Adaptive Decisioning in Pega: Evaluating Online Learning Algorithms for Real-Time Personalization
DOI:
https://doi.org/10.15662/IJARCST.2022.0502003Keywords:
Adaptive Decisioning, Pega Adaptive Decision Manager, Online Learning Algorithms, Real-Time Personalization, Bayesian Updating, Exploration–Exploitation Tradeoff, Uplift ModelingAbstract
Adaptive decisioning has emerged as a transformative capability in modern enterprise platforms, allowing systems to personalize customer experiences in real time. Pega’s Adaptive Decision Manager (ADM) represents one of the most advanced implementations of online learning within enterprise decisioning systems. Unlike traditional batch-trained machine learning models, ADM continuously updates propensities, explores alternative actions, and learns directly from customer responses. This research provides a comprehensive evaluation of the online learning algorithms that underpin Pega’s adaptive decisioning framework, analyzing their mathematical properties, personalization outcomes, real-time stability, convergence behavior, and operational implications. Through structural analysis, quantitative metrics, data tables, and visual diagrams, the study examines how ADM balances exploration and exploitation, adjusts propensities based on incremental reward signals, and optimizes next-best-action selection. Insights offer practical guidance for organizations seeking to maximize uplift, conversion, and customer lifetime value using AI-driven personalization strategies.References
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