I gave a talk this evening on tactics and techniques to improve organic click through rate at the wonderful MKGO event in Milton Keynes. You can get the slides here. But one thing I mentioned as being critical to improving click through rate is a true understanding of what your click through rate actually is to start with.
CTR studies are insightful in many ways. A couple definitely worth checking out are this one from Advanced Web Ranking and this one from Moz. But you can’t take a study as a true reflection of what YOUR click through rate should be for any given query. There are so many variables now – much more so than a few years ago.
We’ve got featured snippets of different types, more visual organic listings (video thumbnails, for example), different devices to consider, some queries return maps, some return answers from Google above the results… the list is seemingly endless.
So while a study can give you a loose idea of click through rates on the whole or by category, if you’re serious about optimising yours, you’ll need to look at your own data.
But really, that’s not an overly useful figure. In this case, there are thousands upon thousands of different queries/keywords generating those clicks, each with different positions, types of results pages and similar.
At the bottom, we can see “1-10 of 999.” 999 is the limit for queries that Search Console will show. It means that there were at least 999 queries sending impressions or clicks to this page. And even from the ones we see here, we can see click through rates and positions are hugely variable.
So even a page level report on click through rate doesn’t necessarily yield the granular level of data you need to get a true measure on click through rate.
That’s why I measure click through rate at a query level. And I take a lot of data to also work out what click through rate is per query at various different positions.
The position there shows 4.6. But this is a global average. And this site targets a UK audience. If it shows up outside the UK, the position is generally low (skewing our overall averages) and that audience isn’t relevant for us. So we now filter to show only UK impressions and clicks.
The average position in the UK (which we are most concerned with) is 1.9 over the timeframe we’re looking at.
This is a useful figure.
What this tells us is that from an average position of 1.9 click through rate is 12.2%. But I need to know how that click through varies with variances in the position.
Because if I want to test things that influence click through rate, I have to allow for the fact that other things might change too. What if I make a page title change and then my ranking drops a place? Obviously, my CTR will drop too. But if I have data about historic click through rates at multiple positions, I can benchmark my “new” click through rate and assess whether it is better or worse than CTR at that position before my changes.
It’s not perfect. And you might not have position data for loads of positions. But where you have the data, extracting it at least gives you something to benchmark any future performance against.
So let’s export that information.
You’re going back the same way you did in the new version, “queries,” report and then filtering by country.
You’ll then get a report filtered for that one query you selected, filtered for UK only and showing the clicks, impressions, CTR and position data by day.
Below I’ve shown you (using an export on another query, which has more varied position data) what I then do with those exports.
The export looks like this.
You’ve got CTR by position for a specific query on your website. That’s much more insightful than a study for you. It means that you can analyse with a little more accuracy the impact of any CTR optimisation work you do even if things also fluctuate by a position or two.