Measuring Temporal Fluidity in Credit-Card Customer Personas: An Exposure-Aware Markov Framework with a Pre-Registered Audit of Predictive Value

Static e-commerce segmentation leaves category change in payment data unmeasured. We analyze 96.19 million credit-card transactions, including online-shopping categories but no channel flags, across 13 quarters for 229,586 customers, measuring transition exposure, set turnover, retention, and novelty. Temporal homogeneity was rejected (${\chi }^2\left(132\right)=\mathrm{57,222.2}$), and a second-order Markov model outperformed a first-order model in held-out log loss (0.886 vs. 0.913). The hidden Markov benchmark matched within ±0.005 log loss, but only two of five fold intervals excluded zero; the nonhomogeneous specification did not improve. A pre-registered audit found a mean-spending ΔR² of 0.0009 over the original baseline after recency correction; neither three primary decision targets nor the auxiliary adoption target reached the area under the receiver operating characteristic curve (AUC) threshold of ΔAUC 0.01 in any fold. One of three gates passed; its increment fell below the residual criterion with lagged category-change information. The re-analysis added nonlinear learners, recent-window and monthly resolutions, and spending-change and category-entropy targets; all increments stayed below 0.01. Equivalence tests placed 95% upper bounds at 0.0038, 0.0017, and 0.0032, bounding increments below materiality. We contribute an exposure-aware audit reporting this bound on payment-category data for e-commerce analytics.