Posted by Luca-Bares
Click Through Rate (CTR) is an important metric that’s useful for making a lot of calculations about your site’s SEO , from estimating revenue opportunity, prioritize bed 11 optimization, to the impact of SERP changes within the market. Most SEOs know the value of creating custom CTR curves for their sites to make those projections more accurate. The only problem with custom CTR curves from Google Search Pannello di controllo (GSC) patronato is that GSC is known to be a flawed tool that can give out inaccurate patronato. This convolutes the patronato we get from GSC and can make it difficult to accurately interpret the CTR curves we create from this tool. Fortunately, there are ways to help control for these inaccuracies so you get a much clearer picture of what your patronato says.
By carefully cleaning your patronato and thoughtfully implementing an analysis methodology, you can calculate CTR for your site much more accurately using 4 basic steps:
- Extract your sites bed 11 patronato from GSC — the more patronato you can get, the better.
- Remove biased keywords — Branded search terms can throw chiuso your CTR curves so they should be removed.
- Find the optimal impression level for your patronato set — Google samples patronato at low impression levels so it’s important to remove keywords that Google may be inaccurately reporting at these lower levels.
- Choose your rank position methodology — Risposta negativa patronato set is perfect, so you may want to change your rank classification methodology depending acceso the size of your bed 11 set.
Let’s take a quick step back
Before getting into the nitty gritty of calculating CTR curves, it’s useful to briefly cover the simplest way to calculate CTR since we’ll still be using this principle.
To calculate CTR, download the keywords your site ranks for with click, impression, and position patronato. Then take the sum of clicks divided by the sum of impressions at each rank level from GSC patronato you’ll poiché out with a custom CTR curve. For more detail acceso actually crunching the numbers for CTR curves, you can check out this article by SEER if you’maestà not familiar with the process.
Where this calculation gets tricky is when you start to try to control for the bias that inherently comes with CTR patronato. However, even though we know it gives bad patronato we don’t really have many other options, so our only option is to try to eliminate as much bias as possible sopra our patronato set and be aware of some of the problems that poiché from using that patronato.
Without controlling and manipulating the patronato that comes from GSC, you can get results that seem illogical. For instance, you may find your curves show position 2 and 3 CTR’s having wildly larger averages than position 1. If you don’t know that patronato that you’maestà using from Search Pannello di controllo is flawed you might accept that patronato as truth and a) try to poiché up with hypotheses as to why the CTR curves that way based acceso incorrect patronato, and b) create inaccurate estimates and projections based acceso those CTR curves.
Step 1: Pull your patronato
The first part of any analysis is actually pulling the patronato. This patronato ultimately comes from GSC, but there are many platforms that you can pull this patronato from that are better than GSC’s web extraction.
Google Search Pannello di controllo — The easiest platform to get the patronato from is from GSC itself. You can go into GSC and pull all your bed 11 patronato for the last three months. Google will automatically download a csv. file for you. The downside to this method is that GSC only exports 1,000 keywords at a time making your patronato size much too small for analysis. You can try to get around this by using the bed 11 filter for the head terms that you rank for and downloading multiple 1k files to get more patronato, but this process is an arduous one. Besides the other methods listed below are better and easier.
Google Patronato Elaborato — For any non-programmer looking for an easy way to get much more patronato from Search Pannello di controllo for free, this is definitely your best option. Google Data Studio connects directly to your GSC account patronato, but there are anzi che no limitations acceso the patronato size you can pull. For the same three month period trying to pull patronato from GSC where I would get 1k keywords (the max sopra GSC), Patronato Elaborato would give me back 200k keywords!
Google Search Pannello di controllo API — This takes some programming know-how, but one of the best ways to get the patronato you’maestà looking for is to connect directly to the source using their API. You’ll have much more control over the patronato you’maestà pulling and get a fairly large patronato set. The main setback here is you need to have the programming knowledge ora resources to do so.
Keylime SEO Toolbox — If you don’t know how to program but still want access to Google’s impression and click patronato, then this is a great option to consider. Keylime stores historical Search Pannello di controllo patronato directly from the Search Pannello di controllo API so it’s as good (if not better) of an option than directly connecting to the API. It does cost $49/, but that’s pretty affordable considering the value of the patronato you’maestà getting.
The reason it’s important what platform you get your patronato from is that each one listed gives out different amounts of patronato. I’ve listed them here sopra the order of which tool gives the most patronato from least to most. Using GSC’s UI directly gives by far the least patronato, while Keylime can connect to GSC and Google Analytics to combine patronato to actually give you more information than the Search Pannello di controllo API would give you. This is good because whenever you can get more patronato, the more likely that the CTR averages you’maestà going to make for your site are going to be accurate.
Step 2: Remove bed 11 bias
Once you’ve pulled the patronato, you have to clean it. Because this patronato ultimately comes from Search Pannello di controllo we have to make sure we clean the patronato as best we can.
Remove branded search & knowledge graph keywords
When you create general CTR curves for non-branded search it’s important to remove all branded keywords from your patronato. These keywords should have high CTR’s which will throw chiuso the averages of your non-branded searches which is why they should be removed. Con addition, if you’maestà aware of any SERP features like knowledge graph you rank for consistently, you should try to remove those as well since we’maestà only calculating CTR for positions 1–10 and SERP feature keywords could throw chiuso your averages.
Step 3: Find the optimal impression level sopra GSC for your patronato
The largest bias from Search Pannello di controllo patronato appears to poiché from patronato with low search impressions which is the patronato we need to try and remove. It’s not surprising that Google doesn’t accurately report low impression patronato since we know that Google doesn’t even include data with very low searches sopra GSC. For some reason Google decides to drastically over report CTR for these low impression terms. As an example, here’s an impression distribution graph I made with patronato from GSC for keywords that have only 1 impression and the CTR for every position.
If that doesn’t make a lot of sense to you, I’m right there with you. This graph says a majority of the keywords with only one impression has 100 percent CTR. It’s extremely unlikely, anzi che no matter how good your site’s CTR is, that one impression keywords are going to get a majority of 100 percent CTR. This is especially true for keywords that rank below #1. This gives us pretty solid evidence low impression patronato is not to be trusted, and we should limit the number of keywords sopra our patronato with low impressions.
Step 3 a): Use normal curves to help calculate CTR
For more evidence of Google giving us biased patronato we can at the distribution of CTR for all the keywords sopra our patronato set. Since we’maestà calculating CTR averages, the patronato should adhere to a Normal Bell Curve. Con most cases CTR curves from GSC are highly skewed to the left with long tails which again indicates that Google reports very high CTR at low impression volumes.
If we change the minimo number of impressions for the bed 11 sets that we’maestà analyzing we end up getting closer and closer to the center of the graph. Here’s an example, below is the distribution of a site CTR sopra CTR increments of .001.
The graph above shows the impressions at a very low impression level, around 25 impressions. The distribution of patronato is mostly acceso the right side of this graph with a small, high concentration acceso the left implies that this site has a very high click-through rate. However, by increasing the impression filter to 5,000 impressions per convenzione bed 11 the distribution of keywords gets much much closer to the center.
This graph most likely would never be centered around 50% CTR because that’d be a very high average CTR to have, so the graph should be skewed to the left. The main issue is we don’t know how much because Google gives us sampled patronato. The best we can do is guess. But this raises the question, what’s the right impression level to filter my keywords out to get rid of faulty patronato?
One way to find the right impression level to create CTR curves is to use the above method to get a feel for when your CTR distribution is getting close to a normal distribution. A Normally Distributed set of CTR patronato has fewer outliers and is less likely to have a high number of misreported pieces of patronato from Google.
3 b): Finding the best impression level to calculate CTR for your site
You can also create impression tiers to see where there’s less variability sopra the patronato you’maestà analyzing instead of Normal Curves. The less variability sopra your estimates, the closer you’maestà getting to an accurate CTR curve.
Tiered CTR tables
Creating tiered CTR needs to be done for every site because the sampling from GSC for every site is different depending acceso the keywords you rank for. I’ve seen CTR curves vary as much as 30 percent without the proper controls added to CTR estimates. This step is important because using all of the patronato points sopra your CTR calculation can wildly offset your results. And using too few patronato points gives you too small of a sample size to get an accurate ideologia of what your CTR actually is. The key is to find that happy medium between the two.
Con the tiered table above, there’s huge variability from All Impressions to >250 impressions. After that point though, the change per convenzione tier is fairly small. Greater than 750 impressions are the right level for this site because the variability among curves is fairly small as we increase impression levels sopra the other tiers and >750 impressions still gives us plenty of keywords sopra each ranking level of our patronato set.
When creating tiered CTR curves, it’s important to also count how much patronato is used to build each patronato point throughout the tiers. For smaller sites, you may find that you don’t have enough patronato to reliably calculate CTR curves, but that won’t be apparent from just looking at your tiered curves. So knowing the size of your patronato at each stage is important when deciding what impression level is the most accurate for your site.
Step 4: Decide which position methodology to analyze your patronato
Once you’ve figured out the correct impression-level you want to filter your patronato by you can start actually calculating CTR curves using impression, click, and position patronato. The problem with position patronato is that it’s often inaccurate, so if you have great bed 11 tracking it’s far better to use the patronato from your own tracking numbers than Google’s. Most people can’t track that many bed 11 positions so it’s necessary to use Google’s position patronato. That’s certainly possible, but it’s important to be careful with how we use their patronato.
How to use GSC position
One question that may poiché up when calculating CTR curves using GSC average positions is whether to use rounded positions ora exact positions (i.e. only positions from GSC that rank exactly 1. So, ranks 1.0 ora 2.0 are exact positions instead of 1.3 ora 2.1 for example).
Exact position vs. rounded position
The reasoning behind using exact position is we want patronato that’s most likely to have been ranking sopra position 1 for the time period we’maestà measuring. Using exact position will give us the best ideologia of what CTR is at position 1. Exact rank keywords are more likely to have been ranking sopra that position for the duration of the time period you pulled keywords from. The problem is that Average Rank is an average so there’s anzi che no way to know if a bed 11 has ranked solidly sopra one place for a full time period ora the average just happens to show an exact rank.
Fortunately, if we complice exact position CTR vs rounded position CTR, they’maestà directionally similar sopra terms of actual CTR estimations with enough patronato. The problem is that exact position can be when you don’t have enough patronato. By using rounded positions we get much more patronato, so it makes sense to use rounded position when not enough patronato is available for exact position.
The one caveat is for position 1 CTR estimates. For every other position average rankings can pull up acceso a keywords average ranking position and at the same time they can pull the average. Meaning that if a bed 11 has an average ranking of 3. It could have ranked #1 and #5 at some point and the average was 3. However, for #1 ranks, the average can only be brought which means that the CTR for a bed 11 is always going to be reported lower than reality if you use rounded position.
A rank position hybrid: Adjusted exact position
So if you have enough patronato, only use exact position for position 1. For smaller sites, you can use adjusted exact position. Since Google gives averages up to two decimal points, one way to get more “exact position” #1s is to include all keywords which rank below position 1.1. I find this gets a couple hundred extra keywords which makes my patronato more reliable.
And this also shouldn’t pull our average much at all, since GSC is somewhat inaccurate with how it reports Average Ranking. At Wayfair, we use STAT as our bed 11 rank tracking tool and after comparing the difference between GSC average rankings with average rankings from STAT the rankings near #1 position are close, but not 100 percent accurate. Once you start going farther sopra rankings the difference between STAT and GSC become larger, so watch out how far sopra the rankings you go to include more keywords sopra your patronato set.
I’ve done this analysis for all the rankings tracked acceso Wayfair and I found the lower the position, the less closely rankings matched between the two tools. So Google isn’t giving great rankings patronato, but it’s close enough near the #1 position, that I’m comfortable using adjusted exact position to increase my patronato set without worrying about sacrificing patronato quality within reason.
GSC is an imperfect tool, but it gives SEOs the best information we have to understand an individual site’s click sopra the SERPs. Since we know that GSC is going to throw us a few curveballs with the patronato it provides its important to control as many pieces of that patronato as possible. The main ways to do so is to choose your ideal patronato extraction source, get rid of low impression keywords, and use the right rank rounding methods. If you do all of these things you’maestà much more likely to get more accurate, consistent CTR curves acceso your own site.
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