Customer Clustering Using Fuzzy K-Means and Cluster Evaluation via MADM to Improve Sales and Marketing Performance

Document Type : Original Article

Authors

1 Corresponding Author. Msc. Faculty of Engineering, College of Farabi, University of Tehran, Tehran, Iran. hmohammadi@ut.ac.ir

2 MSc. Faculty of Engineering, College of Farabi, University of Tehran, Tehran, Iran. ali.rezaeii137878@gmail.com

3 Associate Professor, Department of Management and Accounting, College of Farabi, University of Tehran, Tehran, Iran. reza.fathi@ut.ac.ir

10.22091/jemsc.2026.15223.1343

Abstract

This applied, exploratory study uses data-driven CRM to improve sales and marketing at Sina Fidar Kimia Company (49 customers). Customers are segmented with K-Means and fuzzy C-Means, then the clusters are evaluated using Multi-Attribute Decision Making (MADM). The approach integrates three steps: clustering, fuzzification, and criterion weighting via the Best–Worst Method (BWM). The novelty lies in combining cluster-wise BWM weighting with higher-order (type-3) fuzzification to reflect uncertainty in expert-based CRM indicators and to derive segment-specific managerial actions. Eleven indicators are analyzed, including purchase method, on-time payment, legal status (individual/legal entity), credit backing, reputation, brand, expert judgments (commercial manager and specialists), purchase share, and economic factors. Fuzzification is performed with a type-3 fuzzy logic model in logistic form using composite sine–cosine membership functions (Sheffer type 4) to capture nonlinear relationships more flexibly than triangular/trapezoidal functions. Based on inputs from 14 experts, BWM weights are calculated within clusters. Results identify five distinct customer clusters with different priority indicators, enabling targeted sales and marketing actions and supporting personalized service recommendations to enhance profitability, satisfaction, and loyalty.

Keywords

Main Subjects

References

Adji, H. (2025). Clustering marketing strategy based on applications for power upgrading customers. Jurnal Ilmiah Manajemen Kesatuan, 13(1), 57-64. https://doi.org/10.37641/jimkes.v13i1.3029
Aghaee, A., Aghaee, M., Fathi, M. R., Shoa'bin, S., & Sobhani, S. M. (2021). A novel fuzzy hybrid multi-criteria decision-making approach for evaluating maintenance strategies in petrochemical industry. Journal of Quality in Maintenance Engineering, 27(2), 351–365. https://doi.org/10.1108/JQME-04-2019-0036
Al-Dabbas, L., Al-Tarawneh, H., & Al-Rawashdeh, T. (2023). Customer personality segmentation using K-means clustering. In 2023 International Conference on Information Technology (ICIT) (pp. 537–543). IEEE. https://doi.org/10.1109/icit58056.2023.10225996
Chen, T., Subrahmanyam, S., Singh, K., Aravindhan, S., Sivaraman, R., & Iswanto, H. (2023). Prioritizing factors affecting regional competitiveness in industrial clusters. International Review, (1-2), 99-112. https://doi.org/10.5937/intrev2302105c
Ebrahimi, E., Fathi, M. R., & Irani, H. R. (2016). A new hybrid method based on fuzzy Shannon's entropy and fuzzy COPRAS for CRM performance evaluation (Case: Mellat Bank). Interdisciplinary Journal of Management Studies, 9(2), 333–358. https://doi.org/10.22059/ijms.2016.56414
Fang, C., & Liu, H. (2021). Research and application of improved clustering algorithm in retail customer classification. Symmetry, 13, Article 1789. https://doi.org/10.3390/sym13101789
Hüseynov, N. (2023). Application of cluster analysis of bank customers. Intellect Innovations. Investments, (3), 72-82. https://doi.org/10.25198/2077-7175-2023-3-72
Kasem, M., Hamada, M., & Taj-Eddin, I. (2023). Customer profiling, segmentation, and sales prediction using AI in direct marketing. Neural Computing and Applications, 35, 1–11. https://doi.org/10.1007/s00521-023-09339-6
Kerimli, F., & Isa-Zade, S. (2021). Cluster development as a significant perspective of entrepreneurial promotion in petrochemical industry. Azerbaijan Oil Industry, (2), 44-53. https://doi.org/10.37474/0365-8554/2021-2-44-53
Ling, L., & Weiling, C. (2025). Enhancing segmentation: A comparative study of clustering methods. IEEE Access, 13, 47418–47439. https://doi.org/10.1109/access.2025.3550339
Mim, S., & Logofătu, D. (2022). A cluster-based analysis for targeting potential customers in a real-world marketing system. In 2022 IEEE 18th International Conference on Intelligent Computer Communication and Processing (ICCP) (pp. 159–166). IEEE. https://doi.org/10.1109/iccp56966.2022.10053985
Nsarallahi, M., Fathi, M. R., & Faghih, A. (2018). Designing a model for evaluating marketing channels based on the fuzzy best-worst and fuzzy EDAS methods. Journal of Business Management, 10(3), 695–712. https://doi.org/10.22059/jibm.2017.238125.2684
Panda, A., Rout, P., & Gautam, A. (2024). Optimizing marketing strategy by performing customer segmentation. In 2024 International Conference on Advances in Computing Research on Science Engineering and Technology (ACROSET) (pp. 1–6). IEEE. https://doi.org/10.1109/acroset62108.2024.10743643
Perapu, P. (2025). Customer segmentation using K-means clustering for personalized marketing campaigns. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 11(3), 810-815. https://doi.org/10.32628/cseit25113344
Pradana, M., & Ha, H. (2021). Maximizing strategy improvement in mall customer segmentation using K-means clustering. Journal of Applied Data Sciences, 2, 19–25. https://doi.org/10.47738/jads.v2i1.18
Saxena, A., Agarwal, A., Pandey, B., & Pandey, D. (2024). Examination of the criticality of customer segmentation using unsupervised learning methods. Circular Economy and Sustainability, 4, 1447–1460. https://doi.org/10.1007/s43615-023-00336-4
Sharma, S., & Desai, N. (2023). Data-driven customer segmentation using clustering methods for business success. In 2023 4th IEEE Global Conference for Advancement in Technology (GCAT) (pp. 1–7). IEEE. https://doi.org/10.1109/gcat59970.2023.10353367
Sri, P., Paulson, J., & Srinivasareddy, M. (2023). Customer segmentation analysis for improving sales using clustering. International Journal of Science and Research Archive, 9(2), 708-715. https://doi.org/10.30574/ijsra.2023.9.2.0663
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
Wasilewski, A., Juszczyszyn, K., & Suryani, V. (2024). Multi-factor evaluation of clustering methods for e-commerce application. Egyptian Informatics Journal, 28, 100562. https://doi.org/10.1016/j.eij.2024.100562
Jafari, M., Akhavan, P., & Akbari, A. H. (2026). Enhancing supply chain agility and performance through big data analytics: the role of digitalization and top management support. International Journal of Productivity and Performance Management, 1-22. https://doi.org/10.1108/IJPPM-06-2025-0557 
Tavakkoli-Moghaddam, R., Akbari, A. H., Tanhaeean, M., Moghdani, R., Gholian-Jouybari, F., & Hajiaghaei-Keshteli, M. (2024). Multi-objective boxing match algorithm for multi-objective optimization problems. Expert Systems with Applications, 239, 122394. https://doi.org/10.1016/j.eswa.2023.122394 
Send comment about this article
CAPTCHA Image

Files

Share

How to cite

Statistics