Machine Learning Applications in Marketing Innovation: A Narrative Review of Global Trends and Challenges

Abstract

Machine learning (ML) is increasingly recognized as a transformative force in marketing innovation, reshaping organizational strategies and consumer engagement across multiple industries. This study reviews the literature on ML applications in marketing, with the objective of synthesizing evidence on predictive analytics, consumer segmentation, personalization, sentiment analysis, and automation. A systematic search was conducted across Scopus, Web of Science, and Google Scholar using targeted keywords such as “machine learning,” “marketing innovation,” “predictive analytics,” and “consumer behavior.” Inclusion criteria focused on peer-reviewed articles published between 2018 and 2025 that explicitly examined ML applications in marketing contexts. The review highlights that algorithms such as Long Short-Term Memory networks, Random Forest, and k-means clustering improve predictive accuracy and segmentation, while generative models and natural language processing enhance personalization, dynamic pricing, and content generation. Findings reveal significant differences between developed and developing countries, where advanced infrastructures support rapid adoption, while resource constraints and digital literacy barriers impede implementation. The discussion further identifies systemic implications, including shifts in organizational processes, policy challenges related to privacy and regulation, and workforce skill gaps. Limitations in current research include insufficient focus on local contexts, cultural factors, and long-term impacts of ML adoption. The study concludes by recommending targeted policies, ethical frameworks, and future research agendas that prioritize inclusivity, equity, and sustainability in ML-driven marketing practices. These strategies are critical for ensuring that ML contributes not only to marketing efficiency but also to broader economic and social progress.