A cohort is a group of customers at a certain point in time.
The Cohort Report allows you to analyse the behaviour of customer cohorts over time.
Fig 1: The Cohort Report
Access the Cohort Report dashboard through the top navigation, under Reports.
Fig 2: Access Cohort Report
You can form these cohorts based on any of the following grouping criteria. All date-based groups are grouped in the months of following that criteria. So, for example if you select date of first order, your contacts (or segmented contacts) will be grouped based on the month of their first order.
- Date of first order
- Date of first visit
- Date subscribed
- Date acquired
- Lifecycle Stage
- First Visit Medium
- First Visit Name
- First Visit Campaign
- First Visit Domain
- Subscriber Status
- Order Count
- Country of Last Order
The Cohort Report analyses your customers by month (month of first order, months they were at a lifecycle stage, etc.) and looks at their behaviour over time.
If you select "Group by First Order", on the left side of the table below, you see the number of customers who made their first order for each given month (column 'date of first order').
Fig 3: Customers grouped by Date of First Order
- 'Cohort Size' is the number of customers acquired (=made their first order) in the related month (eg. 1,734 new customers acquired in September 2015).
- 'Average LTV' is the average lifetime value (=total revenue) generated by each customer in the cohort over the following months listed (month 1, 2, 3, etc.)
- 'Month 1' will be the first month the contacts entered and were in the cohort. For example, for the September 2015 cohort, Month 1 will be September 2015, Month 2 will be October 2015, Month 3 will be November 2015, etc.
Note on Date Acquried:
Fig 4: Group by Date Acquired option
The date acquired for each contact is based on the earliest value from the following dates:
- Date of first order
- Date contact was first seen (ie. first visit)
- On the contact listing we set the acquisition date if it isn’t already set based on the highest priority listing of either:
- Opt-in date of the listing
Other 'Group by' date are calculated by their own logic.
This is an example of the cohort table grouped by Date acquired. In the second column of the table, you see the number of customers who were acquired (this doesn't include resubscribers) in each given month.
Fig 5: Customer cohorts Group by Date Acquired option
In the above examples we had selected the metric Total Revenue, that displayed the total revenue for each cohort over the pan of months (6 months, 12 months or 24 months).
Fig 6: Options for a Cohort Report
In addition to showing the total revenue generated for each cohort, you can select:
Fig 7: Metrics you can display for each Cohort
- Total Orders
- Customers Gained (useful if you group by First visit)
- Customers Repeated
- Average Order Value
You can also tick the box on the top right to show cumulative numbers - it can be quite useful if you want to see the repeat rate increasing over time if you select 'Customers Repeated' for example.
Note on Customers Repeated
The Customers Repeated metric counts any customers who have made their second order and adds them in the count under the month they made that second order. It doesn't count customers who have made a third or a fourth, (etc.) order. As that customer has already been counted as a repeat customer for their second order.
Fig 8: Customer Repeated when 'cumulative' is unchecked
However, if you check the cumulative checkbox, the report will show the customers repeated carried on all along the following months.
Fig 7: Customer repeated when 'cumulative' is checked
Finally, you can filter this report on any saved segments or create a new customer filter.
This tool allows you to quickly understand what marketing campaigns are bringing in the highest spending customers or those that have higher retention rates. Here are four examples of customer segments you can look at using the cohort grid:
1. Customers by acquisition source
Using the filter, you can segment customers by their first visit source to understand which channels are bringing in customers with high repeat rates, for example, customers who came from paid ads:
2. Average Order Value
Analyse how the Average Order Value of your customers is growing over time as some of them become repeat customers.
You can export this data into a CSV.
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