CUSTOMER SEGMENTATION USING MACHINE LEARNING

Abstract

RFM (Recency,Frequency, Monetary) analysis is a method to identify high-response customers in marketing promotions, and to improve overall response rates, which is well known and is widely applied today. Less widely understood is the value of applying RFM scoring to a customer database and measuring customer profitability. RFM analysis is considered significant also for the banks and their specific units like online shopping A customer who has visited an online shopping site Recently (R) and Frequently (F) and created a lot of Monetary Value (M) through payment and standing orders is very likely to visit and make payments again. After evaluation of the customer’s behaviour using specific RFM criteria the RFM score is correlated to the online shopping, with a high RFM score beingmore beneficial to the online shopping as well as in the future. Data mining methods can be considered as tools enhancing the online shopping RFM analysis of the customers in total as well as specific groups like the users of online shopping KEYWORDS-Data Mining online shopping, RFM analysis, Clustering