, tools or any thing ! and we saw some websites are in the first page and some others are in next pages of the Google.

Today I want to write about the reason of this rating system and answer to this question that why some websites has higher rating and are in the first page of the Google.

Google.com has a page rank system that evaluate website stats and content and visitors and many other methods that are secret and after checking all stats each website give a rank for each page.

Please note that the Google rank is for pages not for website ! This page ranks is between 0 and 10 that top websites has higher numbers. For example Google.com page rank is 10.

PageRank was developed at Stanford University by Larry Page (hence the name Page-Rank) and Sergey Brin as part of a research project about a new kind of search engine. Sergey Brin had the idea that information on the web could be ordered in a hierarchy by “link popularity”: a page is ranked higher as there are more links to it It was co-authored by Rajeev Motwani and Terry Winograd. The first paper about the project, describing PageRank and the initial prototype of the Google search engine, was published in 1998: shortly after, Page and Brin founded Google Inc., the company behind the Google search engine. While just one of many factors that determine the ranking of Google search results, PageRank continues to provide the basis for all of Google’s web search tools.

PageRank has been influenced by citation analysis, early developed by Eugene Garfield in the 1950s at the University of Pennsylvania, and by Hyper Search, developed by Massimo Marchiori at the University of Padua. In the same year PageRank was introduced (1998), Jon Kleinberg published his important work on HITS. Google’s founders cite Garfield, Marchiori, and Kleinberg in their original paper.

A small search engine called “RankDex” from IDD Information Services designed by Robin Li was, since 1996, already exploring a similar strategy for site-scoring and page ranking. The technology in RankDex would be patented by 1999 and used later when Li founded Baidu in China. Li’s work would be referenced by some of Larry Page’s U.S. patents for his Google search methods.

If you download Google toolbar you can check online your website or any website’s page rank for free.

SERP Rank
The Search engine results page (SERP) is the actual result returned by a search engine in response to a keyword query. The SERP consists of a list of links to web pages with associated text snippets. The SERP rank of a web page refers to the placement of the corresponding link on the SERP, where higher placement means higher SERP rank. The SERP rank of a web page is not only a function of its PageRank, but depends on a relatively large and continuously adjusted set of factors (over 200), commonly referred to by internet marketers as “Google Love”. Search engine optimization (SEO) is aimed at achieving the highest possible SERP rank for a website or a set of web pages.

With the introduction of Google Places into the mainstream organic SERP, PageRank plays little to no role in ranking a business in the Local Business Results. While the theory of citations is still computed in their algorithm, PageRank is not a factor since Google ranks business listings and not web pages.

 

The Alexa.com also has own ranking system too, each website has a ranking number as a Google ranking system but lower number has higher rank in Alexa.com

The websites has under 100,000 rank is better than others.

And now you may ask how can I increase my website page rank in Alexa and Google.com ?

The answer of this question is just this word : SEO

SEO is key of the success in internet and eCommerce. SEO service will increase your page rank and will increase your sale and visitors.

You will have real visitor with SEO.

Each SEO expert has his/her own secrets and methods that use them for the websites and webmasters order SEO service to him/her

For example you have a Job agency in one street in one city in California and have a website for your job and need to receive real website visitors from this street and now a SEO expert will optimize your website pages to have a visitors from this street !

 

You can find many useful info about page rank

]]> http://indexhttp.com/2011/10/28/google-and-alexa-pagerank/feed/ 2 http://indexhttp.com/2011/07/14/seo-as-a-international-markets/ http://indexhttp.com/2011/07/14/seo-as-a-international-markets/#comments Thu, 14 Jul 2011 06:42:15 +0000 www.SEOsecrets.info http://indexhttp.com/?p=57 Continue reading →]]> Optimization techniques are highly tuned to the dominant search engines in the target market. The search engine’s market shares vary from market to market, as does competition. In 2003, Danny Sullivan stated that Google represented about 75% of all searches.

In markets outside the United States, Google’s share is often larger, and Google remains the dominant search engine worldwide as of 2007. As of 2006, Google had an 85-90% market share in Germany.  While there were hundreds of SEO firms in the US at that time, there were only about five in Germany. As of June 2008, the marketshare of Google in the UK was close to 90% according to Hitwise. That market share is achieved in a number of countries.

As of 2009, there are only a few large markets where Google is not the leading search engine. In most cases, when Google is not leading in a given market, it is lagging behind a local player. The most notable markets where this is the case are China, Japan, South Korea, Russia and the Czech Republic where respectively Baidu, Yahoo! Japan, Naver, Yandex and Seznam are market leaders.

Successful search optimization for international markets may require professional translation of web pages, registration of a domain name with a top level domain in the target market, and web hosting that provides a local IP address. Otherwise, the fundamental elements of search optimization are essentially the same, regardless of language.

 Wikipedia

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