Artificial Intelligence–Driven Customer Journey Optimization and Conversion Performance: A Comprehensive Theoretical and Empirical Synthesis

Abstract

The accelerating integration of artificial intelligence into digital marketing and customer experience management represents one of the most transformative shifts in contemporary business strategy. As digital ecosystems become increasingly complex and customer attention spans continue to decline, organizations face mounting pressure to understand, predict, and influence consumer behavior across fragmented, multi-touchpoint journeys. This research article develops a comprehensive, theory-driven, and empirically grounded examination of artificial intelligence–enabled customer journey analytics, conversion rate optimization, and cohort-based performance measurement within digital commerce environments. Drawing strictly on the provided body of academic literature, industry reports, and institutional publications, the study synthesizes insights from marketing science, data analytics, machine learning, and digital experience optimization to articulate how AI-driven systems reshape the identification, interpretation, and activation of customer journey data.

The article begins by situating the problem within the context of declining attention spans and weakening conversion performance, emphasizing the structural limitations of traditional analytics approaches in capturing non-linear, behaviorally complex customer journeys. It then explores the theoretical foundations of customer journey design, satisfaction formation, trust, and conversion behavior, highlighting how AI-driven learning models extend these theories by enabling adaptive, real-time, and predictive decision-making. A detailed methodological framework is presented, explaining how automated cohort analysis, machine learning–based KPI selection, and digital experience monitoring can be operationalized without reliance on mathematical formalism, instead emphasizing conceptual rigor and interpretive depth.

The findings section offers a descriptive synthesis of observed outcomes reported across the literature, demonstrating that AI-driven optimization consistently enhances conversion efficiency, customer retention, CAC payback dynamics, and experiential coherence when implemented strategically. The discussion critically interrogates these findings, addressing ethical concerns, data governance challenges, organizational readiness, and the risk of algorithmic opacity. The article concludes by articulating a future research agenda focused on explainable AI, trust-centered journey orchestration, and the convergence of customer analytics with broader digital transformation initiatives. By offering an integrative, deeply elaborated, and publication-ready contribution, this study advances academic and managerial understanding of AI’s role in redefining customer journey optimization in the digital economy.