AI-Driven Trade Promotion Optimization and Financial ROI in CPG Firms: A Thematic and Analytical Review

Trade promotion accounts for a substantial proportion of marketing expenditure within the consumer-packaged goods (CPG) sector yet historically suffers from inefficiencies and opaque return on investment (ROI). This study presents an evidence-informed thematic and analytical synthesis of artificial intelligence (AI)-driven Trade Promotion Optimization (TPO), examining its financial and operational implications. The review traces the evolution from traditional promotion management to AI-enabled predictive systems integrating machine learning, pricing optimization, and enterprise analytics. A structured AI-Driven Trade Promotion Value Realization (AI-TPO-VR) framework is introduced to link data infrastructure, algorithmic intelligence, operational integration, and measurable financial outcomes. Analytical modeling formalizes ROI estimation through incremental profit, cost savings, and inventory efficiency metrics. The findings indicate that AI enhances forecasting precision, reduces promotional leakage, improves margin performance, and strengthens cross-functional coordination. However, challenges related to data governance, organizational transformation, and ethical AI deployment remain critical determinants of success. The study concludes with strategic recommendations and outlines future research directions toward autonomous, prescriptive trade promotion systems.