Customer Profilling for Precision Marketing using RFM Method, K-MEANS algorithm and Decision Tree


  • Sularso Budilaksono Universitas Persada Indonesia YAI
  • Jupriyanto Jupriyanto STMIK Nusa Mandiri
  • M.Anno Suwarno Universitas Persada Indonesia YAI
  • I Gede Agus Suwartane Universitas Persada Indonesia YAI
  • Lukman Azhari Muhammadiyah Tangerang University
  • Achmad Fauzi Muhammadiyah Tangerang University
  • Mahpud Mahpud Muhammadiyah Tangerang University
  • Novita Mariana Stikubank University
  • Maya Syafriana Effendi Persada Indonesia YAI University




Precision Marketing;, Customer Profiling, RFM (Recency, Frequency, Monetary), K-Means Algorithm, Decision Tree Algorithm, K Nearest Neighbor Algorithm.


Precision marketing is the companys ability to offer products specifically made to customers. This decision can give the company the ability to attract customers to always buy continuously. This study presents a trend model for accurately predicting monthly supply quantities / The method used in the first stage is the RFM (Recency, Frequency, Monetary) method for selecting attributes to group customers into different groups. The output of the first stage is clustered using the K-Means Algorithm. The output of clustering is then classified using the Decision Tree and compared with the K Nearest Neighbor method. The dataset that is processed is sales data from Syifamart As-Syifa Boarding School in Subang with 351,158 rows of data. The clustering process produces 4 optimal clusters. The four clusters are then classified using the Decision Tree algorithm to determine the potential and non-potential characteristics of each customer.

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Breiman, L. (2017). Classification and Regression Trees. New York: Routledge.

Chen, D., Sain, S. L., & Guo, K. (2012). Data mining for the online retail industry: Acase study of RFM model-based customer segmentation using data mining. .Journal of Database Marketing & Customer Strategy Management, 19(3), , 197–208.

Cheng, C. H., & Chen, Y. S. (2009). Classifying the segmentation of customer value via RFM model and RS theory. Expert Systems with Applications, 36(3), 4176–4184.

Iriana, R. (2007). Strategic, Operational, and Analytical Customer Relationship Management. Journal of Relationship Marketing.

Mesforoush, A., & Tarokh, M. J. (2013). Customer profitability segmentation forSMEs case study: network equipment company. .International Journal of Researchin Industrial Engineering, 30–44.

Wei, J. T., Lee, M. C., Chen, H. K., & Wu, H. H. (2013). Customer relationshipmanagement in the hairdressing industry: An application of data miningtechniques. .Expert Systems with Applications, 40(18), 7513–7518.

Zhang, D., Zhou, X., Leung, S. C., & Zheng, J. (2010). Vertical bagging decision trees model for credit scoring. Expert Systems with Applications 37(12),7838–7843.


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How to Cite

Budilaksono, S., Jupriyanto, J., Suwarno, M., Suwartane, I. G. A. ., Azhari, L. ., Fauzi, A. ., Mahpud, M., Mariana, N. ., & Effendi, M. S. . (2021). Customer Profilling for Precision Marketing using RFM Method, K-MEANS algorithm and Decision Tree. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 6(1), 191-200.