AI-DRIVEN MARKETING ANALYTICS FOR RETAIL STRATEGY: A SYSTEMATIC REVIEW OF DATA-BACKED CAMPAIGN OPTIMIZATION

Abstract

Artificial intelligence (AI) has emerged as a transformative force in retail marketing, fundamentally reshaping how organizations design, implement, and optimize campaign strategies. This umbrella review synthesizes findings from 72 peer-reviewed systematic reviews and meta-analyses published between 2010 and 2024, providing a comprehensive, macro-level evaluation of how AI is applied within marketing analytics to enhance retail performance. The reviewed literature spans a wide array of AI techniques—including supervised learning, unsupervised learning, deep learning, reinforcement learning, and natural language processing (NLP)—and their respective roles in improving campaign forecasting, real-time adaptability, customer segmentation, personalization, sentiment analysis, and attribution modeling. The study finds that supervised learning algorithms are widely utilized to predict campaign performance metrics such as conversion rates and customer retention, while deep learning models, particularly LSTM and CNN, are applied in modeling sequential consumer behavior and enhancing journey personalization. Reinforcement learning is frequently employed to enable real-time decision-making in campaign delivery and loyalty programs, while unsupervised clustering methods like K-means and DBSCAN are central to AI-enabled psychographic and behavioral segmentation. Additionally, NLP techniques—especially transformer-based models like BERT and GPT—are instrumental in analyzing sentiment, identifying intent, and optimizing conversational engagement across digital touchpoints. A key contribution of this review is the synthesis of emerging research that addresses legal and ethical implications, with a particular focus on regulatory frameworks such as the General Data Protection Regulation (GDPR) and California Consumer Privacy Act (CCPA). These regulations have prompted shifts in AI marketing system design, leading to increased transparency, and consumer control. The study offers valuable insights for scholars, practitioners, and policymakers seeking to understand the scope, effectiveness, and governance of AI-driven marketing analytics in retail contexts.