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Dergiler / Black Sea Journal of Engineering and Science / 2026 / Cilt: 9 Sayı: 5

Analyzing Customer Lifetime Value in Digital Banking: A PPF-AHP Based Criterion Prioritization Approach

Ünzile Meram Huyut(BAHCESEHIR UNIVERSITY)0009-0007-8737-4521
Gül Tekin Temur(BAHCESEHIR UNIVERSITY)0000-0003-3853-0974
Sayfa
2546–2553
Yayın Tarihi
2026-09-15
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

The research findings aim to provide bank managers with a scientifically grounded roadmap for budget planning, product development, and risk-based pricing strategies.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:In the modern banking ecosystem, shaped by digital transformation and intense competition, focusing limited resources on high-value customers is a fundamental requirement for corporate sustainability.
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Temel Bulgular & Veri:In this context, the concept of "Customer Lifetime Value" (CLV) serves as a strategic compass.
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Sonuç & Katkı:The research findings aim to provide bank managers with a scientifically grounded roadmap for budget planning, product development, and risk-based pricing strategies.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:PPF-AHP · Decision support systems · Banking

Özet

In the modern banking ecosystem, shaped by digital transformation and intense competition, focusing limited resources on high-value customers is a fundamental requirement for corporate sustainability. In this context, the concept of "Customer Lifetime Value" (CLV) serves as a strategic compass. However, the criteria determining a customer's value to a bank consist not only of financial ratios but also of difficult-to-measure, subjective elements such as risk perception, digital adaptation, and loyalty. The primary objective of this study is to prioritize six main criteria; Financial Capacity, Risk Profile, Spending Behaviours, Digital Integration, Loyalty, and Demographic Factors and their associated sub-criteria that shape credit card customer value in the banking sector using the Proportional Picture Fuzzy Analytic Hierarchy Process (PPF-AHP) method. Based on the insights of three senior industry experts, this model mathematically demonstrates which criteria are most decisive in customer valuation and which are of secondary importance. The research findings aim to provide bank managers with a scientifically grounded roadmap for budget planning, product development, and risk-based pricing strategies.

Abstract

In the modern banking ecosystem, shaped by digital transformation and intense competition, focusing limited resources on high-value customers is a fundamental requirement for corporate sustainability. In this context, the concept of "Customer Lifetime Value" (CLV) serves as a strategic compass. However, the criteria determining a customer's value to a bank consist not only of financial ratios but also of difficult-to-measure, subjective elements such as risk perception, digital adaptation, and loyalty. The primary objective of this study is to prioritize six main criteria; Financial Capacity, Risk Profile, Spending Behaviours, Digital Integration, Loyalty, and Demographic Factors and their associated sub-criteria that shape credit card customer value in the banking sector using the Proportional Picture Fuzzy Analytic Hierarchy Process (PPF-AHP) method. Based on the insights of three senior industry experts, this model mathematically demonstrates which criteria are most decisive in customer valuation and which are of secondary importance. The research findings aim to provide bank managers with a scientifically grounded roadmap for budget planning, product development, and risk-based pricing strategies.

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Bu Makaleye Atıf Yap

Huyut, Ü. M., Temur, G. T. (2026). Analyzing Customer Lifetime Value in Digital Banking: A PPF-AHP Based Criterion Prioritization Approach. Black Sea Journal of Engineering and Science, Cilt: 9 Sayı: 5, 2546–2553. https://doi.org/10.34248/bsengineering.1956717

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Kaynakça (17)

  1. Aggarwal, A. G., & Yadav, S. (2020). Customer segmentation using Fuzzy-AHP and RFM model. 2020 8th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), 77–80. https://doi.org/10.1109/ICRITO48877.2020.9197903
  2. Atanassov, K. T. (1986). Intuitionistic fuzzy sets. Fuzzy Sets and Systems, 20(1), 87–96. https://doi.org/10.1016/S0165-0114(86)80034-3
  3. Chang, K.-H., Chung, H.-Y., Wang, C.-N., Lai, Y.-D., & Wu, C.-H. (2023). A new hybrid Fermatean fuzzy set and entropy method for risk assessment. Axioms, 12(1), Article 58. https://doi.org/10.3390/axioms12010058
  4. Cuong, B. C. (2014). Picture fuzzy sets. Journal of Computer Science and Cybernetics, 30(4), 409–420. https://doi.org/10.15625/1813-9663/30/4/5032
  5. Kahraman, C. (2024). Proportional picture fuzzy sets and their AHP extension: Application to waste disposal site selection. Expert Systems with Applications, 238, Article 122354. https://doi.org/10.1016/j.eswa.2023.122354
  6. Kutlu Gündoğdu, F., & Kahraman, C. (2019). Spherical fuzzy sets and spherical fuzzy TOPSIS method. Journal of Intelligent & Fuzzy Systems, 36(1), 337–352. https://doi.org/10.3233/JIFS-181401
  7. Liu, Y. (2024). The influencing factors of customer satisfaction with credit card center of China CITIC Bank. The 1st Thailand-Sino International Conference and The 17th National and International Academic Conference “New Quality Productive Forces and Sustainable Innovation”, 731–743. https://e-research.siam.edu/kb/the-influencing-factors-of-customer-satisfacation-with-credit-card-center/
  8. Razaq, A., Komal, L., Alhamzi, G., & Baidar, A. W. (2026). A novel complex Fermatean fuzzy formalism with improved score function and aggregation operators. Scientific Reports, 16, Article 13559. https://doi.org/10.1038/s41598-026-40838-3
  9. Saaty, T. L. (1977). A scaling method for priorities in hierarchical structures. Journal of Mathematical Psychology, 15(3), 234–281. https://doi.org/10.1016/0022-2496(77)90033-5
  10. Senapati, T., & Yager, R. R. (2020). Fermatean fuzzy sets. Journal of Ambient Intelligence and Humanized Computing, 11(2), 663–674. https://doi.org/10.1007/s12652-019-01377-0
  11. Singh, S., Murthi, B. P. S., Rao, R. C., & Steffes, E. M. (2024). Risk-adjusted lifetime value: Adjusting for customer riskiness using a single metric. International Journal of Bank Marketing, 42(7), 1695–1717. https://doi.org/10.1108/IJBM-12-2023-0645
  12. Yager, R. R. (2013, June 24–28). Pythagorean fuzzy subsets. In 2013 Joint IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS) (pp. 57–61). IEEE. https://doi.org/10.1109/IFSA-NAFIPS.2013.6608375
  13. Yager, R. R., & Alajlan, N. (2017). Approximate reasoning with generalized orthopair fuzzy sets. Information Fusion, 38, 65–73. https://doi.org/10.1016/j.inffus.2017.02.005
  14. Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338–353. https://doi.org/10.1016/S0019-9958(65)90241-X
  15. Zadeh, L. A. (1975). The concept of a linguistic variable and its application to approximate reasoning—I. Information Sciences, 8(3), 199–249. https://doi.org/10.1016/0020-0255(75)90036-5
  16. Zdorovyi, M., & Horokh, O. (2025). Customer lifetime value analytics in digital banking: Approaches, models, and practical implementation. VII International Scientific and Theoretical Conference “Scientific Method: Reality and Future Trends of Researching”, 32–41.
  17. Zhang, Q., & Zhang, H. (2024). Assessing agri-food waste valorization challenges and solutions considering smart technologies: An integrated Fermatean fuzzy multi-criteria decision-making approach. Sustainability, 16(14), Article 6169. https://doi.org/10.3390/su16146169