How to measure the real click-through rate for a CTA in Google Analytics
How Do You Measure Your CTA Click-Through Rate?
Clicks on call-to-action buttons can be overrated if they don’t result in a conversion, but a good click-through rate on a button is a strong indicator of how relevant and engaging your offer is to visitors. That’s why it’s important to record clicks on call-to-actions by setting up click events in Google Tag Manager. It also allows you to measure drop-off rates at each step in your conversion funnel. This will allow you to use data from Google Analytics to help optimise your conversion rate.
For pay-per-click (PPC) campaigns, a good click-through rate (CTR) goes towards your keyword’s expected CTR and contributes to an ad’s Quality Score or Relevance Score. The Quality Score is a key factor in your cost per click (CPC) and when multiplied by your maximum bid it gives you your ad rank in the keyword auction. This means your click-through rate directly impacts on your acquisition costs and the profitability of PPC campaigns.
What is the click-through rate?
The click-through rate is a ratio which indicates the frequency with which visitors who see an ad or call-to-action (CTA) end up clicking on it. For a PPC ad this is calculated by dividing the total number of clicks on an ad by the total number of impressions (i.e. the number of people who had the opportunity to see the ad). This is then multiplied by 100 to give you a percentage CTR.
The CTR for a button on a website is usually calculated by dividing the total number of unique visitors (Google Analytics refers to these as users) by the number of users who click on a button. For example, 3,991 users landed on our webpage which has four CTAs. Given that CTA 1 received clicks from 1660 users we can employ the formula CTR = ( 1,660 / 3991) x 100 = 42%. Is this an accurate reflection of reality though?
The problem with this formula is that it assumes that all users had the opportunity to see the call-to-action on the screen. Given that most web users are on smartphones, this is no longer a reasonable assumption to make.
Even with a desktop site it’s not always possible to locate all CTAs above the fold, but this is even more the case when a user is on a mobile device. In addition, websites increasingly use dynamic content to target certain segments with more relevant and different content. This means only users who are targeted with the content will have the opportunity to see certain CTAs.
How should Click-Through Rates for Call-To-Actions be Measured?
To measure the real click-through rate for a CTA we must first identify how many users have an opportunity to see an element. We can do this by using Google Tag Manager to create an element visibility trigger which only fires an event when an element with a pre-set ID or CSS selector appears on the user’s screen.
This is especially easy to do if you get a developer to add unique IDs to each element on a page. You can use CSS selectors, but these are more easily broken by changes to your website. We can then take the total number of users clicking on the CTA and divide it by that number of users where the visibility tag fired when the CTA appeared in the user’s browser.
For example, with CTA 1 this will be (1,660 / 3,560) x 100 = 47%. This is significantly different than the 42% we had originally calculated. But look at CTA 3 and 4. Because these CTAs were much less likely to be seen by users the CTR’s is massively under-estimated by the normal method of calculating the metric.
When you are using dynamic content, you may also want to get a developer to add an ID to the top of the page for each scenario. This will allow you to measure the potential number of users for each cohort. Once we understand the total number of users who are in each cohort, we can then calculate the ratio of visibility of each CTA.
This is calculated by taking the number of users who had the opportunity to see a CTA and dividing it by the total number of users in the cohort who would have been served the CTA. This allows us to understand how visible a CTA is and so estimate the proportion of users who don’t have an opportunity to see the element on the page.
For example, for CTA 3 this would be (743 / 3,991) x 100 = 25%. Together with the real CTR, we can use this information to inform decisions about whether to move a CTA’s location on the page.
Total Clicks or Unique Clicks?
When you compare total click events with unique click events you will notice that the former is a much bigger number than the later. That’s because total click events for a given CTA will include all clicks from a user during a single session. This might be because they couldn’t make their mind up and they wanted to research each option on multiple occasions.
Total clicks may also include events which generate error messages when users have incorrectly completed a form. This can generate multiple click events before the user succeeds in proceeding to the next step in the journey.
Unique click events only count the first click by a user during a single session. Unique events tell you how many users or sessions triggered an event. This avoids duplication of click events caused by errors or users going back and forward during a session. For this reason, I prefer to use unique events to measure click through rates.
Create a Funnel Report in Data Studio
Now that you are measuring click events you can use this data to create an automated complex funnel visualisation in Data Studio. This enables you to calculate drop-off rates at both a micro-conversion level within in each page and a macro-conversion level by calculating your overall conversion rate. By using Data Studio, you can save yourself hours and days by automating the process of providing crucial management information on user interactions in your conversion funnel.
- About the author: Neal (@northresearch) provides web analytics and CRO consultancy services and has worked in many sectors including financial services, online gaming and retail. He has helped brands such Hastings Direct, Manchester Airport Group Online and Assurant Solutions Ltd to improve their digital marketing measurement and performance.
- Neal has had articles published on website optimisation on CXL and Usabilla.com. As an ex-market research and insight manager he also had posts published on the GreenBook Blog research website. If you wish to contact us please send an email to firstname.lastname@example.org. You can follow us on Twitter @conversionupl, see Neal’s LinkedIn profile or connect on Facebook.