Machine Learning Applications in Marketing: Literature Review and Research Agenda

Currently, machine learning applications in marketing allow to optimize strategies, personalize experiences and improve decision making. However, there are still several research gaps, so the objective is to examine the research trends in the use of machine learning in marketing. A bibliometric analysis is proposed to assess the current scientific activity, following the parameters established by PRISMA-2020. Machine learning applications in marketing have experienced steady growth and increased attention in the academic community. Key references, such as Miklosik and Evans, and prominent journals, such as IEEE Access and Journal of Business Research, have been identified. A thematic evolution towards big data and digital marketing is observed, and thematic clusters such as "digital marketing", "interpretation", "prediction", and "healthcare" stand out. These findings demonstrate the continued importance and research potential of this evolving field.当前，机器学习（Machine Learning）在营销领域的应用可实现策略优化、体验个性化，并助力提升决策水平。然而当前该领域仍存在若干研究空白，因此本研究旨在探析机器学习在营销领域应用的研究趋势。本研究拟采用文献计量分析（Bibliometric Analysis）方法，依据PRISMA-2020设定的规范参数评估当前学术研究活跃度。机器学习在营销领域的应用呈现稳步增长态势，且受到学术界的广泛关注。本研究已甄别出Miklosik与Evans等核心参考文献，以及IEEE Access、"Journal of Business Research"（商业研究期刊）等权威期刊。研究观察到该领域的主题演化正朝向大数据与数字营销方向发展，且涌现出"数字营销""阐释""预测"以及"医疗保健"等突出的主题集群。上述研究结果彰显了这一新兴领域的持续重要性与研究潜力。