There can be few sites that wouldn’t benefit from increasing the proportion of users who visit the site more than once. Return visitors don’t come with new acquisition costs and will generally have higher conversion rates, making visitor loyalty a hot metric to monitor.
No matter how great your content is, the uncomfortable truth is that a large proportion of users probably leave your website on their first visit and never come back. Finding ways to identify what engage users and what drives them away can bring significant gains.
So, which is your best page for building visitor loyalty?
It sounds like a simple question that can be answered with a quick visit to Google Analytics, but finding the answer might be harder than you think. Most people would look for this answer by looking at visitor loyalty by landing page. What that report actually shows you though is how many visits the user had made before returning to the site via that page, rather than how many they made as a consequence of arriving from it.
The problem is in the way that Google stores visitor data. Landing page is stored per visit, so the concept of a user’s “original landing page” doesn’t exist.
Enter the custom variables
Google Analytics custom variables are a great thing, allowing you to pull date and reports based on up to five variables of your choosing. Variables can be set to be tracked per page, per visit or, usefully for our purposes per visitor.
Limitations of this approach
The main limitation of this approach is that it doesn’t come with a time-machine. Because it is dealing with an entirely new variable you can only start collecting data from the minute you add the code. That means that there is no way to go back and see past data.
That said, even with just a couple of days worth of data on hand we’re finding this a useful extra metric.
How to start collecting this data
Here is the snippet:
Google Analytics gives you 5 slots for tracking custom variables (called Keys). The code above uses Key 1. If you are already using Key 1 (most people aren’t) then you can change the first 1 in the snippet to another number 1-5. For example, to use Key 2 you would use:
A handy plugin for WordPress users
For those readers using WordPress, here is an even easier way to add that code: A plugin that adds it for you. The plugin is provided without support or warranty, but if it’s useful you are welcome to it. Let us know though – it’s always nice to hear if this stuff is useful. Download the plugin.
How to access the data
Once you have had a few new visitors to the site you get some new data in Analytics to work from.
To view this got to Audience -> Custom -> Custom Variables
If you used the default Key 1 you will see the heading “Original Landing Page” in the table. If not then select “key 2” “key 3” etc to match the custom variable slot that you specified. (see (1) in next screen shot).
Once you can see the options for ‘Original Landing Page’ (labelled as (2)) click that to view the data.
What you are now presented with is all the data for visit during the time range you are viewing. Interesting, but overwhelming on a busy site. To get to the most interesting data we want to apply a quick filter.
Click ‘Advanced’ next to the search box just underneath the graph.
Set the filter as : Include ‘Count of Visits’ Greater than 1 then click apply. What you now have is a table showing the content that most frequently brings people back to your website.
Sort by Visits (default) and you will see the content that most return visitors land on.
Sort by Count of Visits and you’ll see the content that brings people back most frequently.
It’s worth noting that you can then save this as a custom report to make is easier to get back at the data in the future.
How to use these insights
The original concept of this report was to analyse a blog that had very low visitor loyalty rates. Despite having high quality content users were rarely returning for a second visit. We wanted to analyse what was different for those users who did return so that we could help the site owner replicate that success and build visitor loyalty.
We’ll literally be looking for patterns in the content that does consistently bring users back to the site. What style of content is it? What tone? How is it presented compared with other content? What topic areas are covered? What else in on the page? Etc. With enough data in hand we are confident that we’ll be able to bring additional insight to the content strategy of the site.