Explainable AI in Marketing Analytics: Balancing Predictive Power and Managerial Trust

Abstract

This article examines how explainable artificial intelligence can reconcile the tension between predictive performance and managerial trust in contemporary marketing analytics. Drawing on a synthesis of prior studies, it maps how artificial intelligence and machine learning are deployed for targeting, personalization, resource allocation, and customer journey optimization, while highlighting the persistent opacity of complex black box models. The review identifies key families of explanation techniques, including global and local model explanations, feature importance analysis, and counterfactual reasoning, and discusses how these tools can make algorithmic decision paths intelligible for managers. The findings show that explainable artificial intelligence can enhance error diagnosis, perceived fairness, and confidence in model outputs, yet also reveal that technical transparency alone is insufficient to overcome algorithm aversion. The article argues that effective use of explainable artificial intelligence in marketing requires integrated governance arrangements that combine powerful predictive models with human oversight, user control, and clear accountability structures. These conditions support more responsible data driven marketing decisions.