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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM64610

AI-POWERED CUSTOMER CHURN PREDICTION SYSTEM USING MACHINE LEARNING

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM64610

Volume:

10 |

6 |

Jun 2026

Pages:

1-4

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM64610

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

KASI VIKAS BABU

KASI VIKAS BABU

MASTER OF COMPUTER APPLICATIONS

GIET ENGNEERING COLLEGE (A)

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Mentors

BR

B.Srinivasa Rao

MASTER OF COMPUTER APPLICATIONS

GIET ENGNEERING COLLEGE (A)

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Abstract

This project presents an AI-Powered customer churn prediction system that uses some machine learning algorithms to identify customers who are likely to stop using a product or service. Customer retention plays a vital role in the success of modern businesses, as losing long lasting customers can impact the company growth and revenue. The prediction can be achieved by analyzing customer related data, including transaction records , usage behavior , and demographic information to discover patterns associated with churn. Advanced Machine Learning algorithms such as Logistic regression, Decision tree and Random forest to predict churn with higher precision. The proposed system enhances customer retention, optimizes business performance and supports data-driven decision-making. Prediction helps businesses can implement targeted strategies such as personalised offers and improve services to enhance customer satisfaction and reduce churn , overall this project demonstrate how Artificial Intelligence and Machine Learning can be effectively utilized to enhance customer relationship management and support sustainable business growth

KEYWORDS

Artificial Intelligence(AI) Machine Learnining Customer Churn Prediction Predictive Analytics Customer Retention Data Mining Classiffication Algorithms Logistic Regression Decision Tress Random Forest Customer Behaviour Analytics Business Intelligence Feature Engineering Predictive Modeling Customer Relationship Management Real-time Analytics Customer Satisfaction Revenue Optimazation and Business Growth

Cite This Article

KASI VIKAS BABU, B.Srinivasa Rao, "AI-POWERED CUSTOMER CHURN PREDICTION SYSTEM USING MACHINE LEARNING," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 6, pp. 1-4, Jun 2026. https://doi.org/10.55041/IJSREM64610

Full Article/Conclusion

The AI-powered customer churn prediction system provides an effective and intelligent solution for identifying customers who are likely to discontinue a product. It provides an effective solution for predicting customer churn using machine learning algorithms. The project analyses the large amount of customer data to identify patterns. The proposed system enables businesses to take proactive retention measures, such as personalised offers and improved customer support, before customers leave. It enhances customer satisfaction and loyalty, improves efficiency through automation and reduces manual efforts. The system supports real-time insights for better decision - making, overall the project demonstrates how AI and Machine Learning can be successfully applied to improve customer retention, increase profitability and support sustainable business growth.

References

[1]. Abbasimehr H, Setak M, Tarokh M (2011) A neuro-fuzzy classifier for customer churn prediction. International Journal of Computer Applications 19(8):35–41 [2]. Adwan O, Faris H, Jaradat K, Harfoushi O, Ghatasheh N (2014) Predicting customer churn in telecom industry using multilayer preceptron neural networks: Modeling and analysis. Life Science Journal 11(3):75–81 [3]. Ahmad AK, Jafar A, Aljoumaa K (2019) Customer churn prediction in telecom using machine learning in big data platform. Journal of Big Data 6(1):28 [4]. Archambault, D., Hurley, N., Tu, C.T.: Churnvis: visualizing mobile telecommunications churn on a social network with attributes. In: 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013), pp. 894–901. IEEE (2013) [5]. Asthana P (2018) A comparison of machine learning techniques for customer churn prediction. International Journal of Pure and Applied Mathematics 119(10):1149–1169 [6]. Aziz R, Verma C, Srivastava N (2018) Artificial neural network classification of high dimensional data with novel optimization approach of dimension reduction. Annals of Data Science 5(4):615–635 [7]. Brându¸soiu, I., Toderean, G., Beleiu, H.: Methods for churn prediction in the pre-paid mobile telecommunications industry. In: 2016 International conference on communications (COMM), pp. 97–100. IEEE (2016) [8]. Burez J, Van den Poel D (2009) Handling class imbalance in customer churn prediction. Expert Systems with Applications 36(3):4626–4636 [9]. Chen, H., Chiang, R.H., Storey, V.C.: Business intelligence and analytics: From big data to big impact. MIS quarterly pp. 1165–1188 (2012) [10]. Coussement K, De Bock KW (2013) Customer churn prediction in the online gambling industry: The beneficial effect of ensemble learning. Journal of Business Research 66(9):1629–1636

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Publication Date

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