A Comparative Analysis of Serial and Parallel Data Mining Approaches for Customer Churn Prediction in Telecom

Abstract

In the ever-evolving landscape of the telecommunications industry, where customer churn poses a significant challenge, the role of data mining in predicting and mitigating churn has become paramount. Concurrently, the telecommunications sector is also grappling with the relentless menace of fraud, requiring rapid detection and prevention measures. This research paper presents a comprehensive comparative analysis of serial and parallel data mining approaches for customer churn prediction within the telecom sector. In the first section, we clarify the key approaches and techniques used in data mining for predicting customer turnover, including logistic regression, decision trees, random forests, and neural networks. Serial data mining is investigated with its inherent limits in terms of processing time, scalability, and real-time applicability, which is often done on a single processor core. On the other hand, a detailed analysis of parallel data mining, made possible by multi-core architectures or distributed computing clusters, is presented. We emphasize the potential advantages of parallel processing, such as more computational resources, faster processing, scalability, and real-time capabilities. The paper explores the nuances of parallel data mining implementation in the context of telecommunications data, highlighting the difficulties and expenses involved in establishing and maintaining a parallel infrastructure. The study examines how quick fraud detection and fraud prevention can be accomplished by utilizing parallel data mining’s real-time capabilities. Real-time applications for fraud prevention include proactive customer service, proactive pricing schemes, network quality monitoring, and personalized advice. Performance parameters, such as accuracy, precision, recall, and F1-score, are tested using real-world telecom datasets for the comparison study. The conclusions of this investigation provide light on the usefulness of serial and parallel methods for predicting client attrition. We also look into how these prediction models’ impact on fraud detection and prevention may spread. In conclusion, this research contributes valuable insights into the practicality and efficacy of serial and parallel data mining approaches for customer churn prediction in telecom, with a specific focus on their implications for fraud detection and prevention. The findings provide a roadmap for telecom companies seeking to optimize their data-driven strategies for customer retention and fraud mitigation in the era of big data and advanced analytics.