Cohort Analysis Using Any Dimension cohort-analysis-using-any-dimension

The Custom Cohort Dimension option allows you to analyze cohorts using dimensions other than time. Compare cohorts by Marketing Channel, campaign region, product page, etc. to better understand how retention (or churn) changes by dimension item.

In this video, we’re going to do a deeper dive into the Custom Dimension Cohort in the Cohort Table in Analysis Workspace. So let’s say I’m a multinational fashion company and we use targeted campaigns to drive users to various platforms to drive engagement. Ad spend is absolutely critical, and so I want to make sure I know which campaigns are performing best. In order to do that, I’m going to come into the Cohort Table in Analysis Workspace, and I’m going to build a quick cohort here to track visits. And then I want to see how that turns into online orders. I can quickly see that data and see how many visitors are turning into online orders. But I want to know which campaigns are driving the orders here. So I’m going to come back and open up the editing settings in Cohort Table. Then I’m going to go to the Advanced settings, click on Custom Dimension Cohort and then add the Campaign Name dimension. Once I add the Campaign Name dimension, I’m going to build. And I can see really quickly here that my Facebook app campaign is performing really well at driving initial traffic. And my campaigns, Top Designer Sale and Ireland Couture are doing a good job of keeping online orders coming month after month. Overall, most of my campaigns are driving fairly consistent repeat online order traffic. So using this information, I can come in and identify the most successful campaigns and then take a closer look at what is making them the most effective. And I want to take those learnings and apply them to my other campaigns and try to boost sales that way. So that’s a great use case for the Custom Dimension Cohort. We could replace Campaign Name with Product, with Pages, or with any other dimension that is critical for side by side quick analysis over time.

For more information, please see the documentation.