The product intelligence cycle: How hybrid recommender systems transform user data into strategic decisions

Saxena, Ankita (2025) The product intelligence cycle: How hybrid recommender systems transform user data into strategic decisions. World Journal of Advanced Engineering Technology and Sciences, 15 (3). pp. 927-936. ISSN 2582-8266

This article explores the evolution of recommender systems from basic personalization tools to strategic decision-making assets within modern business environments. It examines how product intelligence frameworks leverage user behavior data to inform core business strategy across multiple domains. The article presents a theoretical framework for hybrid recommendation architectures, analyzing their comparative effectiveness and implementation methodologies in both B2B and B2C contexts. Through case studies of industry leaders, it illustrates how systematic analysis of user behavior can drive product development and strategic positioning. The article quantifies the business impact of these systems across revenue enhancement, engagement metrics, and product roadmap development, while also examining optimization opportunities in inventory, pricing, and assortment decisions. Looking forward, the article shows emerging trends in AI-powered strategy consultancy, including the integration of foundation models, implementation challenges, competitive implications, and ethical considerations. Throughout, the research emphasizes the transformation of complex behavioral data into actionable strategic insights that deliver measurable competitive advantages.