A Data-Driven Framework for Target Market Selection and Performance Evaluation of a Credit Card Product Using Statistical Analysis

The Indian banking sector is highly competitive. Established banks leverage extensive customer data to maintain market dominance, making product launches by new entrants particularly challenging. Existing research has not produced a unified, end-to-end framework that integrates multi-source banking data analysis with rigorous experimental product val-idation. This paper addresses that gap through a two-phase data-driven framework. Phase 1 performs comprehensive data cleaning and exploratory data analysis (EDA) on a 40,000-record banking dataset comprising customer demographics, transaction history, and credit score information, with the objective of identifying the optimal target market segment. Phase 2 validates product performance through a statistically controlled A/B trial supported by hypothesis testing and confidence interval analysis. The framework introduces context-aware imputation techniques, a budget-constrained trial design methodology, and structured comparative tables to guide decision-making. Results confirm that targeting the 18–25 age group with a tailored credit card product leads to a statistically significant increase in average transaction amounts (95% CI: $226–$245). This study provides a reproducible, evidence-based blueprint for credit card product launches in emerging markets such as India.