Keywords
customer segmen
historical customer data
marketing strategies
service customization
How to Cite
Abstract
Effective customer segmentation is crucial in optimizing marketing strategies, particularly in customer-oriented aesthetic clinics. This research aims to enhance customer segmentation in aesthetic clinics using a K-Means approach based on the RFAM (Recency, Frequency, Average-Monetary) model. This approach is utilized to leverage historical customer data to identify customer segments based on their purchasing behavior, including visit frequency, average purchase amount, and the last time they visited the clinic. The K-Means clustering method maps customers into homogeneous groups, enabling aesthetic clinics to adapt more focused and personalized marketing strategies. The research results indicate insights obtained from the analysis and interpretation of RFAM conducted on 493 data points, resulting in the formation of two distinct clusters. In Cluster 1, denoting low loyalty, there are 156 customers, while Cluster 2 comprises 337 customers, reflecting high loyalty. Practical implications of this research include improvements in service customization and promotions tailored to customer needs and preferences. In conclusion, the K-Means approach based on the RFAM model can be utilized as an effective tool to enhance customer segmentation in the aesthetic clinic industry.
References
M. S. Kasem, M. Hamada, and I. Taj-Eddin, ‘Customer profiling, segmentation, and sales prediction using AI in direct marketing’, Neural Comput. Appl., vol. 36, no. 9, pp. 4995–5005, Mar. 2024, doi: 10.1007/s00521-023-09339-6.
P. S. Paays, ‘The Effect of Service Recovery aAccessibility on Customer Satisfaction and Loyalty’, J. Ekon. Bisnis Manaj. Dan Akunt. JEBMA, vol. 4, no. 1, pp. 539–554, 2024.
A. Dewi Rana, Q. Chloe Milano Hadisantoso, and A. Suganda Girsang, ‘RFM-T Model Clustering Analysis in Improving Customer Segmentation’, Int. J. Comput. Digit. Syst., vol. 16, no. 1, pp. 1–11, 2024.
S. A. Laga, I. R. Mukhlis, D. Hermansyah, G. Suprianto, M. A. Karyawan, and H. Yutanto, ‘Customer Behavior Using RFM Model and K-Means Algorithm in Aesthetic Clinic’, in 2023 Eighth International Conference on Informatics and Computing (ICIC), IEEE, 2023, pp. 1–5. Accessed: Apr. 23, 2024. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/10382095/
J. Li, ‘How Do Information Quality, E-service Quality, And System Quality Enhance Customer Satisfaction for Airbnb?’, Int. J. Educ. Humanit., vol. 13, no. 2, pp. 29–45, 2024.
J. Kim, J. Kim, and S. Hong, ‘G-TRACE: Grouped temporal recalibration for video object segmentation’, Image Vis. Comput., p. 105050, 2024.
P. Kumar and G. Bisaria, ‘Consumer Satisfaction Judgments Based on Credence Services’, in Interdisciplinary Research in Technology and Management, CRC Press, pp. 481–490. Accessed: May 15, 2024. [Online]. Available: https://www.taylorfrancis.com/chapters/edit/10.1201/9781003430469-56/consumer-satisfaction-judgments-based-credence-services-pradeep-kumar-gaurav-bisaria
T. K. Vashishth, K. K. Sharma, B. Kumar, S. Chaudhary, and R. Panwar, ‘Enhancing Customer Experience through AI-Enabled Content Personalization in E-Commerce Marketing’, Adv. Digit. Mark. Era Artif. Intell., pp. 7–32, 2025.
A. Suradi, S. Purwati, M. Zakaria, R. Nurbakti, and F. Mustafa, ‘Analysis of Determinant Factors Customer Loyalty Towards Brand in The Telecommunication Industry With The Digitalization Paradigm’, J. Sistim Inf. Dan Teknol., pp. 36–41, 2024.
D. Li, Y. Li, Y. Fan, and S. Tang, ‘Accurate Segmentation Method for Roadside Lampposts Based on Vehicle-Mounted Lidar Point Clouds’, Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci., vol. 48, pp. 305–310, 2024.
D. S. Ametefe et al., ‘Automatic classification and segmentation of blast cells using deep transfer learning and active contours’, Int. J. Lab. Hematol., p. ijlh.14305, May 2024, doi: 10.1111/ijlh.14305.
A. Amzil, M. Abid, M. Hanini, A. Zaaloul, and S. El Kafhali, ‘Stochastic analysis of fog computing and machine learning for scalable low-latency healthcare monitoring’, Clust. Comput., Feb. 2024, doi: 10.1007/s10586-024-04285-x.
Y. Yuricha, B. S. Salim, S. A. Laga, A. Suroni, and R. Permatasari, Buku Ajar Interaksi Manusia Dan Komputer. PT. Sonpedia Publishing Indonesia, 2024. Accessed: Oct. 30, 2024. [Online]. Available: https://books.google.com/books?hl=en&lr=&id=HFYrEQAAQBAJ&oi=fnd&pg=PA17&dq=info:TDDnds64A4oJ:scholar.google.com&ots=D5urhI9a9G&sig=9pRxJVWz_RxhqDOHs0bsIPcDLd0
A. Apichottanakul, M. Goto, K. Piewthongngam, and S. Pathumnakul, ‘Customer behaviour analysis based on buying-data sparsity for multi-category products in pork industry: A hybrid approach’, Cogent Eng., vol. 8, no. 1, p. 1865598, Jan. 2021, doi: 10.1080/23311916.2020.1865598.
F. Yoseph, N. H. Ahamed Hassain Malim, M. Heikkilä, A. Brezulianu, O. Geman, and N. A. Paskhal Rostam, ‘The impact of big data market segmentation using data mining and clustering techniques’, J. Intell. Fuzzy Syst., vol. 38, no. 5, pp. 6159–6173, 2020.
D. Hermansyah and A. Muklason, ‘Evaluation of hyper-heuristic method using random-hill climbing algorithm in the examination timetabling problem’, in Journal of Physics: Conference Series, IOP Publishing, 2020, p. 022101. Accessed: Apr. 23, 2024. [Online]. Available: https://iopscience.iop.org/article/10.1088/1742-6596/1569/2/022101/meta
M. Furqon, N. Najwa, D. Hermansyah, and M. Zarkasi, ‘Knowledge Graph Modeling in Healthcare: A Bibliometric Analysis’, J. Komput. Terap., vol. 8, no. 1, pp. 113–122, 2022.
V. V. Muthuswamy and K. Manoharan, ‘SDG: Consumer’s Perception Towards Herbal/Organic Products with Respect to Sustainable Development Goals’, J. Lifestyle SDGs Rev., vol. 4, no. 4, pp. e01994–e01994, 2024.
S. A. Laga, E. T. Sihotang, D. Hermansyah, and T. A. Hariyanti, ‘Decision Support System for Selection of An Excellent-Students at Faculty Level in University of X Based on GAP Methods’, J. Adv. Inf. Ind. Technol., vol. 6, no. 1, pp. 1–10, 2024.
O. H. Y. Lam, J. Kattge, S. Tautenhahn, G. Boenisch, K. R. Kovach, and P. A. Townsend, ‘“rtry”: An R package to support plant trait data preprocessing’, Ecol. Evol., vol. 14, no. 5, p. e11292, May 2024, doi: 10.1002/ece3.11292.
N. M. A. Krishnan, H. Kodamana, and R. Bhattoo, ‘Data Visualization and Preprocessing’, in Machine Learning for Materials Discovery, in Machine Intelligence for Materials Science. , Cham: Springer International Publishing, 2024, pp. 25–46. doi: 10.1007/978-3-031-44622-1_2.
S. Arefin et al., ‘Retail Industry Analytics: Unraveling Consumer Behavior through RFM Segmentation and Machine Learning’, in 2024 IEEE International Conference on Electro Information Technology (eIT), IEEE, 2024, pp. 545–551. Accessed: Oct. 30, 2024. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/10609927/
S. Kim, W. Shin, and H.-W. Kim, ‘Predicting online customer purchase: The integration of customer characteristics and browsing patterns’, Decis. Support Syst., vol. 177, p. 114105, 2024.
X. Han, C. Liu, Y. Zhou, K. Tan, Z. Dong, and B. Yang, ‘WHU-Urban3D: An urban scene LiDAR point cloud dataset for semantic instance segmentation’, ISPRS J. Photogramm. Remote Sens., vol. 209, pp. 500–513, 2024.
M. Țichindelean, C. Ogrean, and M. Herciu, ‘DO LOYAL CUSTOMERS BUY DIFFERENTLY? EXAMINING CUSTOMERS’LOYALTY IN A SELF-SERVICE SETTING’, Stud. Bus. Econ. No, vol. 19, p. 1, 2024.
A. K. Pandey, A. Goyal, and N. Sikka, ‘RE-RFME: Real-Estate RFME Model for customer segmentation’, Apr. 26, 2024, arXiv: arXiv:2404.17177. Accessed: May 15, 2024. [Online]. Available: http://arxiv.org/abs/2404.17177
S. Monalisa, Y. Juniarti, E. Saputra, F. Muttakin, and T. K. Ahsyar, ‘Customer segmentation with RFM models and demographic variable using DBSCAN algorithm’, ℡KOMNIKA Telecommun. Comput. Electron. Control, vol. 21, no. 4, pp. 742–749, 2023.
S. Paembonan and H. Abduh, ‘Penerapan Metode Silhouette Coefficient untuk Evaluasi Clustering Obat’, PENA Tek. J. Ilm. Ilmu-Ilmu Tek., vol. 6, p. 48, Sep. 2021, doi: 10.51557/pt_jiit.v6i2.659.
D. Tirthyani, S. Kumar, and S. Vats, ‘Clustering and Unsupervised Learning’, in Recent Trends and Future Direction for Data Analytics, IGI Global, 2024, pp. 119–139. Accessed: Oct. 30, 2024. [Online]. Available: https://www.igi-global.com/chapter/clustering-and-unsupervised-learning/347268
Qomariyah and M. U. Siregar, ‘Comparative Study of K-Means Clustering Algorithm and K-Medoids Clustering in Student Data Clustering’, JISKA J. Inform. Sunan Kalijaga, vol. 7, no. 2, pp. 91–99, May 2022, doi: 10.14421/jiska.2022.7.2.91-99.
N. Nugroho and F. D. Adhinata, ‘Penggunaan Metode K-Means dan K-Means++ Sebagai Clustering Data Covid-19 di Pulau Jawa’, Teknika, vol. 11, no. 3, pp. 170–179, Oct. 2022, doi: 10.34148/teknika.v11i3.502.
M. A. Haq, W. Purnomo, and N. Y. Setiawan, ‘Analisis Clustering Topik Survey menggunakan Algoritme K-Means (Studi Kasus: Kudata)’.
C. Rungruang, P. Riyapan, A. Intarasit, K. Chuarkham, and J. Muangprathub, ‘RFM model customer segmentation based on hierarchical approach using FCA’, Expert Syst. Appl., vol. 237, p. 121449, 2024.
R. Raman, D. Pattnaik, L. Hughes, and P. Nedungadi, ‘Unveiling the dynamics of AI applications: A review of reviews using scientometrics and BERTopic modeling’, J. Innov. Knowl., vol. 9, no. 3, p. 100517, 2024.
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.