Journal Press India^®

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This paper explores the application of Exploratory Data Analytics (EDA) in the banking and finance domain, specifically focusing on analyzing customer churning. The report presents a comprehensive step-by-step analysis of banking and other data using various EDA techniques, including descriptive statistics, data visualization, and correlation analysis. Furthermore, the study investigates the critical issue of customer churning in the banking sector by using three datasets. Churn rate analysis uncovers the proportion of customers who discontinue with the bank within a given period. By identifying the churn rate, financial institutions can assess customer retention performance and set benchmarks for improvement. The study further investigates the factors driving customer churn, including customer demographics, transaction history, and customer satisfaction levels. These insights enable banking and finance professionals to make data-driven decisions, formulate targeted marketing strategies, and design effective customer retention initiatives. The findings from this analysis contribute to the advancement of the banking and finance sector’s ability to enhance customer satisfaction, optimize credit card services, and ultimately drive profitability.

Keywords

Exploratory Data Analytics (EDA), Customer churning, Descriptive statistics, Data visualization, Correlation analysis, Data-driven decision-making, Customer retention

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