Beyond purchase patterns

Harnessing Predictive Analytics to Anticipate Unarticulated Consumer Needs

Bibliographic Data

ID7201061
AuthorsKhaled Alshaketheep (0000-0002-9632-8814, Hashemite University, corresponding author), Hind Al-Ahmed (0000-0001-5179-8575, Imam Mohammad ibn Saud Islamic University), Ahmad Mansour (0000-0002-5849-4838, Al-Ahliyya Amman University)
Year2025
Volume257
Pages105089-105089
Publication date2025-05-22
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueActa Psychologica (JOURNAL)
Journal identifiersISSN: 0001-6918 • E-ISSN: 1873-6297
PublisherElsevier BV (PUBLISHER)
DOI10.1016/j.actpsy.2025.105089
PMID40412311
OpenAlexW4410594438
LanguageEN
Citations received2
References cited22

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

Analytics · Data science · Predictive analytics · Computer Science · Consumer Market Behavior and Pricing · Consumer Retail Behavior Studies · Psychology · Technology Adoption and User Behaviour

Tools

Open DOIOpen Access