INCORPORATING A NEURAL NETWORK PREDICTION MODEL INTO THE ANALYSIS OF GOLD PURCHASING BEHAVIOR AMONG INDIAN WOMEN IN CONTEMPORARY TRENDS

Abstract

While the gold-buying practices of Indian women are deeply rooted in sociocultural traditions, they have evolved due to changing investment trends, economic liberalization, and advancements in digital technology. This study explores the factors that affect the gold purchasing decisions of Indian women in the context of contemporary market trends. Empirical methods were employed to analyze key variables such as age, income, digital literacy, cultural events, and access to fintech. To forecast purchase propensity, a predictive model based on neural networks was combined with survey data through a structured quantitative approach. The results indicate that although contemporary elements such as digital gold platforms, investment diversification, and mobile finance applications are gaining traction, especially among urban and millennial women, traditional incentives like festivals and family gatherings continue to influence demand. The Women's Gold Purchase Propensity Index (WGPPI) exhibited strong predictive accuracy, offering valuable insights to retailers, investors, and policymakers.The study also identifies significant research gaps, such as the necessity for psychographic segmentation, the insufficient consideration of rural behavior, and the impact of regulations on digital gold trust. It concludes by proposing a comprehensive future scope that encompasses lifecycle-based research, behavioral economics, and enhancements to AI models. For stakeholders in the Indian gold ecosystem, this holistic approach offers a reliable roadmap that connects tradition with innovation.