UK SEO Consultant | Just Search's Neil Walker

At Just Search like any SEO Consultancy we get asked questions about clients link building and how many links they have etc. Unfortunately some clients have very little knowledge about SEO (initially anyway) and come up with a lot of assumptions:

Google says I only have 14 links

Yahoo Says I have 500 links

Google Webmaster tool says I have 314 links

The trouble with links is that no one tool can give you an accurate view of how many links you have, however some tools are better than others. Personally there are two tools I use when looking at “link intelligence” and they are of course:

• Majestic SEO www.majesticseo.com
• SEOMoz Linscape (Preferably Open site explorer) www.opensiteexplorer.org

Now I really like Open site explorer, they break links down into domain and page authority which has a ranking factor of 1-100 this ideal for creating link profile charts for you competitors (See Blogstorm)

But today I’m going to talk about Majestic SEO.

Majestic SEO

This is a great tool for finding out details about your own site or your competitors websites. One of the best things I like about it is the backlink history section, this give you a good visual of a site’s history of link building.

However this is where things can get a little confusing; Clients who are aware about Majestic SEO have often thrown question to me such as:

Looking a my links in majestic SEO it has shown that I have dropped links in the past two months

Majestic SEO shows I have a link spike!

I think I’ve lost links some where? Majestic SEO shows this!

So I though the best place to find the answer on information like this was by going straight to the horses Mouth – Dixon Jones

The big Question

In the historical back links section of majestic SEO it has three types of reports:

• Monthly View – default monthly view shows data points for any given month. Since our crawl rates increased considerably since 2006 it is advisable to either use normalized view and/or compare domains with each other ins order to get more comparable data.
  • Cumulative View – cumulative view shows ever increasing totals, this can help see clearer in some cases whether a particular site is catching up with competitor or not.
  • Normalized View – normalized view shows how frequently back links were found in every 1 million crawled pages. This allows to removes the effect of ever increasing crawl rates that may result in increased number of backlinks found every month, but actual share of those links might actually be falling indicating potential decline of interest in that domain. This data is only available for back Links based charts.

If I want to assume how many links have been built over time which view do I use?

For example www.justsearching.co.uk shows data of: 2,633,828 external back links with 2,553 unique IP’s.

The Monthly View shows:
- Is this the actual number of links the site has? (Showing a gradual decrease over the past 5 months)

The Cumulative View shows:
- Is this the actual number of links the site has? (this correlates with the 2,633,828 number)

The Answers

In truth, none of the charts are perfect, because they are headline numbers based on when we found the link, but include all URLs that were deleted when we recrawled – so the numbers include links that no longer exist. However, with this proviso, I would recommend using the referring DOMAINS (rather than backlinks) in cumulative view.

Here’s why:

Not So Good:

The top chart is an estimate of the total number of backlinks, but this can easily be misleading, because a sitewide link from one or more large sites greatly influence the numbers. If you just count each referring domain once, this tends to be a better representation… unless your objective is to JUST show as many links as possible.

- Dixon.

To Clarify this further I asked Dixon the following further questions:

• Is the monthly view a snapshot of links (or domains) each month which the Majestic SEO spiders, where as the cumulative view includes all links (or domains) found at any point?
  - I.e. what  would show a drop in links (Domains)?
  - Do links (domains) ever drop in cumulative view?

• In terms of new links found each month, does you system query the database so it can discount old links from the NEW links found?

Dixon’s Response

The monthly view is the NEW links found each month, which is why the graph goes up and down

The cumulative graph can only go up, as we keep deleted links in the index.

When we FIRST find a link, we make a note of the date. When we re-crawl a link, it will not change either graph one jot.

So in January we find 20 NEW links… both charts show 20 for January.

In February we find 17 NEW links… the monthly chart shows 17 links in January, and the cumulative shows 37 links.

Makes sense? We don’t need to discount old links… the cumulative graph is just the monthly graph of new links discovered totaled up

- Dixon Jones

Part way Conclusion

So basically Majestic SEO spiders the web and where it finds a link to a website it notes it, the monthly graph shows you how many NEW links it has found each month, this could be a link which has pointed to your site for the past 4 year but never been spiderd and this would be a new link. The cumulative view simply adds up the new links found each month to give a running total.

Disadvantge - The main problem with this is that it can give out of date information i.e. you can think you have more links than you do. But to be fair this is still more accurate than tools such as Yahoo site explorer and with Majestic SEO you get a tonne of other tool s to help analyze links further. In my opinion if you want to look a link numbers then www.opensiteexplorer.org will give you a better idea of how many links a website has at a particular given point, however there of course advantages to recording the information of where you used to have links!

Are you missing the links?

The best part about Majestic SEO is that you can see your old links, simply gothrough the links you have and see if they still exist, if they don’t try and re-establish them.

When I originally bought the domain name www.seomad.com i drop caught it, this was because I wanted to gain a domain with some age, some previous links and I simply liked the fact it said “SEO mad” and though that described me perfectly

Going through my links on Majestic SEO I have found links on sites that have removed the old pages, I have since contacted them talked about why they shouldn’t remove old pages or at least redirect them, consequently this rekindled relationship gained me another backlink

Moral of the story

Simply no one link intelligence tool is a holy grail in SEO, they all require interpretation in order to use them in the best way, above is just one useful way of using majestic SEO’s data – Enjoy!

I read an interesting post on Dave Naylor’s blog on Wednesday by David Whitehouse on using Google Analytics to segment short and long tail keywords using regular expressions. This post was then to be discovered as something initially developed by Ben Gott over at search engine land.

Background

Well I’m really bad with regular expressions, but i am good with Excel, I also love data so I thought about all the post’s I read about Search journeys and how the common conception is that the longer the search query the smaller the search volume but larger the conversion rate.

The image below shows the common conception of keyword type by search volume.

Anyway I want to know, “Does a longer keyword actually give a better Conversion rate?”

It’s a big question so I thought how can Google Analytics and some real data help me.

Setting up the test

OK well the good thing about working for a large Internet marketing company is that I get access to hundreds of Analytics accounts, so I took 13 Analytics accounts who have either e-commerce tracking or goals set up and set out about exporting the data.

Creating a custom report in Analytics
The next part of the post is just a quick overview of how to create a custom Google Analytics report which can pull out the number of conversions per keyword.

Ecommerce Conversion Rate

• Custom Reporting
• Create a new custom report
• Metrics –> Site Usage Visits
• Metrics –> Ecommerce Transactions
• Metrics –> Total Goal Completions
• Dimensions –> Traffic Sources –> Source
• Dimensions –> Traffic Sources –> Keyword
• View Report Google –> Organic
• Take 1 years worth of data
• Add &limit=50000 to top URL
• Advanced Filter –> keyword Excluding –> Company name
• Export –> Data as a CSV

Overview of Data

So we now have the data all pasted into an Excel sheet detailing:

• Keyword
• Visits
• Transactions / Goals Complete

We then put a little Excel trickery into the mix adding a column with the code of:

=IF(LEN(TRIM(A2))=0,0,LEN(TRIM(A2))-LEN(SUBSTITUTE(A2,” “,””))+1)

This adds a column with Query Length i.e. how many word made up the search term i.e. Identifying what are short and long tail keywords.

Finally I created another column dividing the No. of Transactions / Goals Complete by the number of visits, hence giving us a Conversion rate.

Finalizing the Data

Now I have all the information in an Excel table, the simple thing is to create a pivot table of the information.
For my first example I have chosen to pivot Query Length as the row and then No. of Visits and Conversion rate as columns. I have chosen to display the average results in columns to give the overall picture.

seomad.com/SEOBlog/wp-content/uploads/2010/06/long-tail-exel-pivot-table.jpg”>

The Results

The graph below shows the average conversion rate versus the average number of visitors by query length.

Now I used 13 clients data over a total of 166,699 keywords.

We can see a clear picture that from a 1 phrase visit up to a 5 phrase visit the conversion rate is over double.

It’s not as uniform from 5 phrase visits to 10 phrase visits but I think this may shows that people using 5 phrases and above are still unsure about finding the right product or service.

Although the overall trend does show that conversion rate does increase as the search query increases.

Extending the results

The beauty of having data in excel is that it can be manipulated in any way, so I took the data above and filtered the results by removing keywords which had “0″ / “Zero” conversions, just to see what the affect was.

Apart from the obvious of average Conversion rate in the data increasing massively and the same for Average number of visits this does correlate with the overall data but shows a more uniform conversion rate by query length.

Conclusion

From the data presented it does show clear support that the long tail theory in SEO still exists and it is still right to assume niche keywords will drive a higher conversion although they have a lower search volume.

If you have any questions on the data please leave a comment.

For those who are familiar with my last post on Organic CTR will know that the post had some great pick up and is some real nice data.

After getting a number of requests on twitter (@theukseo) people have asked for more analysis from the data, so that’s what I have done.

Background

In my original study on Organic CTR I took a total of 2849 keywords which were analyzed the from the data I exported from webmaster tools.

I then created a table in Excel featuring:

• Query
• Impressions
• Clicks
• CTR
• Avg. Position
• Query Length
• Whole Number position
• Count

The results as per my last post were:

I then decided to go through the 2849 keywords and label them with a “keyword type” i.e.

• Brand
• Specific Product
• General
• Location

Brand
I labeled keywords as “brand” if they were Company names, company name derivatives, brand related searches.

Specific Product
These keywords were labeled as I consider them to be specific product searches, some keywords were very niche and other more generic but they would all be what I consider to be keywords at the end of a search journey.

General
This does account for a large percentage of the keywords analyzed as simply if I wasn’t connived that a keyword was niche, brand or localized then I have identified it as a “General” query.

location
I have labeled all localized keywords as “location”, this includes product and service searches by location.

The Initial Data
It was a very time consuming job to go through 2849 keywords so I hope this post is worth it, but below shows the overview of data assigned to each keyword type.

As you can see the greatest data is assigned to general keywords, however we do have at least 250 keywords analyzed in the other segments which can still give a reasonable picture.

So what Does the data show?

Below shows the overall graph of CTR based on keyword type. We can see that they all follow a similar pattern. However i will expand on the data in more detail.

seomad.com/SEOBlog/wp-content/uploads/2010/06/CTR-segmented-v1.jpg”>

The pivot table

Segmented Analysis of Organic SEO CTR

The next part of the post takes a look at each individual keyword type, firstly lets look at:

Brand:

The first things to notice is that the CTR rate in top position is much higher than the general CTR I displayed across all keywords i.e.

This would indicate to me that people are generally finding the brand related website in the top 2 results, however on branded product searches they are still likely to click onto the other results.

Specific Product Searches

You can see from the table below that the data does shows a slightly higher CTR on Position 1 for specific product searches, but in general the data is very similar to the overall average CTR’s based on position.

It is important to note that a Specific product search doesn’t have to be a longer query string. In my last post I detailed CTR based on Query length, although a specific product search would be suggested as a longer query this is often not the case.

Location based searches

This segment I think was the most interesting as it shows a higher CTR on all positions.

Remember this is simply the out put from webmaster tools and so I’m not saying this is 100% accurate but I think it does give a good idea of CTR by keyword types. Bearing mind that Google will often display a map insert on location based searches:

My conclusion on the location based keywords is that users are more likely to click on a lot more results when looking for something which is location specific.

General

Finally the biggest segment of data was for general keyword searches. By segmenting this data away from the average we can see that Click through rate is generally lower across the board, but the data is very similar to the general average.

Conclusion

The overall conclusion of this post is it show that Organic CTR does change based on keyword search type, however in general they do all follow a similar pattern. I think as an SEO I will particular take focus on location based searches and focus on Google local business centre to increase the retail space on the first page by displaying result in Organic, Local business and Paid listings, therefore maximizing the potential Click through rate to a website from a search query.