DYNAMIC CUSTOMER PROFILING AT SCALE: CONTINUOUS SEGMENTATION FROM REAL-TIME BEHAVIORAL EVENT STREAMS
Keywords:
Dynamic customer profiling, real-time behavioral data, continuous segmentation, streaming analytics, online machine learning, customer behavior modeling, event-driven architecture.Abstract
A dynamic customer profiling framework enabling continuous segmentation from real-time behavioral event streams was developed and evaluated. Traditional batch-based segmentation methods inadequately capture rapid behavioral shifts, resulting in outdated actionable intelligence. A real-time streaming architecture was implemented to ingest, process, and analyze continuous event data, enabling subsecond updates to customer profiles and segment memberships. The results demonstrated that the dynamic profiling system achieved low processing latency, high throughput efficiency, and significantly improved segmentation accuracy compared to batch models. The system effectively captured micro-behavioral variations, supported rapid transitions between segments, and provided more coherent and responsive clustering outcomes. Results confirmed continuous segmentation achieves 78% improved responsiveness and higher cluster coherence (silhouette score 0.64 vs. 0.41) compared to batch approaches, enabling real-time personalization.
