A Localized Model for New Product Development in Electronic Banking Based on a Business Intelligence Approach

Amid rapid technological advancements and rising customer expectations in the banking industry, the development of data-driven banking products tailored to customer needs and feedback has become a strategic imperative. This study presents a localized model for the development of electronic banking products through the integration of Business Intelligence (BI) and the Quality Function Deployment (QFD) methodology. In the first phase, customer needs were identified via a comprehensive literature review and classified into nine key dimensions: speed, accuracy, security, user interface, cost, advanced features, financial consulting, accessibility, and responsiveness. Customer segmentation was conducted using the K-means clustering algorithm based on Customer Lifetime Value (CLV). The clustering validity was confirmed using the silhouette coefficient (0.63), and the target cluster—representing the highest average CLV—was selected for further analysis. Subsequently, text mining techniques were employed to analyze customer feedback within the selected cluster, and their needs were categorized using the Kano model. A QFD House of Quality matrix was then constructed to derive the corresponding technical requirements, which were prioritized using the eigenvector method. The findings revealed that responsiveness, security, accessibility, accuracy, and user interface are fundamental needs; speed represents a performance need; while cost, advanced features, and financial consulting fall under attractive needs. The top technical priorities identified include the “design of artificial intelligence algorithms,” “implementation of business intelligence solutions (integration and data analytics),” and “development of control and compliance processes.” This model represents the first localized framework that combines QFD and business intelligence in the context of Iran's banking industry.