SEGMENTASI PELANGGAN BERBASIS RFM UNTUK PERUMUSAN STRATEGI PEMASARAN PADA INDUSTRI PERCETAKAN

Ari Kristina - ARS University
Yulita Santi - ARS University
Timothy Sulistio - ARS University
Yen Efawati - ARS University
Athasyahri Syawal Fahrezy - Universitas Mulawarman

Abstract


This study aims to formulate marketing strategies in the printing industry through customer segmentation based on Recency, Frequency, and Monetary (RFM). The research problem is the absence of transaction behavior-based customer mapping that can distinguish loyal customers, high-value customers with declining activity, new or potential customers, low-active customers, and churned/passive customers. The novelty of this study lies in integrating active and churned customer separation, RFM indicator construction, comparison of K-Means and K-Medoids methods, and translation of cluster results into operational STP and CRM strategies in the printing industry context. This study applies a descriptive quantitative approach involving data cleaning, order-level deduplication, customer anonymization, RFM construction, active and churned customer separation, data transformation and normalization, clustering, model evaluation, and marketing strategy interpretation. The results show that of 11,268 customers, only 529 customers (4.69%) are active, while 10,739 customers (95.31%) are categorized as churned or passive. The best model is K-Means with k=4, DBI 0.8366, and Silhouette Score 0.4364. The segmentation produces four active customer groups used as the basis for loyalty, high-value customer retention, repeat purchase improvement, and customer reactivation strategies.

 

ABSTRAK

Penelitian ini bertujuan merumuskan strategi pemasaran pada industri percetakan melalui segmentasi pelanggan berbasis Recency, Frequency, dan Monetary (RFM). Permasalahan penelitian adalah belum adanya pemetaan pelanggan berbasis perilaku transaksi yang mampu membedakan pelanggan loyal, pelanggan bernilai tinggi yang mulai menurun aktivitasnya, pelanggan baru atau potensial, pelanggan aktif rendah, dan pelanggan churned/pasif. Kebaruan penelitian ini terletak pada integrasi pemisahan pelanggan aktif dan churned, pembentukan indikator RFM, pembandingan metode K-Means dan K-Medoids, serta penerjemahan hasil cluster ke dalam strategi STP dan CRM yang operasional pada konteks industri percetakan. Penelitian menggunakan pendekatan kuantitatif deskriptif dengan tahapan pembersihan data, deduplikasi order, anonimisasi pelanggan, pembentukan RFM, pemisahan pelanggan aktif dan churned, transformasi dan normalisasi data, clustering, evaluasi model, serta interpretasi strategi pemasaran. Hasil menunjukkan bahwa dari 11.268 pelanggan, hanya 529 pelanggan (4,69%) yang aktif, sedangkan 10.739 pelanggan (95,31%) tergolong churned atau pasif. Model terbaik adalah K-Means dengan k=4, DBI 0,8366, dan Silhouette Score 0,4364. Segmentasi menghasilkan empat kelompok pelanggan aktif yang menjadi dasar strategi loyalitas, retensi pelanggan bernilai tinggi, peningkatan pembelian ulang, dan reaktivasi pelanggan.

Keywords


Customer segmentation, RFM, Marketing strategy, CRM ; Segmentasi pelanggan, RFM, Strategi pemasaran, CRM

Full Text:

PDF

References


Akande, O., Asani, E. O., & Dautare, B. T. (2024). Customer segmentation through RFM analysis and K-means clustering: Leveraging data-driven insights for effective marketing strategy. Ceddi Journal of Information System and Technology, 3(1).

Aulia, S., Muhammad, A. N., & Wibowo, A. (2025). Optimization of customer segmentation with RFM, K-means, and FP-growth for marketing strategy. SINTECH (Science and Information Technology) Journal, 8(2).

Bult, J. R., & Wansbeek, T. (1995). Optimal selection for direct mail. Marketing Science, 14(4), 378–394.

Buttle, F., & Maklan, S. (2019). Customer relationship management: Concepts and technologies (4th ed.). Routledge.

Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). Sage.

Davies, D. L., & Bouldin, D. W. (1979). A cluster separation measure. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-1(2), 224–227.

Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (8th ed.). Cengage Learning.

Ho, T., Nguyen, P., & Tran, M. (2023). An extended RFM model for customer behaviour and demographic analysis in retail industry. Business Systems Research Journal, 14(1), 26–53.

Kaufman, L., & Rousseeuw, P. J. (1990). Finding groups in data: An introduction to cluster analysis. Wiley.

Kotler, P., Armstrong, G., & Opresnik, M. O. (2021). Principles of marketing (18th ed.). Pearson.

Kotler, P., Keller, K. L., & Chernev, A. (2022). Marketing management (16th ed.). Pearson.

Lemon, K. N., & Verhoef, P. C. (2016). Understanding customer experience throughout the customer journey. Journal of Marketing, 80(6), 69–96.

MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. In Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability (Vol. 1, pp. 281–297). University of California Press.

Martiansah, R., Monalisa, S., Muttakin, F., & Fronita, M. (2025). Customer segmentation analysis through RFM-D model and K-means algorithm. Jurnal Sistem Cerdas, 8(1).

Passolving, I. Y., Ali, P. R., Kartawidjaja, M. A., & Sukwadi, R. (2024). Segmentasi pelanggan menggunakan K-means clustering: Menganalisis metrik RFM untuk strategi pemasaran. Jurnal Media Teknik dan Sistem Industri, 9(1).

Putri, Y., Aldo, D., & Ilham, W. (2024). Retail marketing strategy optimization: Customer segmentation with artificial intelligence integration and K-means clustering. Sinkron: Jurnal dan Penelitian Teknik Informatika, 8(4), 2155–2163.

Rousseeuw, P. J. (1987). Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 20, 53–65.

Sitorus, E. R., & Nugraha, I. (2025). Analisis segmentasi pelanggan dengan model RFM dan K-means clustering. Jurnal Teknik Industri Terintegrasi, 8(1).

Syahra, Y., Fadlil, A., & Yuliansyah, H. (2025). Customer segmentation using RFM and K-means clustering to support CRM in retail industry. Sinkron: Jurnal dan Penelitian Teknik Informatika, 9(3).

Verhoef, P. C., Broekhuizen, T., Bart, Y., Bhattacharya, A., Dong, J. Q., Fabian, N., & Haenlein, M. (2021). Digital transformation: A multidisciplinary reflection and research agenda. Journal of Business Research, 122, 889–901.

Wedel, M., & Kannan, P. K. (2016). Marketing analytics for data-rich environments. Journal of Marketing, 80(6), 97–121.

Zahro, N., Maori, N. A., & Wibowo, G. W. N. (2025). Integration of RFM method and K-means clustering for customer segmentation effectiveness. Journal of Dinda: Data Science, Information Technology, and Data Analytics, 5(1).




DOI: https://doi.org/10.59818/kontan.v5i2.2867

Citation Format:

Refbacks

  • There are currently no refbacks.


Office Address

Jl. Adi Flora Raya, Cluster Magnolia Xiana No 27, Komplek Bumi Adipura, Kec. Gede Bage, Kota Bandung.

Phone: (022) 873 553 70 | Mobile: +62 81 57000 699

Payment Information

Bank Rakyat Indonesia (BRI)
Account Name: CV. Widina Media Utama
Account Number: 110701000573565

SINTA Index Google Scholar Garuda Index Crossref BASE Index Dimensions