Development of Advanced Customer Lifetime Value Models in Retail Banking Services

Posted: Feb 15, 2018

• Dr. Prof. Victor Johansson
• Dr. Prof. Victor Torres
• Dr. Prof. Victoria Ivanov

Abstract

The accurate estimation of customer lifetime value (CLV) represents one of the most critical challenges in retail banking, directly influencing strategic decisions regarding customer acquisition, retention, and resource allocation. Traditional CLV modeling approaches have predominantly relied on statistical methods, including probabilistic models, regression analysis, and more recently, machine learning techniques. However, these conventional methodologies face fundamental limitations in capturing the complex, dynamic, and often contradictory nature of customer behavior in modern banking environments. The emergence of quantum computing principles offers a revolutionary framework for reimagining customer value modeling, moving beyond the constraints of classical probability and binary logic that underpin existing approaches. This research introduces a quantum-inspired computational paradigm for CLV modeling that fundamentally transforms how retail banks conceptualize and quantify customer value. The proposed approach addresses several critical shortcomings of traditional models, including their inability to simultaneously represent contradictory customer behaviors, their limited capacity to model complex relationship networks, and their reliance on historical data patterns that may not capture emerging behavioral trends. By representing customers as quantum states and their interactions as quantum operations, our model enables a more nuanced and comprehensive understanding of customer value dynamics. Our work builds upon recent advances in quantum-inspired machine learning while specifically addressing the unique challenges of retail banking customer analytics. The research demonstrates that quantum computational principles, even when implemented on classical hardware, can significantly enhance predictive accuracy and provide deeper insights into customer behavior patterns. This approach represents a substantial departure from existing literature and offers a new theoretical foundation for customer value assessment in financial services.