Comparing Trust Metrics and Value Analysis to Understand Search Rankings

In July 2009, I used some data extracted from Linkscape to attempt to find patterns in ranking position and general domain / URL trust metrics after the Vince update hit the UK SERPS. Though the analysis couldn’t have taken into account the fact that Google since confirmed they use click through rate based data in some, higher volume search results, it did show some patterns in ranking position in comparison to relative trust levels using the Linkscape numbers.

I thought it might be interesting to share some charts I’ve produced in my own studies of rankings with a view to produce some commentary on what, if anything the data tells us today. Bear in mind, any data gathered from a link analysis tool is not going to be as having insight in to a search engine algorithm itself and therefore the data is going to be many more times simpler. To that end I don’t make any recommendations specifically instructing you to do anything other than your own analysis. I’d love to hear your thoughts and your own observations, so drop a comment after you’ve read this post.

The two terms I’ve looked at are “bird feeders” and “flights“. An odd choice perhaps, but “flights” is a term heavily optimised for and extremely competitive term while “bird feeders” is much less so. In my mind, I’ve believed for a long time now that rankings tend to fall into a few broad categories, and that your strategy to acquire a ranking on page one for that term will be influenced heavily by the category it belongs in. By “categories” I actually mean “levels of optimisation / competitiveness”. Let me explain. My categories look a little like this:

Looking at the UK rankings for “Bird Feeders”

I’ve assembled a raw data set with Linkscape and produced a collection of charts based on the data values listed below. In this post I’ve only included what feels relevant, but for the sake of accuracy here’s what I collected in my data set:

Personalised search is not taken into account in any of the ranking values in my data set. How could it be? I used a “clean” machine to get the rankings data so with some luck, we’re looking at default rankings unaffected by adjustments Google may or may not make based on previous search behaviour. For a more detailed primer on how personalised search actually affects your rankings, head over to the Dojo and read: The SEO guide to Google personalized search.

Now, on with the charts:

URL mozTrust

Let’s start with reviewing mozTrust values plotted against rankings. Our x-axis shows ranking positions between 1 to 10, and our Y axis shows the value metric compared. This chart plots the mozTrust value for each ranking URL between 1 and 10 for the term: “bird feeders”.

url moztrust bird feeders

Positions 1 to 6 show a gradual decrease in mozTrust, a decreasing trend. That trend ends with the values between positions 7, 8 and 10. So what’s going on?

URL mozTrust vs Domain mozTrust

URL mozTrust vs Domain mozTrust

If we plot the corresponding domain mozTrust values in the chart, we can get an impression of Linkscape’s overall domain authority value for each ranking URL. The trusted domains at positions 6 and 8 seem to compensate for lower levels of trust at the URL level. This chart seems to reinforce the feeling that overall domain authority can play a major role in the ranking position awarded to a URL. It might not come as a surprise then,  that amongst the rankings for this term, domains such as, and the are effortlessly achieving rankings on page 1 of these results.

mR Passed by Relevant Anchor Text

mozrank passed by relevant anchors

Taking into account that we’re singling out one ranking factor amongst many in this chart, I still really enjoy thinking about the concept of measuring value passed by most relevant anchors. This chart looks at the most valuable inbound links to each URL and filters for inbound anchor text that contains the term “bird feeders”. By looking at the overall value passed (mozRank passed) by relevant inbound anchor, it seems fair to conclude that the higher value a linked-from page can provide to the linked-to page, the better chance you have for ranking for that term. It’s much harder to take synonyms into account, though I believe in many cases, inbound anchor text can influence rankings via synonym based terms too (Example: fly, flights?). It’s an untested theory, but one I’d like to put some more thought into at some point.

Of course, our value passed chart doesn’t provide a nice smooth curve like you’d hope for, owing to many other ranking factors playing a role in the positioning of our URLs. Sometimes, it seems, the volume of external links to the URL (secondary axis) can help compensate for a lower value passed by the relevant anchors (don’t forget that positions 6, 8 and 9 have very high levels of trust in the domain). Perhaps this can help us to explain how low value, high volume link networks still play a role in some search results?

mR passed by relevant anchors vs external link volume

So, I think it’s OK to make a few assertions from this data. For the most part, overall trust levels and authority play a big role in the rankings. Where one of these components appears to be lacking, the other could compensate, i.e a low authority URL can happily rank with a highly trusted domain. Inbound anchor text value can play a role, as can larger numbers of lower value inbound anchors. Cool. If only every ranking was like this…

Looking at the UK rankings for “Flights”

Our next ranking is a totally different and far less predictable beast. Early on it’s much harder to spot patterns, if at all. I think this is largely due to the possibility that since the Vince update,  “flights” is an anomalous and CTR adjusted phrase, while being extremely competitive in the UK SERPS.

URL mozTrust

flights moztrust value chart

A totally different curve of URL mozTrust shows a more or less flat line in the top rankings. It appears that, to be competitive for this term, you need to be building a lot of authority and trust to be competitive. I know that sounds obvious, but do you target high value pages only for links to target this term? I didn’t go 30 rankings deep like I did in the last Vince update study (also based on the term “flights”) but I do have a feeling that should this chart’s x-axis go that far into the data, we might see the same relationship of trust to rankings.

URL mozTrust vs Domain mozTrust

url moztrust vs domain moztrust flights

Again, there’s a much less obvious pattern in the chart above. Domain trust levels are high in every case. The only thing I’m taking away from this chart are positions 1 and 2 appear as if they should be positioned slightly lower down the rankings, based on these values.

mR Passed by Relevant Anchor Text

I think this chart reveals that there is marginally more value being passed via relevant inbound anchors for position 1 than position 2, but it reveals far less of a predictable pattern than the “bird feeders” version. Could this chart show that, regardless of heavy inbound anchor text optimisation, other factors play a much stronger role than you’d expect compared to a less competitive results page? It might be interesting to reveal then, that positions 3 and 5 are very well known airline brands, Easyjet and British Airways.

Searches related to…

searches related to

As our friend Dave of SharkSEO noted in his post at iCrossing: Unlocking Google’s Vince Update, the “Searches related to” box at the base of the search results can give a lot away. In this case, out of the 6 brand names mentioned in our screenshot, 5 of them appear on page 1 of the rankings for the “flights” search query. What’s really interesting is, that when I filter my data for domains that do not have any heavily optimised inbound anchor text in their most valuable inbound links, the outcome looks like this:

moztrust non kw rich domains

Each “brand” domain ranks in order of mozTrust (URL). It’s almost as if the “flights” search rankings are based on two different approaches, one that looks at the value and relevance passed by inbound anchors while the other looks at overall trust with a combination of upstream / downstream search or related user behaviour. While this idea might be viewed as over simplistic or downright “tin foil hat” I do think this method of value analysis is useful, particularly when informing SEO strategy. Needless to say, the “flights” rankings feel less predictable and much more complicated to be working towards, and if you’re in any of the more competitive niches in the UK, the only strong recommendation I make to you is get the data and do some analysis of your own.

Learn More

Builtvisible are a team of specialists who love search, SEO and creating content marketing that communicates ideas and builds brands.

To learn more about how we can help you, take a look at the services we offer.

Stay Updated

Follow: | | |

Tags: , , | Categories: Technical

7 thoughts on “Comparing Trust Metrics and Value Analysis to Understand Search Rankings

  1. Nick Gerner says:


    Always great to see what you have to say :) I like your points about the interplay between page and domain-level factors. And I like what you’re doing with anchor text. Are you getting that from the Linkscape tool or from the anchor-text API? The tool only aggregates data for the first few thousand links (which might be most of them in this case). But it would be really neat to see that stuff from the anchor-text API which aggregates across all links.

    It would be great to see this on more SERPs with averages and error bars. But I don’t think anyone will argue with some of your conclusions:
    * page level and domain level factors are important and interact for ranking
    * relevant anchor text is valuable
    * a weakness in one factor (like relevant anchor text) can be made up for in another (like overall links)


  2. Thanks Nick – much appreciated feedback.

    To answer your question, I’m currently using Linkscape’s web interface, so we’re only looking at the most valuable inbound links. In both cases that is actually most of them – but a complete snapshot would be even more accurate.

    I’ve tried to extract data via the api to MS excel via Data > From Other Sources > XML Data Import. I’d like to assign more time to this after SES as I’m sure it’s possible to set up the equivalent of a SQL style cube, perhaps with a VBA interface.

  3. Nick Gerner says:

    I like your thought about importing from the API directly into Excel. I strongly suspect that this is impossible (or very very difficult) at the moment. I’ve thought more than once that we should better integrate into Excel and SQL cubes.

    That said, Open Site Explorer ( has most of the data available in the Linkscape tool and more and supports download to spreadsheet. So you could pull those down and into excel and then do a neat pivot table.

  4. Marc Levy says:

    Hi Richard,

    This is a really interesting post and I love getting down into research and trying to make sense of all the nitty gritty details that can effect the SERPs. This post really does give away some great data from your research.

    One question (and maybe I have just forgotten something here as I have only just woken up and it’s pretty early right now!) – How do you find the ‘mR Passed by Relevant Anchor Text’ in Linkscape? Did you just average out the mR values assigned to each exact match anchor per ranking domain?

    We met very briefly at Distilled/SEOmoz seminar in London, I have been enjoying seogadget since. Quality reading!

  5. Hi Marc, good to hear from you!

    To answer your question, take a look at the comparison reports (advanced) in Linkscape. There are tabs for key terms and keyphrases by anchor. These reports give the top 8 most popular terms and include a value passed metric.

    Hope that’s of some use!

  6. Marc Levy says:

    Richard thanks for the answer on that… think I have seen it, but I definitely need to go in and pay more attention to it. Cheers!

  7. Nick Gerner says:

    Marc (and Richard), To get a better look at mozRank passed check out the API

    The aggregates on the Linkscape tool are computed only on the top 3k links. From the API you can get a deeper, more complete look.

Leave a Reply

Your email address will not be published. Required fields are marked *