Web Promotion and Algo Cracking Blog

A statistical approach to SEO

When trying to solve the site ranking logic used by search engines, we tried several tools. For keyword density, market leader IBP analyzes the top ten ranked sites under a certain keywords. By comparing the top ranked sites with the site to be ranked, the webmaster obtains useful data.
IBP analyzes keyword density in all parts of a web page: metatags, alt text, body text, header text, from H1 to H6, anchor text and other parts.
However, we felt that this was cumbersome, restricting and not very effective. While some factors pointed out by IBP are true, there are several that are not. Or maybe they were true at some time, but Google and Yahoo changed their algorithms to avoid copycat sites in their top results (SERP).
Our approach was to create a similar product that would consider not only the top ten ranked sites, but all of them. This is 1000 sites for Google, because beyond that is tricky (but not impossible) to get SERPs.
The huge amount of data obtained in that way needed a statistical tool in order to produce useful information. Thus, we prepared our tool to take page ranges and calculate averages. Pages ranked in SERPs 1-10 are compared to 20-30, 40-50 and so on. In that way, we can find those keyword densities that correlate with rank.
Our tool answers questions such as:
- are keywords in the URL important for ranking?
- in the domain?
- in the subdomain?
- in the page name?
- in the different metatags?

Other interesting questions that can be answered with Algo Cracker:

- Which are the optimal keyword densities for the different parts of the web page?
- Does Google penalize/favor sites that use non-standard TLDs?
- Does Google penalize/favor sites that any string of text or code, like .php, .css, Javascript?


The results are beginning to flow. We have now hard data to prove or discard SEO myths or facts, and we are ready to discuss them with other SEO professionals.

We can offer custom raw data to those who have the math ability to extract useful conclusions from them and share them with us.

We also intend to publish some Excel data in this blog, for those who see money at the other end of this thread...

Check Algo Cracker.

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