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

ArXiv ID: 2506.22711 “View on arXiv”

Authors: João B. G. de Brito, Rodrigo Heldt, Cleo S. Silveira, Matthias Bogaert, Guilherme B. Bucco, Fernando B. Luce, João L. Becker, Filipe J. Zabala, Michel J. Anzanello

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.

Keywords: Customer Lifetime Value (CLV), Open Banking, Retention Prediction, Customer Segmentation, Profitability Analysis

Complexity vs Empirical Score

• Math Complexity: 3.0/10
• Empirical Rigor: 7.5/10
• Quadrant: Street Traders
• Why: The paper applies standard machine learning models (XGBoost) and conventional CLV formulas with minimal novel mathematical derivations, focusing on data integration and model performance metrics. Empirical rigor is high due to detailed methodology on model training, cross-validation, and use of real-world Open Banking data with specific performance metrics (RMSE, MAE, PR-AUC).

  flowchart TD
    A["Research Goal: <br>Leverage Open Banking data <br>to estimate Potential CLV"] --> B["Data Collection<br>Open Banking Data<br>Transaction History"]
    B --> C["Methodology: <br>PCLV Framework<br>Retention Prediction & PCM Estimation"]
    C --> D["Compute Potential Contribution Margins<br>from Competitor Data"]
    C --> E["Predict Retention Probability<br>using OB Insights"]
    D & E --> F["Calculate Potential CLV<br>(PCLV)"]
    F --> G["Key Finding: <br>PCLV shows 21.06% upside <br>over Actual CLV"]
    G --> H["Outcome: <br>Strategic tool for targeted marketing<br>& boosting profitability"]