Posts Tagged ‘SEO’

Note to readers: click on the link to download a copy of my analysis of general purpose SEO toolbars.

One of my more unusual pet peeves in SEO is the number of general purpose SEO toolbars available for use. Actually not so much the number, but the fact that each one claims to be the best of the best, each one has its champions in the community, and each reports slightly different data for certain metrics. Which one is best for me and in what situations? Which provides the most accurate data?

I have been asked so many times by customers “What is the best general purpose toolbar for Firefox?” that I finally decided to do a detailed comparison, if nothing else to satisfy my curiosity about:

• how similar the features/functions of these toolbars are.
• how accurate they were (e.g. did they all get the same numbers for similar analyses).
• which one was best in which situation.

I could spend days writing reviews of these toolbars, but very few would read these and I doubt the added information would prove all that useful versus downloading and trying the tools. SEOs are, by definition, very much experimenters. They prefer to test rather than read or guess. On the other hand, a high level visual summary, which can provide a sense of the tools coverage as well as its focus/strengths, would probably help those evaluating tools so they can know which features they should explore in which tool.

Ergo the table below, which shows a feature-by-feature comparison of six popular SEO toolbars for Firefox – FoxySEO Tool, SEMOMoz, SEOBook, SEOpen, SEOQuake, and SEO for Firefox. Hopefully the categories and line items are self-explanatory. If not and I get enough comments, then I will add an addendum explaining the line items – but that would be painful and probably not add a lot of value for most of the audience.

No doubt, readers are going to be upset because I didn’t get to their favorite toolbar, and for that my humble apologies. In the process of this research, I found several more general purpose SEO toolbars, like ToolbarBrowser, which need review. I will have to cover those on in a separate post and I will update the downloadable pdf comparing all of the toolbars as I extend the research.

What I found interesting:

1. There was no single metric/line item that all six toolbars analyzed except the number of backlinks to site in Yahoo! SiteExplorer.
2. Five of the six tools included metrics for PageRank of the current page, pages in Google Cache, number of backlinks to page in SiteExplorer, DMOZ Entries, Keyword Density, and Meta Tag Analysis. These were the most widely shared metrics.
3. Each tool has different strengths and weaknesses. For example:
   • SEOBook’s toolbar clearly has a much deeper set of tools for keyword analysis compared to the others.
   • FoxySEO tool has a broader coverage of metrics in the social media, site performance, and indexing domains.
   • SEOQuake and several others provides the set of analyses for every line item in the SERPS, which is very handy for competitive analysis.
   • SEOMoz, on the other hand, has fewer tools but they cover areas not included in other toolbars, as well as including metrics unique to SEOMoz – e.g. MOZRank, MozTrust. The toolbar also links to the SEOMoz Pro tools on the SEOMoz website, where you can run other, deeper analyses. Clearly, its purpose is not to perform all the analysis “on page” but rather to give a high-level view from which further data can be gleaned by using the more detailed SEOMoz tools. However, those tools (and the toolbar) are only available to paid SEOMoz members.
4. As far as accuracy goes, when checked most of the tools reported consistent data on everything but link-related metrics, where the data tended to vary widely. Yahoo SiteExplorer links probably had the least variation, but when you looked at bookmarks on social media sites or directory entries in DMOZ or Yahoo, you can see results that differ by a factor of 10 in some cases. What is even more interesting, when I manually went to some of these sites and typed in the same query, I actually got a set of results that differed from the data I got in SEOQuake and FoxySEO Tool.

So which toolbar is right for you depends on:

• What metrics you feel are important to you.
• What services you subscribe to.
• How cluttered you like your Firefox screen.
• How much performance degradation you are willing to tolerate. In the case of tools which provide metrics for every line in the SERPs, the wait time can be substantial.

As you can see from the picture, as a self-confessed tool addict I have all of them running – which explains why I never use Firefox for anything but SEO work. Advantage to Chrome for regular browsing and social media work, at least until all the SEO plugins port over to Chrome.

Well, I’m back after the holidays. Here I thought the holidays would be slow and I’d get a chance to write every day. Turns out that was a bad assumption. I was busy as heck with clients who needed it “yesterday.” That’s different than the beginning of the year – so maybe that’s a sign the economy is really improving.

Today’s post is about whether the long-tail is useful for companies with small keyword universes. This became of interest to me when a colleague of mine, Steven Ebin, suggested that this strategy was useful even for small search markets, which had not been my experience.

I define a small keyword universe as one that is less than 1,000 core keywords. The short-tail is defined as keywords that represent 60% of traffic (in my experience usually about 10-15 keywords for small universes, although that can vary substantially). The mid-tail is defined as keywords that represent the next 25% and the long-tail is defined as keywords that represent the balance. I find that many small customers have core keyword universes of this size and distribution, although size of the keyword universe really ties to the specific market, not the size of customer. Be that as it may, my experience is that there is a correlation between size of company and size of keyword market.

Let’s assume that the overall number of monthly clicks for a keyword market, including the long-tail search terms, is 2,200,000 visits.

Next, we know that not everyone clicks – so we need to ignore that traffic. Some aged results reported for AOL in the Webmaster World forums suggest that 46% of searchers don’t click through.

We then have to make some assumptions about where we will rank in each of the positional categories. For purposes of this analysis, I’ll assume that the above average SEO can get an average position of 5 for the short-tail terms, a position of 3 on average of the mid-tail terms, and an average position of 2 for the long-tail terms.

We also have to estimate the clickthrough rates for results in each of these positions in the SERPs. This data varies widely. One study from Cornell University suggests that 56% of searchers who click click on the first result. Some calculations by Jay Geiger suggest that 42.3% of searchers who click click on the first result, and some reported statistics for AOL from the prior-mentioned source show the same number as 23%.

Clickthrough Rates Based on Position in Search Results

We also have to make some assumptions about conversion rates. Estimating conversion rates is hard because it can vary so much by industry, but let’s assume we have a 0.5% conversion rate for our short-tail terms, 1% for our mid-tail terms, and 3% for our long-tail terms. This gives us a weighted-average conversion rate (based on the percentages in each part of the tail) of 1.0%, which is not unrealistic for organic traffic and may even be low. If you play with these numbers, you will see that a reasonable variation in conversion rate assumption on the long-tail doesn’t change the results of the analysis.

Why increase the conversion rate so much for the long tail? Long-tail searches are more “perfected.” The fact that people type in more specific keyword strings indicates that they are further down the decision-making cycle, and thus the conversion rates are significantly higher. The “spread” that I have assumed for the model is actually conservative. We have often seen conversion rate differentials between the short- and long-tail that are even greater.

When we run out the numbers, using all three sets of clickthrough rates we get the following results:

Results of Analysis on Long-Tail Keywords

This analysis shows that the long-tail can potentially double the business for a small company.

We can also look at this same analysis using the length of the keyword string. How does keyword string length relate to location in the tail? Are long keyword strings (3+ words) equivalent to the “long tail”? The answer is “no” – in fact the ratios of searches for length of tail versus length of keyword string are almost the inverse (60/25/15 vs 20/24/56). However, since we could also segment our keyword universe based on length of keyword string and develop a traffic strategy based on that, let’s look at the analysis that way.

Hitwise completed research on the percentage of searches based on number of terms in the keyword string in January 2009 with these results:

Searches By Number of Terms from Hitwise January 2009

It you add the searches with 3+ terms in the keyword, you get 56.06% – so a pretty substantial amount of traffic, but not as high as the 70% I have heard from others.

We also need to adapt the conversion rates to maintain an average 1% across all three categories, so that the analysis between the two approaches is “apples-to-apples.” When you run the analysis based on length of keyword string with a conversion rate to get the same number of conversions as in the prior case, you get the following results:

Results of Analysis for Small Keyword Markets, By String Length

In this case, the results are even more dramatic, with the number of conversions for keywords with three+ terms dwarfing the one- and two-keyword strings. Even if you take the conversion rate for the three+ keywords down to 0.8% (the same as for two keyword search strings), the conversions are still almost double what is in the one-and two-keyword string categories combined.

So the answer to the question is an absolute “Yes” – the long-tail can be a very valuable source of business even for small keyword universes.

In my previous discussions of social media, channel architectures, and branding, I discussed the fact that I am manic about locking down my online brand (onlinematters) because there seems to be some relationship in the universal search engines between the number of posts/the number of sites that I post from under a specific username and how my posts rank.  It is as if there is some measure of trust given to an author the more he publishes from different sites and the more people see/read/link to what he has written.  I am not talking about authority given to the actual content written by the author – that is the core of search.  I am talking instead about using the author's behavior and success as a content producer to change where his content ranks for any given search result on a specific search term.  It is similar, in many ways, to what happened in the Vincent release where brand became a more important ranking factor.  In this case, the author and the brand are synonymous and when the brand is highly valued, then those results would, under my hypothesis, be given an extra boost in the rankings.

This was an instinct call, and while I believed I had data to support the theory, I had no research to prove that perhaps an underlying algorithm had been considered/created to measure this phenomenon in universal search. 

I thus considered myself twice lucky while doing my weekly reading on the latest patents to find one that indicates someone is thinking about the issue of "author rank."  On October 29th, Jaya Kawale and Aditya Pal of Yahoo!  applied for a patent with the name "Method and Apparatus for Rating User Generated Content in Search Results."  The abstract reads as follows:

Generally, a method and apparatus provides for rating user generated content (UGC) with respect to search engine results. The method and apparatus includes recognizing a UGC data field collected from a web document located at a web location. The method and apparatus calculates: a document goodness factor for the web document; an author rank for an author of the UGC data field; and a location rank for web location. The method and apparatus thereby generates a rating factor for the UGC field based on the document goodness factor, the author rank and the location rank. The method and apparatus also outputs a search result that includes the UGC data field positioned in the search results based on the rating factor.

Let's see if we can't put this into English comprehensible to the common search geek.  Kawale and Pal want to collect data on three specific ranking factors and to combine these into a single, weighted ranking factor, that is then used to influence rank ordering based on  what they term "User Generated Content" or UGC.  The authors note that typical ranking factors in search engines today are not suitable foir ranking UGC.  UGC are fairly short, they generally do not have links to or from them (rendering the back-link based analysis unhelpful) and spelling mistakes are quite common.  Thus a new set of factors is needed to adequately index and rank content from UGC.

The first issue the patent/algorithm has to deal with is defining what the term UGC includes.  The patent specifically mentions "blogs, groups, public mailing lists, Q & A services, product reviews, message boards, forums and podcasts, among other types of content." The patent does not specifically mention social media sites, but those are clearly implied. 

The second issue is to determine what sites should be scoured for UGC.  UGC sites are not always easy to identify.  An example would be a directory in which people rank references based on 5-star rating, where that is the only user input.  Is this site easy to identify as a site with UGC?  Not really, but somehow the search engine must make a decision whether this site is within its valid universe.  Clearly, some mechanism for categorizing sites with UGC needs to exist and while Kawale and Pal use the example of blog search as covering a limited universe of sites, their patent does not give any indication of how sites are to be chosen for inclusion in the crawl process.

Now we come to the ranking factors.  The three specific ranking factors proposed by Kawale and Pal are:

• Document Goodness.  The Document Goodness Factor is based on at least one (and possibly more) of the following attributes of the document itself: a user rating; a frequency of posts before and after the document is posted; a document's contextual affinity with a parent document; a page click/view number for the document; assets in the document; document length; length of a thread in which the document lies; and goodness of a child document. 
• Author Rank.  The Author Rank is a measure of the author's authority in the social media realm on a subject, and is based on on or more of the following attributes:  a number of relevant posted messages; a number of irrelevant posted messages; a total number of root documents posted by the author within a prescribed time period; a total number of replies or comments made by the author; and a number of groups to which the author is a member.
• Location Rank.  Location Rank is a measure of the authority of the site in the social media realm.  It can be based on one or more of the following attributes: an activity rate in the web location; a number of unique users in the web location; an average document goodness factor of documents in the web location; an average author rank of users in the web location; and an external rank of the web location.

These ranking factors are not used directly as calculated.  They are "normalized" for elements like document length and then combined in some mechanism to create a single UGC ranking factor. 

The main thing to note – and the item that caught my attention, obviously – is Author Rank.  Note that is has ranking factors that correspond with what I have been hypothesizing exist in the universal search engines.  That is to say, search results are not ranked only by the content on the page, but by the authority of the author who has written them, as determined by how many posts that author has made, how many sites he has made them on, how many groups he or she belongs to, and so on.

Can I say for certain that any algorithm like this has been implemented?  Absolutely not.  But my next task has to be to design an experiment to see if we can detect a whiff of it in the ether.  I'll keep you informed.