Integration of RFM and Dynamic Time Warping for Customer Segmentation in MSMEs Digital Printing

Erna Erviana; Tuga Mauritsius

doi:10.59261/jequi.v8i3.397

Authors

Erna Erviana Universitas Bina Nusantara
Tuga Mauritsius Universitas Bina Nusantara

DOI:

https://doi.org/10.59261/jequi.v8i3.397

Keywords:

DBSCAN, Customer Segmentation, Dynamic Time Warping, K-Means, Monetary Frequency Recency

Abstract

Background: Customer segmentation is an important strategy for Micro, Small, and Medium Enterprises (MSMEs) to improve marketing effectiveness and maintain customer loyalty. However, conventional approaches based on static data are often unable to capture the dynamic nature of customer behavior.

Objective: This study aims to develop a more comprehensive customer segmentation approach by integrating static analysis based on Recency, Frequency, and Monetary (RFM) with dynamic analysis using Dynamic Time Warping (DTW) through a Hybrid Integration Matrix for Agatek Print customers.

Method: This study uses transaction data collected from May 2024 to October 2025, comprising 8,808 transactions from 2,194 customers. The data were analyzed using the K-Means and DBSCAN algorithms for static segmentation and Dynamic Time Warping (DTW) for time-series pattern analysis. Model performance was evaluated using the Silhouette Score.

Results: The results show K-Means outperforms DBSCAN, achieving a Silhouette Score of 0.5491 compared with 0.0103, indicating a more stable clustering structure. The DTW analysis successfully identifies dynamic transaction behavior patterns that are not captured by static approaches, as evidenced by four DTW clusters (2,135, 2, 1, and 56 customers) that do not align with the static RFM groups, including project-based and small-scale retail transaction patterns. The integration of both approaches reveals that most customers belong to segments characterized by low customer value and inactive purchasing behavior combined with project-based transaction patterns.

Conclusion: The hybrid RFM–DTW approach provides a more comprehensive understanding of customer behavior and can serve as a foundation for developing more adaptive marketing strategies in MSME contexts.

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References

Admanegara, R. C., & Handayani, W. (2024). Customer Churn Analysis Using Machine Learning to Improve Customer Retention on Vissie Net. International Journal of Scientific Research and Management (IJSRM), 12(9), 7379–7387. https://doi.org/10.18535/ijsrm/v12i09.em05

Anitha, P., & Patil, M. M. (2022). RFM model for customer purchase behavior using K-Means algorithm. Journal of King Saud University - Computer and Information Sciences, 34(5), 1785–1792. https://doi.org/10.1016/j.jksuci.2019.12.011

Bushra, A. A., & Yi, G. (2021). Comparative Analysis Review of Pioneering DBSCAN and Successive Density-Based Clustering Algorithms. IEEE Access, 9, 87918–87935. https://doi.org/10.1109/ACCESS.2021.3089036

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.2018.09.004

Cioppi, M., Curina, I., Francioni, B., & Savelli, E. (2023). Digital transformation and marketing: a systematic and thematic literature review. Italian Journal of Marketing, 2023(2), 207–288. https://doi.org/10.1007/s43039-023-00067-2

Deldadehasl, M., Hajian Karahroodi, H., & Nekah, P. H. (2025). Customer Clustering and Marketing Optimization in Hospitality: A Hybrid Data Mining and Decision-Making Approach from an Emerging Economy. Tourism and Hospitality, 6(2), 80. https://doi.org/10.3390/tourhosp6020080

Duan, G., & Song, G. (2025). Corporate marketing based on improved depth-weighted k-mean arithmetic and improved extreme gradient boosting tree. International Journal of Industrial Engineering Computations, 16(4), 1143–1154. https://doi.org/10.5267/j.ijiec.2025.6.010

Hassan, M. M., Mollick, S., & Yasmin, F. (2022). An unsupervised cluster-based feature grouping model for early diabetes detection. Healthcare Analytics, 2, 100112. https://doi.org/10.1016/j.health.2022.100112

Kasem, M. S., Hamada, M., & Taj-Eddin, I. (2024). Customer profiling, segmentation, and sales prediction using AI in direct marketing. Neural Computing and Applications, 36(9), 4995–5005. https://doi.org/10.1007/s00521-023-09339-6

Kumar, N. (2025). Intelligent customer segmentation: unveiling consumer patterns with machine learning. Journal of Umm Al-Qura University for Engineering and Architecture, 16(3), 774–783. https://doi.org/10.1007/s43995-025-00180-7

Mamun, M. N. H. (2025). Advancements in machine learning for customer retention: A systematic literature review of predictive models and churn analysis. Journal of Sustainable Development and Policy, 1(01), 250–284.

Nagib, R., & Dewanti, M. C. (2025). Analisis Pengaruh Teknologi Digital Printing Terhadap Efisiensi Produksi Dan Kualitas Hasil Cetak Di B21 Digital Printing. EKBIS (Ekonomi & Bisnis), 13(2), 255–261. https://doi.org/10.56689/ekbis.v13i2.2299

Paparrizos, J., & Bogireddy, S. P. T. R. (2025). Time-series clustering: A comprehensive study of data mining, machine learning, and deep learning methods. Proceedings of the VLDB Endowment, 18(11), 4380–4395.

Paparrizos, J., & Gravano, L. (2017). Fast and Accurate Time-Series Clustering. ACM Transactions on Database Systems, 42(2), 1–49. https://doi.org/10.1145/3044711

Sugumaran, M. (2025). E-Commerce Customer Retention Analysis. Dublin, National College of Ireland.

Tabianan, K., Velu, S., & Ravi, V. (2022). K-Means Clustering Approach for Intelligent Customer Segmentation Using Customer Purchase Behavior Data. Sustainability, 14(12), 7243. https://doi.org/10.3390/su14127243

Ufeli, C. P., Sattar, M. U., Hasan, R., & Mahmood, S. (2025). Enhancing Customer Segmentation Through Factor Analysis of Mixed Data (FAMD)-Based Approach Using K-Means and Hierarchical Clustering Algorithms. Information, 16(6), 441. https://doi.org/10.3390/info16060441

Ullah, A., Mohmand, M. I., Hussain, H., Johar, S., Khan, I., Ahmad, S., Mahmoud, H. A., & Huda, S. (2023). Customer Analysis Using Machine Learning-Based Classification Algorithms for Effective Segmentation Using Recency, Frequency, Monetary, and Time. Sensors, 23(6), 3180. https://doi.org/10.3390/s23063180

Vera, J. F., & Angulo, J. M. (2023). An MDS-based unifying approach to time series K-means clustering: application in the dynamic time warping framework. Stochastic Environmental Research and Risk Assessment, 37(12), 4555–4566. https://doi.org/10.1007/s00477-023-02470-9

Vianna Filho, C., Luiz, A., de Lima, L., & Kleina, M. (2026). RFM model customer segmentation from a graph theory perspective. Quality & Quantity, 1–22.

Wang, H., Ou, Y., Liu, F., Li, X., Cao, Y., Wang, B., He, J., Liu, T., & Wang, Y. (2026). Research on neighbor grid-based adaptive spatiotemporal DBSCAN algorithm. International Journal of Machine Learning and Cybernetics, 17(3), 123.

Wang, S., Sun, L., & Yu, Y. (2024). A dynamic customer segmentation approach by combining LRFMS and multivariate time series clustering. Scientific Reports, 14(1), 17491. https://doi.org/10.1038/s41598-024-68621-2

Wilbert, H. J., Hoppe, A. F., Sartori, A., Stefenon, S. F., & Silva, L. A. (2023). Recency, Frequency, Monetary Value, Clustering, and Internal and External Indices for Customer Segmentation from Retail Data. Algorithms, 16(9), 396. https://doi.org/10.3390/a16090396