Authors: Research Scholar Akash Verma, Associate Professor Dr. Richa Pareek
Abstract: Customer attrition has become a major challenge for organisations operating in highly competitive and digitally driven markets. Retaining existing customers is increasingly recognised as more cost-effective than acquiring new ones, making intelligent attrition management a strategic priority. This study explores the role of predictive analytics and customer segmentation in developing intelligent customer attrition management systems, integrating machine learning, deep learning, explainable artificial intelligence and customer behavioural analytics to improve churn prediction accuracy and support strategic retention decision-making. Twelve studies published between 2024 and 2026 are reviewed and organised into three themes covering predictive analytics and machine learning, segmentation with explainable analytics, and intelligent decision support with risk analytics. The reviewed evidence is consolidated into comparative tables that map each study to its sector, method, principal finding and limitation, alongside a layer-by-layer specification of the proposed framework and an assessment of the data sources and monitoring modes the corpus relies upon. Six research gaps are identified and mapped to corresponding research directions. The framework emphasises real-time analytics, explainable models and personalised retention strategies to enhance customer engagement and long-term profitability, and the findings suggest that intelligent analytics frameworks can improve proactive retention, optimise managerial decision-making and strengthen competitive advantage in dynamic business environments.
