PREDICTIVE MODELLING FRAMEWORKS AND REINFORCEMENT LEARNING FOR ADAPTIVE PERSONALISATION: ARCHITECTURAL ADVANCEMENTS IN CRM SYSTEMS

Customer relationship management (CRM) systems have experienced a profound evolution over the past few decades, emerging from basic data storage solutions into complex, intelligent platforms designed to foster meaningful, ongoing interactions between organizations and their customers [1]. Historically, CRM implementations focused primarily on recording transactional data, such as past purchases, contact details, and complaint logs. Although these early systems helped track key customer touchpoints, their capabilities were constrained. They often lacked the sophistication needed to decipher intricate patterns within customer behavior, and their decision-making processes were typically guided by static rules and manually derived heuristics that were slow to adapt to changing market conditions. As a result, many organizations struggled to keep pace with evolving consumer preferences, shifting competitive landscapes, and the growing complexity of digital ecosystems.