The future of consumer intelligence: A systematic literature review of AI in neuromarketing

Abstrak

Artificial intelligence has substantially advanced neuromarketing's capacity to analyse subconscious consumer cognition and develop novel computational approaches. Yet the field lacks a unified synthesis of AI's effects across methodology, theory, and practice, hindering both theoretical and empirical advancement. This systematic literature review analyzes 95 peer-reviewed studies from Web of Science and Scopus to examine: (1) current AI applications in neuromarketing; (2) predominant methodologies and technologies; (3) AI's impact on foundational theories; (4) research gaps and limitations; and (5) future trajectories for AI-driven neuromarketing. Results highlight fourteen AI applications, such as emotion detection and predictive analytics, shifting neuromarketing from conventional techniques to real-time consumer insights. 72% of research favours conventional methods over deep learning, despite the fact that AI enhances entire workflows from data collection to personalised distribution. Artificial intelligence challenges established theories (Dual-Process, ELM, Prospect Theory) by revealing dynamic cognitive processes, subconscious decision-making, and evolving constructs. Significant gaps remain: limited multimodal integration (20%), underutilized advanced AI (28%), validation issues, and privacy challenges. Federated learning, ecological validation, multimodal integration, privacy regulation, explainable AI, and adaptive frameworks are the six strategic priorities that have been identified. These findings offer a framework for navigating AI's transformative effects on neuromarketing as a developing commercial field while tackling methodological and ethical challenges.

Identifier

• OpenAlex: https://openalex.org/W7201858892
• DOI: https://doi.org/10.1016/j.chbr.2026.101239