First of all, I am going to a tool called Semrush and I am going to see where my website is already ranked.  There are 218 keywords – you can see that the performance is quite stable right now (and over the last 3 months).

Hi, my name is Lukasz Zelezny and I just wanted to share with you how to perform a simple snapshot analysis. That analysis we will perform on my website, – it’s a small website, but for this example I think it’s perfect.

That’s great, but I should probably consider doing something to rank a little bit better.

If I open the full report of, the most important thing here is to see all the keywords that enabled Semrush to find my website. I can very quickly and smoothly export it into an Excel format, and then load Excel…

We’ll remove some columns that we don’t care about at the moment – obviously sometimes they are useful but for this example we will just keep the columns for position, search volume and URL. I’m removing everything that we don’t need for this example and making the others a little bigger.

So, we have 218 keywords, we have a position and a search volume (taken from and the URL, because every time we talk about keywords we need to remember that they have to be present.

Here, I will create TI Max and TI difference. TI stands for Traffic Index and I will explain this to you a little later. Now the most important part of this exercise is to create an additional supportive table, which should contain word position and click-through rate. As we know, the traffic coming from Google is potentially only occurring when you are listed on the first page of the results – probably between position 1 and 10. I understand that there are other factors that can result in the first page only having 8 positions – Google Maps, News Results etc., but we are trying to simplify this.

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There are lots of studies on organic click-through rate models, and that distribution is also different for each and every keyword. If we want to simplify it for this exercise we are going to avoid making any conclusions on this – please forgive me for this simplification, we are using these values just to make this example easy to understand.

So, we have position 1-10 and the click-through rate on each position. If you do a simple chart you will see that when you’re in position 1, the click-through rate is very high, also in position 2, but it doesn’t matter very much where you end up for every subsequent position. So, this area is the area that we really want to fight for.  Now that we know this, we will do a calculation for the Traffic Index. (The TI is not our visits, although it’s correlated to them).

First, we need to find the click-through rate for each position. So, if the position is 2, the click-through rate is 12. If we were to change the position to 4, the rate is 6%. If we convert it into percentages, we can see how it’s automatically picked up. If we put in position 11, it will generate an error, because obviously in this table we don’t have click-through rate for position 11 – however, we can simply say that if there’s an error, if the position is higher than 10, put zero.

Finally, we need to take the search volume and multiply it by the specific click-through rate that depends on the position. If we change it into a normal number, we have the Traffic Index. So, we have a Traffic Index for each keyword now, and  then in the second column we have TI Max (Traffic Index Maximum) which denotes the Traffic Index we could potentially have if the keyword was in position 1. We understand that there are keywords that will simply never be in positon 1, but we need to simplify it for this exercise – we are taking search volume and multiplying it back the click-through rate which is in position 1.

Finally, TI Diff is the difference between TI Max, minus the current TI, so it will always be positive… the lowest volume it can be is zero. Imagine that conference speaker is in position 1, then the TI Max is 21, TI Diff is 0. That’s the strong signal to us to understand that there is nothing we can do right now to make this keyword perform any better except, obviously, for playing a little bit with the meta-description which would give us a better click-through rate. That is not the main focus in this exercise however – we are only talking about position.

The next step is to perform a quick pivot, according to the URLs. We can see all the URLs and what we want here is to make sure it is definitely TI Diff, so we can see straight away that the biggest potential is found on the home page.

There’s been a little error in the export because we can see that we don’t have this here, so we probably need to merge this.

Let’s do that, then. If we get back to this page, I will do a trick – let me replace every occurrence of HTTP with HTTPS. Okay, no matches. Let me try like…that…

Okay! 8 instances and right now we have this done correctly. Let me now try to do this pivot once again.

We are doing ‘pivot’ and we are doing ‘URL’ and we are doing ‘TI Diff’. On the next level, we are adding ‘keyword’ and we are adding ‘position’. It’s important to make sure that here it is not SUM, it’s the Average.

This is pretty much it…. Maybe to make it even more sexy I will add ‘Search Volume’. Let’s collapse this…

That’s all we need! What do we learn from this? We know that if we sort this by TI, the home page has the biggest potential based on the keywords that are already ranking, and we know the average position of this URL based on all the keywords, is position 58. I also will add the count of the keywords here, so we know that there are 42 keywords that are ranking which trigger the URL that we have here.

If we click plus, we can see what the keywords are. The first two are fleetway travel, that’s the company I was working for back in the day. We can sort this by positions from smallest to largest and we can see that website’s status checker, SEO conference speaker, social media speaker and so on… these keywords are ranking somewhere in the top 100. Right now, I can take the keywords like SEO conference speaker and start optimizing the website.

Let’s go here, the HTTP status checker. I can show you what this webpage looks like – it’s very simple, we can put in any address, click check and they will check what the status is. There’s obviously a long article which ensures this website rates well, and many terms related to checking statuses.

Lots of these keywords here will be related to checking HTTP statuses. Then we have another one which is blog topic content title generator – this is another tool that I have – you can go here, open it, write ‘SEO get ideas’ and you can find ideas for the title of your new blog.

Again, here are the keywords related to this page which are very different to the keywords related to HTTP status checker. We have ‘klout score’ which is a tutorial that I wrote, then the ‘conference speaker’ page which is purely about my conferences. Then, ‘social media webinar’ which is quite important, it’s an ongoing webinar that you can join any time you want. I can see that there is some potential, like ‘webinar social media’, ‘free social media’, and again some irrelevant keywords here that I won’t consider adding.

Another one is the SEO audit – I’m using a widget from Semrush so I can go here and I can simply  try to optimize the content which is here. The tool is here, you can enter your domain and email and that will send you a free audit.

You can see here that the ‘free SEO audit’ search volume is 110 which is small, but the keyword volume is highly related to the industry, then ‘Google site audit’, ‘SEO audit expert tool’, ‘website audit tool’ ‘wordpress SEO audit’ – they are keywords that I would probably need to add to this page and we will be talking about this later.

This is how you can perform a simple snapshot analysis. In the next part we will talk about .

Thank you very much, that was me Lukas Zelezny and if you have any questions, comment below and don’t forget to share it with your friends. I hope it will be useful because we are talking here about tech stuff – things you can implement right now, pretty much straight away. Once again, thank you very much – see you in the next part!


Last Updated in 2022-12-28T11:44:32+00:00 by Lukasz Zelezny