Wake Up SEOs, the New Google is Here

I must admit that lately Google is the cause of my headaches.

No, not just because it decided I was not going to be not provided with useful information about my sites. And neither because it is changing practically every tool I got used since my first days as an SEO (Google Analytics, Webmaster Tools, Gmail…). And, honestly, not only because it released a ravenous Panda.

No, the real question that is causing my headaches is: What the hell does Google want to go with all these changes?

Let me start quoting the definition of SEO Google gives in its Guidelines:

Search engine optimization is about putting your site’s best foot forward when it comes to visibility in search engines, but your ultimate consumers are your users, not search engines.

Technical SEO still matters, a lot!

If you want to put your site’s best foot forward and make it the most visible possible in search engines, then you have to be a master in technical SEO.

We all know that if we do not pay attention to the navigation architecture of our site, if we don’t care about the on-page optimization, if we mess up with the rel=”canonical” tag, the pagination and the faceted navigation of our web, and if we don’t pay attention to the internal content duplication, etc. etc., well, we are not going to go that far with Search.

Is all this obvious? Yes, it is. But people in our circle tend to pay attention just to the last bright shining object and forget what one of the basic pillars of our discipline is: make a site optimized to be visible in the search engines.

The next time you hear someone saying “Content is King” or “Social is the new link building”, snap her face and ask her when it was the last time she logged in Google Webmaster Tools.

Go fix your site, make it indexable and solve all the technical problems it may have. Just after done that, you can start doing all the rest.

User is king

Technical SEO still matters, but that does not mean that it is synonym of SEO. So, if you hear someone affirming it, please snap her face too.

No... content is not the only King. User is the King! Image by Jeff Gregory

User and useful have the same root: use. And a user finds useful a website when it offers an answer to her needs, and if its use is easy and fast..

From the point of view that Google has of User, that means that a site to rank:

  1. must be fast;
  2. must have useful content and related to what it pretends to be about;
  3. must be presented to Google so that it can understand the best it can what it is about.

The first point explains the emphasis Google gives to site speed, because it is really highly correlated to a better user experience.

The second is related to the quality of the content of a site, and it is substantially what Panda is all about. Panda, if we want to reduce it at its minimal terms, is the attempt by Google of cleaning its SERPs of any content it does not consider useful for the end users.

The third explains the Schema.org adoption and why Google (and the other Search Engines) are definitely moving to the Semantic Web: because it helps search engines organize the bazillion contents they index every second. And the most they understand really what is your content about, the better they will deliver it in the SERPs.

The link graph mapped

The decline of Link graph

We all know that just with on-site optimization we cannot win the SERPs war, and that we need links to our site to make it authoritative. But we all know how much the link graph can be gamed.

Even though we still have tons of reasons to complain with Google about the quality of SERPs, especially due to sites that ranks thanks to manipulative link building tactics, it is hard for me to believe that Google is doing nothing in order to counteract this situation. What I believe is that Google has decided to solve the problem not with patches but with a totally new kind of graph.

That does not mean that links are not needed anymore, not at all, as links related factors still represent (and will represent) a great portion of all the ranking factors, but other factors are now cooked in the ranking pot.

Be Social and become a trusted seed

In a Social-Caffeinated era, the faster way to understand if a content is popular is to check its “relative” popularity in the social media environment. I say “relative”, because not all contents are the same and if a meme needs many tweets, +1 and likes/share to be considered more popular than others, it is not so for more niche kind of contents. Combining social signals with the traditional link graph, Google can understand the real popularity of a page.

The problem, as many are saying since almost one year, is that it is quite easy to spam in Social Media.

The Facebook Social Graph from Silicon Angle

For this reason Google introduced the concepts of Author and Publisher and, even more important, Google linked them to the Google Profiles and is pushing Google Plus, which is not just another Social Media, but what Google aims to be in the future: a social search engine.

Rel=”author” and Rel=”publisher” are the solution Google is adopting in order to better control, within other things, the spam pollution of the SERPs.

If you are a blogger, you will be incentivized in marking your content with Author and link it to your G+ Profile, and as a Site, you are incentivized to create your G+ Business page and to promote it with a badge on you site that has the rel=”publisher” in its code.

Trusted seeds are not anymore only sites, but can be also persons (i.e.: Rand or Danny Sullivan) or social facets of an entity… so, the closer I am in the Social Graph to those persons//entity the more trusted I am to Google eyes.

The new Google graph

As we can see, Google is not trying to rely only on the link graph, as it is quite easy to game, but it is not simply adding the social signals to the link graph, because they too can be gamed. What Google is doing is creating and refining a new graph that see cooperating Link graph, Social graph and Trust graphand which is possibly harder to game. Because it can be gamed still, but – hopefully – needing so many efforts that it may become not-viable as a practice.

Wake up SEOs, the new Google is here

As a conclusion, let me borrow what Larry Page wrote on Google+ (bold is mine):

Our ultimate ambition is to transform the overall Google experience […] because we understand what you want and can deliver it instantly.

This means baking identity and sharing into all of our products so that we build a real relationship with our users. Sharing on the web will be like sharing in real life across all your stuff. You’ll have better, more relevant search results and ads.

Think about it this way … last quarter, we’ve shipped the +, and now we’re going to ship the Google part.

I think that it says it all and what we have lived a year now is explained clearly by the Larry Page words.

What can we do as SEOs? Evolve, because SEO is not dieing, but SEOs can if they don’t assume that winter – oops – the change of Google is coming.

The New SEO graph

 

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Search Engine Algorithm Basics

A good search engine does not attempt to return the pages that best match the input query. A good search engine tries to answer the underlying question. If you become aware of this you’ll understand why Google (and other search engines), use a complex algorithm to determine what results they should return. The factors in the algorithm consist of “hard factors” as the number of backlinks to a page and perhaps some social recommendations through likes and +1′ s. These are usually external influences. You also have the factors on the page itself. For this the way a page is build and various page elements play a role in the algorithm. But only by analyzing the on-site and off-site factors is it possible for Google to determine which pages will answer is the question behind the query. For this Google will have to analyze the text on a page.

In this article I will elaborate on the problems of a search engine and optional solutions. At the end of this article we haven’t revealed Google’s algorithm (unfortunately), but we’ll be one step closer to understand some advice we often give as an SEO. There will be some formulas, but do not panic. This article isn’t just about those formulas. The article contains a excel file. Oh and the best thing: I will use some Dutch delights to illustrate the problems.

Croquets and Bitterballen
Behold: Croquets are the elongated and bitterballen are the round ones 😉

True OR False
Search engines have evolved tremendously in recent years, but at first they could only deal with Boolean operators. In simple terms, a term was included in a document or not. Something was true or false, 1 or 0. Additionally you could use the operators as AND, OR and NOT to search documents that contain multiple terms or to exclude terms. This sounds fairly simple, but it does have some problems with it. Suppose we have two documents, which consist of the following texts:

Doc1:
“And our restaurant in New York serves croquets and bitterballen.”

Doc2:
“In the Netherlands you retrieve croquets and frikandellen from the wall.”
Frikandellen
Oops, almost forgot to show you the frikandellen 😉

If we were to build a search engine, the first step is tokenization of the text. We want to be able to quickly determine which documents contain a term. This is easier if we all put tokens in a database. A token is any single term in a text, so how many tokens does Doc1 contain?

At the moment you started to answer this question for yourself, you probably thought about the definition of a “term”. Actually, in the example “New York” should be recognized as one term. How we can determine that the two individual words are actually one word is outside the scope of this article, so at the moment we threat each separate word as a separate token. So we have 10 tokens in Doc1 and 11 tokens in Doc2. To avoid duplication of information in our database, we will store types and not the tokens.

Types are the unique tokens in a text. In the example Doc1 contains twice the token “and”. In this example I ignore the fact that “and” appears once with and once without being capitalized. As with the determination of a term, there are techniques to determine whether something actually needs to be capitalized. In this case, we assume that we can store it without a capital and that “And” & “and” are the same type.

By storing all the types in the database with the documents where we can find them, we’re able to search within the database with the help of Booleans. The search “croquets” will result in both Doc1 and Doc2. The search for “croquets AND bitterballen” will only return Doc1 as a result. The problem with this method is that you are likely to get too much or too little results. In addition, it lacks the ability to organize the results. If we want to improve our method we have to determine what we can use other then the presence / absence of a term in a document. Which on-page factors would you use to organize the results if you were Google?

Zone Indexes
A relatively simple method is to use zone indexes. A web page can be divided into different zones. Think of a title, description, author and body. By adding a weight to each zone in a document, we’re able to calculate a simple score for each document. This is one of the first on page methods search engines used to determine the subject of a page. The operation of scores by zone indexes is as follows:

Suppose we add the following weights ​​to each zone:

Zone Weight
title 0.4
description 0.1
content 0.5

We perform the following search query:
“croquets AND bitterballen”

And we have a document with the following zones:

Zone Content Boolean Score
title New York Café 0 0
description Café with delicious croquets and bitterballen 1 0.1
content Our restaurant in New York serves croquets andbitterballen 1 0.5
Total 0.6

Because at some point everyone started abusing the weights assigned to for example the description, it became more important for Google to split the body in different zones and assign a different weight to each individual zone in the body.

This is quite difficult because the web contains a variety of documents with different structures. The interpretation of an XML document by such a machine is quite simple. When interpreting an HTML document it becomes harder for a machine. The structure and tags are much more limited, which makes the analysis more difficult. Of course there will be HTML5 in the near future and Google supports microformats, but it still has its limitations. For example if you know that Google assigns more weight to content within the <content> tag and less to content in the <footer> tag, you’ll never use the <footer> tag.

To determine the context of a page, Google will have to divide a web page into blocks. This way Google can judge which blocks on a page are important and which are not. One of the methods that can be used is the text / code ratio. A block on a page that contains much more text than HTML code contains probably the main content on the page. A block that contains many links / HTML code and little content is probably the menu. This is why choosing the right WYSIWYG editor is very important. Some of these editors use a a lot of unnecessary HTML code.

The use of text / code ratio is just one of the methods which a search engine can use to divide a page into blocks. Bill Slawski talked about identifying blocks earlier this year.

The advantage of the zone indexes method is that you can calculate quite simple a score for each document. A disadvantage of course is that many documents can get the same score.

Term frequency
When I asked you to think of on-page factors you would use to determine relevance of a document, you probably thought about the frequency of the query terms. It is a logical step to increase weight to each document using the search terms more often.

Some SEO agencies stick to the story of using the keywords on a certain percentage in the text. We all know that isn’t true, but let me show you why. I’ll try to explain it on the basis of the following examples. Here are some formulas to emerge, but as I said it is the outline of the story that matters.

The numbers in the table below are the number of occurrences of a word in the document (also called term frequency or tf). So which document has a better score for the query: croquets and bitterballen ?

croquets and café bitterballen Amsterdam
Doc1 8 10 3 2 0
Doc2 1 20 3 9 2
DocN
Query 1 1 0 1 0

The score for both documents would be as follows:
score(“croquets and bitterballen”, Doc1) = 8 + 10 + 2 = 20
score(“croquets and bitterballen”, Doc2) = 1 + 20 + 9 = 30

Document 2 is in this case closer related to the query. In this example the term “and” gains the most weight, but is this fair? It is a stop word, and we like to give it only a little value. We can achieve this by using inverse document frequency (tf-idf), which is the opposite of document frequency (df). Document frequency is the number of documents where a term occurs. Inverse document frequency is, well, the opposite. As the number of documents in which a term grows, idf will shrink.

You can calculate idf by dividing the total number of documents you have in your corpus by the number of documents containing the term and then take the logarithm of that quotient.

Suppose that the IDF of our query terms are as follows:
Idf(croquets)            = 5
Idf(and)                   = 0.01
Idf(bitterballen)         = 2

Then you get the following scores:
score(“croquets and bitterballen”, Doc1) = 8*5  + 10*0.01 + 2*2 = 44.1
score(“croquets and bitterballen”, Doc2) = 1*5 + 20*0.01 + 9*2 = 23.2

Now Doc1 has a better score. But now we don’t take the length into account. One document can contain much more content then another document, without being more relevant. A long document gains a higher score quite easy with this method.

Vector model
We can solve this by looking at the cosine similarity of a document. An exact explanation of the theory behind this method is outside the scope of this article, but you can think about it as an kind of harmonic mean between the query terms in the document. I made an excel file, so you can play with it yourself. There is an explanation in the file itself. You need the following metrics:

  • Query terms – each separate term in the query.
  • Document frequency – how many documents does Google know containing that term?
  • Term frequency – the frequency for each separate query term in the document (add this Focus Keyword widget made by Sander Tamaëla to your bookmarks, very helpful for this part)

Here’s an example where I actually used the model. The website had a page that was designed to rank for “fiets kopen” which is Dutch for “buying bikes”. The problem was that the wrong page (the homepage) was ranking for the query.

For the formula, we include the previously mentioned inverse document frequency (idf). For this we need the total number of documents in the index of Google. For this we assume N = 10.4 billion.

An explanation of the table below:

  • tf = term frequency
  • df = document frequency
  • idf = inverse document frequency
  • Wt,q = weight for term in query
  • Wt,d = weight for term in document
  • Product = Wt,q * Wt,d
  • Score = Sum of the products

The main page, which was ranking: http://www.fietsentoko.nl/

term Query Document Product
tf df idf Wt,q tf Wf Wt,d
Fiets 1 25.500.000 3.610493159 3.610493159 21 441 0.70711 2.55302
Kopen 1 118.000.000 2.945151332 2.9452 21 441 0.70711 2.08258
Score: 4.6356

The page I wanted to rank: http://www.fietsentoko.nl/fietsen/

term Query Document Product
tf df idf Wt,q tf Wf Wt,d
Fiets 1 25.500.000 3.610493159 3.610493159 22 484 0.61782 2.23063
Kopen 1 118.000.000 2.945151332 2.945151332 28 784 0.78631 2.31584
Score: 4.54647

Although the second document contains the query terms more often, the score of the document for the query was lower (higher is better). This was because the lack of balance between the query terms. Following this calculation, I changed the text on the page, and increased the use of the term “fietsen” and decreased the use of “kopen” which is a more generic term in the search engine and has less weight. This changed the score as follows:

term Query Document Product
tf df idf Wt,q tf Wf Wt,d
Fiets 1 25.500.000 3.610493159 3.610493159 28 784 0.78631 2.83897
Kopen 1 118.000.000 2.945151332 2.945151332 22 484 0.61782 1.81960
Score: 4.6586

After a few days, Google crawled the page and the document I changed started to rank for the term. We can conclude that the number of times you use a term is not necessarily important. It is important to find the right balance for the terms you want to rank.

Speed up the process
To perform this calculation for each document that meets the search query, cost a lot of processing power. You can fix this by adding some static values ​​to determine for which documents you want to calculate the score. For example PageRank is a good static value. When you first calculate the score for the pages matching the query and having an high PageRank, you have a good change to find some documents which would end up in the top 10 of the results anyway.

Another possibility is the use of champion lists. For each term take only the top N documents with the best score for that term. If you then have a multi term query, you can intersect those lists to find documents containing all query terms and probably have a high score. Only if there are too few documents containing all terms, you can search in all documents. So you’re not going to rank by only finding the best vector score, you have the have your statics scores right as well.

Relevance feedback
Relevance feedback is assigning more or less value to a term in a query, based on the relevance of a document. Using relevance feedback, a search engine can change the user query without telling the user.

The first step here is to determine whether a document is relevant or not. Although there are search engines where you can specify if a result or a document is relevant or not, Google hasn’t had such a function for a long time. Their first attempt was by adding the favorite star at the search results. Now they are trying it with the Google+ button. If enough people start pushing the button at a certain result, Google will start considering the document relevant for that query.

Another method is to look at the current pages that rank well. These will be considered relevant. The danger of this method is topic drift. If you’re looking for bitterballen and croquettes, and the best ranking pages are all snack bars in Amsterdam, the danger is that you will assign value to Amsterdam and end up with just snack bars in Amsterdam in the results.

Another way for Google is to use is by simply using data mining. They can also look at the CTR of different pages. Pages where the CTR is higher and have a lower bounce rate then average can be considered relevant. Pages with a very high bounce rate will just be irrelevant.

An example of how we can use this data for adjusting the query term weights is Rochio’s feedback formula. It comes down to adjusting the value of each term in the query and possibly adding additional query terms. The formula for this is as follows:
Rochhio feedback formula

The table below is a visual representation of this formula. Suppose we apply the following values ​​:
Query terms: +1 (alpha)
Relevant terms: +1 (beta)
Irrelevant terms: -0.5 (gamma)

We have the following query:
“croquets and bitterballen”

The relevance of the following documents is as follows:
Doc1   : relevant
Doc2   : relevant
Doc3   : not relevant

Terms Q Doc1 Doc2 Doc3 Weight new query
croquets 1 1 1 0 1 + 1 – 0        = 2
and 1 1 0 1 1 + 0.5 – 0.5  = 1
bitterballen 1 0 0 0 1 + 0 – 0         = 1
café 0 0 1 0 0 + 0.5 – 0     = 0.5
Amsterdam 0 0 0 1 0 + 0 – 0.5     = -0.5  = 0

The new query is as follows:
croquets(2) and(1) bitterballen(1) cafe(0.5)

The value for each term is the weight that it gets in your query. We can use those weights in our vector calculations. Although the term Amsterdam was given a score of -0.5, the adjust negative values back to 0. In this way we do not exclude terms from the search results. And although café did not appear in the original query, it was added and was given a weight in the new query.

Suppose Google uses this way of relevance feedback, then you could look at pages that already rank for a particular query. By using the same vocabulary, you can ensure that you get the most out of this way of relevance feedback.

Takeaways
In short, we’ve considered one of the options for assigning a value to a document based on the content of the page. Although the vector method is fairly accurate, it is certainly not the only method to calculate relevance. There are many adjustments to the model and it also remains only a part of the complete algorithm of search engines like Google. We have taken a look into relevance feedback as well. *cough* panda *cough*. I hope I’ve given you some insights in the methods search engine can use other then external factors. Now it’s time to discuss this and to go play with the excel file 🙂

Have a good day!!!

source: http://www.seomoz.org

How Google’s Panda Update Changed SEO Best Practices Forever

It’s here! Google has released Panda update 2.2, just as Matt Cutts said they would at SMX Advanced here in Seattle a couple of weeks ago. This time around, Google has – among other things – improved their ability to detect scraper sites and banish them from the SERPs. Of course, the Panda updates are changes to Google’s algorithm and are not merely manual reviews of sites in the index, so there is room for error (causing devastation for many legitimate webmasters and SEOs).

A lot of people ask what parts of their existing SEO practice they can modify and emphasize to recover from the blow, but alas, it’s not that simple. In this week’s Whiteboard Friday, Rand discusses how the Panda updates work and, more importantly, how Panda has fundamentally changed the best practices for SEO. Have you been Panda-abused? Do you have any tips for recuperating? Let us know in the comments!

Panda, also known as Farmer, was this update that Google came out with in March of this year, of 2011, that rejiggered a bunch of search results and pushed a lot of websites down in the rankings, pushed some websites up in the rankings, and people have been concerned about it ever since. It has actually had several updates and new versions of that implementation and algorithm come out. A lot of people have all these questions like, “Ah, what’s going on around Panda?” There have been some great blog posts on SEOmoz talking about some of the technical aspects. But I want to discuss in this Whiteboard Friday some of the philosophical and theoretical aspects and how Google Panda really changes the way a lot of us need to approach SEO.

So let’s start with a little bit of Panda history. Google employs an engineer named Navneet Panda. The guy has done some awesome work. In fact, he was part of a patent application that Bill Slawski looked into where he found a great way to scale some machine learning algorithms. Now, machine learning algorithms, as you might be aware, are very computationally expensive and they take a long time to run, particularly if you have extremely large data sets, both of inputs and of outputs. If you want, you can research machine learning. It is an interesting fun tactic that computer scientists use and programmers use to find solutions to problems. But basically before Panda, machine learning scalability at Google was at level X, and after it was at the much higher level Y. So that was quite nice. Thanks to Navneet, right now they can scale up this machine learning.

What Google can do based on that is take a bunch of sites that people like more and a bunch of sites that people like less, and when I say like, what I mean is essentially what the quality raters, Google’s quality raters, tell them this site is very enjoyable. This is a good site. I’d like to see this high in the search results. Versus things where the quality raters say, “I don’t like to see this.” Google can say, “Hey, you know what? We can take the intelligence of this quality rating panel and scale it using this machine learning process.”

Here’s how it works. Basically, the idea is that the quality raters tell Googlers what they like. They answer all these questions, and you can see Amit Singhal and Matt Cutts were interviewed by Wired Magazine. They talked about some of the things that were asked of these quality raters, like, “Would you trust this site with your credit card? Would you trust the medical information that this site gives you with your children? Do you think the design of this site is good?” All sorts of questions around the site’s trustworthiness, credibility, quality, how much they would like to see it in the search results. Then they compare the difference.

The sites that people like more, they put in one group. The sites that people like less, they put in another group. Then they look at tons of metrics. All these different metrics, numbers, signals, all sorts of search signals that many SEOs suspect come from user and usage data metrics, which Google has not historically used as heavily. But they think that they use those in a machine learning process to essentially separate the wheat from the chaff. Find the ones that people like more and the ones that people like less. Downgrade the ones they like less. Upgrade the ones they like more. Bingo, you have the Panda update.

So, Panda kind of means something new and different for SEO. As SEOs, for a long time you’ve been doing the same kind of classic things. You’ve been building good content, making it accessible to search engines, doing good keyword research, putting those keywords in there, and then trying to get some links to it. But you have not, as SEOs, we never really had to think as much or as broadly about, “What is the experience of this website? Is it creating a brand that people are going to love and share and reward and trust?” Now we kind of have to think about that.

It is almost like the job of SEO has been upgraded from SEO to web strategist. Virtually everything you do on the Internet with your website can impact SEO today. That is especially true following Panda. The things that they are measuring is not, oh, these sites have better links than these sites. Some of these sites, in fact, have much better links than these sites. Some of these sites have what you and I might regard, as SEOs, as better content, more unique, robust, quality content, and yet, people, quality raters in particular, like them less or the things, the signals that predict that quality raters like those sites less are present in those types of sites.

Let’s talk about a few of the specific things that we can be doing as SEOs to help with this new sort of SEO, this broader web content/web strategy portion of SEO.

First off, design and user experience. I know, good SEOs have been preaching design user experience for years because it tends to generate more links, people contribute more content to it, it gets more social signal shares and tweets and all this other sort of good second order effect. Now, it has a first order effect impact, a primary impact. If you can make your design absolutely beautiful, versus something like this where content is buffeted by advertising and you have to click next, next, next a lot. The content isn’t all in one page. You cannot view it in that single page format. Boy, the content blocks themselves aren’t that fun to read, even if it is not advertising that’s surrounding them, even if it is just internal messaging or the graphics don’t look very good. The site design feels like it was way back in the 1990s. All that stuff will impact the ability of this page, this site to perform. And don’t forget, Google has actually said publicly that even if you have a great site, if you have a bunch of pages that are low quality on that site, they can drag down the rankings of the rest of the site. So you should try and block those for us or take them down. Wow. Crazy, right? That’s what a machine learning algorithm, like Panda, will do. It will predicatively say, “Hey, you know what? We’re seeing these features here, these elements, push this guy down.”

Content quality matters a lot. So a lot of time, in the SEO world, people will say, “Well, you have to have good, unique, useful content.” Not enough. Sorry. It’s just not enough. There are too many people making too much amazing stuff on the Internet for good and unique and grammatically correct and spelled properly and describes the topic adequately to be enough when it comes to content. If you say, “Oh, I have 50,000 pages about 50,000 different motorcycle parts and I am just going to go to Mechanical Turk or I am going to go outsource, and I want a 100 word, two paragraphs about each one of them, just describe what this part is.” You think to yourself, “Hey, I have good unique content.” No, you have content that is going to be penalized by Panda. That is exactly what Panda is designed to do. It is designed to say this is content that someone wrote for SEO purposes just to have good unique content on the page, not content that makes everyone who sees it want to share it and say wow. Right?

If I get to a page about a motorcycle part and I am like, “God, not only is this well written, it’s kind of funny. It’s humorous. It includes some anecdotes. It’s got some history of this part. It has great photos. Man, I don’t care at all about motorcycle parts, and yet, this is just a darn good page. What a great page. If I were interested, I’d be tweeting about this, I’d share it. I’d send it to my uncle who buys motorcycles. I would love this page.” That’s what you have to optimize for. It is a totally different thing than optimizing for did I use the keyword at least three times? Did I put it in the title tag? Is it included in there? Is the rest of the content relevant to the keywords? Panda changes this. Changes it quite a bit. 😉

Finally, you are going to be optimizing around user and usage metrics. Things like, when people come to your site, generally speaking compared to other sites in your niche or ranking for your keywords, do they spend a good amount of time on your site, or do they go away immediately? Do they spend a good amount of time? Are they bouncing or are they browsing? If you have a good browse rate, people are browsing 2, 3, 4 pages on average on a content site, that’s decent. That’s pretty good. If they’re browsing 1.5 pages on some sites, like maybe specific kinds of news sites, that might actually be pretty good. That might be better than average. But if they are browsing like 1.001 pages, like virtually no one clicks on a second page, that might be weird. That might hurt you. Your click-through rate from the search results. When people see your title and your snippet and your domain name, and they go, “Ew, I don’t know if I want to get myself involved in that. They’ve got like three hyphens in their domain name, and it looks totally spammy. I’m not going to get involved.” Then that click-through rate is probably going to suffer and so are your rankings.

They are going to be looking at things like the diversity and quantity of traffic that comes to your site. Do lots of people from all around the world or all around your local region, your country, visit your website directly? They can measure this through Chrome. They can measure it through Android. They can measure it through the Google toolbar. They have all this user and usage metrics. They know where people are going on the Internet, where they spend time, how much time they spend, and what they do on those pages. They know about what happens from the search results too. Do people click from a result and then go right back to the search results and perform another search? Clearly, they were unhappy with that. They can take all these metrics and put them into the machine learning algorithm and then have Panda essentially recalculate. This why you see essentially Google doesn’t issue updates every day or every week. It is about every 30 or 40 days that a new Panda update will come out because they are rejiggering all this stuff. 🙂

One of the things that people who get hit by Panda come up to me and say, “God, how are we ever going to get out of Panda? We’ve made all these changes. We haven’t gotten out yet.” I’m like, “Well, first off, you’re not going to get out of it until they rejigger the results, and then there is no way that you are going to get out of it unless you change the metrics around your site.” So if you go into your Analytics and you see that people are not spending longer on your pages, they are not enjoying them more, they are not sharing them more, they are not naturally linking to them more, your branded search traffic is not up, your direct type in traffic is not up, you see that none of these metrics are going up and yet you think you have somehow fixed the problems that Panda tries to solve for, you probably haven’t.

I know this is frustrating. I know it’s a tough issue. In fact, I think that there are sites that have been really unfairly hit. That sucks and they shouldn’t be and Google needs to work on this. But I also know that I don’t think Google is going to be making many changes. I think they are very happy with the way that Panda has gone from a search quality perspective and from a user happiness perspective. Their searchers are happier, and they are not seeing as much junk in the results. Google likes the way this is going. I think we are going to see more and more of this over time. It could even get more aggressive. I would urge you to work on this stuff, to optimize around these things, and to be ready for this new form of SEO. 🙂

Google Panda 3.1 Update : 11/18

Friday afternoon, sometime after 4pm I believe, Google tweeted that they pushed out a “minor” Panda update effecting less than one-percent of all searches.

The last time Google said a Panda update was minor, it turned out to be pretty significant.

That being said, we should have named it 3.0 – in fact, I spoke to someone at Google who felt the same. So I am going to name this one 3.1, although it does make it easier to reference these updates by dates.

Panda Updates:

 

For more on Panda, see our Google Panda category.

Forum discussion at WebmasterWorld.

How to Get Quality Backlinks, Some Must Know Best Tips!

Do you know how to get backlinks? I am sure you know how to get some backlinks, but it is important to know different methods of getting backlinks. Search engines, including Google, look at a few key components when determining page rank for websites. One of those components is how many backlinks a site has, and the quality of those backlinks. The more backlinks and the higher the quality the better your site will rank. This article is going to discuss different methods on how to get backlinks.

I am writing this article for two purposes. One is to help you possibly learn a couple of new ways to get backlinks and the other is to give myself a quality backlink. Yes, that is right, writing articles can give you backlinks. Even better if you publish your article in an article directory such as EzineArticles, you are going to get a good quality backlink. To get backlinks with articles you write an informative article about your niche or website. Then include a link or two to your site in the resource box. Article writing can be beneficial in two ways, you are sharing useful information and getting backlinks to help your website get a good page ranking.

A relatively easy way that you can build backlinks is to search for blogs or other articles that are related to your website. Read the blog post or the article and see if you can add an insightful comment. When you leave the comment you will include a link to your website. Just be sure that you are actually leaving quality comments. Do not spam the author of the material you are commenting on. Just as I am sure you would rather not have people spam your site, please do not spam others.

The last tip I have for you today, is to include your website address in forums that you participate in. You can simply place your website in your signature line, and then every time leave you leave a post your site will be there. Do not just start joining random forums and leaving spam. The search engines are smart and they will pick up on that. Just make sure to have a signature line in forums that you do visit.

Building backlinks is a very important part of SEO. It is one step that many people overlook because it can be time consuming and tedious. I have just shared with you a few methods on how to get backlinks to your site. If you really want to increase page rank for your site I suggest working on building backlinks slowly. Do not bombard your site with a whole bunch at once. A slow drip of backlnks will be more natural and it will help your site to move up in the ranks and stay there.