Machine Learning based prototype for Customer Segmentation using RFM

Abstract

One way to hike consumer satisfies the services of the company provides is through an use of the customer relationship management (CRM) system. It can be difficult to determine the proper info what customer requires from data in your CRM system. Businesses can use data mining processes to segment and retrieve important customer information. Basis of consumer's RFM (Recency, Frequency, and Monetary) score, we can classify the customer segmentation. The RFM model has been utilised as the foundation for client segmentation in a number of research. However, the approaches suggested in earlier research are extremely particular to particular businesses, the score of RFM range employed as likewise more arbitrary. Additionally, is organizations grow, problems arise with RFM scoring. Measurements of RFM scores require periodic corrections, and current techniques make these corrections difficult. Determine a correct RFM score range, this study provided a unique technique that used a combination of K-Means and the Davies-Bouldin Index (DBI), circumventing the shortcomings of previous methods. As the amount of data rises, the suggested technique makes it easier to calculate RMF ratings. This is based on research conducted in the telecom industry. The K-Means method used in this study also produced the correct RFM score range which is depended on the ideal K values of the K-Means algorithm. The proposed solution only depends on each customer's RFM value from the corresponding data, so it can be used in different industries.