Emotion-Driven Intelligent Segmentation for Hyper-Personalized Customer Experiences: A Rule Mining Approach

Abstract

The study examines the integration of emotion analysis and association rule mining to facilitate intelligent customer segmentation, aiming to deliver hyper-personalized experiences. A sample comprising 250 respondents has been utilized to analyse the relationship between emotional sentiment and behavioral attributes in shaping consumer preferences and engagement patterns. The analysis of sentiment and emotion has been employed to classify textual feedback into contemporary emotional categories, such as joy, anticipation, and anger, thus providing a nuanced understanding of customer perceptions. In combination, association rule mining was used to highlight encoded patterns and interconnections amongst the demographic characteristics, behavioral responses, and emotional states. The findings suggest a strong correlation between customer sentiments and emotional expressions, and also their purchasing tendencies, including a preference for personalized offers and engagement with AI-powered chatbots. The insights gained the identification of independent emotional-behavioral customer segments, each characterized by specific personalization requirements. The integration of rule-based approach with emotion mining in the study displays an effective technique for extracting actionable insights, thereby improving customer targeting and engagement strategies. The findings would help in developing scholarly discussion and practical implementations in marketing analytics, highlighting the significance of emotional intelligence in data-driven personalization.