Earticle

초록

영어

This study proposes an AI system that predicts customer churn based on telecommunications customer data and automatically generates personalized retention strategies for each customer. Recently, due to market saturation and intensified competition in the telecommunications industry, retaining existing customers has become more important than acquiring new ones. As a result, predicting customer churn in advance and responding effectively has become a key factor in maintaining a company’s competitiveness. However, the causes of customer churn are diverse, including dissatisfaction with pricing, service quality issues, lack of benefits, and customer service experiences. In particular, much of this information exists in the form of unstructured text data such as consultation records and VOC (Voice of Customer), making systematic analysis difficult. To address this issue, this study utilizes natural language processing (NLP) techniques to analyze the reasons for customer churn and convert them into quantitative measures. Specifically, deep learning-based language models such as DistilBERT and RoBERTa were used to process customer text data and perform sentiment analysis. Based on the results, a churn_score representing the customer’s churn risk was calculated. This approach enables the transformation of previously qualitative customer dissatisfaction factors into quantitative indicators for analysis. In addition, this study employs the LightGBM algorithm for structured data-based churn prediction. This model considers various factors such as customer pricing plans, usage patterns, and contract duration to predict churn probability, offering both high predictive performance and efficiency. Furthermore, beyond simply predicting churn, this study designs a system that automatically generates customized retention strategies tailored to each customer based on the analysis results. This allows companies to establish more timely and appropriate response strategies for individual customers. In conclusion, this study proposes an integrated approach that combines unstructured text data and structured data to analyze and predict customer churn, while also providing actionable retention strategies. This system can significantly enhance data-driven customer retention strategies in the telecommunications industry and is expected to be applicable to various other industries in the future.

목차

Abstract
Introduction
Methods
(1) Overall System Architecture
(2) Churn Cause Analysis (NLP-based)
(3) Churn_score Calculation and Model Comparison
(4) Customer Churn Prediction Model (LightGBM)
(5) Retention Strategy Generation
(6) Churn Risk and Strategy Analysis by Cause
Conclusion
Discussion and Implications
Limitations and Future Research Directions
Acknowledgments
References

키워드

저자

• PARK KEWNWOO [ Hanyang University, College of Business ]
• SHIN MINSOO [ Hanyang University, College of Business ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국경영정보학회 정기 학술대회 [KMIS Conference]

• 간기

  반년간

• 수록기간

  1990~2026

• 십진분류

  KDC 325 DDC 658