AI-DRIVEN DIGITAL SERENDIPITY AND ITS IMPACT ON SERVICE MARKETING AND CONSUMER SATISFACTION

Abstract

AI-driven personalization in service marketing has revolutionized endorsement systems by delivering extremely precise recommendations tailored to separate preferences. Conversely, this precision frequently restricts users within filter bubbles, decreasing exposure to novel experiences and restrictive emotional engagement, which can render relations transactional. Addressing this issue, AI-driven digital serendipity familiarises measured randomness into recommendation algorithms, fostering meaningful unexpected detections that improve engagement, consumer delight, and long-term brand loyalty. An experimental design related a standard predictive system with an AI model integrating stochastic divergence nodes. Participants were randomly dispersed through service domains, getting stimuli during key exploration stages. The commendation logic balanced significance and novelty using a calibrated stochasticity ratio. Behavioural metrics such as dwell time, click-through rates, and navigation patterns were restrained alongside Likert-scale assessments of satisfaction and emotional delight. Statistical analyses comprising mediation analysis, regression, and ANOVA assessed relationships, group differences, and the impact of serendipitous references. Consequences confirmed that AI-driven serendipity suggestively enhanced engagement and satisfaction: click-through rates rose from 12.4% to 20.8%, dwell time increased from 4.2 to 7.1 minutes, exploration depth grew by 85.7%, and "lucky find" experiences extended 34.3%. Emotional delight scores enhanced from 3.2 to 4.4, and brand advocacy rose to 62%, with regression analyses showing strong effects (β = 0.38, Adj. R² = 0.78). Despite these implications, future research must discuss long-term behavioural validation, ethical suggestions, cross-cultural applicability, and dynamic optimization of stochasticity over various service environments to sustain meaningful engagement.