Data mining framework for the identification of profitable customer based on recency, frequency, monetary (RFM)

Abstract

Recency, Frequency, and Monetary (RFM) are behavior-based models used to predict customer behavior based on shopping activities. RFM model determines a customer's value for the company, which has been widely applied in various fields, especially the field of marketing. The RFM model was integrated with data mining to support customer segmentation and classification. The data mining techniques used were Self Organizing Maps (SOM), K-means, and Decision Tree C4.5. The framework was tested with 562 data of online customers who have purchased items in Indonesia's e-commerce platform. Three customer segments were distinguished, namely High Value Customer, Medium Value Customer, and Customer at Risk. The measure of goodness of segments formed was evaluated with the Davies-Bouldin index (0.472), cross validation (81%) and confusion matrix (74.48%).