Analisis Segmentasi Pelanggan Ritel Online Menggunakan K-Means Clustering Berdasarkan Model Recency, Frequency, Monetary (RFM)

Penulis

  • Adzriel Yusak Noah Rumapea, Universitas Trisakti,  Indonesia
  • Dian Pratiwi, Universitas Trisakti,  Indonesia
  • Syandra Sari, Universitas Trisakti,  Indonesia

Kata Kunci:

Customer Segmentation, RFM (Recency, Frequency, Monetary) Model, Customer Profile, K-Means Clustering

Abstrak

Customer segmentation is the process of grouping customers based on similar characteristics. It plays a critical role in marketing strategy, enabling businesses to understand consumer behavior and enhance the effectiveness of campaigns. The Recency, Frequency, Monetary (RFM) model and the K-Means Clustering algorithm have proven to be effective in identifying different customer segments. This study uses online retail transaction data from the period 2010-2011. The RFM model is employed to calculate customer value based on recency, frequency, and monetary metrics. Subsequently, the K-Means Clustering algorithm is applied to normalized data to group customers into several segments. The results reveal the existence of three distinct customer segments. These segments, characterized by varying traits, provide a clearer understanding of consumer behavior. By understanding the characteristics of each segment, companies can design more effective and personalized marketing strategies. Furthermore, this study contributes to the advancement of knowledge in the field of data analysis.

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Referensi

Abadi, S., et al. "Application model of k-means clustering: Insights into promotion strategy of vocational high school." International Journal of Engineering & Technology, vol. 7, no. 2.27 Special Issue, 2018, pp. 182–187. doi: 10.14419/ijet.v7i2.11491.

Chaudhary, P. "Data Mining System, Functionalities and Applications: A Radical Review." International Journal of Distributed and Parallel Systems, vol. 1, no. 1, 2015, pp. 32–44.

Harani, N. H., Prianto, C., and Nugraha, F. A. "Segmentasi Pelanggan Produk Digital Service Indihome Menggunakan Algoritma K-Means Berbasis Python." Jurnal Manajemen Informatika, vol. 10, no. 2, 2020, pp. 133–146. doi: 10.34010/jamika.v10i2.2683.

Puspitasari, N., Widians, J. A., and Setiawan, N. B. "Customer segmentation using bisecting k-means algorithm based on recency, frequency, and monetary (RFM) model." Jurnal Teknologi dan Sistem Komputer, vol. 8, no. 2, 2020, pp. 78–83. doi: 10.14710/jtsiskom.8.2.2020.78-83.

Siagian, R., Sirait, P. S. P., and Halima, A. "E-Commerce Customer Segmentation Using K-Means Algorithm and Length, Recency, Frequency, Monetary Model." Journal of Informatics Telecommunication Engineering, vol. 5, no. 1, 2021, pp. 21–30. doi: 10.31289/jite.v5i1.5182.

R. D. Hastuti, I. Waspada, P. W. Wirawan, and N. Bahtiar, “Designing computer-assisted problem-based learning (CAPBL) environment for performance analysis of isolation forest algorithm,” J. Phys. Conf. Ser., vol. 1524, no. 1, 2020, doi: 10.1088/1742-6596/1524/1/012103.

H. Ge and L. Jin, “Human Posture Recognition Based on Isolation Forest Algorithm,” Adv. Transdiscipl. Eng., vol. 37, pp. 276–284, 2023, doi: 10.3233/ATDE230149.

G. ASLANTAŞ, M. GENÇGÜL, M. RUMELLİ, M. ÖZSARAÇ, and G. BAKIRLI, “Customer Segmentation Using K-Means Clustering Algorithm and RFM Model,” Deu Muhendis. Fak. Fen ve Muhendis., vol. 25, no. 74, pp. 491–503, 2023, doi: 10.21205/deufmd.2023257418.

Muningsih, E., & Kiswati, S. (2018). Sistem aplikasi berbasis optimasi metode elbow untuk penentuan clustering pelanggan. Joutica, 3(1), 117.

Aminazadeh, M., & Noorbehbahani, F. (2022). City intersection clustering and analysis based on traffic time series. In 2022 12th International Conference on Computer and Knowledge Engineering (ICCKE) (pp. 274-281). IEEE. https://doi.org/10.1109/ICCKE57176.2022.9960065

Liu, F., & Deng, Y. (2020). Determine the number of unknown targets in open world based on elbow method. IEEE Transactions on Fuzzy Systems, 29(5), 986-995.

A. Z. Faridee and V. P. Janeja, “Measuring Peer Mentoring Effectiven,” Am. J. o, vol. 15, no. 2, pp. 7–22, 2020.

N. H. Harani, C. Prianto, and F. A. Nugraha, “Segmentasi Pelanggan Produk Digital Service Indihome Menggunakan Algoritma K-Means Berbasis Python,” J. Manaj. Inform., vol. 10, no. 2, pp. 133–146, 2020, doi: 10.34010/jamika.v10i2.2683.

Pradana, M. G., & Ha, H. T. (2021). Maximizing Strategy Improvement in Mall Customer Segmentation using K-means Clustering. Journal of Applied Data Sciences, 2(1), 19–25. https://doi.org/10.47738/jads.v2i1.18

R. Siagian, P. S. Pahala Sirait, and A. Halima, “E-Commerce Customer Segmentation Using K-Means Algorithm and Length, Recency, Frequency, Monetary Model,” J. Informatics Telecommun. Eng., vol. 5, no. 1, pp. 21–30, 2021, doi: 10.31289/jite.v5i1.5182.

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Diterbitkan

2024-12-18

Cara Mengutip

Yusak Noah Rumapea, A. ., Pratiwi, D., & Sari, S. (2024). Analisis Segmentasi Pelanggan Ritel Online Menggunakan K-Means Clustering Berdasarkan Model Recency, Frequency, Monetary (RFM). Jurnal Sains Dan Teknologi, 6(3), 292-299. Diambil dari https://ejournal.sisfokomtek.org/index.php/saintek/article/view/4607

Terbitan

Vol 6 No 3 (2024): Jurnal Sains dan Teknologi

Bagian

Article

Lisensi

Hak Cipta (c) 2024 Adzriel Yusak Noah Rumapea, Dian Pratiwi, Syandra Sari

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