Abstract
Customer churn, or loss of a client, is an issue in telecommunication, overseeing an account, and e-commerce. Acquiring new clients is a part more costly than holding existing ones. Thus, churn prediction becomes a commercial technique. Classic strategies such as logistic regression, decision trees, and random forests are a extraordinary beginning but fall short in addressing to course imbalance, moving behaviors, and requiring actionable outcomes. Recent studies have advanced churn prediction in three ways. First, ensemble approaches such as XGBoost, LightGBM, and CatBoost provide palatable execution, particularly when utilized in conjunction with oversampling strategies such as SMOTE and ADASYN. Second, hybrid deep learning models that combine CNNs, BiLSTMs, and attention mechanisms can superior learn adjacent, progressive, and global features with advanced recall and F1 scores. Third, online learning approaches allow models to diligently learn in real-time of progressing client behavior. This article describes these progressions and proposes a system for proactive upkeep utilizing data analytics. It is found that ensemble models have over 85-90% balanced accuracy, and hybrid profound models provide excellent execution. Versatility, feature importance, and interpretability are found to be of crucial importance for being of practical use.
Keywords
Customer churn, Hybrid Approaches, Deep Learning, XGBoost, LightGBM
Citations
IRE Journals:
Ranveer Kumar Singh, Shivam Chaudhari, Rajesh Raghav, Saranya Raj "Data Analytics for Proactive Customer Retention" Iconic Research And Engineering Journals Volume 9 Issue 6 2025 Page 1376-1381 https://doi.org/10.64388/IREV9I6-1712929
IEEE:
Ranveer Kumar Singh, Shivam Chaudhari, Rajesh Raghav, Saranya Raj
"Data Analytics for Proactive Customer Retention" Iconic Research And Engineering Journals, 9(6) https://doi.org/10.64388/IREV9I6-1712929
About IRE Journals
IRE Journals is an open access online journals established with an aim to publish high quality of research work in various diciplines. We have more than 6 years in publications. Over 2500+ papers already published.
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