DataCamp

Customer Segmentation in Python

Name: Customer Segmentation in Python
Availability: InStock
Rating: 4.9 (169 reviews)

4.9(169)21k studentsINTERMEDIATEEnglish

Korshub's take

The Korshub Take

Understanding customer behavior through segmentation allows businesses to tailor products and anticipate user needs. This intermediate course covers behavioral analytics on real retail transaction data, guiding learners through cohort analysis, RFM-style feature preparation, and k-means clustering in Python.

Who It's For

Data analysts, marketers, and Python practitioners wanting to perform customer behavioral analytics and automated segmentation.

Key Takeaways

Cohort Analysis: Track customer retention and behavior trends over time using cohort grids.
Feature Preparation for ML: Preprocess transactional data and transform variables into ML-ready inputs for clustering.
K-Means Clustering: Implement k-means algorithms in Python to group customers into distinct, actionable segments.

About this course

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.