<![CDATA[Background: Banking customer acquisition is often assessed using volume-based indicators, which may encourage growth without adequately considering customer profitability. Although Customer Profitability Analysis (CPA) provides a more comprehensive basis for evaluating customer economic value, its application in banking remains fragmented and predominantly historical, limiting its use in acquisition decision-making. Objective: This study aims to develop an integrated historical and predictive Customer Profitability Analysis model to support value-based customer acquisition decisions in the banking sector. Methods: A qualitative approach was employed using Soft Systems Methodology (SSM) and a comparative case study of two national commercial banks. Data were collected through document analysis and stakeholder insights, focusing on three critical acquisition stages: prospect identification and qualification, needs analysis and solution design, and proposal presentation and negotiation. Results: The findings indicate that CPA implementation remains partial and is constrained by the absence of standardized profitability frameworks, limited predictive capabilities, fragmented data integration, and silo-based decision-making. These limitations result in suboptimal customer acquisition and package-deal decisions. The study therefore develops an integrated conceptual model combining historical and predictive CPA, supported by real-time profitability simulation and cross-functional integration. Conclusion: Integrating historical and predictive CPA into the customer acquisition process can strengthen data-driven, value-oriented decision-making. The proposed model contributes a corporate-level CPA framework, a profitability-based approach for evaluating package deals, and a customer profitability mapping mechanism for acquisition prioritization, supporting more sustainable growth and improved customer portfolio quality.]]>
