In today's competitive world, the success of an organization largely depends on how much it understands its customers' behavior. Understanding each customer individually to better tailor the organizational effort to individual needs is a very expensive task. Based on the size of the organization, this task can be very challenging as well. As an alternative, organizations rely on something called segmentation, which attempts to categorize customers into groups based on identified similarities. This critical aspect of customer segmentation allows organizations to extend their efforts to the individual needs of various customer subsets (if not catering to individual needs), therefore reaping greater benefits.

In this chapter, we will learn about the concept and importance of customer segmentation. We'll then deep dive into learning the various machine learning (ML) methods to identify subgroups of customers based on customer characteristics. We'll implement several projects using the wholesale dataset to understand the ML techniques for segmentation. In the next section, we'll start by learning the foundations of customer segmentation and the need for ML techniques to achieve segmentation. We will cover the following topics as we progress:

• Understanding customer segmentation
• Understanding the wholesale customer dataset and the segmentation problem
• Identifying the customer segments in wholesale customer data using DIANA
• Identifying the customer segments in wholesale customer data using AGNES