Artificial Intelligence (AI) and Predictive Analytics in Marketing: How Machine Learning Algorithms Shape Consumer Behaviour Predictions in the Nigerian Banking Sector.

This conference paper examines how Artificial Intelligence (AI) and predictive analytics shape consumer behaviour prediction in Nigeria's banking sector. Using a quantitative survey design, the study analysed responses from 236 participants with Partial Least Squares Structural Equation Modelling (PLS-SEM) in SmartPLS 4.1.1.2.

The study tested five hypotheses examining the influence of data quality, algorithm accuracy, customer segmentation capability, and real-time analytics on consumer behaviour prediction, with user trust as a moderating variable. Findings indicate that algorithm accuracy (β = 0.377, p < 0.001) and real-time analytics (β = 0.244, p < 0.001) are significant positive drivers of consumer behaviour prediction effectiveness, while data quality and customer segmentation showed no significant direct effects. User trust significantly moderated three of the four AI-driven dimensions, and its inclusion increased the model's explanatory power from R² = 0.582 to R² = 0.712.

The study is grounded in the Technology Acceptance Model (TAM), the Theory of Planned Behaviour (TPB), and Trust Theory. It contributes to marketing scholarship by confirming the central role of trust and predictive AI tools in strategic decision-making, and highlights the need for Nigerian banks to prioritise data transparency, AI explainability, and user-centric design.

Presented at: Maiden International Conference (Virtual), Department of Marketing, Faculty of Business Administration, University of Nigeria, Enugu Campus, July 17–18, 2025.

Conference theme: Marketplace Digital Transformation: Driving Innovation and Success in a Technology-Driven World.

Sub-theme: Artificial Intelligence applications and the future of marketing.

Note: This is the author's full conference paper. The abstract appears in the official Conference Book of Abstracts. Please cite as a conference paper.

Keywords: artificial intelligence, predictive analytics, consumer behaviour, trust, algorithm accuracy, real-time analytics, Nigerian banking sector, PLS-SEM, Technology Acceptance Model, Trust Theory.