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Applying Data Mining Techniques and Extended RFM Model in Customer Loyalty Measurement

Panwad Bunnak, Sotarat Thammaboosadee, and Supaporn Kiattisin
Technology of Information System Management Division, Faculty of Engineering, Mahidol University, Nakhon Pathom, Thailand
Abstract—This paper proposes a loyalty measurement model of individual customer for the benefit in creating of marketing campaign and activities as well as the suitable products and services for customers and establishment of good customer relationship. This study adapts the concept of RFM (Recency- Frequency-Monetary) model and applies to database of customer purchases and the customer type. The business type of selected organization is commercial business. To apply the RFM concept to find customer loyalty according to type of customer, the customer loyalty is partitioned into 5 classes using k-means clustering algorithm and is heuristically assigned customer types: Platinum, Gold, and Silver. Type of customers is then brought into consideration the extending of the RFM Model with customer analytics to make it even better customer classification performance. Finally, the classification system generates decision rules to find out the loyalty of new future customers using C4.5 decision tree algorithm.

Index Terms—customer loyalty, RFM model, k-means, decision tree, CRM

Cite: Panwad Bunnak, Sotarat Thammaboosadee, and Supaporn Kiattisin, "Applying Data Mining Techniques and Extended RFM Model in Customer Loyalty Measurement," Vol. 6, No. 4, pp. 238-242, November, 2015. doi: 10.12720/jait.6.4.238-242
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