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Marketing Analytics Using Machine Learning Techniques

Interpreting the Segmentation Result

Explore how to interpret the results of customer segmentation using K-Means clustering. Learn to analyze average features of segments, assign meaningful segment names, and visualize income distribution and age-income relationships to gain insights into customer groups.

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We have segmented our customers into four groups. Customers in each group share similar characteristics. The more we know about the traits of each segment, the better we will be able to serve them. Therefore, let’s try to analyze each customer segment.

Observations

Let's start by looking at the average of each feature.

Segments characteristics

Python
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
# load dataset containing segmented labels
df_customers_segmented = pd.read_csv('customers_segmented.csv', header=0, index_col='CustomerID')
df_customers_analysis = df_customers_segmented.groupby('Segment').mean().round(2)
print(df_customers_analysis.head())

Explanation

  • In line 9, we calculate the average of the segmented customers using the groupby() function. ...