The Cohort Analysis report is one of the most useful features of Google Analytics. It is an analytical model which is used to study the behavior of a group of users experiencing the same common event over a time period.  In the digital marketing world, we often measure a performance metric for different audience types based on a date range. 

Suppose you want to analyze the result of your advertising performance and ask critical questions such as: Do they come back? Do they remain engaged over time?  Do they end up converting over time?

Today, I am going to walk you through two use cases of cohort analysis so you can get started analyzing right away.

Use Case 1: Tracking Result of A Specific Advertising Campaign

The first case we have here is analyzing the performance of your advertising campaign.

Before we start doing cohort analysis, you need to have the following items configured/prepared in order for our analyses to achieve maximum accuracy:

1. Your overall user experience plan for people viewing your campaign. This should at least include an impression stage (which counts as Day 0), and a remarketing stage (which can happen as early as Day 1).

Impression stage, the stage of the inbound methodology where you use your expertise to create content and conversations that start meaningful relationships with the right people.

You can use blogs, search engine marketing and social media marketing to attract people into your funnel.

Remarketing stage, this stage building a relationship and establishing trust between the audience and your brand.

For example, you can use Email Marketing, Retargeting, Social Media to further affect them and make them your customer.

If you’re looking for a more sophisticated marketing calendar platform, I suggest looking into collaborative tools such as CoSchedule.

2. A segment created for your campaign specifically. This is usually done by adding a utm_campaign tag to the end of all your links related to this campaign (learn more here), and creating a segment of all traffic coming in containing that specific campaign name. 

You should also consider adding a “new user only” filter to your segment, to remove the potential impact of returning users on your overall customer behavior (see below on how to do that).

With those two steps prepared, we can now go into the Cohort Analysis report in Google Analytics to start our analyses.

Since we are using the Google Merchandise Store for our illustrations and I do not have access to their marketing plans specifically, we are going to cover most of the steps with plain text.

If your user journey is significantly larger than 12 days, you might need to use both the day cohort size and the week cohort size for your analysis (but the principle is the same), else you only need the first one.

Then, you want to adjust the date range so the first day of your campaign is visible as a row in your cohort table.

Now, what you need to do is align your user experience plan with each row of the cohort analysis.

For example, if your campaign starts on July 1st, you will want to first look at data at Day 0 on the July 1st cohort, which is an illustration of how many users/sessions/transactions occurred on that specific day.

Then, starting with that row, you want to average the metric from that point up for all columns (as illustrated below). We cannot use Google Analytics’ sum here since it includes incomplete data and dates in which your campaign hasn’t started.

This data serves as a baseline for your analysis and gives you a general idea of how you are doing if you are not doing anything to improve your user experience.

The important thing to notice here is that right now, over 12 days later, you can no longer do ANYTHING to improve that retention.

The only thing you can do is test alternative content to see if you can improve the experiences of new users that are coming into your website starting TOMORROW — and the baseline we just established can help you do exactly that.

Now, what you need to do is identify potential areas of customer experience that you want to improve on. And then, try different things every couple of days in an attempt to improve your cohort metrics, and then wait to see if your metrics are increasing in different parts of your customer journey.

The key here, especially if you don’t have a lot of users to test with, is to test a few variations slowly. While you might want to get as many variations in as possible, too little data for comparison will have a significantly higher chance of giving you the incorrect signal of what is working and what is not.

In the next few days, you will see your result coming in for those days you ran experiments on. Based on the results, rinse and repeat until you get your desired outcome. 

Use Case 2: Use Cohort Activity As A Metric

Now let’s talk about the second case, which applies mostly to people who are trying to use cohort analysis as a metric-indicator of how well you are retaining your users over a long period of time.

To do this, I would recommend using the “week” cohort option.

This analysis is, in fact, significantly easier than the previous one — you just need to look at the cohort table carefully:

To reduce analysis load, I would recommend only analyzing your data through the fourth week after the initial cohort, as data retention data gradually becomes too little to matter in future weeks (depending on your overall traffic size).

What you want to do is to look at each column from top to bottom to identify an overall trend in changes of the cohort metric (retention rate in this case).

When looking through the data, you need to be perfectly aware that many of those ups and downs might be simply due to natural fluctuations in your data or external noises. 

Therefore, you only should look at the overall trend of the data (I recommend plotting it in Excel or Tableau), unless there is a significant outlier that deserves a look (+/-30% compare with the average) .

In general, if you are not doing anything drastic on your website such as changing some major pages or launching a user retention campaign, this number is very likely to stay constant throughout the weeks.

However, if you perform potential actions that will increase your retention such as redesigning the website or launching an email retention campaign, this analysis will be a very good before/after measure of how effective your efforts are.

If you want to find additional ways to track user retention on your website, I would recommend looking into metrics such as XXDayActiveUser or simply number of returning sessions — those metrics are usually easier, but less sophisticated alternatives compared to the analysis introduced here.

Hope the analyses introduced here can provide actual value or inspiration for the analytics at your own company.

The power of cohort analysis lies in the fact that it enables not only to view which customers leave and when they leave, but also to understand why the customers leave, so that you can fix it. That’s how one can identify how well the users are being retained and also determine the primary factors driving the growth, engagement and revenue.