Abstract

Media performance forecasting is critical for optimizing advertising spend and maximizing return on investment (ROI) across diverse client portfolios. This paper presents a comprehensive review and conceptual framework for developing client portfolio management models tailored to media performance forecasting. Leveraging insights from portfolio theory, marketing analytics, and media planning, the framework integrates client segmentation, risk-return trade-offs, and predictive analytics to support strategic resource allocation. The study synthesizes over 100 references to delineate current methodologies, challenges, and emerging trends. It emphasizes the importance of data-driven decision-making and scalable frameworks in dynamic media environments. The proposed framework aims to enable marketers and agencies to balance client objectives with media channel uncertainties and optimize forecasting accuracy. This review-style paper provides a foundational blueprint for academic researchers and practitioners seeking to enhance media planning and client portfolio management capabilities.