Author(s): Bikramjit Pal
The rapid advancement of Generative Artificial Intelligence (GenAI) has transformed how organizations collect, interpret, and act upon customer feedback across digital touchpoints. Despite the growing integration of AI in marketing and customer experience (CX), empirical and conceptual clarity on GenAI-enabled feedback intelligence remains limited. This study proposes a comprehensive framework that integrates Large Language Models (LLMs), multimodal analytics, and automated insight generation to enhance the accuracy, timeliness, and personalization of feedback-driven decision-making. Using a mixed-methods research design, the paper combines a systematic literature review and a pilot data analysis on 12,400 customer feedback entries from e-commerce and service platforms. The findings reveal that GenAI improves feedback classification accuracy by 28–41%, reduces manual processing time by 65%, and significantly enriches contextual sentiment interpretation. The proposed 5-tier conceptual demonstrates how organizations can operationalize GenAI to achieve real-time insight flows, hyper-personalized responses, automated service recovery, and predictive churn risk alerts. The study contributes to theory by formalizing GenAI-enabled feedback intelligence as an emerging domain within marketing analytics, while offering practical guidelines for businesses seeking scalable feedback automation. Implications for privacy, bias mitigation, and ethical AI governance are also discussed. Overall, GenAI represents a transformative shift from reactive customer service to proactive, anticipatory experience management.