How are the customers of today different from those acquired last year? 6 months ago? 3 months ago? This common question is at the heart at what analyzing cohorts can help solve. There are many ways to breakdown customer cohorts, but the message is the same. How do newer customers trend vs older customers? Are they better, worse, the same? Depending on your answer will effect how you plan and execute on that plan over time. At Rubix, we value a good cohort analysis and help your company easily identify these trends with minimal effort. Here’s (3) different ways we help you analyze cohorts:
Standard Grid
The most common way to evaluate cohorts is to place the data on a tabular grid. It’s starts with the first column as the month of your beginning cohort and each column after being either the number of months or the exact month after the start of that cohort. Let’s take an example using number of months, if a cohort began in 1/1/2021 then the 1st column will be “month 0” or “month 1”. Why “month 0” or “month 1”? Your metric will determine the starting point. If measuring retention, it is best to start at “month 1”. If measuring revenue, it is best to start at “month 0”. In this instance we will track revenue, so let’s start at “month 0” (aka 1/1/2021). At this point we can now compare the initial month performance against prior months and any future months (where the data exists). How and why months are better or worse is a factor on your changing business and any new types of initiatives that follow. Using this tabular view, these changes can be viewed easily and quantified faster.
Smoothed Curve
This view is commonly used in comparison with the standard grid. What makes it a powerful tool in your arsenal is how easily it can be used to compare longer term trends over time. Monthly, year-over-year comparisons can be found quicker, and if there is a single month trend that looks better or worse then it can be easily identified. In this case, each line in the graph represents a new cohort month and the x axis represents the number of months out from the start of that cohort. The y axis represents the value at that month for the cohort. You can see the trend line that differs and then refer to the tabular grid for more detail, at the same time! Cool, right? Well it gets better when combined with our 3rd view.
Stacked Line
This view takes the concept of the smoothed line and creates a breakout by the months away from cohort start. Each line now represents the months away from the beginning cohort and the x axis shows the cohort month. Now you can analyze the over time change of specific month performance and see if it is better or worse. An example question that can be answered is “Do we see further drop off in terms of cumulative revenue from cohorts 2 months out over time?”. If yes, then there needs to be an effort to understand which campaigns can be run to bring back customers in month 2. If no, then great! There’s still more to learn but now it’s about how to get incremental growth over time for future cohorts.
Whether your company focuses on e-commerce or subscription sales, the use case for analyzing cohorts is the same. The more information at your disposal to analyze, the faster you can make decisions and continue to grow your business. Our goal is to help your business scale with the right tools in place and at a price that fits your budget. Start your free (no credit card required) 30-day trial here: https://www.rubix3.app/sign-up-premium.
