Hybrid ML Models for High-Fidelity Customer Behavior Prediction Using CRM Data Streams and Cloud-Native Feature Stores

Abstract

Customer relationship management platforms generate continuous streams of behavioral signals that reveal how individuals browse, evaluate, purchase, and disengage across digital and physical channels, yet many predictive systems continue to rely on static historical datasets and isolated algorithms that inadequately capture temporal dependencies, contextual shifts, and cross channel interactions, leading to delayed insights and inconsistent personalization outcomes. This study argues that high fidelity customer behavior prediction cannot be achieved through incremental model tuning alone but requires an integrated architectural approach that unifies streaming data ingestion, governed feature engineering, and complementary learning paradigms within a scalable cloud environment. To address this need, the paper introduces a hybrid machine learning framework that combines real time CRM data streams with cloud native feature stores to enable low latency feature computation, standardized feature reuse, and reproducible experimentation across analytical teams, while simultaneously supporting both batch and online inference workflows. The proposed design incorporates heterogeneous models including gradient boosted trees, sequence aware deep neural networks, and probabilistic estimators to capture short term intent signals, long term behavioral trends, and rare anomaly patterns that often precede churn or conversion events. Empirical patterns suggest that the coordinated use of shared feature repositories and hybrid ensembles improves predictive stability, reduces feature drift, enhances generalization under evolving customer behavior, and shortens decision latency when compared with single model or siloed architectures. In addition to accuracy gains, the framework emphasizes operational reliability through feature lineage tracking, version control, governance checkpoints, and elastic cloud orchestration, thereby strengthening explainability, auditability, and scalability for enterprise deployment. The findings indicate that feature stores and hybrid modeling should be regarded as structural capabilities that transform how customer intelligence systems are designed rather than as auxiliary implementation choices. By synthesizing advances in machine learning systems, real time data engineering, and customer analytics, this research contributes a unified architectural blueprint, methodological guidance, and evaluation protocol that can inform future studies on personalization, churn prevention, and decision intelligence, offering both scholars and practitioners a practical foundation for building trustworthy and high performance behavioral prediction platforms in modern cloud native ecosystems.