Case Study Part 2 – Terms and Definitions
One of Matt’s Many SEO Silo Ranking Case Study:
- Case Study Part 1- The Objective
- Case Study Part 2 – Terms and Definitions
- Case Study Part 3 – Strategic Scenario
- Case Study Part 4 – Test Overview
- Case Study Part 5 – New Silo Architecture Test
- Case Study Part 6 – Site Speed Test
- Case Study Part 7 – Freebase Verify Test
- Case Study Part 8 – Onpage Optimization Test
- Case Study Part 9 – Engagement Test
What do our case study terms mean & how are they calculated?
The Visibility score is an average that reflects the progress of your website, as a whole, in organic search, across all targeted keywords and all search engines.
To calculate the Visibility score, a number of points are assigned to each of the first 30 SERPS positions, as follows:
Position 1 = 30 points
Position 2 = 29 points
Position 30 = 1 point
Positions below 30 have 0 points.
Here is an example of SASSI’s site visibility across the entire Keyword DNA (all keywords across all search engines):
The 0 – 4000 index on the left is the visibility score and at the bottom is time.
This metric shows us how we are swallowing up more industry related keywords, (i.e. site visibility on search engines) than our competitor whose site has been online since 2009-01-30 according to Domain Tools: https://whois.domaintools.com/blisshd.com
site keywords ranking visibility
The blue is SASSI & the green is one of the competitors.
This chart shows how SASSI is ranking for more keywords as time goes by; due to implementing silo architecture the way it is taught in the Technical Foundation 1 Course.
The higher the visibilities index in this case, the more keywords in top positions.
The Visibility score (on left – bottom to top) is the sum of the points given to an URL by each search engine. For example, if we have 3 search engines, we give 3×30=90 points. Now, with a URL that has the following positions:
Search engine 1: Position 1 = 30 points
Search engine 2: Position 10 = 21 points
Search engine 3: Position 32 = 0 points
The Visibility score is 51 points. The visibility percentage in this case would be 51/90 = 0.5666 = 56.66% which approximates to 57%.
The Average Rank is the sum of all the ranking positions divided by the number of keywords. When a keyword is not ranked, its value in this formula is calculated as the search depth (expressed as the number of results) + 1.
For example, if a keyword is not ranked and the search depth is set to 5 pages (50 results), the value of this keyword in the formula would be 50 + 1 = 51.
With a search depth of 10 pages (100 results), the value assigned for a keyword not ranked would be 100 + 1 = 101.
In this example above you can see that over time the keywords we tracking are increasing as an average in comparison to the competitor. On the left we have the ranking position index. The lower the number the better the site is performing as a whole in all the search engines.
What this means in simple terms is if we look at all the keywords on average; our site would sit at position 49 vs our competitor which sits at 92. This gives us a feeling of what direction, as a whole the site is moving up in the serps. In this view the lower the number on the left index the better your site is performing on average.
The Net Gain is the sum of positions that the keywords went up. For example, if you have one keyword that went up 5 positions, one that went up 2 positions and one that went up 4 positions, you would have a Net Gain of 11. The calculation is: 5 + 2 + 4 = 11.
The Net Loss is the sum of positions that the keywords went down. For example, if you have one keyword that went down 3 positions, one that went down 4 positions and one that went down 2 positions, you would have a Net Loss of 9. The calculation is: 3 + 4 + 2 = 9.
Note: If the website didn’t rank for a keyword on a previous comparison date, but now it ranks for that keyword, the Net Gain is calculated this way:
search depth (expressed as the number of results) + 1 – the previous position. For example, if the website was not ranked for a given keyword on the previous comparison date, but now it ranks on the 35th position, with a search depth of 5 pages (50 results), the Net Gain will be 50 + 1 – 35 = 16.
Note: If the website no longer ranks for a keyword (but it ranked on the previous comparison date), the Net Loss is calculated this way: search depth (expressed as the number of results) + 1 – previous position. For example, if the website ranked for a given keyword on the 49th position and it no longer ranks, with a search depth of 5 pages (50 results), the Net Loss will be 50 + 1 – 49 = 2.
Click Share Score
The Click share score estimates the number of searchers who may click on your website result in a SERP when searching for a specific keyword. This value is displayed as percentage, according to the position of your website for the keyword in question.
On the left index is the click share percentage. The bottom is time & this data gives an indication of how your Meta title and Meta description is attracting clicks. By analysing this data you can find the weakest call to actions and increase the click through rate to your site. A 1% increase in traffic has a direct impact on the potential conversions of the site. You can see that with our 1st draft of the site we are performing better than our competitor.
Studies show that if your website is listed on the first position in a SERP, 17.16% of the people who are searching for a keyword will click on your site. The same studies offer information for positions 2 – 10 in the SERP, assigning 9.94% clicks for the result on the second position, 7.64% clicks for the result on the third position, etc.
Click share percentages for ranking positions are mapped as follows:
Position 1 = 17.16%
Position 2 = 9.94%
Position 3 = 7.64%
Position 4 = 5.31%
Position 5 = 3.5%
Position 6 = 1.63%
Position 7 = 1.09%
Position 8 = 1.04%
Position 9 = 0.44%
Position 10 = 0.51%
The estimated Click share score for positions out of top 10 is 0.00%
Click share is also calculated across groups of keywords. For a group of keywords and a search engine, the Click share score is the average click share per keyword, for all the keywords ranking in the first 10 positions. All keywords are also considered a group of keywords in AWR Cloud.
When a website ranks with multiple URLs for the same keyword, only the best position is taken into account for calculating the Click share score.
The Estimated visits can be calculated for a keyword and a search engine by multiplying the related Click share value of that keyword with the Google AdWords search volume.
The Estimated visits for a group of keywords and a search engine are the sum of estimated visits for each keyword of that group.
Google Algorithm Effects on the Site
You can clearly see that silo architecture, theming your site correctly & by applying the persuasion architecture is very powerful and thrives in the Google Land. This is a very important point. Often people develop sites and they try applying every technique under the sun to get their sites ranked. The reality of this is there is a massive increase in the IT debt plus it starts becoming very difficult to really gauge what the effects of your actions are thus throwing accountability for expenditure out the door.
** RED BAR in chart represents the Google Filter ordered below
Author photo drop
‘in the news’ box
Google algo effects on WR1 Money Site
You can see in the chart above that the red bars indicate when google released updates. The purple line indicates the visibility of the site. The index on the left is the ranking factor change percentage.
What I want to demonstrate at this point is even through all the google updates the SASSI site has increased in visibility. This has all been achieved with two citation backlinks as shown later on in the case study.
This case study will hopefully change the way you think about building your sites and will assist you in reducing the costs to get results one wants.
Remember you don’t need to use a nuclear bomb in a fist fight. Assess the situation and respond accordingly. Keep the ‘Super Trump cards’ for when you need them. So with the definitions of what we are speaking about clarified let’s get started.
– Matt Da Cruz
Continue to: Case Study Part 3 – Strategic Scenario