Application of Formal Concept Analysis and Clustering Algorithms  to Analyze Customer Segments

I Gede Bintang Arya Budaya; I Komang  Dharmendra; Evi Triandini

doi:10.29207/resti.v9i2.6184

I Gede Bintang Arya Budaya Institute of Technology and Business STIKOM Bali
I Komang Dharmendra Institute of Technology and Business STIKOM Bali
Evi Triandini Institute of Technology and Business STIKOM Bali

DOI: https://doi.org/10.29207/resti.v9i2.6184

Keywords: RFM Analysis, K-Means Clustering, Gaussian Mixture Model, Data-Driven Marketing, Customer Hierarchical Relationships

Abstract

Business development cannot be separated from relationships with customers. Understanding customer characteristics is important both for maintaining sales and even for targeting new customers with appropriate strategies. The complexity of customer data makes manual analysis of the customer segments difficult, so applying machine learning to segment the customer can be the solution. This research implements K-Means and GMM algorithms for performing clustering based on the Transaction data transformed to the Recency, Frequency, and Monetary (RFM) data model, then implements Formal Concept Analysis (FCA) as an approach to analyzing the customer segment after the class labeling. Both K-Means and GMM algorithms recommended the optimal number of clusters as the customer segment is four. The FCA implementation in this study further analyzes customer segment characteristics by constructing a concept lattice that categorizes segments using combinations of High and Low values across the RFM attributes based on the median values, which are High Recency (HR), Low Recency (LR), High Frequency (HF), Low Frequency (LF), High Monetary (HM), and Low Monetary (LM). This characteristic can determine the customer category; for example, a customer that has HM and HR can be considered a loyal customer and can be the target for a specific marketing program. Overall, this study demonstrates that using the RFM data model, combined with clustering algorithms and FCA, is a potential approach for understanding MSME customer segment behavior. However, special consideration is necessary when determining the FCA concept lattice, as it forms the foundation of the core analytical insights.

Downloads

Download data is not yet available.

References

S. Gupta, T. Justy, S. Kamboj, A. Kumar, and E. Kristoffersen, “Big data and firm marketing performance: Findings from knowledge-based view,” Technol Forecast Soc Change, vol. 171, p. 120986, 2021.

S. Munusamy and P. Murugesan, “Modified dynamic fuzzy c-means clustering algorithm–Application in dynamic customer segmentation,” Applied Intelligence, vol. 50, no. 6, pp. 1922–1942, 2020.

H. Chen, L. Zhang, X. Chu, and B. Yan, “Smartphone customer segmentation based on the usage pattern,” Advanced Engineering Informatics, vol. 42, p. 101000, 2019.

Y. Deng and Q. Gao, “A study on e-commerce customer segmentation management based on improved K-means algorithm (Retraction of Vol 18, Pg 497, 2020),” 2022, SPRINGER HEIDELBERG TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY.

F. Leisch, S. Dolnicar, and B. Grün, Market segmentation analysis: Understanding it, doing it, and making it useful. 2018.

M. T. Ballestar, P. Grau-Carles, and J. Sainz, “Customer segmentation in e-commerce: Applications to the cashback business model,” J Bus Res, vol. 88, pp. 407–414, 2018.

E. Y. L. Nandapala and K. P. N. Jayasena, “The practical approach in Customers segmentation by using the K-Means Algorithm,” in 2020 IEEE 15th international conference on industrial and information systems (ICIIS), IEEE, 2020, pp. 344–349.

J. Wu et al., “An empirical study on customer segmentation by purchase behaviors using a RFM model and K-means algorithm,” Math Probl Eng, vol. 2020, 2020.

R. M. Saputra and H. Darmawan, “EFFECTS OF URBANIZATION AND THE GROWTH OF MICRO, SMALL, AND MEDIUM ENTERPRISES (MSMES) AS SUPPORTS FOR THE ECONOMY OF INDONESIA IN AN URBAN CONTEXT,” Journal of Social Political Sciences, vol. 4, no. 2, pp. 201–214, 2023.

F. Rinaldi, S. Maarif, S. Thamrin, and A. A. Supriyadi, “Role of Micro, Small, and Medium Enterprises (MSMEs) in Supporting National Defense from Economic Perspective,” Journal of Positive School Psychology, pp. 8914–8920, 2022.

T. BELHADJ, “Customer Value Analysis Using Weighted RFM model: Empirical Case Study.,” Al Bashaer Economic Journal, vol. 7, no. 3, 2021.

M. Kovacs, D. Buryakov, and V. Kryssanov, “An Unsupervised Approach for Customer Need Assessment in E-commerce: A Case Study of Japanese Customer Reviews,” in Proceedings of the 2021 6th International Conference on Cloud Computing and Internet of Things, 2021, pp. 41–48.

D. Gerritsen and R. van Olderen, “Events and customer relationship marketing.,” 2020.

M. Alves Gomes and T. Meisen, “A review on customer segmentation methods for personalized customer targeting in e-commerce use cases,” Information Systems and e-Business Management, vol. 21, no. 3, pp. 527–570, 2023.

I. G. Harsemadi, D. P. Agustino, and I. G. B. A. Budaya, “Klasterisasi Pelanggan Tenant Inkubator Bisnis STIKOM Bali Untuk Strategi Manajemen Relasi Dengan Menggunakan Fuzzy C-Means,” JTIM: Jurnal Teknologi Informasi dan Multimedia, vol. 4, no. 4, pp. 232–243, 2023.

D. P. Agustino, I. G. Harsemadi, and I. G. B. A. Budaya, “Edutech Digital Start-Up Customer Profiling Based on RFM Data Model Using K-Means Clustering,” Journal of Information Systems and Informatics, vol. 4, no. 3, pp. 724–736, 2022.

D. P. Agustino, I. G. B. A. Budaya, I. G. Harsemadi, I. K. Dharmendra, and I. M. S. A. Pande, “Comparison of the DBSCAN Algorithm and Affinity Propagation on Business Incubator Tenant Customer Segmentation,” Jurnal Sisfokom (Sistem Informasi dan Komputer), vol. 12, no. 2, pp. 315–321, 2023.

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.

S. F. Djun, I. G. A. Gunadi, and S. Sariyasa, “Analisis Segmentasi Pelanggan pada Bisnis dengan Menggunakan Metode K-Means Clustering pada Model Data RFM,” JTIM: Jurnal Teknologi Informasi dan Multimedia, vol. 5, no. 4, pp. 354–364, 2024.

E. Patel and D. S. Kushwaha, “Clustering cloud workloads: K-means vs gaussian mixture model,” Procedia Comput Sci, vol. 171, pp. 158–167, 2020.

Y. Fu, X. Liu, S. Sarkar, and T. Wu, “Gaussian mixture model with feature selection: An embedded approach,” Comput Ind Eng, vol. 152, p. 107000, 2021.

A. Braud, A. Buzmakov, T. Hanika, and F. Le Ber, Formal Concept Analysis. Springer, 2021.