Cohort Analytics in this context is the measurement of how often a user returns to a website over a given period of time. By understanding how well you retain your users, the better you will understand how best to monetise them – which is what we are all chasing in digital publishing.
This is not the same as the standard vanity stats that you may find in Google Analytics or Adobe’s SiteCatalyst for return visits or visitor retention, this will give you a more detailed understanding.
Also, if done correctly, you can use cohort analysis to measure not only general users to your website but also registered users, logged in users, users that purchase or convert to something.
Cohort analytics is quite a new concept for digital publishing. In the past, CPMs have been high enough for publishers to only have to worry about unique users, visits and pages. But now, with the advent of Google Adsense and Facebook Ads, advertisers can now target audiences, so now publishers need to focus on other ways to measure and monetise audiences.
How to measure retention
Cohort Analytics is not something that is available out of the box with most standard web analytics tools. Unfortunately it takes a bit of hacking to get it to work with Google Analytics – even then, it is quite limited as you can only measure over five units of time. This will all become apparent shortly.
In addition, this explanation will only give you a basic overview for general tracking.
Google Analytics allows you to configure custom variables – of which there are five – and these segments can be persistent over a number of visits.
Custom variables should be set per time period that you would wish to track. In this instance it would track user retention over a rolling 5 month period.
Here is some example code for month one using the first of five custom variables:
*** CODE ***
pageTracker._setCustomVar( 1, // This custom var is set to slot #1 for the first month. "Month", // The top-level name for your online content categories. Required parameter. "January 2011", // Sets the value of "January 2011" to "Month" for this particular aricle. Required parameter. 1 // Sets the scope to visitor level.
*** END CODE ***
Once this code is in your site, it will need to change each month. The two paramters that will need to change are the first and third variables where each will increment when month 2 begins.
*** CODE ***
pageTracker._setCustomVar( 2, // This custom var is set to slot #2 for the second month. "Month", // The top-level name for your online content categories. Required parameter. "February 2011", // Sets the value of "February 2011" to "Month" for this particular month. Required parameter. 1 // Sets the scope to visitor level.
*** END CODE ***
Once this has been implemented correctly and has gathered the relevant correct data, you will see some hopefully nice results. In addition, if you are clever with your naming and strategy you will be able to measure much more than this.
Hopefully you will see something like the image above after a period of time. What the above table shows is how many of the users return in the following months from their first visit.
The reason for the Month 1 statistics all being 100% is that all users in Month 1 are new. In Month 2 it shows how many users from Month 1 returned in Month 2. Month 3 highlights how many users returned to the site in Month 3.
By fully understanding how long your users keep coming back to your site, you can really start to focus on some new metrics.
You will be able to work out the Lifetime Value (LTV) of your users which would help you to work out how much you may want to spend on marketing. By understanding this, you can ensure that your marketing stays profitable.
You can also start to focus your development attention on lengthening the lifetime value of your users.