In relational email marketing, the use of cohort analysis can help us understand how users behave based on openings, clicks, unsubscriptions and conversions over time, and thus predict future behavior.
In the following example we present a cohort analysis with the aim of making the concept and its application more understandable.
Let’s suppose that we are in the month of July and we want to analyse the behaviour of users registered in the last 6 months by relating the date of registration to the gmx email list newsletter with the buyers obtained thanks to email marketing actions in the following months. First, we will create the cohorts. In this case, they will be the groups of users registered in each month (column “RM0”).
cohort analysis
Next, in the “CM0” column we enter the number of buyers belonging to the cohort who have made at least one purchase in the same month in which they registered. In CM0% we have the relative data. In CM+1 we enter the buyers of the cohort who made at least one purchase in the month following their registration. In M+1% we have the relative data, and so on. We project this mechanism for each of the cohorts until the date on which the analysis will end (in this example, this date is the last month for which we have data, that is, June or CM+5).
Next, we calculate what % of the total number of users we have acquired have purchased at least once. For example, in month CM+4 we have 46 buyers. These buyers are users registered in January and February, that is, 1,645 users. This means that 2.80% of registered users will purchase in month + 4.
It is useful, once we have the data in the table, to present the information visually with graphics. If we analyze the data from the example in question, we notice some interesting aspects: it is 3 months after registration when our email marketing actions generate more buyers. The cohort corresponding to the month of June has a very different behavior from the others in CM0. Was there any special offer? What was the origin of the registrations? Was there advertising on TV?
cohort analysis
Finally, this cohort analysis will allow us to predict/estimate how many buyers we will have in the coming months of January, February, March, April, May and June based on the users we attract in those months.
Knowing the average order amount per buyer, we can then project the income generated by each new user acquired in the months following registration, and this, linked to the CAC (Customer Acquisition Cost), will allow us to know if the investment in Email Marketing (customer acquisition) is > or < the income generated per customer throughout their life cycle.
