Qualitative Cohort Analysis
Do you know how your Google Adwords-acquired customer segments grow their LTV compared to those customers acquired from organic search? Have you ever thought of performing a
cohort analysis on different customer segments side by side in the same report? If so, a
qualitative cohort analysis helps you answer those questions.
This topic dives into what a qualitative cohort is, why you might be interested in building this analysis, and how you can create it in Commerce Intelligence.
qualitative cohorts, anyway?
Cohort analysis in general can be broadly defined as the analysis of user groups that share similar characteristics over their life cycles. It allows you to identify behavioral trends across different user groups.
See cohort analysis.
cohort analyses in Commerce Intelligence group users together by a common date (for example, the set of all customers who made their first purchase in a given month). A
qualitative cohort is a little different: it is a user group that is defined by a characteristic that is not time-based. Examples include:
Cohort Analysis Builder is optimized for grouping cohorts using a time-based characteristic. This is great for analyses focusing on a specific segment of user (for example, all users who were acquired via a paid search campaign). In the
Cohort Analysis Builder, you can (1) focus in on that specific user group, and (2)
cohort on a date (like their first order date).
However, if you want to analyze the cohort behavior of multiple user segments in the same cohort report (
paid search versus
organic search vs direct traffic, perhaps?), this more advanced analysis can be constructed in the
qualitative cohort report in the
Report Builder involves the Adobe analyst team creating some advanced calculated columns on the necessary tables.
To build these, submit a support ticket (and reference this article!). Here is what you need to know:
metric you want to perform your cohort analysis with and what table it uses (example:
Revenue, built on the
user segments you want to define and where that information lives in your database (example: different values of
User's referral source, native to the
users table and relocated down to the
cohort date you want your analysis to use (example: the
User's first order date timestamp). This example would allow us to look at each segment and ask
How does a user's revenue grow in the months following their first order date?.
time interval that you want to see the analysis over (example:
quarters after the
User's first order date).
Once the Adobe analyst team responds to the above, you have a couple of new advanced calculated columns to build out your report! Then you can follow the below directions to do this.
First, you want to add the metric you are interested in cohorting, once for each
cohort you are analyzing. In this example, you want to see cumulative
Revenue made in the months after a customer’s first order, segmented by the
User's referral source. This means that, for each segment, you add one
Revenue metric and filter for the specific segment:
Second, you should make two changes to the time options of the report:
time interval to
None. This is because you eventually group by the time interval as a dimension instead of using the usual time options.
time range to the window of time that you want the report to cover.
In this example, you look at an
all time view of
Revenue. After this, you should end up with a series of dots:
Third, you adjust to set up the
cohorts. Based on the
cohort date and
time interval you specified to the Adobe analyst team, you have a dimension in your account that performs the
cohort dating. In this example, that custom dimension is called
Months between this order and customer's first order date. Using this dimension, you should:
Group by the dimension with the
group by option
Select all values of the
dimension in which you are interested
Show top/bottom option, select the top X months that you are interested in, and sort by the
Months between this order and customer's first order date dimension
Now, you can able to see one line for each
cohort that you specified. Check out the example now – you see the
Revenue contributed by users of each referral source,
grouped by the number of months between their first order and any subsequent order. The example also added a
Cumulative perspective to see the
cohorts' aggregate growth - look at the results table for more granularity.
What does this tell us? Here, the specific referral source
Paid search is valuable in the first month of a customer’s purchasing lifetime, but fails to retain its customer base with repeat revenue. While
Direct Traffic starts off at a lower amount, revenue in subsequent months actually accumulates at a similar pace.
No matter how you dice it,
cohort analysis is a powerful tool in your analysis toolbox. This type of analysis can yield some interesting insights about your business that traditional
time-based cohorts may not, enabling you to make better data-driven decisions.