HBFC Bank aims to accelerate its growth by converting existing depositors into personal loan customers while retaining them. The project analyzes a dataset of 5000 customers, exploring demographic details, customer-bank relationships, and responses to the last personal loan campaign. The primary goal is to construct a predictive model identifying prospective customers likely to apply for personal loans, laying the foundation for actionable insights from historical customer data.
This project showcases advanced skills in statistical analysis, data visualization, and proficiency in tools like Excel for data manipulation. Utilizing industry-relevant techniques such as creating categorical variables, scatter plots, and pivot tables, it demonstrates a comprehensive understanding of both the financial industry and data analytics. Key terms like "personal loan uptake," "median income disparity," and "optimizing marketing campaign" underscore the project's focus on actionable insights, customer segmentation, and strategic recommendations.
The analysis reveals that 9.6% of the bank's customer base has availed Personal Loans. Key findings include diverse age and income distributions, a positive correlation between age and experience, and insights into top customer locations. Specific customer segments, such as those with Fixed Deposits and Credit Cards but no Personal Loan, are identified. The comparative analysis of median income highlights income disparities between customers with and without personal loans. Pivot table analyses provide nuanced insights into relationships between education levels and personal loan uptake. These results form the basis for strategic recommendations aimed at optimizing future personal loan marketing campaigns.