Customer Cohort Analysis
Last updated
Last updated
Do you wonder if the changes that you’ve made in your business impacted new customers or do you want to understand the needs of your user base or identify trends? That and much more you can do with our new model, Customer Cohort Analysis.
A cohort is a subset of users or customers grouped by common characteristics or by their first purchase date. Cohort analysis is a type of behavioral analytics that allows you to track and compare the performance of cohorts over time.
With Graphite, you are only a few steps away from your Cohort model. Once you have selected your dataset, it is time to enter the parameters into the model. The Time/Date Column represents a time-related column.
After that, you have to select the Aggregation level.
For example, if monthly aggregation is selected, Graphite will generate Cohort Analysis with a monthly frequency.
Also, your dataset must contain Customer ID and Order ID/ Trx ID columns as required model parameters.
Last but not least, you have to select the Monetary (amount spent) variable, which represents the main parameter for your Cohort Analysis.
Additionally, you can break down and filter Cohorts by a business dimension (variable) which you select after you enable the checkbox.
That's it, your first Customer Cohort Analysis model is ready.
Now we will go through Model Results, which consists of 3 tabs: Cohorts, Repeat by, and Details.
After you run your model, the first tab that appears is the Cohorts Tab.
We are going to see different metrics such as the Number of Customers, the Percentage, the Amount, and the Cumulative Amount, but there are 3 more metrics: the Average Order Value, the Cumulative Average Order Value, and the Average Revenue per Customer.
Depending on the metric (the default is No of Customers), the results are presented through a graphic representation of her heatmap and the heatmap.
In the example above, groups of customers are grouped by year when they made their first purchase. Column 0 represents the number of customers per cohort (i.e. 4255 customers made their first purchase in 2018). Now we can see their activity year to year: 799 customers came back in 2019, 685 in 2020, and 118 in 2021.
If you switch your metric to Percentage, you will get results in percentages.
Let's track our Monetary column (in our case total amount spent per customer) and switch metric to Amount to see how much money our customers spend through the years.
As you can see above, customers that made their first order in 2018 have spent 46.25M, and 799 customers that came back in 2019 have spent 12.38M. I
In case you want to track the total amount spent through the years, switch metric to Amount (Cumulative).
Basically, we tracked the long-term relationships that we have for our given groups (cohorts). On the other hand, we can compare different cohorts at the same stage in their lifetime. For example, for all the cohorts, we can see how much the average revenue per customer two years after they made their first purchase: the average revenue per customer in the cohort from 2019 (12.02K) is almost half less than from 2018 (21.05K). Here is an opportunity to see what went wrong and make a new business strategy.
In case you broke down and filtered cohorts by a variable with less than 20 distinct values (parameter Repeat by in Model Scenario), for each value you will get a separate Cohort Analysis in the Repeat by Tab.
All the values related to the Cohorts and Repeat by Tabs, with much more, can be found on the Details Tab, in the form of a table.
Now it's your turn to track your customer's behavior, see when is the best time for remarketing, and how to improve customer retention.
