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Will You Add? - Google Page Rank Explained
A funnel is a good way to think of the marketing process. The top is very broad and consists of using mass marketing techniques. Mass marketing includes items like mass mailings, national advertising, billboards, and airing commercials during the Super Bowl. The marketing message is sent to a broad audience with little or no discretion. Mass marketing is very expensive, difficult to evaluate, and has a low return on investment. Mass marketing is a stab in the dark, like trying to find needles in a haystack. The next section of the funnel narrows the target audience into market segments. The communications still appeal to a large audience, but they are at least targeted to a specific market segment by means of the chosen media.Cat Fancy magazine, ESPN, and the Oxygen website each target a specific segment of the market: cat lovers, sports enthusiasts, and women, respectively. Next, we narrow the target audience further into niche or specialty markets. Harley Davidson, Barbie, and Macintosh computers each appeal to a specialty area of the market. The bottom of the funnel is the customized or one-to-one area of the funnel. The consumer being on a first-name basis with the product or service provider often characterizes these markets. Mike the mechanic, Bill the barber,
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 it's 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
You can start an online business by selling information on the internet. This Information is usually in the form of an e-book. Writing an e-book is similar to writing a book except that e-books contain significantly less pages. A lot of people are making money online by starting websites and selling e-books, and a lot of them make a steady and profitable income. However, by writing your own you could make a much more substantial incomeCreate Your OwnYou can build a much larger income by creating and publishing your own e-book instead of simply re-selling other people's e-books.When you write your own e-book you have total control of not only the text but also the advertisements and links you want to use to generate interest in your website. By publishing your own e-book you gain many advantages and the potential to tap into to several means of increasing your income.Become an ExpertBy writing e-books you can generate a lot of knowledge on a particular subject and become something of an expert. This can be very helpful because people generally prefer to buy products from people that they consider to be experts on the subject. By being able to offer your own quality and informative e-book you can establish yourself as someone who knows what they are talking a
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 webpage's 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 (SERP's). 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 website's 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 page's PR is unless they have the Google Toolbar installed (or use an online page rank checker). Since page rank is part of Google's 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:
- Link Farms - pages containing long lists of unrelated links set up for the sole purpose of manipulating search engine rankings and page rank
- Doorway Pages - orphaned webpages either on the same website or distributed throughout the internet stuffed with keywords containing links to the offender's site. Used to artificially inflate the back link count for a website.
- Free For All Links Pages - a type of link farm where, as the name implies, anyone is free to post their link. Once a valuable way to spread the word about your website, abuse through auto submissions has rendered these sites worthless and are now viewed as search engine SPAM.
- Automated or Hosted Link Exchanges - sites that offer to provide "hundreds" of back links to your site instantly. Generally you will have to install some html code on your website to display their directory and in return anyone else who has this code installed on their website will be displaying your link. This is a case where "if it sounds too good to be true it is". The search engine's are wise to this technique and watch for unnatural "spikes" in the number of backlinks pointing to a website. In actuality it is possible to inflate your page rank with this technique but if the search engine's wise up to your practices (and they always do eventually) you risk being dropped from their index or black holed in their rankings.
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 it's 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. Let's 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 it's 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 possible? Let's 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 it's 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 f
Recently a very innovative and bright business-marketing student had a brilliant idea for her marketing project. Building a filtration and reverse osmosis system to clean car wash water at a car wash and then reuse it for all the other water needs at the car wash such as landscaping, toilets and pressure washing the concrete and facilities. But the question is if someone produces such a system to do this, can they sell it and will car wash owners buy it?Business Marketing Student Paula Chavis has the whole thing worked out as she uses this innovative concept of hers for her project. She will have a team of sales people call on customers who are interested from advertising, website, catalogs and other sources.Now then one other point is that will also be needed to sell it will have to be a plan to PR the water conservation story to the community with a PR kit to make the sales team the choice for the carwash owner. Since there are other similar systems which the carwash owner once they have the idea may purchase instead, so this PR kit should include a plaque for their carwash lobby that they recycle and reuse water, some graphics for their website and press releases which your sales person hand delivers to their local newspaper too. That is one benefit, which will have t
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:
- Link Farms - pages containing long lists of unrelated links set up for the sole purpose of manipulating search engine rankings and page rank
- Doorway Pages - orphaned webpages either on the same website or distributed throughout the internet stuffed with keywords containing links to the offender's site. Used to artificially inflate the back link count for a website.
- Free For All Links Pages - a type of link farm where, as the name implies, anyone is free to post their link. Once a valuable way to spread the word about your website, abuse through auto submissions has rendered these sites worthless and are now viewed as search engine SPAM.
- Automated or Hosted Link Exchanges - sites that offer to provide "hundreds" of back links to your site instantly. Generally you will have to install some html code on your website to display their directory and in return anyone else who has this code installed on their website will be displaying your link. This is a case where "if it sounds too good to be true it is". The search engine's are wise to this technique and watch for unnatural "spikes" in the number of backlinks pointing to a website. In actuality it is possible to inflate your page rank with this technique but if the search engine's wise up to your practices (and they always do eventually) you risk being dropped from their index or black holed in their rankings.
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 it's 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. Let's 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 it's 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 possible? Let's 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 it's 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
Achieve Herbal Medicine Careers in the United States and Canada. With the demand for alternative and complementary medicine on the rise, individuals that are interested in pursuing herbal medicine careers will find it is essential for aspiring healers to acquire appropriate education and training from one of several natural health schools in order to land any number of herbal medicine careers.Herbal medicine careers today offer a variety of professional fields including positions as herbalists, naturopaths, natural healing practitioners, Chinese medicine practitioners, homeopathic practitioners, Ayurvedic practitioners, and related fields in iridology.Individuals seeking to fulfill their dreams of entering herbal medicine careers must first get adequate education. In most herbal medicine courses, students will study a wide variety of subjects including but not limited to coursework in Ayurvedic medicine, botanical medicine, Chinese medicine, phytochemistry, plant compounds, cell chemistry, and pharmacy (herbal). Depending on which healing field you wish to engage, herbal medicine careers allow you to become your own boss in an entrepreneurial healing arts practice; and for primary healthcare providers, a welcome addition of herbal medicine education can add to the se
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 it's 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. Let's 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 it's 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 possible? Let's 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 it's 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
When a product is first launched into a market a firm will have to decide what price to charge.Penetration pricing This strategy uses a very low price to enter the market and gain market share. It makes sense if there are cost advantages to producing on a large scale. It can also be beneficial if the market is price sensitive, so that a lower price generates significantly higher sales.Price skimming This strategy uses a high price to enter the market. Even though the price is high, some people may still be eager to try a new product. Once sales from this group of people have been exhausted, the price can be dropped to attract a new segment. When this segment is exhausted the price can be cut again. A price skimming strategy is appropriate if the firm can protect its idea or invention so that competitors cannot enter with a cheaper version. It may be protected using a trademark (which protects the firm logo) or a patent (which protects a new invention). Price skimming also makes sense if the market is particularly price sensitive, so that a price cut would not generate a large increase in sales. This strategy is often used with new technology: the latest computer or computer accessory enters the market with a high price which then falls quite rapidly a year or so later.
Implications
Having a basic understanding of the algorithm we can now draw a few conclusions about page rank and it's 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 possible? Let's 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 it's 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
Gone are the days when people used to encircle job listings in newspapers and wait for interview calls. Finding a job in India has become so much easier after the web has entered the public domain, says RK Sachdeva, CEO, Tecumseh India Pvt Ltd. He goes on to add that e-recruitment has a major role to play in the hiring of a manpower of 2000 which is a part of the Indian branch of this multi national company.The Wikipedia encyclopedia defines the 'World Wide Web' as a global, read-write information space. Indeed it is this space that has enabled us to connect and interact with people across the globe, in a matter of a few minutes. Amongst the population of these web-users are a number of employees and employers.In today's world of liberalization and globalization, the internet provides the workforce with a common platform to interact and communicate. Time plays a very crucial role in the progressive economy and e-recruitment ensures that hiring is a convenient process.TV18 took up a 50% stake in JobStreet.com India which is an online recruitment company. TimesJobs.com, which is a part of India's biggest media house The Times of India, broke away from its tie up with Career Builder of the US and has emerged to be the second biggest online recruitment agency. Facts l
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 it's 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 shouldn't 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.
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