Customer Segmentation Through RFM Analysis and K-Means Clustering: Leveraging Data-Driven Insights for Effective Marketing Strategy

Oluwatobi Akande; Emmanuel Oluwatobi Asani; Bestman Dautare

doi:10.56134/jst.v3i1.81

Authors

Oluwatobi Akande Baze University
Emmanuel Oluwatobi Asani Landmark University
Bestman Dautare Landmark University

Keywords:

Customer Relationship Management, Machine Learning, Data Analysis
doi

https://doi.org/10.56134/jst.v3i1.81

Abstract

Consumer segmentation is a very effective methodology that could enable organizations to gain a deeper comprehension of their consumer base and customize their tactics accordingly in order to cater to their unique requirements. Through the process of categorizing clients according to common attributes, organizations can acquire valuable knowledge regarding their requirements, inclinations, and purchasing behaviors. This comprehension empowers firms to develop focused marketing strategies and provide customized experiences that foster customer loyalty and enhance revenue generation. The prevalent categories of criteria in consumer segmentation encompass demographic, psychographic, geographic, and behavioral factors, whilst the prevailing methodologies for constructing customer segments involve rule-based and cluster-based techniques. Rule-based segmentation involves the utilization of pre-established rules to allocate clients into distinct segments. Conversely, cluster-based segmentation employs statistical techniques to identify inherent clusters or groups within the customer population. This research investigates the application of the K-Means clustering technique for the purpose of segmenting customer behavioral data into several categories, namely Platinum, Gold, Silver, Bronze, or Bad. The clustering approach employed demonstrated a notable degree of accuracy and precision. Through the implementation of an appropriate strategy for customer segmentation, organizations have the potential to strengthen their product offers, concentrate their marketing communications, and augment client loyalty.

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References

Agranovskii, A. V, & Silukov, A. P. (2021). Comparative Analysis of Results of Modern Classification Algorithms Usage for Determining the Type of Physical Activity Based on Integrated Sensors Data. Wave Electronics and Its Application in Information and Telecommunication Systems (WECONF), 1–5. https://doi.org/https://doi.org/10.1109/WECONF51603.2021.9470661

Ali, R., Farhat, T., Abdullah, S., & Akram, S. (2023). Customer Segmentation for Targeted Marketing: A Comparative Analysis of Clustering Techniques. 2023 25th International Multitopic Conference (INMIC), 1–6. https://doi.org/https://doi.org/10.1109/INMIC60434.2023.10466067

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/https://doi.org/10.1016/j.jksuci.2019.12.011

Arya, R. K., Rajput, A., Singh, S., Mohan, K. M., Choudhry, S., Babu, Y. M., Kumar, P., & Dong, J. (2023). Open-Source Visualization Tools for Antenna Engineers. IEEE Microwaves, Antennas, and Propagation Conference (MAPCON), 1–6. https://doi.org/https://doi.org/10.1109/MAPCON58678.2023.10464133

Awalina, E. F. L., & Rahayu, W. I. (2023). Optimalisasi Strategi Pemasaran dengan Segmentasi Pelanggan Menggunakan Penerapan K-Means Clustering pada Transaksi Online Retail. Jurnal Teknologi Dan Informasi, 13(2), 122–137. https://doi.org/https://doi.org/10.34010/jati.v13i2.10090

Belorkar, A., Guntuku, S. C., Hora, S., & Kumar, A. (2020). Interactive Data Visualization with Python: Present your data as an effective and compelling story. Packt Publishing.

Buzzacchi, L., Milone, F. L., Paolucci, E., & Raguseo, E. (2023). How to react to a shock? Effects of Airbnb hosts’ choices and market segmentation at the time of Covid-19. Information & Management, 60(7), 103857. https://doi.org/https://doi.org/10.1016/j.im.2023.103857

Chen, A. H.-L., & Gunawan, S. (2023). Enhancing Retail Transactions: A Data-Driven Recommendation Using Modified RFM Analysis and Association Rules Mining. Applied Sciences, 13(18), 10057. https://doi.org/https://doi.org/10.3390/app131810057

Chen, H., Zhang, L., Chu, X., & Yan, B. (2019). Smartphone customer segmentation based on the usage pattern. Advanced Engineering Informatics, 42, 101000. https://doi.org/https://doi.org/10.1016/j.aei.2019.101000

Chronopoulos, D. K., Lukas, M., & Wilson, J. O. S. (2020). Consumer spending responses to the COVID-19 pandemic: An assessment of Great Britain. Available at SSRN 3586723. https://doi.org/https://doi.org/10.2139/ssrn.3586723

Dzulhaq, M. I., Sari, K. W., Ramdhan, S., Tullah, R., & others. (2019). Customer segmentation based on RFM value using K-means algorithm. 2019 Fourth International Conference on Informatics and Computing (ICIC), 1–7. https://doi.org/doi: 10.1109/ICIC47613.2019.8985726

Fadilah, A. T. (2019). Implementasi Algoritma K-Means Clustering Untuk Targeting Ads Studi Kasus: Data Pelanggan Asuransi Mobil Ibm Watson Analyti. Fakultas Sains Dan Teknologi UIN Syarif Hidayatullah Jakarta.

Grunig, J. E., & Repper, F. C. (2013). Strategic management, publics, and issues. Excellence in Public Relations and Communication Management, 117–157. https://doi.org/https://doi.org/10.4324/9780203812303

Harahap, M., Lubis, Y., & Situmorang, Z. (2021). Analisis Pemasaran Bisnis dengan Data Science: Segmentasi Kepribadian Pelanggan berdasarkan Algoritma K-Means Clustering. Data Sciences Indonesia (DSI), 1(2), 76–88. https://doi.org/https://doi.org/10.47709/dsi.v1i2.1348

Hartoyo, H., Manalu, E., Sumarwan, U., & Nurhayati, P. (2023). Driving success: A segmentation of customer admiration in automotive industry. Journal of Open Innovation: Technology, Market, and Complexity, 9(2), 100031. https://doi.org/https://doi.org/10.1016/j.joitmc.2023.100031

Hu, X., Liu, A., Li, X., Dai, Y., & Nakao, M. (2023). Explainable AI for customer segmentation in product development. CIRP Annals, 72(1), 89–92. https://doi.org/https://doi.org/10.1016/j.cirp.2023.03.004

Hu, X., Shi, Z., Yang, Y., & Chen, L. (2020). Classification method of internet catering customer based on improved RFM model and cluster analysis. 2020 IEEE 5th International Conference on Cloud Computing and Big Data Analytics (ICCCBDA), 28–31. https://doi.org/https://doi.org/10.1109/ICCCBDA49378.2020.9095607

Jaiswal, D., Mohan, A., & Deshmukh, A. K. (2023). Cash rich to cashless market: Segmentation and profiling of Fintech-led-Mobile payment users. Technological Forecasting and Social Change, 193, 122627. https://doi.org/https://doi.org/10.1016/j.techfore.2023.122627

Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255–260. https://doi.org/https://doi.org/10.1126/science.aaa8415

Joung, J., & Kim, H. (2023). Interpretable machine learning-based approach for customer segmentation for new product development from online product reviews. International Journal of Information Management, 70, 102641. https://doi.org/https://doi.org/10.1016/j.ijinfomgt.2023.102641

Kanoujia, S., & Karuppanan, P. (2024). Depression Detection in Speech Using ML and DL Algorithm. IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), 2, 1–5. https://doi.org/https://doi.org/10.1109/IATMSI60426.2024.10503510

Kuo, R. J., Alfareza, M. N., & Nguyen, T. P. Q. (2023). Genetic based density peak possibilistic fuzzy c-means algorithms to cluster analysis-a case study on customer segmentation. Engineering Science and Technology, an International Journal, 47, 101525. https://doi.org/https://doi.org/10.1016/j.jestch.2023.101525

Kursan Milaković, I. (2021). Purchase experience during the COVID-19 pandemic and social cognitive theory: The relevance of consumer vulnerability, resilience, and adaptability for purchase satisfaction and repurchase. International Journal of Consumer Studies, 45(6), 1425–1442. https://doi.org/https://doi.org/10.1111/ijcs.12672

Lewaaelhamd, I. (2024). Customer segmentation using machine learning model: an application of RFM analysis. Journal of Data Science and Intelligent Systems, 2(1). https://doi.org/https://doi.org/10.47852/bonviewJDSIS32021293

Lezhnina, O., & Kismihók, G. (2022). Latent class cluster analysis: Selecting the number of clusters. MethodsX, 9, 101747. https://doi.org/https://doi.org/10.1016/j.mex.2022.101747

Ma, J., Nault, B. R., & Tu, Y. P. (2023). Customer segmentation, pricing, and lead time decisions: A stochastic-user-equilibrium perspective. International Journal of Production Economics, 264, 108985. https://doi.org/https://doi.org/10.1016/j.ijpe.2023.108985

McKinney, W. (2012). Python for data analysis: Data wrangling with Pandas, NumPy, and IPython. O’Reilly Media, Inc.

Mehrabioun, M., & Mahdizadeh, B. M. (2021). Customer retention management: A complementary use of data mining and soft systems methodology. Human Systems Management, 40(6), 897–916. https://doi.org/https://doi.org/10.3233/HSM-201075

Mouton, J. P., Ferreira, M., & Helberg, A. S. J. (2020). A comparison of clustering algorithms for automatic modulation classification. Expert Systems with Applications, 151, 113317. https://doi.org/https://doi.org/10.1016/j.eswa.2020.113317

MR, A. P. A. S., Sodik, J., Saripudin, M., Rejeki, S. M., & Firdaus, U. (2024). Kajian untuk Segmentasi Customer Bank dengan Algoritma K-Means. Karimah Tauhid, 3(3), 3899–3906.

Muchardie, B. G., Gunawan, A., & Aditya, B. (2019). E-commerce market segmentation based on the antecedents of customer satisfaction and customer retention. 2019 International Conference on Information Management and Technology (ICIMTech), 1, 103–108. https://doi.org/doi: 10.1109/ICIMTech.2019.8843792

Myburg, M. E. (2023). Using recency, frequency and monetary variables to predict customer lifetime value with XGBoost.

Nabella, R. O., & others. (2022). Analisis Segmentasi Konsumen Menggunakan Metode K-Means Clustering Berdasarkan Model RFM Dan Pengetahuan Data Lainnya Sebagai Rekomendasi Strategi Pemasaran (Studi Kasus: Yayasan Xyz). Fakultas Teknik.

Nandapala, E. Y. L., & Jayasena, K. P. N. (2020). The practical approach in Customers segmentation by using the K-Means Algorithm. IEEE 15th International Conference on Industrial and Information Systems (ICIIS), 344–349. https://doi.org/https://doi.org/10.1109/ICIIS51140.2020.9342639

Onumanyi, A. J., Molokomme, D. N., Isaac, S. J., & Abu-Mahfouz, A. M. (2022). AutoElbow: An automatic elbow detection method for estimating the number of clusters in a dataset. Applied Sciences, 12(15), 7515. https://doi.org/https://doi.org/10.3390/app12157515

Putra, R. F., Zebua, R. S. Y., Budiman, B., Rahayu, P. W., Bangsa, M. T. A., Zulfadhilah, M., Choirina, P., Wahyudi, F., & Andiyan, A. (2023). Data Mining: Algoritma dan Penerapannya. PT. Sonpedia Publishing Indonesia.

Ramadhan, H. M. T., Pertiwi, A., Defanka, G. A., & Sari, A. P. (2023). Clustering RFM (Recency, Frequency, Monetary) Publisher Gim Menggunakan Algoritma K-Means. Prosiding Seminar Nasional Informatika Bela Negara, 3, 8–18.

Ramkumar, P., Kalamani, P., Valarmathi, C., & Devi, M. S. (2021). An effective analysis of data clustering using distance-based K-means Algorithm. Journal of Physics: Conference Series, 1979(1), 12015. https://doi.org/https://doi.org/10.1088/1742-6596/1979/1/012015

Rojlertjanya, P. (2019). Customer segmentation based on the RFM analysis model using k-means clustering technique: a case of IT solution and service provider in Thailand. Bangkok University.

Rungruang, C., Riyapan, P., Intarasit, A., Chuarkham, K., & Muangprathub, J. (2024). RFM model customer segmentation based on hierarchical approach using FCA. Expert Systems with Applications, 237, 121449. https://doi.org/https://doi.org/10.1016/j.eswa.2023.121449

Russell, A. D., Chiu, C.-Y., & Korde, T. (2009). Visual representation of construction management data. Automation in Construction, 18(8), 1045–1062. https://doi.org/https://doi.org/10.1016/j.autcon.2009.05.006

Sarikaya, A., Gleicher, M., & Szafir, D. A. (2018). Design factors for summary visualization in visual analytics. Computer Graphics Forum, 37(3), 145–156. https://doi.org/https://doi.org/10.1111/cgf.13408

Silalahi, H. D., Hasibuan, D., & Sinambela, M. (2023). Segmentasi Pelanggan Online Dengan Metode K-Means Clustering. METHOTIKA: Jurnal Ilmiah Teknik Informatika, 3(1), 13–21.

Steiss, A. W. (2019). Strategic management for public and nonprofit organizations. Routledge. https://doi.org/https://doi.org/10.4324/9781482275865

Susilo, D. D., Hilabi, S. S., Priyatna, B., & Novalia, E. (2024). Implementasi Data Mining dalam Pengelompokan Data Pembelian Menggunakan Algoritma K-Means Pada PT. Otomotif 1. Jutisi: Jurnal Ilmiah Teknik Informatika Dan Sistem Informasi, 13(1).

Talaat, F. M., Aljadani, A., Alharthi, B., Farsi, M. A., Badawy, M., & Elhosseini, M. (2023). A Mathematical Model for Customer Segmentation Leveraging Deep Learning, Explainable AI, and RFM Analysis in Targeted Marketing. Mathematics, 11(18), 3930. https://doi.org/https://doi.org/10.3390/math11183930

Wang, C. (2022). Efficient customer segmentation in digital marketing using deep learning with swarm intelligence approach. Information Processing & Management, 59(6), 103085. https://doi.org/https://doi.org/10.1016/j.ipm.2022.103085

Wicaksono, Y. (2019). Segmentasi Pelanggan Bisnis Dengan Multi Kriteria Menggunakan K-Means. Indonesian Journal of Business Intelligence (IJUBI), 1(2), 45–53. https://doi.org/https://doi.org/10.21927/ijubi.v1i2.872

Yuan, Y., Dehghanpour, K., Bu, F., & Wang, Z. (2020). A data-driven customer segmentation strategy based on contribution to system peak demand. IEEE Transactions on Power Systems, 35(5), 4026–4035. https://doi.org/https://doi.org/10.1109/TPWRS.2020.2979943