Cohort analysis is one of the most powerful methods to analyse how a particular group of customers engage with your business. While you can unlock many other insights, a few primary objectives could be -
Estimate the lifetime value of your customers
Analyse the impact of your marketing strategy on your customer cohorts.
Understanding the Cohort analysis
We have grouped all acquired customers into cohorts by when they made their first purchase.
👉 Decoding the cohort analysis of a test store below
Definitions
Acquisition period - When new customers were acquired. This is how the cohorts are created. In the above screenshot, we have Nov and Dec cohort.
New customers - How many new customers are there in the cohort.
Repeat % - The percentage of new customers who have made at least 1 additional order after their first order.
Orders - Total number of orders from a particular cohort in the selected timeframe
Revenue - Total revenue from a particular cohort in the selected timeframe
First-order - This variable shows the value of the selected metric. In the given screenshot, by default, it takes the Cumulative revenue per customer which is the total revenue from all acquired customers in their first order divided by the total number of new customers.
Period after 1st order - This could be months/quarter/year after the first order and can be flexibly selected to analyze the shorter or longer time periods.
For Example - This screenshot shows that there are 2320 new customers acquired in the month of Dec 2020 out of which 28% of customers made repeat purchases over the subsequent months. Also, The cumulative revenue per customer is $38 in the first order which increased to $39 within the same month(0 months after 1st order) and increased to $43 one month after the first order.
👉 Comparing different customer cohorts
Imagine you're increasing ad spends to increase your new customer's acquisition or let's say, you're launching new products to see if these products are helping you retain more of your new customers and helping you increase the repeat rate, you can do all this and much more by comparing the customer cohorts.
For example - In the above screenshot, When you compare Dec 2020 and Jan 2021 cohorts, you see Jan 2021 cohort has a higher number of new customers and also has a higher repeat rate.
👉 Additional Tools
Duration - You can analyse your cohorts at a monthly, quarterly, or yearly level. The shorter time durations help when the customer behaviour is changing considerably faster. This is generally used when your marketing team does rapid testing and experimentation.
Whereas, the longer time durations help in quickly giving you a birds-eye view of how the business has progressed. Remember to keep a longer timeframe like 2+years if you're choosing quarterly or yearly durations.
Filters - You can choose to analyse the customers from a specific sales channel, countries, order tags, or customer tags. You can include or exclude these values using filters to filter out relevant first-orders.
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