DOI : https://doi.org/10.18517/ijaseit.15.3.12816

Customer Behavior Analysis for Forecasting Customer Attrition: An Artificial Intelligence Approach

Waleed A Hammood (1), Omar A Hammood (2), Salwana Mohamad Asmara (3), Muhammad Shukri Che Lah (4), Rasyidah (5)
(1) Department of Information Technology, College of Computer Science and Information Technology, University of Anbar, Ramadi, Iraq
(2) Business Administration Department, Faculty of Administration and Economics, University of Fallujah, Fallujah, Iraq
(3) Faculty of Computing, Universiti Malaysia Pahang Al-Sultan Abdullah, Pahang, Malaysia
(4) Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn, Malaysia
(5) Department of Information Technology, Politeknik Negeri Padang, Sumatera Barat, Indonesia
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[1]
W. A. Hammood, O. A. Hammood, S. M. Asmara, M. S. Che Lah, and Rasyidah, “Customer Behavior Analysis for Forecasting Customer Attrition: An Artificial Intelligence Approach”, Int. J. Adv. Sci. Eng. Inf. Technol., vol. 15, no. 3, pp. 680–685, Jun. 2025.
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In the telecom sector, the phases of acquisition, build-up, peak, decline, and attrition are commonly included in the customer's lifetime. Nevertheless, telecom businesses often struggle to analyze the vast amounts of client data they generate, which hinders their ability to forecast customer attrition accurately and leads to revenue loss. To help customer retention managers anticipate customer attrition and develop effective retention strategies, this research aims to design a predictive model that supports their efforts. As an empirical and iterative process, Artificial Intelligence (AI) and Machine Learning (ML) were used to train several models and enhance the accuracy of churn prediction. Many technologies used today, such as artificial intelligence (AI), machine learning (ML), and data mining, were developed during the digital era. To conduct various research studies to predict customer churn, mobile operators can utilize "Big Data" from customer records, a defining feature of the telecom industry. Based on each user's distinct attributes, the model interface predicts the likelihood of churn and supports various tasks. This research provides a framework for creating successful retention strategies and sheds light on the variables that cause customers to leave the telecom industry. The study's findings encourage telecom firms to become more competitive and promote long-term growth by strengthening customer strategy and improving predictive performance. Using the Tel-data dataset, the study successfully applied AI and ML techniques, such as logistic regression, to create a prediction model that could reliably identify consumers at risk of leaving. This enables businesses to conduct targeted retention campaigns, thereby enhancing client loyalty and satisfaction.

B. R. Amin, S. Taghizadeh, M. S. Rahman, M. J. Hossain, V. Varadharajan, and Z. Chen, “Cyber attacks in smart grid–dynamic impacts, analyses and recommendations,” IET Cyber-Phys. Syst. Theory Appl., vol. 5, pp. 321–329, 2020, doi:10.1049/iet-cps.2019.0103.

F. E. Mashi, M. M. Rosli, H. Haron, W. A. Hammood, and S. A. Aliesawi, “Multi-stages of Alzheimer’s disease classification using deep learning,” in Proc. Int. Conf. Innov. Entrep. Comput. Eng. Sci. Educ. (InvENT), 2024, pp. 458–469, doi: 10.2991/978-94-6463-589-8_42.

R. Alhamad, E. Almajali, and S. Mahmoud, “Electrical reconfigurability in modern 4G, 4G/5G and 5G antennas: A critical review of polarization and frequency reconfigurable designs,” IEEE Access, vol. 11, pp. 29215–29233, 2023, doi:10.1109/access.2023.3260073.

W. A. Hammood et al., “Factors influencing the success of information systems in flood early warning and response systems context,” Telkomnika Telecommun. Comput. Electron. Control., vol. 18, no. 6, pp. 3083–3090, 2020, doi:10.12928/telkomnika.v18i6.14666.

W. A. Hammood et al., “Data analysis of an exploring the information systems success factors for early warning systems adoption,” Babylonian J. Mach. Learn., vol. 2024, pp. 102–111, 2024.

W. A. Hammood et al., “User authentication model based on mobile phone IMEI number: A proposed method application for online banking system,” in Proc. 2021 Int. Conf. Softw. Eng. Comput. Syst. (ICSECS) and 4th Int. Conf. Comput. Sci. Inf. Manage. (ICOCSIM), 2021, pp. 411–416, doi:10.1109/icsecs52883.2021.00081.

J. Lappeman, M. Franco, V. Warner, and L. Sierra-Rubia, “What social media sentiment tells us about why customers churn,” J. Consum. Mark., vol. 39, no. 5, pp. 385–403, 2022, doi:10.1108/JCM-12-2019-3540.

W. A. Hammood et al., “Determinants influencing e-payment adoption amidst COVID-19: A conceptual framework,” in Proc. 2023 7th Int. Conf. New Media Stud. (CONMEDIA), 2023, pp. 39–43, doi: 10.1109/conmedia60526.2023.10428488.

W. A. Hammood et al., “A review of user authentication model for online banking system based on mobile IMEI number,” IOP Conf. Ser.: Mater. Sci. Eng., vol. 769, Art. no. 012061, 2020, doi:10.1088/1757-899X/769/1/012061.

M. A. Ismail, “A GPU accelerated parallel genetic algorithm for the traveling salesman problem,” J. Soft Comput. Data Min., vol. 5, pp. 137–150, 2024, doi: 10.15199/48.2024.01.21.

N. F. Idris and M. A. Ismail, “A review of homogenous ensemble methods on the classification of breast cancer data,” Prz. Elektrotech., vol. 2024, 2024, doi: 10.15199/48.2024.01.21.

S. W. Fujo, S. Subramanian, M. A. Khder et al., “Customer churn prediction in telecommunication industry using deep learning,” Inf. Sci. Lett., vol. 11, no. 1, p. 24, 2022, doi:10.18576/isl/110120.

T. A. Tang et al., “DeepIDS: Deep learning approach for intrusion detection in software defined networking,” Electronics, vol. 9, Art. no. 1533, 2020, doi: 10.3390/electronics9091533.

W. A. Hammood, A. Aminuddin, O. A. Hammood, K. H. Abdullah, D. Sofyan, and M. Rahardi, “Conceptual model of internet banking adoption with perceived risk and trust factors,” Telkomnika Telecommun. Comput. Electron. Control., vol. 21, no. 5, pp. 1013–1019, 2023, doi:10.12928/telkomnika.v21i5.24581.

J. Watson-Daniels et al., “Predictive churn with the set of good models,” arXiv Prepr., arXiv:2402.07745, 2024, doi:10.48550/arXiv.2402.07745.

M. BinJubier, M. A. Ismail, M. Othman, S. Kasim, and H. Amnur, “Optimizing genetic algorithm by implementation of an enhanced selection operator,” JOIV Int. J. Inform. Vis., vol. 8, no. 3–2, pp. 1643–1650, 2024, doi: 10.62527/joiv.8.3-2.3449.

M. Dzulkurnain, A. Aminuddin, W. Hammood, K. Abdullah, and M. A. Miah, “Optimizing students’ practical skills through project-based learning: Case study in vocational high schools,” Int. J. Eval. Res. Educ., vol. 13, no. 5, p. 3151, 2024, doi:10.11591/ijere.v13i5.28694.

N. N. M. Azam, M. A. Ismail, M. S. Mohamad, A. O. Ibrahim, and S. Jeba, “Classification of COVID-19 symptoms using multilayer perceptron,” Iraqi J. Comput. Sci. Math., vol. 4, no. 4, pp. 100–110, 2023, doi: 10.52866/ijcsm.2023.04.04.009.

Y. Suh, “Machine learning based customer churn prediction in home appliance rental business,” J. Big Data, vol. 10, Art. no. 41, 2023, doi:10.1186/s40537-023-00721-8.

A. M. Hasan, N. M. Noor, T. H. Rassem, A. M. Hasan, and W. A. Hammood, “A combined weighting for the feature-based method on topological parameters in semantic taxonomy using social media,” IOP Conf. Ser.: Mater. Sci. Eng., vol. 769, Art. no. 012002, 2020, doi:10.1088/1757-899X/769/1/012002.

W. A. Hammood, R. A. Arshah, S. M. Asmara, H. Al Halbusi, O. A. Hammood, and S. Al Abri, “A systematic review on flood early warning and response system (FEWRS): A deep review and analysis,” Sustainability, vol. 13, Art. no. 440, 2021, doi:10.3390/su13010440.

H. Farman, S. R. Talpur, U. Amjad, G. Shankar, U. E. Laila, and L. Naseem, “Leveraging machine learning and deep learning models for proactive churn customer retention,” VFAST Trans. Softw. Eng., vol. 12, no. 4, pp. 70–86, 2024, doi:10.21015/vtse.v12i4.1928.

R. Liu et al., “An intelligent hybrid scheme for customer churn prediction integrating clustering and classification algorithms,” Appl. Sci., vol. 12, Art. no. 9355, 2022, doi:10.3390/app12189355.

T. Kimura, “Customer churn prediction with hybrid resampling and ensemble learning,” J. Manage. Inf. Decis. Sci., vol. 25, no. 1, pp. 1–23, 2022.

A. Dingli et al., “Comparison of deep learning algorithms to predict customer churn within a local retail industry,” Int. J. Mach. Learn. Comput., vol. 7, no. 5, pp. 128–132, 2017, doi:10.18178/ijmlc.2017.7.5.634.

N. Alboukaey et al., “Dynamic behavior based churn prediction in mobile telecom,” Expert Syst. Appl., vol. 162, Art. no. 113779, 2020, doi: 10.1016/j.eswa.2020.113779.

N. S. Nordin and M. A. Ismail, “A hybridization of butterfly optimization algorithm and harmony search for fuzzy modelling in phishing attack detection,” Neural Comput. Appl., vol. 35, no. 7, pp. 5501–5512, 2023, doi: 10.1007/s00521-022-07957-0.

A. Cucus, W. P. Sari, A. Aminuddin, D. I. S. Saputra, W. A. Hammood, and R. F. A. Aziza, “Systematic review of IT risk management using a scientometric approach,” in Proc. 2023 6th Int. Conf. Inf. Commun. Technol. (ICOIACT), Yogyakarta, Indonesia, Nov. 2023, pp. 126–131, doi:10.1109/icoiact59844.2023.10455938.

O. A. Nizam, M. Nafaa, and W. A. Hammood, “RESP: Relay suitability-based routing protocol for video streaming in vehicular ad hoc networks,” Int. J. Comput. Commun. Control, vol. 14, no. 1, pp. 21–38, 2019, doi: 10.15837/ijccc.2019.1.3211.

R. A. Arshah, W. A. Hammood, and A. Kamaludin, “An integrated flood warning and response model for effective flood disaster mitigation management,” Adv. Sci. Lett., vol. 24, no. 10, pp. 7819–7823, 2018, doi: 10.1166/asl.2018.13024.

K. R. Awad, M. K. Obed, and M. K. Abed, “The analysis of the effect of information technology on the economic growth in Iraq (2006–2018),” in Proc. 2020 2nd Annu. Int. Conf. Inf. Sci. (AiCIS), Fallujah, Iraq, Nov. 2020, pp. 183–190, doi:10.1109/AiCIS51645.2020.00038.

Y. M. Al-Sabaawe, W. A. Husien, and A. A. Hammadi, “Three phases strategy of electronic management application: A proposed model,” in Proc. 2020 2nd Annu. Int. Conf. Inf. Sci. (AiCIS), Fallujah, Iraq, Nov. 2020, pp. 148–156, doi:10.1109/AiCIS51645.2020.00033.

K. H. Abdullah, F. S. Abd Aziz, R. Dani, W. A. Hammood, and E. Setiawan, “Urban pollution: A bibliometric review,” ASM Sci. J., vol. 18, 2023, doi: 10.32802/asmscj.2023.1440.

O. A. Hammood, M. N. M. Kahar, W. A. Hammood, R. A. Hasan, M. A. Mohammed, A. A. Yoob, and T. Sutikno, “An effective transmit packet coding with trust-based relay nodes in VANETs,” Bull. Electr. Eng. Inform., vol. 9, no. 2, pp. 685–697, 2020, doi:10.11591/eei.v9i2.1653.

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