As a Data Analyst consulting for a banking institution, I conducted an in-depth analysis of a dataset containing 13.6 million customer records to uncover insights into customer behavior and identify cross-selling opportunities for the bank's products. Leveraging Python and its extensive libraries, I performed data cleaning and visualization tasks, which included standardizing column names, addressing missing values, and creating a refined dataset of 949,609 unique customers. For further analysis, I exported the cleaned dataset to a CSV file and imported it into Power BI. Within Power Query, I further refined the data, categorizing customers by age and income using conditional and calculated columns. Employing Data Analysis Expressions (DAX), I calculated the total number of products owned by each customer. Through this analysis, I identified key cross-selling opportunities, such as marketing debit cards to current account holders and promoting pension accounts to payroll account owners. I recommended targeting marketing efforts toward high-representation age and income groups, optimizing the product portfolio, and enhancing the customer experience. These strategic actions are expected to drive business growth and significantly increase the bank's revenue.