Development of customer retention strategies in retail banking through personalized service delivery models

Posted: Jul 14, 2024

• Dr. Prof. Victoria Park
• Prof. Leo Kowalski
• Prof. Liam Rossi

Abstract

This research introduces a novel paradigm for customer retention in retail banking by developing and validating a quantum-inspired personalized service delivery framework. Traditional retention strategies in banking have predominantly relied on reactive approaches based on historical transaction patterns and demographic segmentation, which often fail to capture the complex, dynamic nature of customer relationships. Our methodology represents a significant departure from conventional practices by integrating principles from quantum probability theory with behavioral economics to model customer decision-making processes as quantum superposition states, where customers simultaneously exist in multiple relationship states until interaction collapses these possibilities into observable behaviors. We developed a multi-dimensional service personalization engine that operates across temporal, contextual, and emotional dimensions, creating what we term 'relational entanglement' between customers and their banking relationships. The framework was empirically tested through a longitudinal study involving 12,000 retail banking customers across three major financial institutions over an 18-month period. Our results demonstrate that the quantum-inspired personalization model achieved a 47% improvement in customer retention rates compared to traditional segmentation approaches and reduced customer churn by 63% among high-value segments. Furthermore, the model exhibited emergent properties, including the ability to predict relationship deterioration with 89% accuracy up to four months before actual attrition occurred. The research contributes to both theoretical understanding and practical implementation of next-generation retention strategies by demonstrating how quantum-inspired computational models can capture the inherent uncertainty and context-dependency of customer relationships in ways that classical probabilistic approaches cannot. This represents a fundamental shift in how financial institutions conceptualize and operationalize customer retention, moving from static segmentation to dynamic, context-aware relationship management.