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Sham analysis optimization

Posted by Michael Martinez on February 6, 2008 in SEO Theory

So Andy Beard called me out yesterday on the subject of whether SEO Theory is really better indexed than most SEO blogs. He resorted to a smoke-and-mirror tactic of inplying that articles on SEO Theory are not indexed because some date archive pages (all duplicate content pages that are poorly linked through Wordpress’ default architecture) do not currently appear in Google’s index.

The fact that you can find all the articles by searching for their titles and other content didn’t prevent people from proclaiming his post as another milestone in SEO analysis. I admit to rolling my eyes, of course, because his analysis was so shallow and amateurish but I still rebutted his claims in a comment that Andy has yet to publish. I suppose allowing me to show his readers that he engaged in blog sleight-of-hand wasn’t good for link baiting or something.

Rand Fishkin at least admitted that allowing me to express my “contrarian point of view” on SEOmoz was good for links (and that is why SEOmoz has an unrestricted link in SEO Theory’s blogroll).

Sham analysis is a mainstay in popular SEO blogs and forums. People show you only the facts they want you to see so that you’ll agree with them, link to them, possibly even translate their sham analysis and help the world perceive them as experts.

I’ve known SEO experts, Senators, and Andy Beard is no SEO expert. Not if I have to judge his expertise by how he argues his points.

There are other sham analysts in the business. Some of them are very well known. A lot of them are not very well known. I’ve been looking at SEO blogs for a long time and there are days when I just want to cringe and crawl under a rock for shame.

Even people who make a very good effort to look at all sides of an issue will tenaciously hold to debunked markers of value. Halfdeck, for example, who has performed a lot of interesting SEO experiments and documented them, clings to the Google Toolbar PR value in dogged fashion. There but for the references to PR goes an SEO with whom I could often agree on many points.

On the other hand, Toolbar PR analysis doesn’t earn as much credit as it deserves. Halfdeck is among a handful — an extremely small handful — of SEOs who could be credited with staying on top of Toolbar PR analysis during Google’s Fall 2007 purge of paid links. About the only thing Toolbar PR analysis is good for — these days — is figuring out which sites Google thinks sell or buy links, in my not-so-humble opinion.

But inspired by Halfdeck’s love of TBPR values and Google’s only real indicator of who might have been nailed for buying or selling links, we undertook our own study of Toolbar PR data. We found that there tends to be no correlation between Google Toolbar values and anything else, except in one apparently anomalous area. It’s anomalous only in that it stands out from other sets of Toolbar PR data we evaluated.

People can fake Toolbar PR. Google can penalize Toolbar PR. Toolbar PR is normally updated only once every 2-4 months. You don’t have much hope of learning anything useful from Toolbar PR unless you look at historical trends by Web site. History teaches many lessons. Few SEOs learn them.

There will always be people who measure the optimized Web by Google Toolbar PR, Alexa Rank, and other implausible metrics. Some members of the SEO community have asked whether the aggregate values of these metrics might show us something. In my opinion, if you see similar trends in disparate data sources, you’re probably seeing a valid correlation.

Rand Fishkin openly questions the value of third-party metrics and I agree with many of his concerns. But we’re now talking about analyzing analytical evaluations. That is, the Google Toolbar PR values are derived from other data — so are the Alexa Ranks, Compete Ranks, etc. Every metric we have to work with is a derivative value that separates us from the raw data.

We’re now engaged in the business compiling metrics on metrics to determine which metrics work best or if the metrics work better together. These Second Derivative valuations are mathematically valid concepts. For example, acceleration is a commonly used Second Derivative. Rand Fishkin’s PageStrength tool is an example of a Second Derivative (which I would further qualify as a first generation second derivative, but let’s not get off on a tangent).

SEO analysis needs to be detailed and thorough. You cannot fully explain why search results change. There is no one in the SEO community who is capable of explaining exactly why search results change. There are a lot of people who can give you a pretty decent checklist of possible causes.

If we cannot explain the causes of changes, then how useful is our even most detailed and thoughtful analysis? Well, such analysis helps us to determine which possible causes of change are plausible causes of change. Plausible cause is usually enough for us to take the next step in forming a plan of action. SEO is more effective when it rules out as many possible causes as can reasonably be ruled out, or when it includes as many possible cause as can be plausibly accepted.

Sham analysis favors the emotional point of view. I want to prove Michael Martinez is wrong so I will make my case on these facts. It’s easy enough to prove I’m wrong by showing that the facts disagree with me. For example, in Andy Beard’s sham analysis all he revealed was that I have some date archve pages that Google wasn’t indexing (and may never index for all we know — I don’t do anything to entice Google to archive those pages).

So what? Did Andy show that most SEO blogs are at least as well indexed as SEO Theory? Nope. Did he show that SEO Theory is poorly indexed? Nope. After all, if you can find the majority of SEO Theory’s articles in the search results, it’s well indexed. How you find the articles doesn’t matter, but I would prefer that you find the individually archived posts and those URLs are the ones I link to.

NOTE: All this has nothing to do with whether my sweeping generalization — that SEO Theory is better indexed than most SEO blogs — is true. To prove that statement to be true, I would first have to make a convincing case that I can find all the SEO blogs. However, I find many SEO blogs that are not indexed in Google, including one SEO blog whose owner a few months ago wrote disparaging remarks about me on Sphinn. I’ve always been of the opinion that if your SEO blog is not indexed in Google then your opinion on SEO doesn’t count for much.

Now, if I say that SEO analysis should be thorough and detailed, what do I hope to get from it? I’m glad I asked that.

1. SEO analysis can be used to evaluate the competitiveness of search results.
2. SEO analysis can be used to evaluate the search visibility of a Web site.
3. SEO analysis can be used to evaluate the popularity of an expression.
4. SEO analysis can be used to evaluate the connectivity of a Web document.
5. SEO analysis can be used to evaluate the methods a search engine uses to find and index content.
6. SEO analysis can be used to identify trends in market interest.

And it doesn’t stop there but I have to. A thorough analysis should be more than a page long. A lot of my site analyses have run between 30 and 50 pages, depending on how many queries and how much historical analysis were involved in the process. I do snapshot analysis all the time, as do most of you. We know deep in our hearts that snapshot analysis leaves much to be desired but there simply isn’t time enough in a day to do it all right.

If you spend your SEO career doing nothing more than snapshot analysis, after your 1,000th analysis you will see things you never imagined in your 1st analysis. Practice makes perfect. Hence, the more in-depth analysis you do throughout your career, the better your snapshot analytical instincts will become.

Nonetheless, snapshot analysis is prone to failure and usually leads us down the wrong path. That is, we miss things — often huge glaring things — in snapshot analysis. Missing things invalidates our conclusions, although we can say that the invalidation is measurable or quantifiable in terms of degrees.

Nonetheless, what most SEO bloggers present to you each day is snapshot analysis, usually distorted snapshot analysis if they are feeling passionate about their topic. Every blogger has a good day once in a while. Every blogger has a bad day once in a while. Statistically, most of us provide mediocre analytical thoughts in our blogs every day. In that respect the playing field stays mostly level, except where some people have more visibility than others.

Unfortunately for the SEO community, however, snapshot analysis tends to be compelling and persuasive. That is why so many SEO myths become popular quickly. That is why so many SEO tricks of the trade become outdated quickly. People don’t question the snapshot analysis. They don’t ask for citations (unless I make a sweeping generalization). They don’t ask, “Did you tell us everything or did you conveniently leave out the fact that the SEO Theory articles you imply cannot be found can actually be found in very quick, simple searches?”

Snapshot analysis may work on blogs but it doesn’t work in search results. That is why some SEO bloggers whine about Google penalizing their sites when, in fact, they just didn’t bother to optimize their “missing” blog posts to rank for particular queries.

You don’t optimize a blog by getting 100 other blogs to link to you. You optimize a blog by focusing on your keywords, providing good natural emphasis, and not accusing search engines of penalizing you just because you don’t rank for every possible expression you can think of.

If this industry is going to establish standards, it needs to set some solid guidelines for making compelling arguments so that they seem credible. When outrageously bad analysis is posted on popular blogs and people link to it with praise, you know there is a real problem in the industry.

SEOs need to be completely skeptical and objective in their analyses. They need to present all the facts available and not hide the inconvenient truths that contradict their points of view. They need to accept that a conjecture may not hold up to closer scrutiny without pinning their hearts and egos to the failure of the conjecture.

Most conjectures turn out to be wrong. It should be a matter of course or standard operational procedure in our community that we treat snapshot analysis as a first step toward understanding phenomena and not as the next big breakthrough in SEO science.

That is not how real science works.