K-Means Kümeleme Algoritması ve RFM Modeli Kullanarak Müşteri Segmentasyonu

Yıl 2023, , 491 - 503, 15.05.2023

Gözde Aslantaş , Mustafacan Gençgül , Merve Rumelli , Mustafa Özsaraç , Gözde Bakırlı

https://doi.org/10.21205/deufmd.2023257418

Cited By: 3

Öz

Hedef müşterinin belirlenmesi ve ihtiyaçlarının karşılanması, müşteri segmentasyonunda önemli noktalardır. Yenilik-Sıklık-Tutar (RFM) Analizi ve K-Means kümeleme algoritması, müşteri davranışını analiz eden müşteri segmentasyonu için kullanılan popüler yöntemlerdir. Çalışmamızda, ev cihazlarının, RFM bileşenlerini temsil edecek şekilde özelliklerini çıkararak K-Means kümeleme algoritmasını RFM modeline uyarladık. Böylece, benzer RFM özelliklerine sahip müşteriler aynı kümelere atanırken, benzer olmayan RFM özelliklerine sahip müşteriler farklı kümelere atanmıştır. Deneylerde, kümeleme çalışmasının, belirlenen Silhouette Skorunu geçerek başarılı olduğu gözlenmiştir. Ortaya çıkan kümeler, bir müşterinin işletme için ne kadar değerli olduğunu ölçen Müşteri Yaşam Boyu Değeri (CLV) metriğine göre sıralanmış ve adlandırılmıştır.

Anahtar Kelimeler

Müşteri Segmentasyonu, RFM Model, K-Means Algoritması, Müşteri Yaşam Boyu Değeri

Customer Segmentation Using K-Means Clustering Algorithm and RFM Model

Öz

The key points in customer segmentation are determining target customer groups and satisfying their needs. Recency-Frequency-Monetary (RFM) analysis and K-Means clustering algorithm are the popular methods for customer segmentation when analyzing customer behavior. In our study, we adapt the K-means clustering algorithm to RFM model by extracting features that represent RFM aspects of home appliances. Customers with similar RFM-oriented features are assigned to the same clusters, while customers with non-similar RFM-oriented features are assigned to different clusters. In the experiments, clustering achieved the determined threshold for Silhouette Score. The resulting clusters were ranked and named by Customer Lifetime Value (CLV) metric, which measures how valuable a customer is to the business.

Anahtar Kelimeler

Customer Segmentation, RFM Model, K-Means Algorithm, Customer Lifetime Value

Kaynakça

• [1] Xian, Z., Keikhosroiani, P., XinYing, C., Li, Z. 2022. An RFM Model Using K-Means Clustering to Improve Customer Segmentation and Product Recommendation. Keikhosroiani, P., ed. Handbook of Research on Consumer Behavior Change and Data Analytics in the Socio-Digital Era, IGI Global, Malaysia, pp. 124-145. DOI: 10.4018/978-1-6684-4168-8.ch006
• [2] Li, P., Wang, C., Wu, J., Madleňák, R. 2022. An E-commerce Customer Segmentation Method Based on RFM Weighted K-means. International Conference on Management Engineering, Software Engineering and Service Sciences (ICMSS), 61-68. DOI: 10.1109/ICMSS55574.2022.00017
• [3] Anitha, P., Malini M.P. 2022. RFM Model for Customer Purchase Behavior Using K-Means Algorithm, Journal of King Saud University - Computer and Information Sciences, Vol. 34, No. 5, p. 1785–1792. DOI: 10.1016/j.jksuci.2019.12.011
• [4] Mensouri D., Azmani A., Azmani M., 2022. K-Means Customers Clustering by Their RFMT and Score Satisfaction Analysis, International Journal of Advanced Computer Science and Applications, Vol. 13, No. 6, p. 469–476. DOI: 10.14569/ IJACSA.2022.0130658
• [5] Wan, S., Chen, J., Qi, Z., Gan, W., Tang, L. 2022. Fast RFM Model for Customer Segmentation. In Companion Proceedings of the Web Conference (WWW '22), Association for Computing Machinery, New York, NY, USA, 965–972. DOI: 10.1145/3487553.3524707
• [6] Wu, J., Shi, L., Lin, W-P., Tsai, S-B., Li, Y., Yang, L., Xu, G. 2020. An Empirical Study on Customer Segmentation by Purchase Behaviors Using a RFM Model and K-Means Algorithm, Mathematical Problems in Engineering, Vol. 2020, p. 1-7. DOI: 10.1155/2020/8884227
• [7] Hushes, A. M. 1994. Strategic Database Marketing 4e: The Masterplan for Starting and Managing a Profitable, Customer-Based Marketing Program. Mc Graw-Hill, 608p.
• [8] Tabianan, K., Velu, S., Ravi, V. 2022. K-Means Clustering Approach for Intelligent Customer Segmentation Using Customer Purchase Behavior Data, Sustainability, Vol. 14, No. 12. p. 1-15. DOI: 10.3390/su14127243
• [9] Chen, A., Liang, Y-C., Chang, W-J., Siauw, H-Y., Minanda, V. 2022. RFM Model and K-Means Clustering Analysis of Transit Traveller Profiles: A Case Study, Journal of Advanced Transportation, Vol. 2022, p. 1-14. DOI: 10.1155/2022/1108105
• [10] Dewabharata, A. 2022. Customer Segmentation Using the K-Means Clustering as a Strategy to Avoid Overstock in Online Shop Inventory. Proceedings of the 1st International Conference on Contemporary Risk Studies, ICONIC-RS, 31 March-1 April, South Jakarta, DKI Jakarta, Indonesia, 1-12. DOI: 10.4108/eai.31-3-2022.2320688
• [11] Huang, Y., Zhang, M., He, Y. 2020. Research on Improved RFM Customer Segmentation Model Based On K-Means Algorithm. 5th International Conference on Computational Intelligence and Applications (ICCIA), 19-21 June, 24-27. DOI: 10.1109/ICCIA49625.2020.00012
• [12] Dedi, A., Dzulhaq, M.I., Sari, K.W., Ramdhan, S., Tullah, R., Sutarman 2019. Customer Segmentation Based on RFM Value Using K-Means Algorithm. Fourth International Conference on Informatics and Computing (ICIC), 1-7. DOI: 10.1109/ICIC47613.2019.8985726
• [13] Vohra, R., Pahareeya, J., Hussain, A., Ghali, F., Lui, A. 2020. Using Self Organizing Maps and K-Means Clustering Based on RFM Model for Customer Segmentation in the Online Retail Business. Intelligent Computing Methodologies, 16th International Conference, Bari, Italy, October 2–5, Springer-Verlag, Berlin, Heidelberg, 484–497. DOI: 10.1007/978-3-030-60796-8_42
• [14] Maryani, I., Riana, D., Astuti, R.D., Ishaq, A., Sutrisno, Pratama, E.A. 2018. Customer Segmentation based on RFM model and Clustering Techniques with K-Means Algorithm. Third International Conference on Informatics and Computing (ICIC), 1-6. DOI: 10.1109/IAC.2018.8780570
• [15] Kotler, P. 1974. Marketing during Periods of Shortage, Journal of Marketing, Vol. 38, No. 3, p. 20-29. DOI: 10.1177/002224297403800305
• [16] Shih, YY., Liu, CY. 2003. A Method for Customer Lifetime Value Ranking — Combining the Analytic Hierarchy Process and Clustering Analysis. Database Marketing & Customer Strategy Management, Vol. 11, No. 2, p. 159–172. DOI: 10.1057/PALGRAVE.DBM.3240216
• [17] Monalisa, S., Nadya, P., Novita, R., 2019. Analysis for Customer Lifetime Value Categorization with RFM Model, Procedia Computer Science, Vol. 161, p. 834-840. DOI: 10.1016/J.PROCS.2019.11.190
• [18] Haenlein, M., Kaplan, AM., Beeser, Aj., 2007. A Model to Determine Customer Lifetime Value in a Retail Banking Context, European Management Journal, Vol. 25,No.3,p. 221-234. DOI: 10.1016/J.EMJ.2007.01.004
• [19] He, X., Li, C., 2016. The Research and Application of Customer Segmentation on E-Commerce Websites, 6th International Conference on Digital Home (ICDH), 203-208. DOI: 10.1109/ICDH.2016.050
• [20] Dwyer, FR. 1997. Customer Lifetime Valuation to Support Marketing Decision Making, Journal of Direct Marketing, Vol. 11, No. 4, p. 6-13. DOI: 10.1002/(SICI)1522-7138(199723)11:4<6::AID-DIR3>3.0.CO;2-T
• [21] Khajvand, M., Zolfaghar, K., Ashoori, S., Alizadeh, S., 2011. Estimating Customer Lifetime Value based on RFM Analysis of Customer Purchase: Case Study, Procedia Computer Science, Vol. 3, p. 57-63. DOI: 10.1016/J.PROCS.2010.12.011
• [22] Hidalgo, P., Manzur, EF., Olavarrieta, S., Farias, P. 2008. Customer Retention and Price Matching: The AFPs Case, Journal of Business Research, Vol. 61, No. 6, p. 691-696. DOI: 10.1016/J.JBUSRES.2007.06.046
• [23] Shih, YY., Liu, DR. 2008. Product Recommendation Approaches: Collaborative Filtering via Customer Lifetime Value and Customer Demands, Expert Systems with Applications: An International Journal, Vol. 35, No. 1-2, p. 350-360. DOI: 10.1016/J.ESWA.2007.07.055
• [24] De Amorim, RC, Hennig, C. 2014. Recovering the Number of Clusters in Data Sets with Noise Features using Feature Rescaling Factors, Information Sciences, Vol. 324, p. 126-145. DOI: 10.1016/J.INS.2015.06.039
• [25] Leonard, K., Rousseeuw, P. 1990. Partioning Around Medoids (Program PAM). pp 68-125. Leonard, K., Rousseeuw, P., ed. 1990. Finding Groups In Data, Wiley, New York , 335p.