Developing Ad Impact Assessment Models Using Pre/Post-Survey Data Analytics

This paper presents a comprehensive study on developing advanced advertising impact assessment models leveraging pre- and post-survey data analytics. Advertising effectiveness measurement remains crucial for optimizing marketing investments, yet conventional methods often fail to capture nuanced consumer behavior shifts. Our proposed framework integrates statistical, machine learning, and causal inference approaches to analyze survey data collected before and after ad exposure. This approach enables deeper understanding of consumer perception changes, purchase intent, and brand awareness, supported by large-scale empirical validation. Results demonstrate improved accuracy and interpretability over traditional models, offering practical implications for marketing professionals and researchers. Future directions include integrating multi-modal data sources and real-time adaptive assessment mechanisms.