Ultimate guide on using cohort analysis and enhanced ecommerce to understand users’ behaviours

Ultimate guide on using cohort analysis and enhanced ecommerce to understand users’ behaviours

Daily lot of users come to your website, some of them make a purchase and some of them abandon your site without checking out. Both the users enter the website with the intention of making a purchase on your website. But one of them chose to proceed with the check out, the other abandoned. User disengagement is the harsh reality of your online business. No matter what you do to retain your customers sooner or later they will move on. If you constantly send email news letters to your subscribers, you will definitely loose some email subscribers with each new news letter.

With all these things happening, there comes a lot of questions like, what you can do to reduce the shopping cart abandonment? What was their average order value like? Did they spend more money on the website? When is the best time to re-engage with your users? Did they spend more money on the website? What is the rate at which you should acquire new users to maintain your website conversion rate?

Through cohort analysis you get answers to all such questions.

We always talk about user engagement, but we rarely discuss about user disengagement. It is a harsh reality of your online business. Whatever you do to retain your customers sooner or later they will move on. The bigger your subscribers base, more subscribers you are going to loose with each new newsletter. By seeing this pattern over and over again, you will conclude that ‘user churn’ is normal. As the technology is constantly changing, new and powerful competitors’ emergence is just a matter of time and you may end up loosing your customer for good.

You can not completely stop user disengagement from occurring, but you can certainly reduce the disengagement rate. By focusing on this you can effectively reduce your customer attrition and re-engage with them before it is too late. You need to do lot more than re-engage with existing users. You also need to constantly find new users.

It is always good to have returning users to your site, but you need lot more new users and that too all the time because majority of users eventually disengaged from your website and become less profitable over time for no apparent reason. As you loose upto a dozen of subscribers with each news letter, you also gain new subscribers which are 5 to 10 times more than the subscribers.

You not only need to constantly find new ways to re-engage with your existing users, but you should also need a constant flow of new users to compensate for the users lost over time and to maintain your conversion rate. You should not stop acquiring new traffic at any point and become content with the existing with the existing traffic, no matter how much traffic you are currently getting.

All about cohort analysis:

The cohort analysis is all about user disengagement. A cohort is a group of users who showed common characteristics, attributes or experience in a particular time frame. The characteristics of users are time bound because same users can show different characteristics in different time period. All users who visit your website from a particular country belong to the ‘same country’ cohort. Similarly, all users who visit your website for the first time on a particular date like jan 4, 2015 belong to the ‘jan 4, 2015’ cohort. A user can be a member of multiple cohorts at the same time depending upon how you segment and interpret the data.

Cohort analysis report in google analytics:

You actually can analyze your cohort’s behaviour through any google analytics report. But cohort analysis report has been specially developed for analyzing cohort’s behaviour of users is the cohort analysis report. This report is especially useful in understanding the behaviour of different cohorts in response to time sensitive, short term marketing campaigns like Christmas sales, new email campaigns, cyber Monday etc.

Cohort type:

Cohort type is the dimension that characterizes the cohorts. You can select only one type at a time. Consequently through cohort analysis report you can analyse the behaviour of only one type of cohort i.e. the group of users with same acquisition date. Acquisition date is the date when users started their very first session on your website.

Cohort size:

Cohort size is the size of selected cohort. At present only one cohort type is available in the cohort analysis report, which is acquisition date. So, cohort size has been defined only in terms of time frame either by day, by week or by month.

Cohort size by day – all users who were acquired on the same day.

Cohort size by week – all users who were acquired during the same week.

Cohort size by month – all users who were acquired during the same month.


Metrics present in the cohort analysis are divided into three categories. ‘Per user’, ‘Retention’, ‘Total’. Each category contains several metrics. Only ‘Retention’ category contains just one metric called ‘user retention’. User retention is the percentage of users in a cohort who returned in the Nth day, week or month. User retention is the default metric in cohort analysis report and you can only analyse one metric at a time in this report.

Date range:

Date range is the time period for which the cohort data should be displayed. The value of the data range depends upon the ‘cohort size’ selected. If the ‘cohort size’ is ‘by day’ then the value of the ‘data range’ could be set to any one of the following:

  1. last 7 days

  2. last 14 days

  3. last 21 days

  4. last 30 days

If cohort size is ‘by month’ then the value of the date range should be set to any one of the following:

  1. last month

  2. last 2 months

  3. last 3 months

Cohort analysis and data sampling issues:

Cohort analysis report suffer from data sampling issues much more than any other google analytics reports especially, when advanced segments are applied to it. If you use advanced segments, they create data sampling issues. The advanced segments can greatly skew your cohort data.

If you are looking out for any help with your business and fix your Google analytics and conversion issues, feel free to contact us at info@librawebsolutions.com. At Libra web solutions we successfully transformed around 80 ecommerce ventures for customers from UK, Australia, India, Hong Kong, Singapore since 2008.

Written by

<p>Digital transformational leader into Digital Payments & omni channel commerce, Software Services & Delivery using Web & Social, Mobile, Analytics, Could Platforms.Hands on 360 degree implementation of applications ranging from Commerce, Marketing, Sales, Service and Billing and back office operations</p>