PREDICTIVE ANALYTICS FOR CUSTOMER CHURN PREVENTION IN THE RETAIL SECTOR
DOI:
https://doi.org/10.53555/f4y4jt58Keywords:
Customer Churn, Predictive Analytics, Retail Analytics, Machine Learning, Customer RetentionAbstract
Customer churn is one of the most critical challenges faced by the retail industry in today’s highly competitive and data-driven market environment. Retaining existing customers is significantly more cost-effective than acquiring new ones, making churn prevention a strategic priority for retail organizations. This research paper explores the application of predictive analytics as a powerful tool for identifying, analyzing, and preventing customer churn in the retail sector. By leveraging historical customer data, transactional behavior, demographic variables, and engagement patterns, predictive models can forecast the likelihood of customer attrition with high accuracy. The study emphasizes the role of machine learning algorithms such as logistic regression, decision trees, random forests, and gradient boosting techniques in churn prediction. A structured research methodology is adopted involving data collection, preprocessing, model development, and evaluation. The results demonstrate that predictive analytics enables retailers to proactively target at-risk customers and implement personalized retention strategies, thereby enhancing customer lifetime value and profitability. The findings highlight the significance of integrating predictive insights into strategic decision-making and customer relationship management systems. The paper concludes by emphasizing that predictive analytics not only supports churn reduction but also fosters long-term customer loyalty and sustainable competitive advantage in the retail industry.
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