The Role of Explainable AI (XAI) in Marketing Decision-Making and Strategic Business Intelligence
1 Department of Marketing, Faculty of Management Sciences, Imo State University, PMB 2000.
2 Department of Accounting, Ekiti State University (EKSU), Ado-Iworoko P.M.B. 5363, Ado-Ekiti 360101, Ekiti State, Nigeria.
3 Department of Public Administration, Kaduna State Polytechnic, Kaduna State Nigeria.
Review
Open Access Research Journal of Multidisciplinary Studies, 2021, 01(01), 050-059.
Article DOI: 10.53022/oarjms.2021.1.1.0019
Publication history:
Received on 24 March 2021; revised on 23 May 2021; accepted on 27 May 2021
Abstract:
Explainable Artificial Intelligence (XAI) has emerged as a transformative force in the application of artificial intelligence within marketing and strategic business intelligence. As organizations increasingly adopt complex machine learning models to drive decision-making, the need for transparency, interpretability, and accountability becomes paramount. Traditional black-box models, while powerful, often lack the clarity required for stakeholders to trust and act upon AI-generated insights. This opacity poses risks not only to managerial confidence but also to customer trust, regulatory compliance, and ethical responsibility. XAI addresses this gap by offering methodologies that elucidate the inner workings of AI systems, thereby enhancing trust, compliance, and strategic alignment. By making AI decisions more understandable, XAI empowers managers to validate predictions, regulators to ensure fairness, and customers to engage with confidence in personalized experiences. The growing importance of XAI is underscored by the increasing reliance on AI-driven tools for customer segmentation, predictive analytics, and personalized marketing. In highly competitive markets, where consumer expectations for transparency and ethical practices are rising, XAI provides a critical bridge between technical sophistication and human interpretability. It enables organizations to not only harness the predictive power of advanced algorithms but also to explain outcomes in ways that align with cognitive and ethical frameworks. This manuscript explores the theoretical foundations of XAI, its core methodologies, and its practical applications in marketing and business intelligence. Through detailed case studies and a comprehensive literature review, we examine how XAI contributes to improved customer segmentation, personalized marketing, and ethical decision-making. The paper further highlights the strategic implications of XAI adoption, emphasizing its role in fostering sustainable competitive advantage, regulatory compliance, and long-term customer trust. Finally, we outline future directions for research and practice, positioning XAI as a cornerstone of responsible and effective AI integration in the evolving digital economy.
Keywords:
Black-box model; Artificial Intelligence; Customer segmentation; Data protection; Business Intelligence
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Copyright © 2021 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0