Automatic and Adaptive Segmentation of Customer in R framework using K-means Clustering Technique

In the past few years, e-commerce business has placed a greater emphasis on delivering the better customer service. Building stronger customer relationships aids businesses in generating profits as well as customer happiness and retention. Customer segmentation is a useful tool for identifying unmet customer demand. This work solves the customer segmentation problem by improving the performance of K-means clustering algorithm by optimizing Sum of Squared Error using Elbow method. To aid K-means Clustering in calculating the optimal number of clusters, the Sum of Squared Error is used.