Predicting Consumer Adoption of Luxury Products via Instagram Marketing: A Machine Learning Approach

In the contemporary digital landscape, social media platforms have radically reshaped consumer-brand interactions, with Instagram emerging as a pivotal channel for luxury brand marketing. This study investigates the adoption of luxury products through Instagram by integrating psychological, social, and demographic predictors with advanced analytical methodologies. Utilizing a comprehensive dataset of 205 respondents, we examine how Instagram engagement metrics-such as time spent on the platform, influencer marketing, sponsored advertisements, and interactive content-impact consumer adoption behaviours. Employing multiple statistical techniques including multiple linear regression, logistic regression, and path analysis, alongside machine learning models like Support Vector Machines (SVM) and decision trees, we identify the primary drivers of purchase intention and brand trustworthiness in the luxury context. Notably, while surface-level engagement metrics show limited predictive power, factors such as brand trust, prestige, uniqueness, and personalized recommendations have strong influences on consumer purchase intention. The SVM model accurately predicts adoption likelihood with 87% accuracy, underscoring the efficacy of psychological and social variables as discriminators. Additionally, sentiment analysis of qualitative feedback reveals predominantly positive consumer attitudes toward Instagram marketing strategies. Our findings highlight the nuanced role of brand-related psychological constructs and demographic variables in shaping luxury product adoption, offering actionable insights for marketers aiming to optimize Instagram's potential in luxury branding. This integrative approach advances both theoretical understanding and practical applications, reinforcing the importance of authenticity, emotional connection, and targeted psychographic segmentation in luxury digital marketing.