A STUDY ON CUSTOMER CHURN RATE PREDICTION ANALYSIS AT NPL BLUESKY AUTOMOTIVE PVT LTD NAGPUR

Abstract

Customer churn is a significant concern for businesses in the automotive sector, as it directly impacts profitability and growth. The study explores predictive techniques for analysing customer churn at NPL Bluesky Automotive Pvt Ltd, located in Nagpur, focusing on understanding factors contributing to customer attrition and developing accurate predictive models. By leveraging data analytics and machine learning algorithms, this research identifies patterns in customer behaviour that influence churn, such as purchasing habits, service frequency, and customer satisfaction levels. Various statistical tools and predictive models, including logistic regression, decision trees, and random forests, are evaluated for their efficacy in forecasting customer churn. Data from the company’s CRM systems were analysed, which included customer demographics, purchase history, and service interactions. The study also examines the role of customer relationship management (CRM) strategies in reducing churn rates and enhancing customer loyalty. The findings of this research will aid NPL Bluesky Automotive Pvt Ltd in designing targeted retention strategies, improving customer satisfaction, and ultimately reducing churn. Furthermore, the results highlight the potential of machine learning in optimizing customer retention strategies, offering valuable insights into how automotive companies can leverage data-driven decisions to maintain a loyal customer base. The paper concludes with recommendations for integrating churn prediction models into the company’s operational processes to ensure a proactive approach to customer retention.