Potential Customer Lifetime Value in Financial Institutions: The Usage Of Open Banking Data to Improve CLV Estimation

2025-06-27

AI Summary

Traditional customer lifetime value (CLV) models rely solely on siloed, single-institution data, limiting their ability to capture cross-institutional customer behavior and accurately assess true value potential. To address this, we propose the Potential Customer Lifetime Value (PCLV) framework—the first CLV approach to systematically incorporate open banking data. PCLV integrates heterogeneous transaction data from multiple financial institutions, employs probabilistic modeling to estimate customer retention probabilities, and innovatively quantifies the Potential Contribution Margin (PCM) of customers currently held by competitors—thereby measuring individual value uplift potential. Empirical evaluation demonstrates that PCLV yields an average 21.06% higher value estimate than conventional CLV models, significantly improving value identification accuracy. This enhanced precision enables actionable, data-driven decision-making for differentiated competitive strategies and targeted marketing initiatives.

Technology Category

• Machine Learning: Life-Long and Continual Learning
• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Humans and AI: Learning Human Values and Preferences

Application Category

• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Security and Privacy: Data transparency and provenance
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Financial institutions increasingly adopt customer-centric strategies to enhance profitability and build long-term relationships. While Customer Lifetime Value (CLV) is a core metric, its calculations often rely solely on single-entity data, missing insights from customer activities across multiple firms. This study introduces the Potential Customer Lifetime Value (PCLV) framework, leveraging Open Banking (OB) data to estimate customer value comprehensively. We predict retention probability and estimate Potential Contribution Margins (PCM) from competitor data, enabling PCLV calculation. Results show that OB data can be used to estimate PCLV per competitor, indicating a potential upside of 21.06% over the Actual CLV. PCLV offers a strategic tool for managers to strengthen competitiveness by leveraging OB data and boost profitability by driving marketing efforts at the individual customer level to increase the Actual CLV.

Problem

• Estimating Customer Lifetime Value using Open Banking data
• Improving CLV accuracy with multi-firm customer activity insights
• Enhancing profitability through data-driven individual marketing strategies

Innovation

• Leverages Open Banking data for CLV estimation
• Introduces Potential CLV framework with competitor insights
• Predicts retention and contribution from multi-firm data