Customer Segmentation based on RFM model and Clustering Techniques With K-Means Algorithm

Abstraksi

 Every day there is a transaction process performed by Customer. The process generates a lot of data where there are 82,648 transactions from the month of January-December 2017. This study aims to perform customer segmentation on Nine Reload Credit by utilizing data mining process based on RFM model and by using techniques Clustering. The algorithm used for cluster formation is K-Means algorithm. K-Means produces a visual cluster model with the Rapidminer 5.2 tools that represent the number of customers in each cluster by using RFM (Recency, Frequency, and Monetary) attributes. From 82,648 transactions that were then processed, based on RFM model it resulted in 102 Customers. Furthermore, we analyzed cluster by using K-Means algorithm with the result of 63 Customers in Cluster 1 and 39 Customers in Cluster 2. The result of this research can be used by company to know customer category, and then the company will know how to maintain the customer owned. 

 

Kata Kunci: ; RFM Model; Cluster Analysis; Customer Segmentation; K-Means Algorithm.

URI
https://ieeexplore.ieee.org/document/8780570

Bidang ilmu
Data Mining

References

[1]   Maryani, Ina, and Dwiza Riana. 2017. “Clustering and Profiling of Customers Using RFM for Customer Relationship Management Recommendations.” 2017 5th International Conference on Cyber and IT Service Management, CITSM 2017, 2–7. https://doi.org/10.1109/CITSM.2017.8089258. [2]  Tama, Bayu Adhi. 2010. “Penetapan Strategi Penjualan Menggunakan Association Rules Dalam Konteks CRM.” Jurnal Generic Vol. 5 (No.1):35–38. [3]   Hand, David J. 2007. “Principles of Data Mining.” Drug Safety 30 (7):621–22. https://doi.org/10.2165/00002018200730070-00010. [4]     Ramamohan, Y, K Vasantharao, C Kalyana Chakravarti, and a S K  

 Ratnam. 2012. “A Study of Data Mining Tools in Knowledge Discovery Process.” International Journal of Soft Computing and Engineering 2 (3):191–94. [5]  Wongchinsri, Pornwatthana, and Werasak Kuratach. 2016. “A Survey -Data Mining Frameworks in Credit Card Processing.” 2016 13th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, ECTICON 2016. https://doi.org/10.1109/ECTICon.2016.7561287. [6]   Peiman Alipour Sarvari, Alp Ustundag, and Hidayet Takci. 2014. “Performance Evaluation of Different Customer Segmentation Approaches Based on RFM and Demographics Analysis.” Kybernetes 43 (8):1209–23. https://doi.org/10.1108/K-01-2015-0009 [7]   Rachid, et al. 2015. “Combining RFM Model and Clustering Techniques for Customer Value Analysis of a Company selling online.” 2015 12th  International Conference of Computer Systems and Applications (AICCSA) 2015,1-6.  [8]   Liu Jiali and Du Hyung. 2010. “Study on Airline Customer Value Evaluation Based on RFM Model (2010).” 2010 International Conference On Computer Design And Appliations (ICCDA 2010) ,278-281 [9]   Aviliani, U. Sumarwan, I. Sugema, and A. Saefuddin. 2011. “Segmentasi Nasabah Tabungan Mikro Berdasarkan Recency, Frequency, dan Monetary : Kasus Bank BRI.” Finance and Banking Journal 13 (1):95– 109. [10] Kusrini Luthfi, Ema Taufiq. 2009. Algoritma Data Mining. Edited by Theresia Ari Prabawati. Yogyakarta: C.V Andi OFFSET. https://books.google.co.id/books?id=Ojclag73O8C&pg= PA3&dq=data+mining+adalah&hl=id&sa=X&ved=0ah UKEwijrefgpYnZAhXBPY8KHWeJCQ4Q6AEIKzAA# v=onepage&q=data mining adalah&f=false. [11] Lubis, Abdul Haris. 2016. “Model Segmentasi Pelanggan Dengan Kernel K-Means Clustering Berbasis Customer Relationship Management.” Jurnal & Penelitian Teknik Informatika 1:36–41. [12] Rahman, Aulia Tegar; Wiranto ;Rini Anggrainingsih. 2017. “Coal Trade Data Clustering Using K-Means ( Case Study PT . Global Bangkit Utama )” 6 (1):24–31.