Volume 5 • Issue 1 • PP: 24–30 • 2025
Credit-Card Engagement Segmentation for Embedded FinTech Product Strategy: Evidence from Expenditure Microdata
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Digital card products create business value only when issued accounts translate into sustained, responsible use. This paper develops an expenditure-based segmentation model for embedded FinTech card strategy using a real credit-card micro-dataset. Average monthly card expenditure is treated as an observable engagement outcome and is examined alongside income, age, and home-ownership status. The empirical design combines descriptive portfolio profiling, robust regression, cross-validated prediction, and product-action mapping. The results show that income is the strongest observed driver of monthly spend, but the relationship is nonlinear and does not fully explain customer heterogeneity. A quartile-based segmentation separates low-use, developing-use, active-use, and premium-use customers, with mean monthly expenditure increasing from 39.92 to 666.35 across the four operating segments. The analysis argues that card engagement should be managed as a portfolio state rather than as a simple activation metric. The study contributes a transparent business-analytics framework that links observed card expenditure to embedded-finance decisions, including activation support, limit calibration, reward design, and repayment-aware engagement monitoring.
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