Customer Segmentation Project using K-prototypes with Code Source

In this tutorial, we will explain and implement a customer segmentation project using K-prototypes.

In this article, you will discover what customer segmentation is, why companies use it, and how to create a customer segmentation project using K-prototypes algorithm.

After completing this tutorial, you will know:

• Customer segmentation Definition

• Why segment customers?

• Customer segmentation types

• Why using machine learning in customer segmentation

• Project implementation using python

Let’s start.

Customer segmentation Definition

Customer segmentation is the practice of dividing a customer base into groups of individuals that are similar in specific ways, such as age, gender, interests, and spending habits.

Why segment customers?

• Learning about customers and their specific needs.
• Create target campaigns and products for each segment.
• Improve customer support by understanding their challenges.
• Increase customer loyalty.
• Identify valuable customers.
• Identify new opportunities for products and services.

Types of customer segmentation

Why using machine learning in customer segmentation

• Machine learning models can process customer data and discover recurring patterns across various features. In many cases, machine learning algorithms can help marketing analysts find customer segments that would be very difficult to spot through intuition and manual examination of data.
• Instead of manually analyzing large amounts of data to look for patterns, you can simply allow the ML program to do the task for you.
• Machine learning can find hidden patterns a human marketer might not see
• Machine learning can automatically update your segments. 

Clustering Techniques

Project Code Source

You can find the GitHub code source of the project in this repository: https://github.com/tech-data/cutomer-segmentation/blob/main/Customer%20Segmentation-Kprototypes.ipynb