A custom dashboard in Google Analytics you can use for checking your checkout process
If you have an ecommerce web site you know that the performance of the checkout is critical. If anything goes wrong with the checkout it will be costing you money.
That’s why first thing every morning, before I even look at the sales figures, I look at my clients' ‘Checkout’ custom dashboards.
It gives me an instant health check. I can clearly see if something has gone wrong with any of the stages in the checkout, such as the payment gateway or the address lookup system. Or if something is wrong with a promotional offer codes and people who use it are unable to buy…
All I have to do is to take a quick look at this one-stop dashboard to see if any of the ‘sparkline’ charts has an unusual looking spike. I can instantly see if something is out of line, and exactly which part of the checkout has a problem. I really like reports like these. They’re what I call ‘read and react‘ reports.
Build a report like this and you’ll find it’s very powerful. You may already have read enough to know what to do, but I’ve explained how to do it below. Follow these instructions and you will be able to spot if there’s anything odd happening with your checkout by looking at one clear report.
Which means that… You can jump in and fix the problem before too much money has been lost.
How To Make Your Read and React Checkout Dashboard
The secret of this technique is to set up a sequence of Google Analytics funnels, one for each stage, and then build a dashboard showing the abandon rate for each of those funnels.
The idea of using a series of separate funnels has been around since 2010.
The original concept involved using a Google Analytics Custom Report to present the information in a way which made all the important numbers easy to see, but it did not include any visualisation of the abandon rate from the different stages.
But now we can use GA Custom Dashboards to show all the individual abandon rates, trended over time, on a single screen.
Being able to spot the changes in the trend compared to normal performance adds the final touch which transforms ‘detailed analysis’ data into a ‘read and react’ operational report. You also get a bonus of being reminded of the relative abandon rates from the different stages so you can see where the friction points are.
Each time you look at this dashboard you can see what’s going on in a split second. You can instantly tell if you need to fix anything.
At peak periods it’s a good idea to check back later and set the date range to include ‘today’ so you can keep an eye on things.
Once you’ve tried this technique you can apply it in lots of ways. In the example here, I’ve extended the concept to show some extra very useful data which is relevant to checkout problems.
In the first column I’ve got the ‘alarm bell’ step by step abandon rates. These are all ‘metric’ widgets which include the sparklines trend charts which are the key to this technique. I’ve arranged the widgets in order of the likelihood of trouble, not the real life order of the stages. You can just drag and drop them to suit your preference.
The middle column contains a ‘table’ widget which might also provide evidence of something odd, or at least interesting, going on with the checkout. It shows the overall checkout abandon rate by source/medium of the visit. I find it useful to be constantly aware of how some channels bring visitors who are far more motivated to make it all the way through the checkout than others.
In the third column I’ve got some ‘line chart’ widgets because they allow me to compare two metrics. In this example they are comparing the abandon rate from just the cart page with the abandon rate for the rest of the checkout. Or the abandon rate from just the cart page with the abandon rate of the checkout as a whole.
You could extend the idea behind the middle ‘table’ widget further. For example, on many sites it would be useful to have another widget showing the abandon rates from just the cart page, broken down by source. If your cart page contains an offer code box, for example, a sudden spike in the cart page abandon rate from one source is a strong indication that there’s probably a ‘bad’ code being promoted there. In that situation you can either create an extra version of the code which matches the one which people are trying to use, or try to promote the correct code via the same source.
In the video below you’ll see me building another variation on this layout. Once again the first column contains the ‘alarm bell’ sparklines, but the table widgets in the middle can be used to gain a wide range of operational insights which you can use to modify or correct promotional campaigns straight away.
You can include up to 12 widgets on each dashboard. What you choose to show in the tables should be decided on the basis of what you’re actually putting your resources into now. This is a living dashboard: you should change it to align with what matters now and what you can still change.
Can you see the theme there? This dashboard is not just a read and react tool for spotting technical errors. It’s also a very useful tool for getting an understanding of the motivation of the visitors from different channels. The biggest variations in checkout performance, in my experience, have very little to do with the technicalities of the site and everything to do with the strength of people’s desire to buy. Persuasion and motivation can have more influence on abandon rates than the position or wording of the ‘checkout now’ button.
The original Lunametrics post gives you excellent instructions on how to set up the sequence of funnels.
And this video shows how to use those funnels in a custom dashboard like the one above:
Updates on Checkout Abandon Rate Dashboards in Google Analytics
[January 2012] Someone has just pointed out that it can be extremely useful to look at checkout abandon rates by browser for each stage. At the moment there’s no easy way to do this in the dashboard itself. You can’t use Advanced Segments in dashboards. And even if you could, you really need to be able to segment by browser version, not browser, as this screenshot shows:
Having to configure each segment first and only being able to see four browser versions at a time would make using segments tedious, even if it was possible.
So the way I do this is to configure the widget to link through to a Google Analytics custom report containing a tab which allows me to see the abandon rate broken down by browser and then by browser version. I also have a tab showing a source/medium breakdown, which is the one I use more often.
You can import an example of such a report into your own GA profiles by logging into GA and then clicking this link:
This example shows the rates for two goals (14 and 15 in this case) so that it can be used as the link from two widgets. That suits the way I work. This is intended as an example to get you started: you must edit the report to show the goals which you have configured in the relevant profiles.
Resources for Horizontal Funnels and Custom Dashboards
Tim Leighton-Boyce has been using analytics, customer surveys and usability testing to help improve ecommerce webs sites for 15 years. Originally he worked for direct mail companies. These days he’s a consultant. Follow Tim on Twitter or connect with him on LinkedIn. I’m also active on Google Plus.
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