Machine Learning For Real-Time Brand Health Tracking: A Framework For Ai-Augmented Brand Equity Management

Abstract

This study develops and empirically validates a framework for integrating machine learning into real-time brand health tracking, advancing AI-augmented brand equity management. Recognizing the limitations of conventional periodic brand audits, the proposed framework leverages predictive analytics and continuous data streams to provide dynamic, actionable insights into brand performance. To assess the framework's practical relevance and perceived efficacy, a structured survey was administered to 289 brand and marketing professionals in Pune City, Maharashtra, India. The instrument utilized a five-point Likert scale to capture respondent evaluations of the framework's core components, including predictive accuracy, real-time responsiveness, and decision-support utility. The analysis employed one-sample t-tests to determine whether mean perceptions significantly exceeded a neutral baseline, and Pearson correlations to examine the interrelationships among key framework dimensions. The results robustly support the study's hypotheses. The aggregate mean for the primary hypothesis (H1) was approximately 3.66, indicating favorable professional endorsement of the framework's foundational principles. Furthermore, significant positive correlations, ranging from 0.603 to 0.712, were observed among the framework's constituent elements, confirming the internal consistency and convergent validity of the proposed model. Both H1 and H2 were supported, demonstrating that brand professionals recognize the value of machine learning for enhancing the timeliness and strategic depth of brand health monitoring. These findings suggest that the proposed framework offers a viable pathway for organizations to transition from reactive brand management to a proactive, data-driven approach, thereby strengthening long-term brand equity. The study contributes to the marketing literature by providing an evidence-based blueprint for AI integration in brand management practice (Joseph et al., 2025; Mannaru, 2025).