SEGMENTASI PELANGGAN BERDASARKAN ANALISIS RFM (RECENCY, FREQUENCY AND MONETARY INDEXES) DAN ANALISIS DEMOGRAFI
DOI:
https://doi.org/10.47775/ictech.v18i2.285Kata Kunci:
segmentasi, klastering, RFM, demografiAbstrak
Perusahaan mencoba mengenali kebutuhan masyarakat dan pelanggan dengan lebih tepat, salah satu metode yang digunakan adalah CRM. Untuk bisa membedakan kebutuhan banyak pelanggan dan menjalin interaksi antara produsen dan konsumen, harus memahami pelanggan melalui perilaku dan transaksinya. Salah satu metode yang digunakan untuk melakukan segmentasi pelanggan menggunakan model RFM dan metode klastering yaitu K-Means. Sementara demografi digunakan sebagai data pendukung untuk melakukan segmentasi pelanggan. Studi ini menunjukkan bahwa bobot atribut RFM mempengaruhi kinerja asosiasi aturan secara positif. Selain itu, untuk mendapatkan segmen pelanggan yang lebih akurat, disarankan untuk menggunakan kombinasi RFM tertimbang dan atribut demografis. Oleh karena itu, metodologi yang diusulkan menghasilkan hasil dan skor terbaik yaitu sebesar 0.284 dengan jumlah rule yang dikembangkan sebanyak 2491, sehingga pentingnya RFM tertimbang dan data demografi dalam cluster telah terbukti.
Referensi
Asroni, Ronald Adrian. 2015. Penerapan Metode KMeans Untuk Clustering Mahasiswa Berdasarkan Nilai Akademik Dengan Weka Interface Studi Kasus Pada Jurusan Teknik Informatika UMM Magelang. Jurnal Ilmiah Semesta Teknika. Vol. 18 (1), 76-82.
Berry, M. J. A. and Linoff, G. S., 2018. Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management. Second edition.
Buttle. 2014. Customer Relationship Management. Malang:Bayu Media.
Cermak P. 2015. Customer Profitability Analysis and Customer Life Time Value Model:Portofolio Analysis. University of Economics, Prague.
Chung, Y.C., & Chen, S.J. (2016). Study on customer relationship management activities in Taiwan tourism factories. Total Quality Management & Business Excellence, 27(5-6), 581–594. doi:10.1080/14783363.2015.1019341
Coussement, K., Van den Bossche, F. A. M., & De Bock, K. W. (2014). Data accuracy’s impact on segmentation performance: Benchmarking RFM analysis, logistic regression, and decision trees. Journal of Business Research, 67(1), 2751–2758. doi:10.1016/j.jbusres.2012.09.024
Dursun, A., & Caber, M. (2016). Using data mining techniques for profiling profitable hotel customers: An application of RFM analysis. Tourism Management erspectives, 18, 153–160. doi:10.1016/j.tmp.2016.03.001
Fallis, A. (2013). No Title No Title. Journal of Chemical Information and Modeling (Vol. 53). doi:10.1017/CBO9781107415324.004
Farris P. W., Bendle N. T., Pfeifer P. E., and Reibstein D. J. 2008. Marketing Metric. Jakarta:Akademia.
Ghodousi, M., Alesheikh, A. A., & Saeidian, B. (2016). Analyzing public participant data to evaluate citizen satisfaction and to prioritize their needs via K-means, FCM, and ICA. Cities, 55, 70–81.
He, X. and Li, C., 2016. The Research and Application of Customer Segmentation on Ecommerce Websites.
Hosseini, Z. Z., & Mohammadzadeh, M. (2016). Knowledge discovery from patients’ behavior via clustering-classification algorithms based on weighted eRFM and CLV model: An empirical study in public health care services. Iranian Journal of Pharmaceutical Research, 15(1), 355–367
Kantardzic, Mehmed. 2011. Data Mining: Concepts, Models, Methods, and Algorithms.New Jersey:John Wiley & Sons, Inc., Hoboken.
Kim, E., and Lee, B. 2007. An Economic Analysis of Customer Selection and Leverage Strategies in A Market where Network Externalities Exist. Decision Support Systems. 44 (1) 124-134.
Margianti, E. S., Refianti, R., Mutiara, A. B., Nuzulina, K., Technology, I., & Technology, I. (2016). AFFINITY PROPAGATION AND RFM-MODEL FOR CRM ’ S DATA ANALYSIS, 84(2), 272–282
Monalisa, S. 2017. Klasterisasi Customer Lifetime Value Dengan Model Lrfm menggunakan Algoritma KMeans. Jurnal Teknologi Informasi dan Komputer, 5(2).
Nettleton, D. (2014). Chapter 13 – CRM –Customer Relationship Management and Analysis. In Commercial Data Mining (pp. 195–208). doi:10.1016/B978-0-12-416602-8.00013-3
Onur, D., Ejder, A., and ZekiAtil, B. 2018. Customer Seg- mentation by Using RFM Model and Clustering Methods: A Case Study in Retail Industry. International Journal of Contemporary Economics and Administrative Sciences, 1-20.
Selvi, K.R and Ravi, R. 2013. The Organizational Achieving Customer Lifetime Value through Customer Relationship Management. Asia Pacific Journal of Marketing and Management, 2(6).
Soltani, Z., & Navimipour, N. J. (2016). Customer relationship management mechanisms: A systematic review of the state of the art literature and recommendations for future research. Computers in Human Behavior, 61, 667–688. doi:10.1016/j.chb.2016.03.008
Venkatesan, R. and Kumar, V. (2014). A Customer Lifetime Value Framework for Customer Selection and Resource Allocation Strategy. Journal Marketing, 68. 106-125.
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