Build retention and churn tables
You have learned the main elements of the customer lifetime value calculation and certain variations of it. Now you will use use the monthly cohort activity dataset to calculate retention and churn values, which you will then explore and later use to project average customer lifetime value.
The pandas library has been loaded as pd and the cohorts_counts dataset has been imported. Feel free to explore it in the console.
Diese Übung ist Teil des Kurses
Machine Learning for Marketing in Python
Anleitung zur Übung
- Extract cohort sizes from the first column of
cohort_counts. - Calculate retention by dividing the cohort counts with the cohort sizes.
- Calculate churn by subtracting 1 and the retention rates.
- Print the retention table.
Interaktive Übung
Vervollständige den Beispielcode, um diese Übung erfolgreich abzuschließen.
# Extract cohort sizes from the first column of cohort_counts
cohort_sizes = cohort_counts.___[:,0]
# Calculate retention by dividing the counts with the cohort sizes
retention = cohort_counts.___(cohort_sizes, axis=0)
# Calculate churn
churn = 1 - ___
# Print the retention table
print(___)