Uncovering Customer Heterogeneity Through Behavioral Segmentation: Linking Digital Service Usage Patterns to Purchase Behavior

Digital service environments generate extensive longitudinal behavioral data that can reveal meaningful differences in how customers engage with services over time. This study investigates customer heterogeneity through a multidimensional behavioral segmentation approach that integrates service usage intensity, behavioral variability, temporal trajectories, engagement, and relationship maturity, and examines whether the resulting segments differ in observed purchase behavior. Using longitudinal customer data from a telecommunications service environment covering 2023–2025, billing and service usage records were transformed into customer-level behavioral features. After data quality assessment and preprocessing, 974 customers with sufficient behavioral histories and complete information were included in the analysis. Cluster tendency was assessed using the Hopkins statistic, while alternative clustering algorithms and cluster solutions were evaluated through internal validity criteria, stability analysis, resampling, and PCA-based robustness checks. A three-cluster K-means solution yielded stable, balanced, and behaviorally interpretable segments. Purchase rates differed significantly across the segments (χ2(2) = 9.783, p = 0.0075), ranging from 22.18% to 33.43%. The findings indicate that rapidly increasing service usage does not necessarily correspond to higher observed purchase rates; rather, established, high-intensity multi-service engagement is associated with higher observed purchase rates.