Python Tutorial: Calculate cohort metrics

Key Takeaways

great we have assigned the cohorts and calculated the monthly offset for the metrics now we will learn how to calculate business metrics for these customer cohorts we will start by using the cohort camps table from our previous lesson to calculate customer retention then we will calculate the average purchase quantity the retention metric measures how many customers from each of the cohort have returned in the subsequent months we will use the data frame called cohort counts which we created in the previous lesson our first step is to select the first column which is the total number of customers in the cohort next we calculate the ratio of how many of these customers came back in the subsequent months which is the retention rate one word of caution you will see that the first month's retention by definition will be hundred percent for all cohorts this is because the number of active customers in the first month is actually the size of the cohort let's get down to coding will select the first column from the table and store it as cohort sizes then we will use the divide function on the cohort counts data frame and ask the cohort sizes we set the access parameter to zero to ensure that we divide along the row axis finally we round the ratio to three digits and multiply it by hundred to make it look like a percentage with these simple commands we have completed retention rate metric calculation let's take a look at it as you can see the first column has a hundred percent retention rate for all cohorts as expected we can now compare the retention rate over time and across cohorts to evaluate the health of our customers shopping habits let's take a look at another example let's step back a little bit and go back to our original online data set we will show you how to calculate other metrics for these cohorts these are almost identical lines of code you've seen in the previous slide where we created the customer counts what's different is that in this case we will calculate the average quantity first we create a group I object with cohort month and cohort index and store it as grouping then we call this object select the quantity column and calculate the average then we store the results as cohort data then we reset the index before calling the pivot function to be able to access the columns now stored as indices finally we create a pivot table by passing cohort month to the index parameter cohort index to the columns parameter and the quantity to the values parameter let's round it up to one digit and see what we get here we go you are now fully equipped to manipulate transaction transactional customer data and draw powerful insights now you will practice what you've learned so far and we'll build new analysis