Alizadeh Zoeram, A., & Karimi Mazidi, A. R. (2018). New Approach for Customer Clustering by Integrating the LRFM Model and Fuzzy Inference System.
Iranian Journal of Management Studies,
11(2), 351-378.
https://doi.org/10.22059/ijms.2018.242528. 672839
Babaiyan, V., & Sarfarazi, S. A. (2019). Analyzing Customers of South Khorasan Telecommunication Company with Expansion of RFM to LRFM Model.
Journal of Artificial Intelligence and Data Mining,
7(2), 331-340.
https://doi.org/10.22044/ jadm.2018.6035.1715
Brahmana, R. W. S., Mohammed, F. A., & Chairuang, K. (2020). Customer segmentation based on RFM model using K-means, K-medoids, and DBSCAN methods.
Lontar Komputer: Jurnal Ilmiah Teknologi Informasi,
11(1), 32-43.
https://doi.org/10.24843/LKJITI. 2020.v11.i01.p04
Buckinx, W., & Van den Poel, D. (2005). Customer base analysis: partial defection of behaviourally loyal clients in a non-contractual FMCG retail setting.
European Journal of Operational Research,
164(1), 252-268.
https://doi.org/10.1016/j.ejor.2003.12.010
Christy, A. J., Umamakeswari, A., Priyatharsini, L., & Neyaa, A. (2021). RFM ranking – An effective approach to customer segmentation.
Journal of King Saud University - Computer and Information Sciences,
33(10), 1251-1257.
https://doi.org/10.1016/j.jksuci.201 8.09.004
Delangizan, S., Papzan, A., & Armand, S. (2022). Design and Development of a Model for Commercialization of Organic Products Based on Fundamental Theory (Case Study: Kermanshah Province).
Karafan Quarterly Scientific Journal,
18(4), 33-48.
https://doi.org/10.48301/kssa.2021.271211.1375
Edelstein, H. (2001). Building profitable customer relationships with data mining. In
Customer Relationship Management: The Ultimate Guide to the Efficient Use of CRM. Vieweg+Teubner Verlag.
https://doi.org/10.1007/978-3-322-84961-8_26
Ernawati, E., Baharin, S., & Kasmin, F. (2021, November 28).
A review of data mining methods in RFM-based customer segmentation. 2nd Annual Conference of Science and Technology, Malang, Indonesia.
https://doi.org/10.1088/1742-6596/1869/1/012085
Huang, Y., Zhang, M., & He, Y. (2020, June 19-21).
Research on improved RFM customer segmentation model based on K-Means algorithm. 2020 5th International Conference on Computational Intelligence and Applications, Beijing, China.
https://doi.org/10. 1109/ICCIA49625.2020.00012
Hwang, H., Jung, T., & Suh, E. (2004). An LTV model and customer segmentation based on customer value: a case study on the wireless telecommunication industry.
Expert Systems with Applications,
26(2), 181-188.
https://doi.org/10.1016/S0957-4174(03 )00133-7
Jonker, J-J., Piersma, N., & Van den Poel, D. (2004). Joint optimization of customer segmentation and marketing policy to maximize long-term profitability.
Expert Systems with Applications,
27(2), 159-168.
https://doi.org/10.1016/j.eswa.2004.01.010
Kao, Y-T., Wu, H-H., Chen, H-K., & Chang, E-C. (2011). A case study of applying LRFM model and clustering techniques to evaluate customer values.
Journal of Statistics and Management Systems,
14(2), 267-276.
https://doi.org/10.1080/09720510.2011 .10701555
Kim, S-Y., Jung, T-S., Suh, E-H., & Hwang, H-S. (2006). Customer segmentation and strategy development based on customer lifetime value: A case study.
Expert Systems with Applications,
31(1), 101-107.
https://doi.org/10.1016/j.eswa.2005.09.004
Liu, D-R., & Shih, Y-Y. (2005). Integrating AHP and data mining for product recommendation based on customer lifetime value.
Information & Management,
42(3), 387-400.
htt ps://doi.org/10.1016/j.im.2004.01.008
Marisa, F., Ahmad, S. S. S., Yusof, Z. I. M., Hunaini, F., & Aziz, T. M. A. (2019). Segmentation Model of Customer Lifetime Value in Small and Medium Enterprise (SMEs) using K-Means Clustering and LRFM Model.
International Journal of Integrated Engineering,
11(3), 169-180.
https://doi.org/10.30880/ijie.2019.11.03.018
Maryani, I., Riana, D., Astuti, R. D., Ishaq, A., & Pratama, E. A. (2018, October 17-18).
Customer Segmentation based on RFM model and Clustering Techniques With K-Means Algorithm. 2018 Third International Conference on Informatics and Computing, Palembang, Indonesia
https://doi.org/10.1109/IAC.2018.8780570
Saeida Ardakani, S., Konjkav Monfared, A., & Hosseini Tavabe, Z. (2021). A Model of Customer’s Stickiness in Online Retail with Emphasis of Their Perceived Value (Case study: DigiKala).
Karafan Quarterly Scientific Journal,
19(5), 573-595.
https://doi.org/1 0.48301/kssa.2021.287817.1546
Seyedhosseini, S., Gholamian, M., & Maleki, A. (2011). A methodology based on RFM using data mining approach to assess the customer loyalty.
International Journal of Industrial Engineering,
22(2), 171-179.
http://ijiepm.iust.ac.ir/article-1-661-en.html
Tale, A., Khani, H., Zendehdel, T., Hajtalebi, H., Behzadfar, F., & Razaqiazar Azar, M. (2017).
SPSS Software Applied Training. Vandad.
https://www.gisoom.com/book/11321411/
Tavakoli, M., Molavi, M., Masoumi, V., Mobini, M., Etemad, S., & Rahmani, R. (2018, October 12-14).
Customer Segmentation and Strategy Development Based on User Behavior Analysis, RFM Model and Data Mining Techniques: A Case Study. 2018 IEEE 15th International Conference on e-Business Engineering Xi'an, China
https://doi.org/1 0.1109/ICEBE.2018.00027
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. In D. Huang & P. Premaratne (Eds.),
Intelligent Computing Methodologies: 16th International Conference, ICIC 2020, Bari, Italy, October 2–
5, 2020, Proceedings, Part III. Springer Cham.
https://doi.org/10.1007/978-3-030-60 796-8_42
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 Means Algorithm.
Mathematical Problems in Engineering,
2020, 1-7.
https://doi.o rg/10.1155/2020/8884227