Cohort Analysis is a useful for tracking customer behavior towards products and and their performance over time. In marketing, this can be useful tool to analyze customer loyalty by looking closely at their retention rate, life cycle and lifetime value.
Lets see how many of the Superstore Customers are loyal! 🧐
I would like to show two kinds of Analysis. First is to see retention rate by looking at when did the customers made their last purchase after completing their first purchase.
Create the following calculated fields:
Then bring the Difference field into columns and the Customer's First Purchase field into rows. Drag COUNTD of Customer Name both to Text and Color. Also to Detail to also get the quick table calculation for Percent of Total (table across). Then change the marks card to square
On the chart below, we can see that Superstore have retained some loyal customers over the years. For instance, 22 customers who made their first purchase in Nov 2019, is still buying after 37 months, that's 35.48% of the total customers that month.
The second part of analysis, I wanted to show is to see when did the customer made their repeat order after the first purchase.
Create the following calculated fields:
To bring this calculation to view, follow similar step from the first analysis. This time, bring the Difference of First and Second Purchase field into columns and the Customer's First Purchase (in year and month) fields into rows. Similarly drag COUNTD of Customer Name both to Text, Color and Detail (quick table calculation for Percent of Total). Then change the marks card to square.
In the chart below, we can see the another purchasing behavior of the customers. Most customers in 2019 made their second purchase shortly after their first purchase. Similar to the previous chart, there's significant indication that something in Nov 2019 was interesting for the customer.
I worked on similar analysis for training attendance. I really find cohort analysis quite useful in customer behavior analysis and management.
