Leveraging Explainable AI to Enhance Consumer Insight Models in Real-Time Surveys
DOI:
https://doi.org/10.63282/3050-922X.ICRCEDA25-113Keywords:
Explainable AI, Consumer Insights, Real-Time Surveys, Machine Learning Interpretability, SHAP, LIME, Marketing Analytics, AI Transparency, Consumer Behavior Analysis, Data-Driven Decision-MakingAbstract
In the evolving landscape of consumer research, understanding the rationale behind AI-driven predictions is crucial for building trust and facilitating informed decision-making. This paper explores the integration of Explainable Artificial Intelligence (XAI) into real-time survey platforms to enhance consumer insight models. We examine various XAI techniques, such as SHAP and LIME, and their application in interpreting machine learning outcomes. Through case studies and empirical analysis, we demonstrate how XAI fosters transparency, improves user engagement, and refines marketing strategies. The findings underscore the potential of XAI to bridge the gap between complex AI models and consumer understanding, paving the way for more personalized and effective marketing approaches
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