Beyond purchase patterns: harnessing predictive analytics to anticipate unarticulated consumer needs

Area of Science:

• Marketing Strategy
• Business Analytics
• Consumer Behavior

Background:

• Organizations increasingly adopt data-driven strategies.
• Anticipating unarticulated consumer needs is crucial for competitive advantage.
• Artificial intelligence (AI) and predictive analytics offer potential solutions.

Purpose of the Study:

• To investigate how predictive analytics, AI, and diverse data enhance the detection of latent consumer demands.
• To examine the mediating role of predictive analytics in linking data diversity to market performance.
• To explore the moderating effects of customer interface quality and technological innovation.

Main Methods:

• Cross-national survey of 750 digitally active consumers in Jordan, Palestine, and Saudi Arabia.
• Partial Least Squares Structural Equation Modeling (PLS-SEM) to test a conceptual framework.
• Analysis of data diversity, interactive systems, and predictive analytics impact on market performance.

Main Results:

• Predictive analytics significantly mediates the relationship between data diversity and organizational responsiveness.
• Enhanced identification of unexpressed consumer needs and improved market performance were observed.
• Customer interface quality moderates the impact, and technological innovation plays a nuanced role.

Conclusions:

• Integrating AI and diverse data sources with predictive analytics is key to anticipating unarticulated consumer needs.
• The study extends dynamic capabilities theory by offering insights into anticipatory intelligence.
• Combining human-centered design with algorithmic precision translates predictive insights into business value.