Analyzing custom segments
As a final step, you will analyze average values of Recency
, Frequency
and MonetaryValue
for the custom segments you've created.
We have loaded the datamart
dataset with the segment values you have calculated in the previous exercise. Feel free to explore it in the console. pandas
library is also loaded as pd
.
This exercise is part of the course
Customer Segmentation in Python
Exercise instructions
- Calculate the averages for
Recency
,Frequency
andMonetaryValue
for eachRFM_Level
segment. - As the last column, return the size of each segment passing
count
to theMonetaryValue
column next to themean
. - Print the aggregated dataset.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Calculate average values for each RFM_Level, and return a size of each segment
rfm_level_agg = datamart.____('____').____({
'____': '____',
'____': '____',
# Return the size of each segment
'____': ['____', '____']
}).round(1)
# Print the aggregated dataset
print(____)