Search Engine Optimization for Site Owners and Small Businesses

The recently released AOL research data provides some interesting information on clickthroughs as a function of position in the SERPs. Thanks to Richard Hearne over at Red Cardinal, who has imported the data into a MySQL database, I have been able to construct two striking charts.

The first shows the percentage of total clickthroughs versus the position in the SERPs.

It clearly demonstrates the importance of the top position, with the first place site getting 3.5 times the clickthrough rate of the second place site.

The second chart shows the percentage of the first position clickthroughs versus the position in SERPs.

Notice that the ninth position is getting marginally less clickthroughs than the tenth position. This is probably because a few users are not concentrating and click on the tenth result when they really intended to move to the next page of the results.

However the most interesting observation is the dislocation of clickthroughs between tenth and eleventh position. Going from the top of page two, to the bottom of page one, increases the clickthroughs by a factor of 4.5!

Added Position and Clickthrough Tool.

When I first looked at Matt Inman and Rand Fishkin’s Page Strength tool just after its launch the correlation coefficient with PageRank was extremely high at 0.92 on a random sample of 26 urls (see Page Strength). Now the Aviva Directory has published a list of Bob Mutch’s directories showing their Page Strength and simply by adding the current PageRank value to the table it is possible to calculate a correlation coefficient on this larger data set.

Directories with a current PageRank of zero were removed from the table so as not to perturbate the results. Many of them are PageRank zero because they are banned from Google’s index, for example galaxy.com (Page Strength 5), cannylink.com (Page Strength 4), dirone.com (Page Strength 3.5) and so on.

After the removal of all directories with a current PageRank of zero the remaining 277 were plotted on an x,y chart and the correlation coefficient calculated. The results were as follows:

The correlation coefficient was lower than in the previous data set at 0.78 but still significant. I had a quick look at some of the outliers like topicalbeach.com with a PageRank of 5 and a Page Strength of 1.5 and found that in this case Aviva Directory had misreported the Page Strength which is actually 4.5. Validating the data would increase the correlation coefficient.

Users of the Page Strength tool have reported a variety of anomalies because the Yahoo search operator linkdomain:domain.tld used by the tool does not always return correct results. Sorry Matt and Rand but I cannot see this tool as anything other than a very unreliable proxy for the already unreliable PageRank metric.

I read Ten Levels of Hangover in the bitter defeat blog some time ago but forgot about it until I read Rand’s recent Levels of Search Marketing Knowledge. Levels of anything seems like a fun idea to me so here is my version of The Ten Levels of SEO.

The Ten Levels of SEO
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“There is a great satisfaction in building good tools for other people to use”. Freeman Dyson (Professor Emeritus, The Institute for Advanced Study) in “Disturbing the Universe” (1979) Chapter 1.

I think Matt Inman and Rand Fishkin would agree with Professor Dyson, which is why they are probably very pleased with their new tool, Page Strength. The real question though is “Is it a good tool?”

Rand says “The Page Strength Tool is designed to replace the often inaccurate and infrequently updated Google PageRank score in the toolbar”. However the first thing I noticed when I plugged in a couple of urls was how little the Page Strength differed from the PR and mostly by similar amounts. So I collected a few random urls during the course of a day and plotted the Page Strength against the PR on an x,y chart. The results were as follows:

On this set of data which is 26 urls the correlation coefficient is 0.92 which is extremely high. This is not so surprising in that they are both dimensionless variables heavily dependent on link counts. It would be interesting to know what the correlation coefficient is with a larger sample set. This is something Rand or Matt should be able to calculate easily since both values for any given url are kept in their database.

PageRank is a lousy predictor of SERPs position because it is keyword independent. However when I saw that Page Strength included at least one keyword related factor in its calculation (position in Google for the first four words in the title tag) I though it would be interesting to see how much better Page Strength was than PR as a predictor. There are lots of ways at looking at this but as all I wanted was a rough indication I looked at the Page Strength and PR values for the first 25 urls in the Google search for |engagement rings| and compared their correlation coefficients. The results looked like this:

PageRank as expected does indeed have a poor correlation with position in the SERPs with a correlation coefficient of 0.441 but Page Strength is even worse with a correlation coefficient of 0.123. On different searches the results would vary and of course the relationship may not be linear as assumed here but despite the statistical liberties Page Strength does not look anything like a possible contender to replace PageRank as a metric.

However there is one very good lesson that can be learned from this not so good tool. Barely two weeks since its launch Rand’s website has obtained hundreds of inbound links from sycophantic bloggers. A very good example of Link Bait in action and a lesson for us all!

If you do one thing to your website this year then follow up on the information in this post. Routine content addition based on long tail mining is not yet mainstream but by starting now, before your competitors do, you will gain a significant advantage.

Earlier this year in the Long Tail Search post I showed how to mine your server logs and use the long tail of search to write new content. A number of readers have asked if there is software available to automate the process for determining the search terms for which you should be writing new content.

For clients, I use a bespoke automated process which is incorporated into a commercial log file analyzer but it is not stand alone and is not publicly available. However Connors Communications an established strategic PR and SEO agency based in NYC are beta testing a stand alone service.

It consists of a single line of JavaScript code placed on every page of your website which enables you to logon to their server and see all the search hits in real-time. The list is automatically refined using a proprietary algorithm which pulls out long tail search terms as ‘suggestions’. These suggestions can be examined and exported to a to-do-list which is essentially the list of topics for your new content.

Remember it is an iterative procedure and the more content you add using this formalized process the more long tail search terms will become apparent - for which you then write even more content, and so on.

Connors Communications are not charging for this service while it is in beta and they intend continuing to provide a free service for all websites whose traffic is under a certain limit. For sites which exceed that limit they will be offering a paid version. Here is where you can register for the beta or read more about the service HitTail.