机器学习(ML)在市场营销中的应用:文献计量学方法。

Q2 Pharmacology, Toxicology and Pharmaceutics
F1000Research Pub Date : 2025-03-17 eCollection Date: 2025-01-01 DOI:10.12688/f1000research.160010.2
Sebastián Cardona-Acevedo, Erica Agudelo-Ceballos, Diana Arango-Botero, Alejandro Valencia-Arias, Juana De La Cruz Ramírez Dávila, Jesus Alberto Jimenez Garcia, Carlos Flores Goycochea, Ezequiel Martínez Rojas
{"title":"机器学习(ML)在市场营销中的应用:文献计量学方法。","authors":"Sebastián Cardona-Acevedo, Erica Agudelo-Ceballos, Diana Arango-Botero, Alejandro Valencia-Arias, Juana De La Cruz Ramírez Dávila, Jesus Alberto Jimenez Garcia, Carlos Flores Goycochea, Ezequiel Martínez Rojas","doi":"10.12688/f1000research.160010.2","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":12260,"journal":{"name":"F1000Research","volume":"14 ","pages":"92"},"PeriodicalIF":0.0000,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11926528/pdf/","citationCount":"0","resultStr":"{\"title\":\"Applications of Machine Learning (ML) in the context of marketing: a bibliometric approach.\",\"authors\":\"Sebastián Cardona-Acevedo, Erica Agudelo-Ceballos, Diana Arango-Botero, Alejandro Valencia-Arias, Juana De La Cruz Ramírez Dávila, Jesus Alberto Jimenez Garcia, Carlos Flores Goycochea, Ezequiel Martínez Rojas\",\"doi\":\"10.12688/f1000research.160010.2\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>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.</p>\",\"PeriodicalId\":12260,\"journal\":{\"name\":\"F1000Research\",\"volume\":\"14 \",\"pages\":\"92\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2025-03-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11926528/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"F1000Research\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.12688/f1000research.160010.2\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2025/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q2\",\"JCRName\":\"Pharmacology, Toxicology and Pharmaceutics\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"F1000Research","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.12688/f1000research.160010.2","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"Pharmacology, Toxicology and Pharmaceutics","Score":null,"Total":0}
引用次数: 0

摘要

目前,市场营销中的机器学习应用允许优化策略,个性化体验和改进决策。然而,仍然存在一些研究空白,因此我们的目标是研究机器学习在市场营销中的应用的研究趋势。根据PRISMA-2020建立的参数,建议采用文献计量学分析来评估当前的科学活动。机器学习在市场营销中的应用经历了稳定的增长,并越来越受到学术界的关注。关键的参考文献,如Miklosik和Evans,以及著名的期刊,如IEEE Access和Journal of Business Research,已经被确定。观察到向大数据和数字营销的主题演变,“数字营销”、“解释”、“预测”和“医疗保健”等主题集群脱颖而出。这些发现证明了这一不断发展的领域的持续重要性和研究潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Applications of Machine Learning (ML) in the context of marketing: a bibliometric approach.

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.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
F1000Research
F1000Research Pharmacology, Toxicology and Pharmaceutics-Pharmacology, Toxicology and Pharmaceutics (all)
CiteScore
5.00
自引率
0.00%
发文量
1646
审稿时长
1 weeks
期刊介绍: F1000Research publishes articles and other research outputs reporting basic scientific, scholarly, translational and clinical research across the physical and life sciences, engineering, medicine, social sciences and humanities. F1000Research is a scholarly publication platform set up for the scientific, scholarly and medical research community; each article has at least one author who is a qualified researcher, scholar or clinician actively working in their speciality and who has made a key contribution to the article. Articles must be original (not duplications). All research is suitable irrespective of the perceived level of interest or novelty; we welcome confirmatory and negative results, as well as null studies. F1000Research publishes different type of research, including clinical trials, systematic reviews, software tools, method articles, and many others. Reviews and Opinion articles providing a balanced and comprehensive overview of the latest discoveries in a particular field, or presenting a personal perspective on recent developments, are also welcome. See the full list of article types we accept for more information.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信