Market Segmentation and Profiling Based on Customer Lifetime Value using Data Mining Techniques: First Bank USA One Case Study

In the current competitive environment, banks are committed to expanding their products and services to maintain profitable and long-term customer relationships. Market segmentation is a widely adopted method for banks to gain insights into customers and effectively satisfy their needs. This study presents a novel approach to marketing segmentation by integrating customer lifetime value (CLV) with data mining techniques. Specifically, we developed a decision tree model using the CART algorithm, taking CLV as the target variable to segment 10,000 customers of First Bank USA One into 8 distinct groups. The study identified key determinants of segmentation as “Mortgage Balance,” “Credit Card Balance,” “Savings Account Balance,” and “Number of Open Accounts.” We extracted the characteristics of the 8 segments to create profiles as ‘Young Savers,’ ‘Budget Limiters,’ ‘Fluid Spenders,’ ‘Constrained Borrowers,’ ‘Balanced Wealth Managers,’ ‘Affluent Credit Users,’ ‘Savvy Accumulators,’ and ‘Mortgage Strategists.’ Among these, “Mortgage Strategists” emerged with the highest CLV, indicating their significant profitability to the bank, while “Young Savers,” although currently showing lower CLV, exhibit promising growth potential over their lifecycle. By leveraging these data-driven insights to tailor marketing strategies, banks can promote customer-centric initiatives, strengthen customer relationships, and foster sustainable long-term growth.