This study develops a framework that translates usage-based customer segmentation into forward-looking targeting prioritization in digital banking. While clustering describes current behavior, it does not indicate which segments should be prioritized under limited outreach capacity.
Using transaction-level data from 42,208 customers of a nationally operating private bank, Agglomerative Hierarchical Clustering with Ward’s linkage is applied to weighted RFM indicators across mobile and internet banking channels. Weights and cluster-level responsiveness coefficients are elicited using the Analytic Hierarchy Process and embedded in a linear allocation model that determines the minimum targeting coverage required to achieve predefined uplift thresholds. Robustness is assessed via Monte Carlo perturbation.
The four-cluster solution reveals strong heterogeneity in digital usage. Responsiveness elicitation produces an asymmetric structure in which a high-intensity segment accounts for a disproportionate share of attainable uplift. Moderate targets can be achieved through concentrated targeting, while broader inclusion becomes necessary at higher performance levels. Results remain stable under substantial parameter variation.
Responsiveness is elicited rather than empirically estimated.
The framework enables targeted digital engagement based on marginal contribution rather than uniform outreach.
The approach supports more balanced targeting by avoiding systematic exclusion of lower-intensity segments and highlighting inclusion-relevant demographic differences.
The study separates descriptive segmentation from forward-looking responsiveness, avoiding circular prioritization based on historical usage. It integrates behavioral clustering, expert elicitation and allocation modeling into a unified decision framework.
