UK Bank's customer data analysis
Using Tableau, I conducted a normal distribution analysis on a UK bank customers dataset to understand how account balances are distributed.
The analysis revealed key patterns, including the average balance most customers hold and outlier accounts with exceptionally high or low balances.
These insights highlighted potential segments for targeted financial products and flagged areas for deeper investigation, such as accounts with unusually low balances indicating dormant or underserved customers.
I developed an interactive customer segmentation analysis by defining quadrants based on age and account balance. The visualization incorporates dynamic filters for gender, job classification, and quadrants, allowing stakeholders to refine the data view. By setting adjustable balance value and age cutoffs, the tool enables stakeholders, such as marketing managers, to redefine quadrant sizes to align with specific strategic goals. This flexibility supports the development of targeted strategies, including customized financial services tailored to different customer groups. This project highlights my ability to create adaptable visualizations that empower decision-makers with actionable insights.
Through clustering analysis based on age and account balance, I segmented customers into three distinct groups, revealing key patterns:
• Cluster 1: 1,815 items with the youngest age and lowest account balance center.
• Cluster 2: 908 items with middle age and the highest account balance center.
• Cluster 3: 1,291 items with the oldest age and a moderate account balance center.
The analysis highlighted a younger group with lower balances, potentially indicating early-stage earners; a middle-aged group with the highest balances, likely representing financially stable individuals; and an older group with moderate balances, often associated with long-term savings or investments. These insights informed actionable strategies for personalized marketing, product development, and enhanced customer engagement tailored to each segment’s unique financial behaviors. This project demonstrated my ability to uncover hidden trends and support data-driven business decisions.