Python Tutorial: Time cohorts

Want to learn more? Take the full course at https://learn.datacamp.com/courses/customer-segmentation-in-python at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- Now we will learn about the most popular cohort analysis type - time cohorts. We will segment customers into acquisition cohorts based on the month they made their first purchase. We will then assign the cohort index to each purchase of the customer. It will represent the number of months since the first transaction. Time-based cohorts group customers by the time they completed their first activity. In this lesson, we will group customers into cohorts based on the month of their first purchase. Then we will mark each transaction based on its relative time period since the first purchase. In this example, we will calculate the number of months since the acquisition. In the next step, we will calculate metrics like retention or average spend value, and build this heatmap. For example, this number means that 24% of the cohort which signed up in August 2011, was active 4 months later. Column one here is the month of first purchase, therefore the retention rate is 100%. This is by definition, as customers had to be active on this month to be assigned to this cohort. A little bit about data. We will use a 20% random sample from an Online retail dataset with half a million transactions. This is a realistic dataset with customer transactions which is commonly used in segmentation. Let's look at the first 5 rows of it. The data contains 7 columns with customer transactions. The main ones we will use are date, price, and the customerID. Now that we have loaded the data, let's build a simple cohort table for time-based cohorts. First, we create a function that truncates a given date object to the first day of the month. Then we apply it to the InvoiceDate and create an InvoiceMonth column. Next, we create a groupby() object with CustomerID and