Customer Segmentation Analysis using K-means Algorithm with Elbow method and Dendrogram

In today’s competitive business environment, it is crucial to do marketing effectively based on the customer buying habits and requirements. By analyzing the buying habits of customers, companies can enhance customer’s shopping experiences. The analysis can be done with the help of a method called customer segmentation. It is the process of grouping customers into different clusters based on similar characteristics. In this paper, we have done customer segmentation using the k-means algorithm which is an unsupervised learning method and it focuses on grouping the data points into clusters where each point resides in a cluster with the nearest centroid. The accuracy for the optimal number of clusters is verified using a dendrogram where the number of clusters is equal to the number of points obtained by drawing a imaginary horizontal line bisecting through the most extended vertical line of the graph. This analysis gives insights based on customer buying habits which helps companies build marketing strategies for the targeted audience that will improve client retention and overall revenue of business.