An Effective Customer Segmentation using RFM Ranking Techniques

Customer segmentation emerged as a technique to develop a relationship between customer buying behavior and company profits. With the ever-growing data within the market and the extreme competition among the companies to capture the market, it has become very important for companies to understand the data accumulated by them wisely and efficiently. This can be achieved by customer segmentation techniques. It is directly related to customer relationship management (CRM) system. Therefore, the presented approach aims to provide a research framework to carry out the evolution of different customer segmentation and their behavior. In this paper, the evaluation of the presented approach is evaluated on the online available retail store dataset for one year. The evaluation is carried out using the different approaches such as k-means and k-Medoids algorithm. Further, the segmentation accuracy is gauged by Silhouette distance and mean square error (MSE). The results analyzed through different techniques using the various parameters and show a relationship between customer behavior and marketing strategies of the companies.