A Business Intelligence Model for Monitoring Campaign Effectiveness in Digital Banking

Abstract

In the evolving landscape of digital banking, the ability to measure and optimize marketing campaign performance is critical to customer acquisition, engagement, and retention. This review paper proposes a business intelligence (BI) model that integrates real-time data analytics, campaign attribution frameworks, and performance dashboards to monitor campaign effectiveness across digital channels. The paper synthesizes literature on BI-driven marketing intelligence, data integration strategies, and customer behavior analytics, with a focus on banking-specific use cases. Emphasis is placed on how predictive analytics, multichannel tracking, and key performance indicator (KPI) alignment empower financial institutions to fine-tune campaigns and maximize return on investment (ROI). The paper also reviews current technological tools and data visualization platforms used in the sector and explores implementation challenges such as data silos, model calibration, and privacy compliance. By consolidating insights from academia and industry, this paper provides a structured foundation for banks to adopt adaptive, insight-driven campaign monitoring models that align with digital transformation goals and regulatory expectations.

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