A common misconception is that people think Cohort analysis is a mere aggregation of data points for the beginning period (March 1ST — March 7th) there is no way to tell whether the session visit in March is from the same person who visited in April. To compute Cohort analysis, you need to identify all users who visited during the initial period (March 1ST — 7th)

You just need to be aware of the fact that all your cohort metrics are inflated a little compared with the most accurate computation when using Google Analytics.

Instead of summing your session or page activities over a fixed time range, cohort analysis describes behaviours of various groups of users OVER TIME on your website — which makes the analysis somewhat harder to interpret and understand without deep understanding.

The second anchor is how long you want to run your analysis for (the lagging period).

If you want to see how users behave one month after their initial visit, your lagging period would be 1 month.

This number is selected largely based on your preferences and your company’s industrial environment.

Overall, it is hard to extract business value from one-time cohort analysis immediately compared to other popular analytics methods.

This is mostly because you cannot really react based on your cohort analysis data.

If you are running, let’s say a funnel analysis, and see some users dropping out of a part of the funnel very rapidly. You can immediately launch a retargeting campaign to those users, while patching the funnel up with some site redesign to make sure you don’t lose those users.

With both the lagging period and cohort determined, we can define the final anchor, which is the termination time of our analysis.

This detail is easy to omit in Google Analytics since it treats an unfinished cohort as finished.