Interactive Course

Customer Segmentation in Python

Learn how to segment customers in Python.

  • 4 hours
  • 17 Videos
  • 55 Exercises
  • 3,887 Participants
  • 4,400 XP

Loved by learners at thousands of top companies:

whole-foods-grey.svg
intel-grey.svg
ikea-grey.svg
dell-grey.svg
rei-grey.svg
deloitte-grey.svg

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.

  1. Recency, Frequency, 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.

  2. 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.

  1. 1

    Cohort Analysis

    Free

    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.

  2. Recency, Frequency, 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.

  3. Data pre-processing 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.

  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.

What do other learners have to say?

Devon

“I've used other sites, but DataCamp's been the one that I've stuck with.”

Devon Edwards Joseph

Lloyd's Banking Group

Louis

“DataCamp is the top resource I recommend for learning data science.”

Louis Maiden

Harvard Business School

Ronbowers

“DataCamp is by far my favorite website to learn from.”

Ronald Bowers

Decision Science Analytics @ USAA

Karolis Urbonas
Karolis Urbonas

Head of Data Science at Amazon

Karolis is currently leading a data science team for Amazon Devices - analyzing behavior of Amazon Echo, Kindle, FireTV and FireTablets customers. He's a data science enthusiast obsessed with machine learning, analytics, neural networks, data cleaning, feature engineering, and every engineering puzzle he can get his hands on. On his free time, he blogs about all these topics at cyborgus.com.

See More
Icon Icon Icon professional info