Beyond purchase patterns: harnessing predictive analytics to anticipate unarticulated consumer needs - Khaled Alshaketheep; Hind Al-Ahmed; Ahmad Mansour (Acta Psychologica)
As organizations transition toward data-driven strategies, the ability to anticipate unarticulated consumer needs has emerged as a critical frontier in strategic marketing. This study investigates how predictive analytics, when integrated with artificial intelligence (AI) and diverse data sources, can enhance firms' capacity to detect and respond to latent consumer demands. Drawing on a cross-national survey of 750 digitally active consumers in Jordan, Palestine, and Saudi Arabia, the research employs Partial Least Squares Structural Equation Modeling (PLS-SEM) to test a conceptual framework linking data diversity, interactive systems, and predictive analytics with market performance. The findings reveal that predictive analytics serves as a powerful mediator between heterogeneous data inputs and improved organizational responsiveness, significantly contributing to the identification of unexpressed needs and enhancement of market performance. Moreover, the study uncovers the moderating role of customer interface quality and the nuanced impact of technological innovation. By extending dynamic capabilities theory and addressing theoretical gaps in the detection of unconscious consumer intent, this research offers a novel perspective on anticipatory intelligence. The results underscore the importance of integrating human-centered design with algorithmic precision to translate predictive insights into actionable business value.