Analyze the segments
Fantastic! Final step - analyzing your segmentation solution - you will analyze the average Recency
, Frequency
, MonetaryValue
and Tenure
values for each of the four segments you have built previously. Take some time to analyze them and understand what kind of customer groups and behaviors they represent.
The RFMT raw data is available as datamart_rfmt
, and the cluster labels from the previous exercise is loaded as cluster_labels
. We have also loaded the pandas
library as pd
.
This exercise is part of the course
Customer Segmentation in Python
Exercise instructions
- Create a new DataFrame by adding a cluster label column to
datamart_rfmt
. - Create a
groupby
element on aCluster
column. - Calculate average RFMT values and segment sizes per each
Cluster
value.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Create a new DataFrame by adding a cluster label column to datamart_rfmt
datamart_rfmt_k4 = datamart_rfmt.____(Cluster=____)
# Group by cluster
grouped = ____.____(['____'])
# Calculate average RFMT values and segment sizes for each cluster
grouped.____({
'Recency': '____',
'Frequency': '____',
'MonetaryValue': '____',
'Tenure': ['mean', '____']
}).round(1)