Artificial Intelligence Tool For Churn Prediction Model and Customer Segmentation Article Swipe

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A. Chandra Sekhar , J. Vignesh , C. Nabi Harshad , G. Mohammad Shaheed , D. Sreekanth , P Manivannan N L Narasimha Reddy , Aleti Vardhan ·

YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.46647/ijetms.2025.v09i02.066

The telecommunications sector has experienced remarkable expansion over the past few decades,driven by rising competition, rapid technological advancements, and ever-changing customerexpectations. To remain competitive in this evolving environment, telecom operators must prioritizecustomer retention and the delivery of personalized services. Two of the most pressing challenges inthis context are churn prediction and customer segmentation. Churn prediction involves identifyingsubscribers who are at high risk of discontinuing their service and migrating to a competitor,allowing providers to develop timely retention strategies. Meanwhile, customer segmentationfocuses on categorizing users into distinct groups based on shared traits and behavioral patterns,which enables more precise marketing campaigns, personalized service offerings, and optimizedpricing models. Traditionally, telecom providers depended on conventional statistical approachesfor churn prediction, which were often manual, limited in scalability, and insufficient for real-timedecision-making. The emergence of Artificial Intelligence (AI) and machine learning hasrevolutionized this landscape, equipping telecom companies with advanced tools to enhance churnforecasting and refine customer segmentation. These technologies have empowered companies toleverage large-scale customer data for more accurate predictions and strategic segmentation,enabling proactive engagement with at-risk customers and fostering long-term loyalty.Consequently, this research focuses on the integration of AI-driven solutions for churn predictionand customer segmentation, which has become vital for telecom firms aiming to minimize customerattrition, strengthen competitive advantage, and deliver an exceptional customer experience in anindustry shaped by constant innovation and fierce market rivalry

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Computer Science

Market Segmentation

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Computer science Market segmentation Artificial intelligence Segmentation Machine learning Business Marketing

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Type: article
Language: en
Landing Page: https://doi.org/10.46647/ijetms.2025.v09i02.066
PDF: https://doi.org/10.46647/ijetms.2025.v09i02.066
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OpenAlex ID: https://openalex.org/W4409709171

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DOI: https://doi.org/10.46647/ijetms.2025.v09i02.066

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Title: Artificial Intelligence Tool For Churn Prediction Model and Customer Segmentation

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Language: en

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Publication year: 2025

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Publication date: 2025-01-01

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Authors: A. Chandra Sekhar, J. Vignesh, C. Nabi Harshad, G. Mohammad Shaheed, D. Sreekanth, P Manivannan N L Narasimha Reddy, Aleti Vardhan

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Landing page: https://doi.org/10.46647/ijetms.2025.v09i02.066

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Concepts: Computer science, Market segmentation, Artificial intelligence, Segmentation, Machine learning, Business, Marketing

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