As a publisher you will invariably want to measure your readers’ loyalty. Do they keep coming back for more, or do they arrive once, never to return again?
In Google Analytics, all traffic data defaults to displaying aggregate, lump traffic in the form of Sessions. At the most basic level, the % New Sessions metric can be useful for breaking down your traffic into the percentage of your visitors who were first-time or repeat readers. For example, a lower % New Sessions figure will show that a greater proportion of visits during the selected date range were return readers, whilst a higher % New Sessions figure will mean that a greater proportion were return visits. However, the process of understanding ongoing readership loyalty is not quite so straightforward, and relying on the % New Sessions metric on its own is in no way insightful as to your readers’ loyalty or visit behaviour.
I much prefer to drill down into specific levels of readership loyalty and analyse them independently. For example, I may create segments of users who visit the blog 5+ times per month, vs 10+ times per month, vs 20+ times per month etc., and then measure the growth of these various loyalty segments over time. I can then analyse which posts are more likely to produce a dedicated, hardcore reader, and which ones consistently fail to do so. However, a how-to guide on how to set up and analyse those segments in Google Analytics would be quite lengthy (and I do cover this in a lot of detail in my book, wink wink).
A quicker, albeit more basic method of measuring readership loyalty can be achieved by heading over to the Audience > Cohort Analysis report.
The Cohort Analysis Report
Cohort Analysis may sound like a scary, complicated name, but there’s really no need to worry. In fact, let’s just ignore that label and keep things as simple as possible. What this report essentially tells you is the percentage of follow-up traffic – repeat visits – that you earn, after the reader’s initial visit.
By default, the report displays readership retention over the past seven days, focussing on all first-time visitors during this period and their subsequent return to your website. Day 0 is the point at which the analysis begins, because it represents the day on which readers first visited your website (the readers’ acquisition date). If you look at the graph, day 0 will always display the highest volume of readers. This is because, when analysing readership loyalty from the point at which these readers first visited your website, you will only ever see less or the same volume of these users return to your website in the days that follow. Hopefully this makes sense – essentially, people cannot clone themselves 🙂
The appearance of the graph will vary from blog to blog. However, do not be concerned if you see a dramatic drop in readers from day 1, with only a relatively small percentage of readers returning to the blog across the remaining 7 days.
Below is an example of one blog’s retention. Notice how there are comparatively few return readers from Day 1 onwards. In fact, the graph remains mostly flat from Day 1, except for a few barely noticeable bumps across the proceeding 7 days.
The following example shows another blog’s retention, this time one that naturally attracts a more loyal reader base from the point at which readers first arrived on the website. Notice that, compared to the previous example, there is a more observable flow of return readers across the successive 7 days:
If you hover your mouse over the points on the graph, you will see a User Retention Rate for each successive day. For example, if your Retention Rate on Day 1 is 2%, then it means that 2% of all readers who discovered your blog returned to read more of your blog the following day. I’ll come back to this shortly.
Underneath the visual graph there is also a colour chart that some Analytics users might find easier to quickly analyse reader retention. Colours that are the darkest blue represent days that have the highest Retention Rate. This chart is more than just a simple visualisation chart: it also breaks down retention performance for each of the different acquisition dates in the past 7 days.
By looking in the left-hand column, you will be able to identify how well you retain your readers, depending on which of the 7 days they first arrived on your blog. In the above example, readers whose first visit to the blog was on 30th November were most likely to return to the blog on the fifth day (4th December), although they were also successful in returning to the blog on the 1st Day (1st December). Likewise, readers whose first visit was on 4th December were very likely to return the following day (5th December).
You can also refer to the overall Retention Rates displayed at the top of the chart, which mirror the Retention Rates displayed in the graph. In example just shown, 5.36% of all first-time readers returned to the blog the following day. On Day 6, 3.11% of them had returned.
This data can help you to quickly identify if certain acquisition days were significantly more likely to prompt a repeat visit amongst first-time readers. If this is the case, then consider if anything unique took place on those days – did you carry out some form of PR activity, publish a post, or were you highly active on Social Media? Perhaps certain activity has influenced your readers’ decision to return to your blog for a second time during a successive day.
Analysing Retention for Longer Periods of Time
So far I’ve only covered readers who first discovered your website in the past 7 days and their retention across those days. You can expand the period of time to analyse by selecting the Date Range drop-down and selecting the length of time of your choice. The options are the last 7, 14, 21 and 30 days. If you were to select 30 days, then the graph will show you how well you have retained readers who first discovered your blog within the past 30 days.
For many websites, 30 days – let alone 7 – will not be enough time to measure ongoing readership loyalty. Many people have busy lives after all, and frequent blog reading may be out of the question for your readers. This time let’s examine a longer period of time by changing the date range. You will notice in the report menu that Cohort Size is set to Day. Click on this and change the Cohort Size to Month.
Now the date range options will update, allowing you to set the analysis period to a wider period of time up to a maximum of three months. This allows you to observe how well you retain your readers over a quarter of a year.
Hover your mouse over each point in the graph to quickly understand the percentage of readers who return to your blog in the three months that follow their initial visit. Also examine the coloured chart to understand in more detail which of the acquisition months were most likely to prompt a repeat visit in the successive months.
In the below example, 0.32% of all readers who first discovered the blog in September were still reading the blog in December. This may sound low, but given that just over 54,900 users visited the blog in September, this equates to 154 highly engaged, repeat readers who were still returning to your blog three months later. This could potentially give this website an indication of its ongoing hardcore reader conversion rate. In other words, at that blog’s current rate, the website’s hardcore, loyal followers grow by around 154 readers in a three month period.
Understanding your readership loyalty is an important part of any ongoing content strategy, whether you’re a blogger, news website or any other type of content publisher. That’s not to say that all publishers require committed readers. The articles which you publish on your blog or website may be better suited to answering questions or providing other forms of one-off content consumption, such as recipes or instructional topics. Such content might not lend itself well to producing dedicated readers.
Use the Cohort Analysis report to identify your current readership loyalty so that you can determine whether you would like to make improvements to your overall Retention Rate. Perhaps there are Social Media activities, on-site changes or engaging topic ideas that may help promote repeat visit behaviour. You can then measure your Retention Rate once you’ve implement those changes, to understand whether they are having a positive effect on overall readership loyalty.
The next step is to drill down into the various levels of loyalty, analyse these readers’ favourite posts and topics, and understand which pages help to produce the most dedicated readers. If you’d like to learn how to increase your readership retention, check out my book Google Analytics for Bloggers & Content Publishers.