A Systematic Literature Review Of Machine Learning Adoption In Emerging Marketing Applications

Abstract

This study presents a comprehensive systematic literature review focusing on the adoption and impact of Machine Learning (ML) in marketing strategies, particularly within the context of emerging markets. As digital transformation accelerates globally, businesses in developing economies are increasingly turning to ML technologies to optimize their marketing efforts, enhance customer engagement, and improve operational efficiencies. By leveraging the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, this study systematically reviewed a total of 45 peer-reviewed articles published between 2018 and 2024. The findings reveal that ML-driven marketing strategies have significantly transformed customer engagement through personalized content, predictive analytics, and automated customer service, leading to improved conversion rates and cost efficiencies. However, the study also highlights key challenges, such as data quality, infrastructure limitations, and regulatory hurdles, which constrain ML adoption in these regions. Notably, while larger enterprises have made strides in integrating ML into their marketing processes, small and medium enterprises (SMEs) continue to face barriers related to limited resources and data accessibility. This review underscores the importance of addressing these challenges to fully leverage ML's potential in emerging markets, emphasizing the need for robust data management, privacy compliance, and strategic investments in technology. The study contributes to the existing body of literature by offering insights into the current state of ML adoption in marketing, identifying opportunities, and outlining practical implications for businesses seeking to enhance their marketing capabilities in rapidly evolving digital environments.