This blog post is organized into a three-part strategy series that will outline what it takes to spend marketing dollars intelligently on your Pay Per Click (PPC) channel. In preparing for this series, I sought out the business acumen of successful entrepreneurs (both real and fictional) and chose to follow Tony Montana’s infamous and proven three-step approach:
Wikipedia, naturally, has an entry about PageRank with more resources you might be interested in. It also covers how some sites using redirection can fake a higher PageRank score than they really have. And since we’re getting all technical — PageRank really isn’t an actual 0 to 10 scale, not behind the scenes. Internal scores are greatly simplified to match up to that system used for visible reporting.
When returning results on a SERP, search engines factor in the “relevance” and “authority” of each website to determine which sites are the most helpful and useful for the searcher. In an attempt to provide the most relevant results, the exact same search by different users may result in different SERPs, depending on the type of query. SERPs are tailored specifically for each user based their unique browsing history, location, social media activity and more.
Search engine advertising is one of the most popular forms of PPC. It allows advertisers to bid for ad placement in a search engine's sponsored links when someone searches on a keyword that is related to their business offering. For example, if we bid on the keyword “PPC software,” our ad might show up in the very top spot on the Google results page.
If you are serious about improving search traffic and are unfamiliar with SEO, we recommend reading this guide front-to-back. We've tried to make it as concise as possible and easy to understand. There's a printable PDF version for those who'd prefer, and dozens of linked-to resources on other sites and pages that are also worthy of your attention.
PageRank has been used to rank spaces or streets to predict how many people (pedestrians or vehicles) come to the individual spaces or streets. In lexical semantics it has been used to perform Word Sense Disambiguation, Semantic similarity, and also to automatically rank WordNet synsets according to how strongly they possess a given semantic property, such as positivity or negativity.
One thing to bear in mind is that the results we get from the calculations are proportions. The figures must then be set against a scale (known only to Google) to arrive at each page’s actual PageRank. Even so, we can use the calculations to channel the PageRank within a site around its pages so that certain pages receive a higher proportion of it than others.
There is one thing wrong with this model. The new pages are orphans. They wouldn’t get into Google’s index, so they wouldn’t add any PageRank to the site and they wouldn’t pass any PageRank to page A. They each need to be linked to from at least one other page. If page A is the important page, the best page to put the links on is, surprisingly, page A [view]. You can play around with the links but, from page A’s point of view, there isn’t a better place for them.
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Webmasters and content providers began optimizing websites for search engines in the mid-1990s, as the first search engines were cataloging the early Web. Initially, all webmasters needed only to submit the address of a page, or URL, to the various engines which would send a "spider" to "crawl" that page, extract links to other pages from it, and return information found on the page to be indexed. The process involves a search engine spider downloading a page and storing it on the search engine's own server. A second program, known as an indexer, extracts information about the page, such as the words it contains, where they are located, and any weight for specific words, as well as all links the page contains. All of this information is then placed into a scheduler for crawling at a later date.