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Course

Customer Segmentation in Python

IntermediateSkill Level

4.8+

Updated 03/2026

Learn how to segment customers in Python.

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PythonData Manipulation4 hr17 videos55 Exercises4,400 XP21,591Statement of Accomplishment

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Course Description

The most successful companies today are the ones that know their customers so well that they can anticipate their needs. Data analysts play a key role in unlocking these in-depth insights, and segmenting the customers to better serve them. In this course, you will learn real-world techniques on customer segmentation and behavioral analytics, using a real dataset containing anonymized customer transactions from an online retailer. You will first run cohort analysis to understand customer trends. You will then learn how to build easy to interpret customer segments. On top of that, you will prepare the segments you created, making them ready for machine learning. Finally, you will make your segments more powerful with k-means clustering, in just few lines of code! By the end of this course, you will be able to apply practical customer behavioral analytics and segmentation techniques.

Prerequisites

Supervised Learning with scikit-learn

1

Cohort Analysis

In this first chapter, you will learn about cohorts and how to analyze them. You will create your own customer cohorts, get some metrics and visualize your results.

Introduction to cohort analysis

How many customers acquired?

Cohort analysis

Assign daily acquisition cohort

Calculate time offset in days - part 1

Calculate time offset in days - part 2

Cohort metrics

Customer retention

Calculate retention rate from scratch

Calculate average price

Visualizing cohort analysis

Visualize average quantity metric

2

Recency, Frequency, and Monetary Value Analysis

In this second chapter, you will learn about customer segments. Specifically, you will get exposure to recency, frequency and monetary value, create customer segments based on these concepts, and analyze your results.

Recency, frequency, monetary (RFM) segmentation

Calculate spend quartiles (q=4)

Calculate recency deciles (q=4)

Calculating RFM metrics

Largest frequency value

Calculate RFM values

Building RFM segments

Calculate 3 groups for recency and frequency

Calculate RFM Score

Analyzing RFM table

Find average value for RFM score segment

Creating custom segments

Analyzing custom segments

3

Data Preprocessing for Clustering

Once you created some segments, you want to make predictions. However, you first need to master practical data preparation methods to ensure your k-means clustering algorithm will uncover well-separated, sensible segments.

Data preprocessing

Assumptions of k-means

Calculate statistics of variables

Managing skewed variables

Detect skewed variables

Manage skewness

Centering and scaling data

Center and scale manually

Center and scale with StandardScaler()

Pre-processing pipeline

Visualize RFM distributions

Pre-process RFM data

Visualize the normalized variables

4

Customer Segmentation with K-means

In this final chapter, you will use the data you pre-processed in Chapter 3 to identify customer clusters based on their recency, frequency, and monetary value.

Practical implementation of k-means clustering

Run k-means

Assign labels to raw data

Choosing the number of clusters

Calculate sum of squared errors

Plot sum of squared errors

Profile and interpret segments

Prepare data for the snake plot

Visualize snake plot

Calculate relative importance of each attribute

Plot relative importance heatmap

End-to-end segmentation solution

Pre-process data

Calculate and plot sum of squared errors

Build 4-cluster solution

Analyze the segments

Final thoughts

Customer Segmentation in Python

Course
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*4.8

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Ella

10 hours ago

Nabil

3 days ago

CHRISTIAN FABIAN

4 days ago

Gonzalo

4 days ago

Jan

6 days ago

Nagham

last week

This course was very insightful and useful for learning so many techniques in segmenting the customers. It also emphasizes the importance of the business logic behind each segmentation, so you don't rely only on numbers.

Ella

Nabil

CHRISTIAN FABIAN

FAQs

What segmentation techniques does this course teach?

You learn cohort analysis, RFM (recency, frequency, monetary value) segmentation, and k-means clustering to group customers based on their purchasing behavior.

What dataset is used in this course?

You work with a real anonymized dataset of customer transactions from an online retailer, applying segmentation methods to actual purchase records.

Do I need machine learning experience before enrolling?

Some familiarity with scikit-learn is expected. The prerequisites include Supervised Learning with scikit-learn, along with pandas, Intermediate Python, and basic statistics.

How does this course prepare data for k-means clustering?

Chapter 3 covers practical preprocessing steps like scaling, handling skewed distributions, and transforming RFM metrics so the clustering algorithm produces well-separated segments.

What types of roles benefit from customer segmentation skills?

Data analysts, marketing analysts, and CRM specialists use these techniques to personalize campaigns, improve retention, and allocate marketing budgets more effectively.

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