Search Engines (SE) know everything about what people have been looking for in the past.
One of the reasons why companies like G, Y en M are valued so much is just for this informational value. SE have large databases of search terms that are categorized by date, time, season, etc. And with intelligent (data-mining) techniques the SE masters will know perfectly what has been going on in period that is behind us. And that is only for the demand site, but the same applies for the supply. The crawlers have investigated the world (wide web) outside and can easily sketch the current supply. Although that basic economics tells us that demand and supply never really match. Taking this aside leaves us with the question: what will the past (demands) tell you about the future (needs) With the growing content on a daily bases, the overload of information is enormous. Consumers, more than business professionals, will shift their searches fast and frequently. This leaves you as a SEO - basically with two possible strategies. The one that best fits your organizational style is the one you should pursue.
In the field of investment there are a few investment strategies. Two of them are well known to the personal investors. The first is technical analysis (TA) and the other fundamental analysis (FA). Fans of the first believe that past information will help to address future investment decisions. The supporters of the fundamental approach will search for undervalued stock and buy them even if that stock is out of favor. In a sense both strategies differ in the fact that the first investor moves with the flow of the market and the last has its own independent approach.
Now back to the business. If you believe that past search information will reveal something about near future search interest, you should engage an active SEO policy. Move with the market (changes) and optimize frequently for changing favors. In that approach you go with the flow. You follow a certain trend and catch up with what is in favor at a specific moment in time. In the other case you (or your company) will have an own path to track irrespectively the market turbulences.
The impact of either policy is high. The first needs an active approach with an agile response to market influences. You need to actively follow the market and make adjustments or follow a SEO that is in line with the current stream. The second is much more relaxed in the sense that the market interferences are taken for granted. You have your own path to follow and you are not bothered by current hypes and emotions.
A mix of both could be an option as long as you do not treat the middle way. This would be aiming at a moving target and will leave you with neither the benefits of the first nor the ones of the second.
If you recognize the above possibilities, the next thing to know is what style best fits your company. With what approach are you most familiar with What are your personal management preferences or what favor reflects the preference of the organization.If you are able to dig this up in advance you will save a lot of SEO-effort and will help you business by addressing energy where it agrees with your organizational style. Your client should also notice the effect.Suppliers of SEO can benefit from this by tailoring their offerings according to the needs and the style preferences of the prospect.
2005 Hans Bool / Astor White
Hans Bool (The Netherlands) is the founder of Astor White a consulting company dedicated to (the human side of) management consulting and e-advice. He has many years of experience in (project) management, consulting and business architecture. He studied economics and has recently published the book: How to manage your organizational portfolio just stick to your rules.
Page Rank (PR) is an algorithm used by Google to compute the relative importance of a particular webpage on the internet and assign it a numeric value from 0 (least important) to 10 (most important). This value is calculated through an iterative analysis of the backlinks to the webpage. If webpage A links to webpage B then webpage B would receive 1 vote towards their page rank.
Fact: Page Rank is calculated on a webpage by webpage basis not on a website by website basis
The importance of the webpage casting a vote and the total number of outgoing links on the webpage casting a vote are the primary factors which determine how much voting share this webpage will transfer to each of the outgoing links on them. Google calculates a webpages page rank by adding up all of the voting shares for that webpage through an iterative calculation.
Page Rank is one of the factors Google utilizes to help determine their Search Engine Ranking Positions (SERPs). It should be noted that this algorithm is only one part of their overall ranking scheme and not necessarily the most important one as many websites would have you believe. The general internet user has no idea about the concept of page rank and are unable to tell what a particular pages PR is unless they have the Google Toolbar installed (or use an online page rank checker). Since page rank is part of Googles search ranking algorithm an understanding of the concept is still important for any webmaster concerned with getting traffic to their site.
Fact: Not all links pointing to a webpage are counted as votes for that webpage
As soon as Google introduced the concept of page rank unsavory webmasters developed ways to manipulate the rankings. These webmasters began creating web pages with the sole purpose of increasing the amount of incoming links pointing to their website.
Common Black Hat SEO Techniques:
How is Page Rank Calculated
When Google introduced the concept of page rank they published the algorithm they were going to use to calculate it. The formula in its current form is known only to the engineers at Google but it is fair to say it closely resembles the following formula.
PR(A) = (1-d) + d(PR(t1)/C(t1) + ... + PR(tn)/C(tn))
While at first glance this equation can seem daunting, in actuality the concept is not that hard to understand. Lets take a minute to break down the formula and see what conclusions can be drawn.
PR(t1)...PR(tn) - the page rank (PR) of each page from page t1 to tn. (each value of t represents 1 link to webpage A)
C(t1)...C(tn) - the number of outgoing links (C) on each page from page t1 to tn
d - damping factor
Quoting from the original Google Page Rank white paper:
The parameter d is a damping factor which can be set between 0 and 1. We usually set d to 0.85.
Knowing what these parameters mean and knowing the value of the damping factor we can simplify the formula from above:
PR(A) = 0.15 + 0.85*(A share of the PR of every webpage linking to page A)
The share each webpage passes to webpage A can be computed by dividing the Page Rank of the webpage linking to page A by the number of outgoing links on that page. Each outgoing link on that page would receive an equal voting share from the total available page rank of the page containing the outgoing link. The total available page rank each webpage has available to transfer to outgoing links is a little less than the total page rank of that page (PR of page * 0.85) which can be easily derived when the damping factor is known.<
Implications
Having a basic understanding of the algorithm we can now draw a few conclusions about page rank and its implications to your website. For instance, it is very possible to have a link on web page X that has a high page rank transferring less page rank voting shares to your website than a link on web page Y with a lower page rank.
How is this possibleLets analyze an example:
Page X - page rank 4, outgoing links 10
Page Y - page rank 8, outgoing links 100
Page X would transfer 0.85(4/10) = 0.34 page rank voting shares to each outgoing link
Page Y would transfer 0.85(8/100) = 0.068 page rank voting shares to each outgoing link
Even though Page X has a much lower page rank value, due to the fact that the number of outgoing links on Page X is so much smaller than on Page Y it actually transfers more page rank voting shares to each outgoing link than Page Y .
Pages with no links back to them would still have a modest page rank value of 0.15 derived from the (1-d) portion of the equation. It is important to note that while this value holds true according to the equation, only Google engineers are privy to the knowledge of whether actual page rank voting share is transferred in this scenario. Google could easily say that pages with no incoming links transfer a page rank voting share of 0 with a click of a mouse and no one would know for sure except them.
Fact: The Google Toolbar displays Page Rank as a base 10 log scale that is not the actual result of the Page Rank calculation
The average page rank of all pages in the index is 1. It is possible to have an actual page rank value in the millions or much smaller than 1 using the page rank formula but the Google toolbar only displays integers from 0 - 10 on its pr meter. Only Google knows how the scale is split up and where the basepoints for each level are. For example, it may take an actual page rank of 10,000 using the formula above to achieve a page rank of 4/10 on the toolbar scale.
Page Rank in Complex Networks
The example above does not actually duplicate a real world example since it is only computing the page rank voting share of the ffa page in an idealized situation where the page rank of the page is already known. In complex networks with links in and links out of webpages the actual page rank for a webpage cannot be known due to the interdependencies each web page has on one another to calculate their page rank.
Think of it as a chicken and the egg situation. The problem can be solved by taking a best initial guess for the page rank value of each webpage in the network and plugging it into the page rank formula. The results of these calculations are then used to calculate the next incremental page rank values for the webpages in the network. This calculation is repeated over and over again until the page rank value approaches a limit. This limit is then the actual page rank for that page. In a complex network like the internet finding the page rank for all webpages can take millions of iterations.
Click here for more detailed examples and an online page rank calculator
It is also worth noting that when a webpage transfers page rank voting shares to another webpage the page rank of the contributing page is not reduced in any way. There is no actual page rank transfer, only a weighted vote is passed to the outgoing links.
Links on webpages with a high page rank and little or no other outgoing links on them but yours will provide the best opportunities to improve your page rank (if that is your goal and it shouldnt be, link for traffic not pr). Make sure to work on your site content and design before approaching other webmasters for links. The bottom line is you need to have a site worth linking to in order to get people to link to it.
Resources
Michael Lawrence is a University of Waterloo Engineering Graduate. Currently his projects include and