Literature Review Analisis Perilaku Pelanggan Menggunakan RFM Model
Abstract
Intisari— Data merupakan aset bagi setiap perusahaan, utamanya adalah Data Pelanggan. Pengolahan data pelanggan selanjutnya dapat disebut sebagai Data Mining. Ketatnya persaingan industri saat ini membuat perusahaan harus cermat didalam mengolah data pelanggan mereka, salah satunya pengolahan data pelanggan untuk operasi CRM. Dimana tujuannya adalah untuk dapat mengenali pelanggan dengan menganalisis perilaku pelanggan. Sehingga ini dapat menjadi suatu investasi bagi perusahaan. Perilaku pelanggan dapat diramalkan dengan menggunakan RFM model. Dan pada penelitian ini penulis melakukan literature review mengenai implementasi RFM Model pada data mining didalam membantu perusahaan untuk lebih memahami pelanggan mereka. Dari hasil review yang dilakukan RFM model pada rentang Tahun 2016 – 2021 lebih banyak dikombinasikan dengan teknik data mining yaitu algoritma clustering, dimana tujuannya adalah untuk mengelompokkan atau melakukan segmentasi pelanggan. Dan dari hasil review penulis juga memformulasikan riset yang dapat dilakukan, yaitu mengkombinasikan RFM model dengan metode TOPSIS.
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References
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[5] D. P. Hidayatullah, R. I. Rokhmawati, and A. R. Perdanakusuma, “Analisis Pemetaan Pelanggan Potensial Menggunakan Algoritma K-Means dan LRFM Model Untuk Mendukung Strategi Pengelolaan Pelanggan ( Studi Pada Maninjau Center Kota Malang ),” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 2, no. 8, pp. 2406–2415, 2018.
[6] A. Muhidin, “Analisa Metode Hierarchical Clsutering dan K-Mean dengan Model LRFMP pada Segmentasi Pelanggan,” SIGMA, J. Teknol. Pelita Bangsa, vol. 7, no. 1, pp. 82–83, 2017.
[7] I. P. A. A. Krishna, R. I. Rokhmawati, and F. Pradana, “Pengembangan Sistem Informasi Marketing Automation Menggunakan RFM Dan Topsis Pada Lotus Asia Tours,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 2, no. 4, p. 18, 2018, [Online]. Available: http://j-ptiik.ub.ac.id/index.php/j-ptiik/article/view/1305.
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[9] H. Ma and D. Gang, “The customer relationship management based on data mining,” WIT Trans. Eng. Sci., vol. 80, pp. 287–294, 2013, doi: 10.2495/aie120341.
[10] W. Bi, M. Cai, M. Liu, and G. Li, “A Big Data Clustering Algorithm for Mitigating the Risk of Customer Churn,” IEEE Trans. Ind. Informatics, vol. 12, no. 3, pp. 1270–1281, 2016, doi: 10.1109/TII.2016.2547584.
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[13] O. Doğan, E. Ayçin, and Z. A. Bulut, “Customer Segmentation by Using RFM Model and Clustering Methods: A Case Study in Retail Industry,” Int. J. Contemp. Econ. Adm. Sci., vol. 8, no. 1, pp. 1–19, 2018, [Online]. Available: www.ijceas.com.
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[17] W. Wahyudin, I. P. A. Wijaya, and I. B. A. Swamardika, “Data Mining for Clustering Revenue Plan Expense Area (APBD) by using K-Means Algorithm,” Int. J. Eng. Emerg. Technol., vol. 2, no. 1, p. 87, 2017, doi: 10.24843/ijeet.2017.v02.i01.p18.
[18] D. Ardiada, P. A. Ariawan, and M. Sudarma, “Evaluation of Supporting Work Quality Using K-Means Algorithm,” IJEET Int. J. Eng. Emerg. Technol., vol. 3, no. 1, pp. 3–6, 2018.
[19] B. Karim, S. Sentinuwo, and A. Sambul, “Penentuan Besaran Uang Kuliah Tunggal untuk Mahasiswa Baru di Universitas Sam Ratulangi Menggunakan Data Mining,” J. Tek. Inform., vol. 11, no. 1, 2017, doi: 10.35793/jti.11.1.2017.16555.
[20] F. Safari, N. Safari, and G. A. Montazer, “Customer lifetime value determination based on RFM model,” Mark. Intell. Plan., vol. 34, no. 4, pp. 446–461, 2016, doi: 10.1108/MIP-03-2015-0060.
[21] J. T. Wei, S. Y. Lin, Y. Z. Yang, and H. H. Wu, “Applying data mining and RFM model to analyze customers’ values of a veterinary hospital,” Proc. - 2016 IEEE Int. Symp. Comput. Consum. Control. IS3C 2016, pp. 481–484, 2016, doi: 10.1109/IS3C.2016.126.
[22] M. Tavakoli, M. Molavi, V. Masoumi, M. Mobini, S. Etemad, and R. Rahmani, “Customer Segmentation and Strategy Development Based on User Behavior Analysis, RFM Model and Data Mining Techniques: A Case Study,” Proc. - 2018 IEEE 15th Int. Conf. E-bus. Eng. ICEBE 2018, pp. 119–126, 2018, doi: 10.1109/ICEBE.2018.00027.
[23] J. Jamal and D. Yanto, “Analisis RFM dan Algoritma K-Means untuk Clustering Loyalitas Customer,” Energy, vol. 9, no. 1, pp. 0–8, 2019.
[24] W. A. Taqwim, N. Y. Setiawan, and F. A. Bachtiar, “Analisis Segmentasi Pelanggan Dengan RFM Model Pada Pt . Arthamas Citra Mandiri Menggunakan Metode Fuzzy C-Means Clustering,” vol. 3, no. 2, pp. 1986–1993, 2019.
[25] S. Monalisa, P. Nadya, and R. Novita, “Analysis for customer lifetime value categorization with RFM model,” Procedia Comput. Sci., vol. 161, pp. 834–840, 2019, doi: 10.1016/j.procs.2019.11.190.
[26] Y. H. Chrisnanto and A. Kanianingsih, “Pengelompokan Ekuitas Pelanggan Berbasis Recency Frequency Monetary (Rfm) Menggunakan K-Means Clustering,” Semin. Nas. Teknol. Inf. dan Komun. 2019 (SENTIKA 2019), vol. 2019, no. Sentika, pp. 13–14, 2019, [Online]. Available: https://fti.uajy.ac.id/sentika/publikasi/makalah/2019/1.pdf.
[27] P. Anitha and M. M. Patil, “RFM model for customer purchase behavior using K-Means algorithm,” J. King Saud Univ. - Comput. Inf. Sci., no. xxxx, 2020, doi: 10.1016/j.jksuci.2019.12.011.
[28] N. Puspitasari, J. A. Widians, and N. B. Setiawan, “Customer segmentation using bisecting k-means algorithm based on recency, frequency, and monetary (RFM) model,” J. Teknol. dan Sist. Komput., vol. 8, no. 2, pp. 78–83, 2020, doi: 10.14710/jtsiskom.8.2.2020.78-83.
[29] H. Bekamiri, M. Mehraeen (Lagzian), A. Pooya, and H. Sharif, “A Stochastic Approach for Valuing Customers in Banking Industry: A Case Study,” Ind. Eng. Manag. Syst., vol. 19, no. 4, pp. 744–757, Dec. 2020, doi: 10.7232/iems.2020.19.4.744.
[30] S. Hwang and Y. Lee, “Identifying customer priority for new products in target marketing: Using RFM model and TextRank,” Innov. Mark., vol. 17, no. 2, pp. 125–136, 2021, doi: 10.21511/im.17(2).2021.12.
[31] F. Juniati, S. Monalisa, R. Zafa, and M. Muslim, “KLASTERISASI CUSTOMER LIFETIME VALUE DENGAN MODEL LRFM MENGGUNAKAN ALGORITMA Fuzzy C-Means,” J. Ilm. Rekayasa dan Manaj. Sist. Inf., vol. 6, no. 1, p. 38, 2020, doi: 10.24014/rmsi.v6i1.8635.
[32] H. Roshan and M. Afsharinezhad, “The New Approach in Market Segmentation by Using RFM Model,” J. Appl. Res. Ind. Eng., vol. 4, no. 4, pp. 259–267, 2017, doi: 10.22105/jarie.2017.91297.1011.
[33] X. Hu, Z. Shi, Y. Yang, and L. Chen, “Classification Method of Internet Catering Customer Based on Improved RFM Model and Cluster Analysis,” 2020 IEEE 5th Int. Conf. Cloud Comput. Big Data Anal. ICCCBDA 2020, pp. 28–31, 2020, doi: 10.1109/ICCCBDA49378.2020.9095607.
[34] J. Wu et al., “An Empirical Study on Customer Segmentation by Purchase Behaviors Using a RFM Model and K -Means Algorithm,” Math. Probl. Eng., vol. 2020, no. November 2017, 2020, doi: 10.1155/2020/8884227.
[35] E. Ismanto and N. Effendi, “Sistem Pendukung Keputusan Penerimaan Karyawan Dengan Metode Simple Additive Weighting (SAW),” SATIN - Sains dan Teknol. Inf., vol. 3, no. 1, p. 1, 2017, doi: 10.33372/stn.v3i1.208.
[36] M. A. Mude, “PERBANDINGAN METODE SAW DAN TOPSIS PADA KASUS UMKM,” Ilk. J. Ilm., vol. 8, no. Agustus, pp. 76–81, 2016.
[37] A. P. Windarto, “Implementasi Metode Topsis Dan Saw Dalam Memberikan Reward Pelanggan,” Klik - Kumpul. J. Ilmu Komput., vol. 4, no. 1, p. 88, 2017, doi: 10.20527/klik.v4i1.73.
[38] G. Wibisono, A. Amrulloh, and E. Ujianto, “Penerapan Metode Topsis Dalam Penentuan Dosen Terbaik,” Ilk. J. Ilm., vol. 11, no. 2, pp. 102–109, 2019, doi: 10.33096/ilkom.v11i2.430.102-109.
[39] A. A. Paypas, R. K. Dewi, and K. C. Brata, “Implementasi Topsis Pada Sistem Rekomendasi Tempat Wisata Pantai Di Sekitar Malang Berbasis Lokasi,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 3, no. 4, pp. 3368–3373, 2019.
[40] A. A. Paypas, R. K. Dewi, and K. C. Brata, “Implementasi TOPSIS Pada Sistem Rekomendasi Tempat Latihan Bela Diri Di Kota Malang Berbasis Lokasi,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 3, no. 4, pp. 3368–3373, 2019.
[41] R. K. Dewi, K. C. Brata, and N. Nabila, “Konsistensi Ranking pada Sistem Rekomendasi Resep Masakan dengan Simple Additive Weighting,” J. Nas. Tek. Elektro dan Teknol. Inf., vol. 8, no. 3, p. 235, 2019, doi: 10.22146/jnteti.v8i3.518.
[42] A. Winata and I. A. Fiqr, “Loyalitas Pelanggan Hotel Emersia Di Bandar,” J. Manaj. Magister, vol. 03, no. 02, pp. 133–149, 2017, [Online]. Available: https://jurnal.darmajaya.ac.id/index.php/jmmd/article/view/989/644.
[43] C. D. Rumiarti and I. Budi, “Customer Segmentation for Customer Relationship Management on Retail Company: Case Study PT Gramedia Asri Media,” J. Sist. Inf., vol. 13, no. 1, p. 1, 2017, doi: 10.21609/jsi.v13i1.525.
[2] M. A. Jihad, “Pemanfaatan Metode Technique for Order Preference By Similiarity To Ideal Solution (Topsis) Untuk Menentukan Pelanggan Terbaik,” J. Inf. dan Komput., vol. 7, no. 1, pp. 1–6, 2019, doi: 10.35959/jik.v7i1.117.
[3] N. Lubis and M. Rizan, “Penerapan Customer Relationship Management (Crm) Dengan Menggunakan Metode Lrfm Analysis,” J. Din. Manaj. DAN BISNIS, vol. 1, no. 2, pp. 1–15, Sep. 2018, doi: 10.21009/JDMB.01.2.06.
[4] I. Sumadikarta and E. Abeiza, “PENERAPAN ALGORITMA K-MEANS PADA DATA MINING UNTUK MEMILIH PRODUK DAN PELANGGAN POTENSIAL (Studi Kasus : PT Mega Arvia Utama),” J. Satya Inform., no. 1, pp. 1–12, 2014.
[5] D. P. Hidayatullah, R. I. Rokhmawati, and A. R. Perdanakusuma, “Analisis Pemetaan Pelanggan Potensial Menggunakan Algoritma K-Means dan LRFM Model Untuk Mendukung Strategi Pengelolaan Pelanggan ( Studi Pada Maninjau Center Kota Malang ),” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 2, no. 8, pp. 2406–2415, 2018.
[6] A. Muhidin, “Analisa Metode Hierarchical Clsutering dan K-Mean dengan Model LRFMP pada Segmentasi Pelanggan,” SIGMA, J. Teknol. Pelita Bangsa, vol. 7, no. 1, pp. 82–83, 2017.
[7] I. P. A. A. Krishna, R. I. Rokhmawati, and F. Pradana, “Pengembangan Sistem Informasi Marketing Automation Menggunakan RFM Dan Topsis Pada Lotus Asia Tours,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 2, no. 4, p. 18, 2018, [Online]. Available: http://j-ptiik.ub.ac.id/index.php/j-ptiik/article/view/1305.
[8] Y. Perwira, “Penentuan Peringkat Pelanggan Terbaik dengan Metode Weighted Product ( Studi Kasus Pt . Asia Raya Foundry ),” vol. 3, no. 1, pp. 138–147, 2019.
[9] H. Ma and D. Gang, “The customer relationship management based on data mining,” WIT Trans. Eng. Sci., vol. 80, pp. 287–294, 2013, doi: 10.2495/aie120341.
[10] W. Bi, M. Cai, M. Liu, and G. Li, “A Big Data Clustering Algorithm for Mitigating the Risk of Customer Churn,” IEEE Trans. Ind. Informatics, vol. 12, no. 3, pp. 1270–1281, 2016, doi: 10.1109/TII.2016.2547584.
[11] B. E. Adiana, I. Soesanti, and A. E. Permanasari, “Analisis Segmentasi Pelanggan Menggunakan Kombinasi Rfm Model Dan Teknik Clustering,” J. Terap. Teknol. Inf., vol. 2, no. 1, pp. 23–32, 2018, doi: 10.21460/jutei.2018.21.76.
[12] A. A. G. B. Ariana, I. K. G. Darma Putra, and L. Linawati, “Perbandingan Metode SOM/Kohonen dengan ART 2 pada Data Mining Perusahaan Retail,” Maj. Ilm. Teknol. Elektro, vol. 16, no. 2, p. 55, 2017, doi: 10.24843/mite.2017.v16i02p10.
[13] O. Doğan, E. Ayçin, and Z. A. Bulut, “Customer Segmentation by Using RFM Model and Clustering Methods: A Case Study in Retail Industry,” Int. J. Contemp. Econ. Adm. Sci., vol. 8, no. 1, pp. 1–19, 2018, [Online]. Available: www.ijceas.com.
[14] I. S. Melati, L. Linawati, and I. A. . Giriantari, “Knowledge Discovery Data Akademik Untuk Prediksi Pengunduran Diri Calon Mahasiswa,” Maj. Ilm. Teknol. Elektro, vol. 17, no. 3, p. 325, 2018, doi: 10.24843/mite.2018.v17i03.p04.
[15] I. G. N. A. S. Mahendra, I. B. L. M. Suta, and M. Sudarma, “Classification of Data Mining with Adaboost Method in Determining Credit Providing for Customers,” IJEET Int. J. Eng. Emerg. Technol., vol. 4, no. 1, pp. 31–36, 2019.
[16] L. R. Atmarani, M. Sudarma, and I. D. Giriantari, “Sistem Opinion Mining dengan Metode Pos Tagging dan SVM Untuk Ekstraksi Data Opini Publik pada Layanan JKBM,” Maj. Ilm. Teknol. Elektro, vol. 16, no. 1, p. 91, 2016, doi: 10.24843/mite.1601.13.
[17] W. Wahyudin, I. P. A. Wijaya, and I. B. A. Swamardika, “Data Mining for Clustering Revenue Plan Expense Area (APBD) by using K-Means Algorithm,” Int. J. Eng. Emerg. Technol., vol. 2, no. 1, p. 87, 2017, doi: 10.24843/ijeet.2017.v02.i01.p18.
[18] D. Ardiada, P. A. Ariawan, and M. Sudarma, “Evaluation of Supporting Work Quality Using K-Means Algorithm,” IJEET Int. J. Eng. Emerg. Technol., vol. 3, no. 1, pp. 3–6, 2018.
[19] B. Karim, S. Sentinuwo, and A. Sambul, “Penentuan Besaran Uang Kuliah Tunggal untuk Mahasiswa Baru di Universitas Sam Ratulangi Menggunakan Data Mining,” J. Tek. Inform., vol. 11, no. 1, 2017, doi: 10.35793/jti.11.1.2017.16555.
[20] F. Safari, N. Safari, and G. A. Montazer, “Customer lifetime value determination based on RFM model,” Mark. Intell. Plan., vol. 34, no. 4, pp. 446–461, 2016, doi: 10.1108/MIP-03-2015-0060.
[21] J. T. Wei, S. Y. Lin, Y. Z. Yang, and H. H. Wu, “Applying data mining and RFM model to analyze customers’ values of a veterinary hospital,” Proc. - 2016 IEEE Int. Symp. Comput. Consum. Control. IS3C 2016, pp. 481–484, 2016, doi: 10.1109/IS3C.2016.126.
[22] M. Tavakoli, M. Molavi, V. Masoumi, M. Mobini, S. Etemad, and R. Rahmani, “Customer Segmentation and Strategy Development Based on User Behavior Analysis, RFM Model and Data Mining Techniques: A Case Study,” Proc. - 2018 IEEE 15th Int. Conf. E-bus. Eng. ICEBE 2018, pp. 119–126, 2018, doi: 10.1109/ICEBE.2018.00027.
[23] J. Jamal and D. Yanto, “Analisis RFM dan Algoritma K-Means untuk Clustering Loyalitas Customer,” Energy, vol. 9, no. 1, pp. 0–8, 2019.
[24] W. A. Taqwim, N. Y. Setiawan, and F. A. Bachtiar, “Analisis Segmentasi Pelanggan Dengan RFM Model Pada Pt . Arthamas Citra Mandiri Menggunakan Metode Fuzzy C-Means Clustering,” vol. 3, no. 2, pp. 1986–1993, 2019.
[25] S. Monalisa, P. Nadya, and R. Novita, “Analysis for customer lifetime value categorization with RFM model,” Procedia Comput. Sci., vol. 161, pp. 834–840, 2019, doi: 10.1016/j.procs.2019.11.190.
[26] Y. H. Chrisnanto and A. Kanianingsih, “Pengelompokan Ekuitas Pelanggan Berbasis Recency Frequency Monetary (Rfm) Menggunakan K-Means Clustering,” Semin. Nas. Teknol. Inf. dan Komun. 2019 (SENTIKA 2019), vol. 2019, no. Sentika, pp. 13–14, 2019, [Online]. Available: https://fti.uajy.ac.id/sentika/publikasi/makalah/2019/1.pdf.
[27] P. Anitha and M. M. Patil, “RFM model for customer purchase behavior using K-Means algorithm,” J. King Saud Univ. - Comput. Inf. Sci., no. xxxx, 2020, doi: 10.1016/j.jksuci.2019.12.011.
[28] N. Puspitasari, J. A. Widians, and N. B. Setiawan, “Customer segmentation using bisecting k-means algorithm based on recency, frequency, and monetary (RFM) model,” J. Teknol. dan Sist. Komput., vol. 8, no. 2, pp. 78–83, 2020, doi: 10.14710/jtsiskom.8.2.2020.78-83.
[29] H. Bekamiri, M. Mehraeen (Lagzian), A. Pooya, and H. Sharif, “A Stochastic Approach for Valuing Customers in Banking Industry: A Case Study,” Ind. Eng. Manag. Syst., vol. 19, no. 4, pp. 744–757, Dec. 2020, doi: 10.7232/iems.2020.19.4.744.
[30] S. Hwang and Y. Lee, “Identifying customer priority for new products in target marketing: Using RFM model and TextRank,” Innov. Mark., vol. 17, no. 2, pp. 125–136, 2021, doi: 10.21511/im.17(2).2021.12.
[31] F. Juniati, S. Monalisa, R. Zafa, and M. Muslim, “KLASTERISASI CUSTOMER LIFETIME VALUE DENGAN MODEL LRFM MENGGUNAKAN ALGORITMA Fuzzy C-Means,” J. Ilm. Rekayasa dan Manaj. Sist. Inf., vol. 6, no. 1, p. 38, 2020, doi: 10.24014/rmsi.v6i1.8635.
[32] H. Roshan and M. Afsharinezhad, “The New Approach in Market Segmentation by Using RFM Model,” J. Appl. Res. Ind. Eng., vol. 4, no. 4, pp. 259–267, 2017, doi: 10.22105/jarie.2017.91297.1011.
[33] X. Hu, Z. Shi, Y. Yang, and L. Chen, “Classification Method of Internet Catering Customer Based on Improved RFM Model and Cluster Analysis,” 2020 IEEE 5th Int. Conf. Cloud Comput. Big Data Anal. ICCCBDA 2020, pp. 28–31, 2020, doi: 10.1109/ICCCBDA49378.2020.9095607.
[34] J. Wu et al., “An Empirical Study on Customer Segmentation by Purchase Behaviors Using a RFM Model and K -Means Algorithm,” Math. Probl. Eng., vol. 2020, no. November 2017, 2020, doi: 10.1155/2020/8884227.
[35] E. Ismanto and N. Effendi, “Sistem Pendukung Keputusan Penerimaan Karyawan Dengan Metode Simple Additive Weighting (SAW),” SATIN - Sains dan Teknol. Inf., vol. 3, no. 1, p. 1, 2017, doi: 10.33372/stn.v3i1.208.
[36] M. A. Mude, “PERBANDINGAN METODE SAW DAN TOPSIS PADA KASUS UMKM,” Ilk. J. Ilm., vol. 8, no. Agustus, pp. 76–81, 2016.
[37] A. P. Windarto, “Implementasi Metode Topsis Dan Saw Dalam Memberikan Reward Pelanggan,” Klik - Kumpul. J. Ilmu Komput., vol. 4, no. 1, p. 88, 2017, doi: 10.20527/klik.v4i1.73.
[38] G. Wibisono, A. Amrulloh, and E. Ujianto, “Penerapan Metode Topsis Dalam Penentuan Dosen Terbaik,” Ilk. J. Ilm., vol. 11, no. 2, pp. 102–109, 2019, doi: 10.33096/ilkom.v11i2.430.102-109.
[39] A. A. Paypas, R. K. Dewi, and K. C. Brata, “Implementasi Topsis Pada Sistem Rekomendasi Tempat Wisata Pantai Di Sekitar Malang Berbasis Lokasi,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 3, no. 4, pp. 3368–3373, 2019.
[40] A. A. Paypas, R. K. Dewi, and K. C. Brata, “Implementasi TOPSIS Pada Sistem Rekomendasi Tempat Latihan Bela Diri Di Kota Malang Berbasis Lokasi,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 3, no. 4, pp. 3368–3373, 2019.
[41] R. K. Dewi, K. C. Brata, and N. Nabila, “Konsistensi Ranking pada Sistem Rekomendasi Resep Masakan dengan Simple Additive Weighting,” J. Nas. Tek. Elektro dan Teknol. Inf., vol. 8, no. 3, p. 235, 2019, doi: 10.22146/jnteti.v8i3.518.
[42] A. Winata and I. A. Fiqr, “Loyalitas Pelanggan Hotel Emersia Di Bandar,” J. Manaj. Magister, vol. 03, no. 02, pp. 133–149, 2017, [Online]. Available: https://jurnal.darmajaya.ac.id/index.php/jmmd/article/view/989/644.
[43] C. D. Rumiarti and I. Budi, “Customer Segmentation for Customer Relationship Management on Retail Company: Case Study PT Gramedia Asri Media,” J. Sist. Inf., vol. 13, no. 1, p. 1, 2017, doi: 10.21609/jsi.v13i1.525.
Published
2022-07-08
How to Cite
WICAKSANA, Putu Aryasuta; SWAMARDIKA, Ida Bagus Alit; HARTATI, Rukmi Sari.
Literature Review Analisis Perilaku Pelanggan Menggunakan RFM Model.
Majalah Ilmiah Teknologi Elektro, [S.l.], v. 21, n. 1, p. 21 - 30, july 2022.
ISSN 2503-2372.
Available at: <https://ojs.unud.ac.id/index.php/jte/article/view/76855>. Date accessed: 18 apr. 2024.
doi: https://doi.org/10.24843/MITE.2022.v21i01.P04.
Section
Articles
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
This work is licensed under a Creative Commons Attribution 4.0 International License
