Using Cohort Analysis in Adobe Analytics

With the latest release of Adobe Analytics, the Analysis Workspace interface now provides a way to conduct cohort analyses. The new Cohort Analysis borrows from an existing one that Adobe had previously made available for mobile implementations, but now it is available for use everywhere and with everything that you have in your Adobe Analytics implementation. In this post, I will provide a quick “how to” since I have been surprised by how few of my Adobe customers are aware of this new functionality.

Cohort Analysis Revisited

A cohort analysis is used when you want to isolate a specific event and then see how often the same folks completing that event go on to complete a future event. In the recent decade, cohort analyses became popular due to social networking tools when they were used to judge the “stickiness” of these new tools.  For example, in the early days of Twitter, people would look to see how often users who tweeted in January were still tweeting in February. In this case, the number of people who tweeted in February was a separate number from those who tweeted in January and then in February, with the latter being “cohorts.”  For more information on this topic, check out the wikipedia page here.

New Cohort Analysis Visualization

Once you are comfortable with cohort analysis as a concept, let’s look at how you can create cohort analyses in the new Adobe Analytics interface. To start, use the left navigation to access the Analysis Workspace feature of the product (note that if you are on an older version of Adobe Analytics, you may not have Analysis Workspace enabled):

In the Analysis Workspace area, you will click the visualizations tab to see all of the potential visualizations:

From here, you will drag the “Cohort Table” visualization over to your reporting canvas and should see this:

At this point, you need to select your timeframe/granularity (i.e. Month, Week, Day) using the drop-down box and then drag over the metric you want visitors to have performed to be included in the cohort. This is done by clicking on the components tab at the top-left:

Keep in mind that you cannot use calculated metrics and some other out-of-box metrics as inclusion metrics, but you can use any of your raw success events. Also, if you click on the “Metrics” link, you can see all metrics and do a search filter, which is very handy. When contemplating your inclusion metric, think about what actions you want visitors to take to be included in the cohort. For example, if you are looking for people who have ordered, you would use the Orders metric, but if you are interested in people who have viewed content on your site, you may use a Content Views success event. As an example, let’s use the latter and build a cohort of visitors who have viewed content on the site and see how many of those visitors come back to the website within x number of days. To do this, we would change the granularity to days, add a Content Views metric as the inclusion metric and then add the Visits metric as the return metric so the cohort analysis looks like this:

You may also notice that you have the option to increase the number of times each metric has to occur before people would be be added to the inclusion or return portion of the cohort. By default the number is one, meaning that the above cohort is looking for cases in which one or more Content Views took place and then one or more return Visits took place. To narrow down the cohort, we could easily increase these numbers to force visitors to have viewed more content to be included in the cohort or returned to the site more than once to be included. But in this example, we’ll keep these set to one and run the report to see this:

Here we can see that on November 3rd, we had 6,964 unique visitors who had Content Views and that of those who viewed content on that day, 13% (892 Visitors) returned to the site within one day (had a return Visit). Keep in mind that all numbers shown in cohort analyses are unique visitor counts. The color shading shows the intensity of the cohort relative to the other cohort cells. By looking horizontally, you can see the drop-off by day for each cohort starting date and as you look vertically, the days will follow a cascading pattern with the newest starting dates having the fewest return dates like this:

Changing the granularity from Day to Week, would work the same way, but have far fewer cohorts unless you extend your timeframe:

Here is an example in which I have made both the inclusion and return metric the same thing (Content Views), but made viewing two pieces of content required to be eligible for the return cohort:

Here you will notice that requiring two return content views reduced the first (Nov 3rd) cohort from 13% down to 9%. You can use these settings to identify interesting patterns. Since you can also make as many cohorts as you want using all of your success events, the amount of information you can glean is enormous.

Putting Cohorts To Use

Once you learn how to generate cohort analyses, you may ask yourself “Ok, now what do I do with these?” That is a valid question. While a blog post isn’t the best venue for sharing all you can do with cohort analyses, let me share a couple ways I would suggest you use them. The first way is to apply segments to your cohorts. For example, you may want to determine if visitors from a specific region perform better than another, or if those using your responsive design pages are more likely to return. Here is an example in which the previous cohort is segmented for Microsoft browsers to see if that makes the cohort better or worse:

In this case, our Nov 3rd cohort went from 13% to 8% just based upon browser. Since you probably have many segments, this provides more ways you can slice and dice these cohorts and adding a segment is as easy as dropping it into the top of your Analysis Workspace page like this:

Keep in mind that any segment you apply will be applied to both the inclusion and return criteria. So in the preceding scenario, by adding a Microsoft Browser segment, the inclusion visitor count only includes those visitors who had a Content View event and used a Microsoft browser and the return visits also had to be from a Microsoft browser.

But my favorite use for cohorts is using a semi-hidden feature in the report. If you have a particular cohort cell (or multiple cells) that you are interested in, you can right-click on it and create a brand new segment just for that cohort! For example, let’s say we look at our original content to return visit cohort:

Now, let’s say something looks suspicious about the Nov 3rd – Day 4 cohort, which is at 3% (top-right cell). We can right-click on it to see this:

Then clicking will show us the following pre-defined segment in the segment builder:

Now you can name and save this segment and use it in any analysis that you may need in the future!  You can also make changes to it if you desire before saving.

While there is much more you can do with cohorts, this should be enough for you to get started and begin playing around with them. Enjoy!