Open Access

An Intelligent Machine Learning Framework for Customer Churn Prediction in CRM Systems

https://doi.org/10.55640/ijidml-v03i07-03

pdf

Mr. Ram Pratap Singh ¹ ,

⁴ Department of Computer Science and Engineering, Lakshmi Narain College of Technology, Bhopal, India

Abstract

In today's highly competitive market environment, organizations face significant challenges in retaining customers due to increasing competition, evolving customer expectations, and unpredictable churn behavior. In order to address these concerns and predict customer turnover using the Telco Customer turnover dataset, this article suggests a CRM system that relies on clever machine learning techniques. To ensure high-quality data for model training, the proposed system incorporates thorough data pretreatment stages such ADASYN class balancing, missing value management, label encoding, and z-score normalisation. Build and test two supervised ML models, XGBoost and Random Forest (RF), to see how well they perform. The trials showed that the Proposed RF Model achieved 97.1% accuracy (ACC), 97.6% precision (PRE), 97.4% recall (REC), and 97.2% F1-score (F1), whereas the Model Proposed XGBoost Model generated 96.9% ACC, 96.7% PRE, 96.6% REC, and 96.3% F1-score. The suggested method outperforms state-of-the-art machine learning and deep learning models for predicting customer attrition. With the help of the suggested framework, businesses may pinpoint consumers who are likely to churn, which in turn allows for more proactive retention measures, happier customers, and more profits in the long run.

Keywords

Customer Churn Prediction, Machine Learning, Predictive Modeling

References

H. Ravilla, “Predictive Analytics for Customer Churn in Salesforce Service Cloud,” in Learning and Analytics in Intelligent Systems, 2026, pp. 14–24. doi: 10.1007/978-3-032-05377-0_2.

M. Mittal, “AI IN RETAIL: TRANSFORMING THE CUSTOMER EXPERIENCE THROUGH INTELLIGENT AUTOMATION,” Int. Res. J. Mod. Eng. Technol. Sci., vol. 07, no. 03, 2025, doi: DOI : https://www.doi.org/10.56726/IRJMETS69338.

H. P. Cyril and S. Kumara, “Identification of Anomalies via Deep Learning-Based Models for High-Dimensional Telecom Traffic Data,” J. Adv. Artif. Intell., vol. 4, no. 1, pp. 24–37, 2026.

D. Patel, “Hybrid Deep Learning Approaches for Personalized Product Recommendation in Online Retail Systems,” in 2026 6th International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET), FEZ, Morocco: IEEE, 2026, pp. 1–6, May. doi: 10.1109/IRASET68627.2026.11538894.

S. K. Anumula, “Next-Gen Supply Chains: A Product Lifecycle Management Based Approach to Resilient and Sustainable Operations,” Int. J. Manag. Value Supply Chain., vol. 16, no. 3, 2025.

K. Dixit, “AI-Assisted Decision Support Systems for Front Office Operations,” ESP J. Eng. & Technol. Adv., vol. 5, no. 1, pp. 112–119, 2025, doi: 10.56472/25832646/JETA-V5I1P114.

N. Bhatt, “A Hybrid Artificial Intelligence Model for Product Recommendation in Online Retail System,” in Artificial Intelligence Systems (AIS 2026), 2026, pp. 1–7.

A. Joon, S. A. Pahune, S. Mathur, and S. Rongala, “Designing Predictive Analytics Frameworks with ML for ERP-Supported E-Commerce Systems,” in 2025 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC), 2025, pp. 1–6. doi: 10.1109/ASSIC64892.2025.11158452.

T. Shah, “Measuring the Profitability and Long-Term Value of BNPL Customers Using Data Analytics,” Int. J. Res. Anal. Rev., vol. 10, no. 1, 2023, doi: 10.56975/ijrar.v10i1.322189.

A. B. Chatterjee, “A Proposed Hybrid Distributed Ledger Architecture for Cross-Border Payments: Design and Conceptual Framework,” Int. J. Emerg. Technol. Comput. Sci. Inf. Technol., vol. 7, no. 1, pp. 210–217, Feb. 2026, doi: 10.63282/3050-9246.IJETCSIT-V7I1P132.

Y. Patel, “Detection of Multi-Account Abuse in E-Commerce Systems Using Behavioral Biometrics,” Am. J. Cogn. Comput. AI Syst., vol. 8, pp. 107–127, 2024.

Kshitij Dixit, “Predictive Analytics in Business Intelligence for Sales Forecasting,” Int. J. Adv. Res. Sci. Commun. Technol., vol. 3, no. 1, p. 981, Sep. 2023, doi: 10.48175/IJARSCT-12750G.

S. Pawar, G. Patil, K. Patel, P. Pawar, S. Khedkar, and B. More, “Falsified News Detection Using Deep Learning Approach,” in 2021 Asian Conference on Innovation in Technology (ASIANCON), 2021, pp. 1–5. doi: 10.1109/ASIANCON51346.2021.9544585.

M. Imani, M. Joudaki, A. Beikmohammadi, and H. R. Arabnia, “Customer Churn Prediction: A Systematic Review of Recent Advances, Trends, and Challenges in Machine Learning and Deep Learning,” 2025. doi: 10.3390/make7030105.

Hirenkumar N. Dholariya, “GVIF: A Governed Vector Intelligence Framework for AI-Driven Cloud Data Modernization in Regulated Financial Systems,” Int. J. Comput. Exp. Sci. Eng., vol. 12, no. 1, Jan. 2026, doi: 10.22399/ijcesen.4797.

A. K. Kishore Varma Alluri, “A Data to Action Architecture for Applying Artificial Intelligence in Salesforce CRM to Enable Real Time Enterprise Decision Automation,” in 2026 7th International Conference on Inventive Research in Computing Applications (ICIRCA), IEEE, Jun. 2026, pp. 2114–2119. doi: 10.1109/ICIRCA69024.2026.11570427.

A. K. K. V. Alluri, “A Systematic Study of Machine Learning Frameworks Enabling Scalable Secure and Explainable Artificial Intelligence in Salesforce CRM Platforms,” in 2026 International Conference on Electronic Systems and Intelligent Computing (ICESIC), IEEE, Mar. 2026, pp. 396–401. doi: 10.1109/ICESIC67389.2026.11496486.

Z. Hudli, R. Kademani, P. Patil, S. Harakuni, and S. Shahapur, “A Predictive Model for Telecom Customer Churn Using Machine Learning,” in 2025 IEEE 4th International Conference for Advancement in Technology (ICONAT), IEEE, Sep. 2025, pp. 1–7. doi: 10.1109/ICONAT66879.2025.11362579.

Y. Kuramannagari, S. Mahale, P. V. M. A. K. Kanamarlapudi, H. N. Veerlapati, M. Gupta, and R. Kumar, “A Comparative Analysis of Machine Learning Models for Customer Churn Prediction in Subscription-Based Businesses,” 2026. doi: 10.1109/iccica67008.2025.11337851.

S. A. Alteer and A. Alariyibi, “Customer Churn Prediction Using Machine Learning: A Case Study of Libyan Internet Service Provider Company,” in 2024 IEEE 4th International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and Computer Engineering (MI-STA), IEEE, May 2024, pp. 605–612. doi: 10.1109/MI-STA61267.2024.10599671.

R. R. Chandan, S. Ramaraj, Sarishma, P. Praba Devi, S. Sj, and E. Indhuma, “A Novel Machine Learning-Based Early Warning Detection System for Business Customer Churn,” in 5th International Conference on Recent Trends in Computer Science and Technology, ICRTCST 2024 - Proceedings, 2024. doi: 10.1109/ICRTCST61793.2024.10578489.

I. N. Nyoman Mahayasa Adiputra and P. Wanchai, “Customer Churn Prediction Using Weight Average Ensemble Machine Learning Model,” in Proceedings of JCSSE 2023 - 20th International Joint Conference on Computer Science and Software Engineering, 2023. doi: 10.1109/JCSSE58229.2023.10202105.

K. D. Singh, P. Deep Singh, A. Bansal, G. Kaur, V. Khullar, and V. Tripathi, “Exploratory Data Analysis and Customer Churn Prediction for the Telecommunication Industry,” in 2023 3rd International Conference on Advances in Computing, Communication, Embedded and Secure Systems (ACCESS), IEEE, May 2023, pp. 197–201. doi: 10.1109/ACCESS57397.2023.10199700.

M. Galal, S. Rady, and M. Aref, “Enhancing Customer Churn Prediction in Digital Banking using Ensemble Modeling,” in NILES 2022 - 4th Novel Intelligent and Leading Emerging Sciences Conference, Proceedings, 2022. doi: 10.1109/NILES56402.2022.9942408.

BlastChar, “Telco Customer Churn,” Kaggle Contributor.

F. Zaka, M. Sabir, L. Khalid, S. Ejaz, and S. Khalid, “Development of XAI-Driven Churn Prediction Framework for Proactive Retention in Telecom,” Int. J. Innov. Sci. Technol., pp. 1916–1934, Aug. 2025, doi: 10.33411/ijist/20257319161934.

V. Chang, K. Hall, Q. A. Xu, F. O. Amao, M. A. Ganatra, and V. Benson, “Prediction of Customer Churn Behavior in the Telecommunication Industry Using Machine Learning Models,” Algorithms, vol. 17, no. 6, 2024, doi: 10.3390/a17060231.

A. El Attar and M. El-Hajj, “Explainable AI-driven customer churn prediction: a multi-model ensemble approach with SHAP-based feature analysis,” Front. Artif. Intell., vol. 9, Feb. 2026, doi: 10.3389/frai.2026.1748799.

M. M. K. Margret*, M. M. Monishapriyadharshini, M. S. Nathies, and M. C. Sriram, “Churn Prediction using Machine Learning-An Analytical CRM Application,” Int. J. Innov. Technol. Explor. Eng., vol. 9, no. 5, pp. 1948–1952, Mar. 2020, doi: 10.35940/ijitee.E2931.039520.

M. G. Abdelhady and K. A. Mohamed, “Leveraging artificial intelligence for predictive customer churn modeling in telecommunications: a framework for enhanced customer relationship management,” Sci. Rep., vol. 15, no. 1, p. 43826, Dec. 2025, doi: 10.1038/s41598-025-30108-z.

Most read articles by the same author(s)

<< < 1 2 3 4 5 6 7 > >>

International Journal of Intelligent Data and Machine Learning

An Intelligent Machine Learning Framework for Customer Churn Prediction in CRM Systems

Abstract

Keywords

References

Most read articles by the same author(s)

Similar Articles