Customer Segmentation is one of the most crucial areas in the field of targeted marketing. This proposed work aims to develop an environment between customers and retailers to set up a beneficial scheme that can help achieve maximum benefit for both customer groups and retailers, for the purpose of creating a win–win situation between customers and retailers. This work focuses on applying RFM Analysis in the field of e-commerce with an overview to segment the customers into different groups. Here, a simple clustering technique using K-means in combination with the Elbow method is applied for the proposed task. Initially, to calculate the ideal number of clusters, the Elbow approach is used, followed by K-means to cluster the group of customers. The customers are divided into groups viz Champions, Can’t Lose, Loyal, promising, require activation, and followed by customers divided into optimal clusters for categorizing the customers into different groups. The experimental results depicted the formation of 3 optimal clusters, in which cluster 1 is more reliable and efficient in terms of loyal customers which contains high monetary amounts and frequency, but low recency values when compared to the other two clusters.