Comparison Of K-Means & Hierarchical Clustering In Customer Segmentation

Identification of customers based on their choices and other behaviors is an important strategy in any organization. This identification may help in approaching customers with specific offers and products. An organization with a large number of customers may experience difficulty in identifying and placing into a record each customer individually. A huge amount of data processing and automated techniques are involved in extracting insights from the large information collected on customers. Clustering, an unsupervised technique in machine learning (ML), helps identify customers based on their key characteristics. In this article, we will discuss the identification and segmentation of customers using two clustering techniques - K-Means clustering and hierarchical clustering. We will see the