RFM Analysis Using K-Means Clustering to Improve Revenue and Customer Retention

Abstract

Customer segmentation is one of the key methods in marketing analytics and has been used to segment customers on various criteria and drive business results. One of the techniques used to segmenting the customers basis the behaviour they have exhibited in the past is RFM Analysis. RFM is an acronym for Recency, Frequency and Monetary value. With the increasing availability of past transactional data, RFM analysis can be effectively used to segment customers and drive subsequent business actions. This study performs customer segmentation on past transactional data using K-Means clustering algorithm in Python and basis the created segments, recommended course of actions is suggested.

Keywords: Customer Segmentation, K-means clustering, RFM Analysis, Python, Euclidean Distance, Centroid, Classification, Machine Learning