Explainable AI in Marketing Analytics: A Governance Framework for Building Consumer Trust in Emerging Digital Markets

Abstract

Organisations today are increasingly using artificial intelligence (AI) tools such as recommendation systems, predictive targeting and automated personalisation to improve marketing decisions and customer engagement. However, many of these technologies operate as ‘black-box’ systems, making it difficult for consumers to understand how algorithmic decisions are made. This lack of transparency can reduce trust in digital platforms. To address this challenge, this article develops a conceptual governance framework that explains how explainable AI (XAI) improves algorithmic transparency, enhances consumer understanding, strengthens perceptions of fairness and ultimately builds consumer trust in AI-driven marketing analytics systems. Unlike existing studies that primarily examine explainability from a technical perspective, this framework positions XAI as a governance mechanism for responsible marketing practices. The framework is particularly relevant for emerging digital markets such as India, where rapid digital adoption is accompanied by increasing concerns about data privacy, accountability and responsible AI use. The study suggests that integrating explainability into marketing analytics can support ethical personalisation, strengthen long-term consumer trust and promote responsible digital engagement. The proposed conceptual governance framework provides a foundation for understanding how XAI supports responsible marketing analytics and offers an analytical basis for future empirical research. The study also provides practical insights for marketers, platform designers and policymakers seeking to develop trustworthy AI-enabled marketing systems.