I ran across a hot topic on Sphinn titled “Google Showing Bias Towards .org TLDs” and thought to take a look at how other sites/people are trying to run tests on what matters to Google. I am always appreciative to see people trying to run tests that go beyond just pure guesses as I’ve stated before about statistical SEO. Below are some of the findings from The Google Cache on what TLDs matter:

Preliminary Results:

The results were quite shocking. The .org subdomains outranked all other extensions. As you can see, the .nets and .coms are intermixed, some not ranking at all, but the .orgs are stacked at the top. While these results must be taken with a grain of salt until they can be verified on a much larger scale, it does indicate that there may be some bias towards the .org top level domain. (many have suspected this) These results have shown true on appx 80% of datacenters we have tested.

[...]

Implications:

1. Further study is definitely needed. Virante will be expanding the number of test subjects greatly and testing with and without subdomains.
2. Considering the costs are quite similar, it may make sense to begin using .orgs, like our good friends at SEOMoz

I’ve gone through my share of econometric papers, so I usually quickly turn my eyes to what the set-ups were to test such an experiment. First, let’s look at the methodology:

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Search Engine Optimization is still in the early stages of turning hypotheses and theories into a form of science. SEO science in my opinion delve beyond just purely stating what has happened or taking guesses, but rather using complicated econometric formulas for regression analysis via panel data. Of course, the idea of running econometric analysis appeals to a very niche audience as correlations and marketing proofs are usually preferred (and in most cases is all that is needed).

Nonetheless, there are consistent thoughts and ideas inside the SEO world on what works for proper optimization and on what search engine itself. What interests me most is algorithmically figuring out what kind of factor affects search engines the most when you can hold other values constant and by what percent. It is within the analytical area of SEO that interests me and not just the marketing and strategy approach (which obviously are just as important).

Effective analysis of the science of SEO would have to take in a large amount of factors that could influence Google, not to mention the areas where Google manually changes its algorithm (a large error factor). Various blackhat and grayhat techniques in heavy traffic and niche markets could easily create a large statistical error, but even more importantly, the constant changes in search engine algorithms would make econometric analysis a limited benefit if not re-analyzed every algorithm update.

Fear not, however, as this has never prevented people from doing time-series analysis for the stock markets and successfully predicting, albeit briefly, where to put one’s money.

Still, there are search strategists and search marketing professionals and experts from Aaron Wall to Michael Martinez that do dig into what works within SEO (only to an extent due to confidentiality areas of course).

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One of the things still missing today within the SEO community is a strong understanding of quantitative analysis and econometrics along with the actual desire to publish a paper within a constantly changing field. SEO is heavily focused on marketing (as it should be), but the idea of figuring out what works, by how much, and for how long seems to be sorely lacking.

In my view, statistical SEO is an open area that can be filled through a kind of econometric SEO, to help reduce the pervasiveness of common myths and rumors that often seem to spring up out of sites such as WebMasterWorld where anything that can go wrong is some kind of penalty rather than actually quantitatively testing what may have occurred.

You will see within this blog various ideas and concepts that will be driven not just out of a marketing perspective but also from a statistically-minded person probably in the forms of equations like the following:

OrgTraf_it = α + β₁*SERP_it + β₂*SEO_it + β₃*(SEO*PPC)_it + … + uit

And coming from the school I learned econometrics from, as far as I am concerned, if the econometrics formula for SEO is not using panel data, then there is no purpose in running the statistical regression for causal purposes. So please remember, correlations do not equal causation!

Therefore, when trying to figure out the right ways to get more traffic and what areas to invest in the most time, only truly statistical SEO analysis can figure out that to a more hard science.

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Search Engine Optimization, though around for about a decade or so now since its inception, is still a relatively new field of work. New enough that I would still qualify SEO as a soft science as there are no publicly detailed statistical studies using regression analysis and other econometrics work on what factors really help sites rank on the top of search engines (This is not to say that there are not any statistical analyses out there, just none using proper regression analyses outside of Excel).

Try searching the following phrases in Google:

• Econometrics and SEO
• SEO regression analysis
• SEO regressions

the lack of any papers on these topics? I’m not surprised as I doubt even 5% of the people I chat with randomly at parties know what SEO or a Search Strategist is—and I live in the heart of Silicon Valley with a large number of computer nerd friends. (more…)

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