Abstract

This paper presents a comprehensive literature-based framework for developing advertising impact assessment models utilizing pre- and post-campaign survey data analytics. While the absence of primary experimental data constraints empirical validation, the paper extensively reviews existing methodologies, analytical techniques, and emerging trends in leveraging survey data for robust ad effectiveness measurement. Key challenges, such as measurement bias and causal inference limitations, are addressed, along with opportunities for advanced modeling using machine learning and econometric approaches. The proposed conceptual framework integrates best practices and state-of-the-art analytics to guide marketers and researchers in optimizing advertising strategies through enhanced data-driven insights.