Online SEO Training | Online SEO Training India | Online SEO Training in India: April 2010

Wednesday, April 21, 2010

off page optimization

Off-page optimization are strategies for search engine optimization that are done off the pages of a website to maximize its performance in the search engines for target keywords related to the page content. Examples of off-page optimization include linking, and placing keywords within link anchor text. Methods of obtaining links can also be considered off-page optimization. These include:
Press releases
Article distribution
Social networking via sites like Digg and Slashdot
Link campaigns, such as asking complementary businesses to provide links
Directory listings
Link exchanges
Three-way linking
One-way linking
Blogging
Forum posting
Multiway linking
Internet Marketing

Page Rank Algoritham

Algorithm
PageRank is a probability distribution used to represent the likelihood that a person randomly clicking on links will arrive at any particular page. PageRank can be calculated for collections of documents of any size. It is assumed in several research papers that the distribution is evenly divided between all documents in the collection at the beginning of the computational process. The PageRank computations require several passes, called "iterations", through the collection to adjust approximate PageRank values to more closely reflect the theoretical true value.
A probability is expressed as a numeric value between 0 and 1. A 0.5 probability is commonly expressed as a "50% chance" of something happening. Hence, a PageRank of 0.5 means there is a 50% chance that a person clicking on a random link will be directed to the document with the 0.5 PageRank.
[edit]Simplified algorithm
How PageRank Works
Assume a small universe of four web pages: A,B, C andD. The initial approximation of PageRank would be evenly divided between these four documents. Hence, each document would begin with an estimated PageRank of 0.25.
In the original form of PageRank initial values were simply 1. This meant that the sum of all pages was the total number of pages on the web. Later versions of PageRank (see the formulas below) would assume a probability distribution between 0 and 1. Here a simple probability distribution will be used- hence the initial value of 0.25.
If pages B, C, and D each only link to A, they would each confer 0.25 PageRank to A. All PageRank PR( ) in this simplistic system would thus gather to A because all links would be pointing to A.
$PR(A)= PR(B) + PR(C) + PR(D).\,$
This is 0.75.
Suppose that page B has a link to page C as well as to page A, while page D has links to all three pages. The value of the link-votes is divided among all the outbound links on a page. Thus, page B gives a vote worth 0.125 to page A and a vote worth 0.125 to page C. Only one third of D's PageRank is counted for A's PageRank (approximately 0.083).
$PR(A)= \frac{PR(B)}{2}+ \frac{PR(C)}{1}+ \frac{PR(D)}{3}.\,$
In other words, the PageRank conferred by an outbound link is equal to the document's own PageRank score divided by the normalized number of outbound links L( ) (it is assumed that links to specific URLs only count once per document).
$PR(A)= \frac{PR(B)}{L(B)}+ \frac{PR(C)}{L(C)}+ \frac{PR(D)}{L(D)}. \,$
In the general case, the PageRank value for any page ucan be expressed as:
$PR(u) = \sum_{v \in B_u} \frac{PR(v)}{L(v)}$ ,
i.e. the PageRank value for a page u is dependent on the PageRank values for each page v out of the set B_u (this set contains all pages linking to page u), divided by the numberL(v) of links from page v.
[edit]Damping factor
The PageRank theory holds that even an imaginary surfer who is randomly clicking on links will eventually stop clicking. The probability, at any step, that the person will continue is a damping factor d. Various studies have tested different damping factors, but it is generally assumed that the damping factor will be set around 0.85.^[5]
The damping factor is subtracted from 1 (and in some variations of the algorithm, the result is divided by the number of documents (N) in the collection) and this term is then added to the product of the damping factor and the sum of the incoming PageRank scores. That is,
$PR(A) = {1 - d \over N} + d \left( \frac{PR(B)}{L(B)}+ \frac{PR(C)}{L(C)}+ \frac{PR(D)}{L(D)}+\,\cdots \right).$
So any page's PageRank is derived in large part from the PageRanks of other pages. The damping factor adjusts the derived value downward. The original paper, however, gave the following formula, which has led to some confusion:
$PR(A)= 1 - d + d \left( \frac{PR(B)}{L(B)}+ \frac{PR(C)}{L(C)}+ \frac{PR(D)}{L(D)}+\,\cdots \right).$
The difference between them is that the PageRank values in the first formula sum to one, while in the second formula each PageRank gets multiplied by N and the sum becomes N. A statement in Page and Brin's paper that "the sum of all PageRanks is one"^[5] and claims by other Google employees^[7] support the first variant of the formula above.
Google recalculates PageRank scores each time it crawls the Web and rebuilds its index. As Google increases the number of documents in its collection, the initial approximation of PageRank decreases for all documents.
The formula uses a model of arandom surfer who gets bored after several clicks and switches to a random page. The PageRank value of a page reflects the chance that the random surfer will land on that page by clicking on a link. It can be understood as a Markov chain in which the states are pages, and the transitions are all equally probable and are the links between pages.
If a page has no links to other pages, it becomes a sink and therefore terminates the random surfing process. However, the solution is quite simple. If the random surfer arrives at a sink page, it picks another URL at random and continues surfing again.
When calculating PageRank, pages with no outbound links are assumed to link out to all other pages in the collection. Their PageRank scores are therefore divided evenly among all other pages. In other words, to be fair with pages that are not sinks, these random transitions are added to all nodes in the Web, with a residual probability of usually d= 0.85, estimated from the frequency that an average surfer uses his or her browser's bookmark feature.
So, the equation is as follows:
$PR(p_i) = \frac{1-d}{N} + d \sum_{p_j \in M(p_i)} \frac{PR (p_j)}{L(p_j)}$
where $p 1, p 2,..., p N$ are the pages under consideration, $M (p i)$ is the set of pages that link to $p i$ , $L (p j)$ is the number of outbound links on page $p j$ , and N is the total number of pages.
The PageRank values are the entries of the dominant eigenvector of the modified adjacency matrix. This makes PageRank a particularly elegant metric: the eigenvector is
$\mathbf{R} = \begin{bmatrix} PR(p_1) \\ PR(p_2) \\ \vdots \\ PR(p_N) \end{bmatrix}$
where R is the solution of the equation
$\mathbf{R} = \begin{bmatrix} {(1-d)/ N} \\ {(1-d) / N} \\ \vdots \\ {(1-d) / N} \end{bmatrix} + d \begin{bmatrix} \ell(p_1,p_1) & \ell(p_1,p_2) & \cdots & \ell(p_1,p_N) \\ \ell(p_2,p_1) & \ddots & & \vdots \\ \vdots & & \ell(p_i,p_j) & \\ \ell(p_N,p_1) & \cdots & & \ell(p_N,p_N) \end{bmatrix} \mathbf{R}$
where the adjacency function $\ell(p_i,p_j)$ is 0 if page $p j$ does not link to $p i$ , and normalised such that, for each i
$\sum_{i = 1}^N \ell(p_i,p_j) = 1$ ,
i.e. the elements of each column sum up to 1 (for more details see the computation section below). This is a variant of the eigenvector centrality measure used commonly in network analysis.
Because of the large eigengap of the modified adjacency matrix above, ^[8] the values of the PageRank eigenvector are fast to approximate (only a few iterations are needed).
As a result of Markov theory, it can be shown that the PageRank of a page is the probability of being at that page after lots of clicks. This happens to equal $t - 1$ where $t$ is the expectationof the number of clicks (or random jumps) required to get from the page back to itself.
The main disadvantage is that it favors older pages, because a new page, even a very good one, will not have many links unless it is part of an existing site (a site being a densely connected set of pages, such as Wikipedia). The Google Directory (itself a derivative of the Open Directory Project) allows users to see results sorted by PageRank within categories. The Google Directory is the only service offered by Google where PageRank directly determines display order.^{[citation needed]} In Google's other search services (such as its primary Web search) PageRank is used to weigh the relevance scores of pages shown in search results.
Several strategies have been proposed to accelerate the computation of PageRank.^[9]
Various strategies to manipulate PageRank have been employed in concerted efforts to improve search results rankings and monetize advertising links. These strategies have severely impacted the reliability of the PageRank concept, which seeks to determine which documents are actually highly valued by the Web community.
Google is known to penalize link farms and other schemes designed to artificially inflate PageRank. In December 2007 Google started activelypenalizing sites selling paid text links. How Google identifies link farms and other PageRank manipulation tools are among Google's trade secrets.
[edit]Computation
To summarize, PageRank can be either computed iteratively or algebraically. Alternatively, the power iteration method^[10]^[11], or power method, can be employed.
[edit]Iterative
In the former case, at $t = 0$ , an initial probability distribution is assumed, usually
$PR(p_i; 0) = \frac{1}{N}$ .
At each time step, the computation, as detailed above, yields
$PR(p_i;t+1) = \frac{1-d}{N} + d \sum_{p_j \in M(p_i)} \frac{PR (p_j; t)}{L(p_j)}$ ,
or in matrix notation
$\mathbf{R}(t+1) = d \mathcal{M}\mathbf{R}(t) + \frac{1-d}{N} \mathbf{1}$ , ()
where $\mathbf{R}_i(t)=PR(p_i; t)$ and $\mathbf{1}$ is the column vector of length $N$ containing only ones.
The matrix $\mathcal{M}$ is defined as
$\mathcal{M}_{ij} = \begin{cases} 1 /L(p_j) , & \mbox{if }j\mbox{ links to }i\ \\ 0, & \mbox{otherwise} \end{cases}$
i.e.,
$\mathcal{M} := (K^{-1} A)^t$ ,
where $A$ denotes the adjacency matrix of the graph and $K$ is the diagonal matrix with the outdegrees in the diagonal.
The computation ends when for some small $ε$
$|\mathbf{R}(t+1) - \mathbf{R}(t)| < \epsilon$ ,
i.e., when convergence is assumed.
[edit]Algebraic
In the latter case, for $t \to \infty$ (i.e., in the steady state), the above equation () reads
$\mathbf{R} = d \mathcal{M}\mathbf{R} + \frac{1-d}{N} \mathbf{1}$ . ()
The solution is given by
$\mathbf{R} = (\mathbf{I}-d \mathcal{M})^{-1} \frac{1-d}{N} \mathbf{1}$ ,
with the identity matrix $\mathbf{I}$ .
The solution exists and is unique for $0 < d <>$ . This can be seen by noting that $\mathcal{M}$ is by construction a stochastic matrix and hence has an eigenvalue equal to one because of thePerron-Frobenius theorem.
[edit]Power Method
If the matrix $\mathcal{M}$ is a transition probability, i.e., column-stochastic with no columns consisting of just zeros and $\mathbf{R}$ is a probability distribution (i.e., $|\mathbf{R}|=1$ , $\mathbf{E}\mathbf{R}=1$ where $\mathbf{E}$ is matrix of all ones), Eq. () is equivalent to
$\mathbf{R} = \left( d \mathcal{M} + \frac{1-d}{N} \mathbf{E} \right)\mathbf{R} =: \widehat \mathcal{M} \mathbf{R}$ . ()
Hence PageRank $\mathbf{R}$ is the principal eigenvector of $\widehat\mathcal{M}$ . A fast and easy way to compute this is using the power method: starting with an arbitrary vector $x (0)$ , the operator $\widehat\mathcal{M}$ is applied in succession, i.e.,
$x(t+1) = \widehat\mathcal{M} x(t)$ ,
until
$| x (t + 1) - x (t) | < ε$ .
Note that in Eq. () the matrix on the right-hand side in the parenthesis can be interpreted as
$\frac{1-d}{N} \mathbf{I} = (1-d)\mathbf{P} \mathbf{1}^t$ ,
where $\mathbf{P}$ is an initial probability distribution. In the current case
$\mathbf{P} := \frac{1}{N} \mathbf{1}$ .
Finally, if $\mathcal{M}$ has columns with only zero values, they should be replaced with the initial probability vector $\mathbf{P}$ . In other words
$\mathcal{M}^\prime := \mathcal{M} + \mathcal{D}$ ,
where the matrix $\mathcal{D}$ is defined as
$\mathcal{D} := \mathbf{D} \mathbf{P}^t$ ,
with
$\mathbf{D}_i = \begin{cases} 1, & \mbox{if }L(p_i)=0\ \\ 0, & \mbox{otherwise} \end{cases}$
In this case, the above two computations using $\mathcal{M}$ only give the same PageRank if their results are normalized:
$\mathbf{R}_{\textrm{power}} = \frac{\mathbf{R}_{\textrm{iterative}}}{|\mathbf{R}_{\textrm{iterative}}|} = \frac{\mathbf{R}_{\textrm{algebraic}}}{|\mathbf{R}_{\textrm{algebraic}}|}$ .

Friday, April 9, 2010

SEO Interview Questions

Death by interview has been known so I wouldn’t recommend asking all of these questions. Pick and choose at will…

Open-ended SEO questions

What is your favourite aspect of SEO?
What is the most difficult aspect of SEO for you?
What has been your biggest mistake in optimising a website for search engines?
What has been you biggest success in SEO?
Do you have your own website(s)? What are they? What is their purpose? How effective have they been?
What business sectors have you previously worked in as a SEO?
What is the most competitive sector you have worked in as a SEO specialist?
Do you like Matt Cutt’s?!

Knowledge of the SEO Industry / engagement in learning about SEO and engagement with the SEO Community

What is your favourite SEO website/blog, and why?
Who do you most respect in the SEO industry, and why?
Who do you least respect in the SEO industry, and why?
Which website do you go to learn something new every time?

Analytics

What Analytics packages have you used?
Talk me through the process of setting conversion goals?
Explain the process of advanced segmentation and an example of why you might use this?
If you could develop a new feature for an analytics package that is not currently/easily available what would it be?

Algorithms

Please explain the PageRank algorithm…
What is the most important aspect to you of the PageRank algorithm for link-building?
What is page segmentation?
What is LSI / LSA and its relevance to SEO?
Explain to me how phrase-base algorithms work? Clustering?
Describe any perceived differences in the main search engines?
Have you noticed any algorithm changes lately that you believe to have affected your rankings? How do you work to protect your online visibility?

Keywords

What process do you typically go through when researching keywords?
How could this process be improved?
How do you carry out competitive analysis of keywords/SERPs as part of the keyword research process?
When targeting keywords on-page, discuss some considerations you might make?

Accessibility

What factors hinder search engines access to a website’s content?
What is the most responsible way of using Flash?
Tell me how you might use the Robots.txt file?
What is the difference between an xml sitemap and an html sitemap?

On-page Ranking Factors

If you were reviewing a landing page, what on-page ranking factors would you consider?
How would you analyse the strength of that page as part of the site?
Are you competent with HTML and CSS?

Onsite Ranking Factors

Talk me through factors you would consider in building an optimised website. (Possible answers might include the discussion around information architecture, site structure, title tags, link structures, keyword relevance, etc).
What are onsite ranking factors for building a successful landing page strategy?
Please provide examples of blackhat SEO techniques?
What are your thoughts on blackhat SEO techniques? What, if any, have you used, or tested?

Offsite Ranking Factors

What would the perfect inbound link look like?
What do you like and not like about link-building?
Explain to me your involvement in link-building in the past?
What approach to link-building have you had most success?

Linkbait Development and Marketing

Would you consider yourself as creative?
Have you ever successfully carried out a linkbait campaign for a client / in-house? What was the success?
Talk me through the process you might go through in developing a linkbait strategy?

Copywriting

Are you confident writing and publishing content online?
Please provide examples of the content that you have written. What was the purpose of this content and what keywords were you targeting?

SEO Tools

What keyword research tools do you use and why?
What is your favourite ‘SEO tool’? Why?
Do you think SEO tools are effective in competitive markets? Why?

Testing

Have you carried-out split-testing / multivariate testing of content?
What did you learn from this process?

Image Optimisation

What factors can you do to encourage the chance of ranking for images?
What is hotlinking? How can this be successfully optimised?

Video optimisation

Have you ever carried out video optimisation?
What are a few considerations of optimising video content?

Social Media Interview Questions

Open-ended Social Media Questions

Which social media sites do you most like? Why?
Which social media sites do you most dislike? Why?
What has been your biggest achievement in SMM?
Do you feel that you are well connected in Social media spheres? Does your social media account carry influence?
Do you prefer to use the same pseudonym across your social media profiles? What are the pros and cons of doing this?
When did you get into social media (and marketing)?
What is your oldest social media profile?
Would you ever sell or buy social media profiles?
What has been the most effective social media marketing campaign you have been involved in? How was it effective? Metrics / exposure / links?
How do you effectively use social media to support SEO campaigns, or vice versa?

PPC Interview questions

Although not strictly SEO, the understanding of the cross-over of visibility in paid-listings can be very important for effective SEO campaigns.
Have you worked on/with PPC accounts? How did it go?
How do you integrate PPC and SEO?
What considerations might you make when carrying out paid search competitive analysis?

Business Acumen and ROI

What are the key performance metrics you have previously reported to clients?
What are effective metrics for highlighting return on investment from SEO?
What would you like to change about the SEO reporting process?
What other areas of business present opportunities for organic search visibility?
If your SEO efforts aren’t getting the visibility you would hope, what would you do?

Pitching for Business

Have you prepared proposals for SEO clients?
Have you presented proposals to SEO clients? How did it go?
What was the biggest mistake you have made in a meeting with a client?
What is your greatest strength when presenting to prospective SEO clients?

Retaining Business

What do you think is the most important factor in retaining a client?
From the previous company that you worked for, what was one process that they could have improved in retaining and gaining clients?

Closing questions

Where do you see yourself in 5 years time?
What are you salary expectations? Availability? Etc, etc…
How did you feel the interview went?!

Search engine optimization

Search engine optimization (SEO) is the process of improving the volume or quality of traffic to a web site or a webpage (such as a blog) from search engines via "natural" or un-paid ("organic" or "algorithmic") search results as opposed to other forms of search engine marketing (SEM) which may deal with paid inclusion. The theory is that the earlier (or higher) a site appears in the search results list, the more visitors it will receive from the search engine. SEO may target different kinds of search, including image search, local search, video search and industry-specific vertical search engines. This gives a web site web presence.

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