{"title":"Analysis and Implications of Adopting AI and Machine Learning in Marketing, Servicing, and Communications Technology","authors":"Priyal J. Borole","doi":"10.4018/ijaiml.338379","DOIUrl":null,"url":null,"abstract":"Methods for machine learning, or ML, are becoming more accessible, and consumer-generated data is on the rise, both of which are transforming marketing strategies. Researchers and marketers still have a long way to go before they fully grasp the myriad ways in which ML applications might help businesses gain and keep an edge in the marketplace. This study systematically evaluates the academic and corporate literature to present a taxonomy of marketing use cases based on machine learning. The authors have discovered 11 common use cases that fall into four distinct groups that reflect the core areas of leverage for machine learning in marketing: shopper fundamentals, consuming experience, decisions, and financial impact. The literature highlights practical implications for researchers and marketers by discussing the taxonomy's found repeating patterns and providing an analytical structure for analyzing it and extension.","PeriodicalId":217541,"journal":{"name":"Int. J. Artif. Intell. Mach. Learn.","volume":"311 ","pages":"1-11"},"PeriodicalIF":0.0000,"publicationDate":"2024-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Int. J. Artif. Intell. Mach. Learn.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4018/ijaiml.338379","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Methods for machine learning, or ML, are becoming more accessible, and consumer-generated data is on the rise, both of which are transforming marketing strategies. Researchers and marketers still have a long way to go before they fully grasp the myriad ways in which ML applications might help businesses gain and keep an edge in the marketplace. This study systematically evaluates the academic and corporate literature to present a taxonomy of marketing use cases based on machine learning. The authors have discovered 11 common use cases that fall into four distinct groups that reflect the core areas of leverage for machine learning in marketing: shopper fundamentals, consuming experience, decisions, and financial impact. The literature highlights practical implications for researchers and marketers by discussing the taxonomy's found repeating patterns and providing an analytical structure for analyzing it and extension.
机器学习(ML)的方法越来越容易获得,消费者生成的数据也在不断增加,这两者都在改变着营销策略。研究人员和营销人员要想完全掌握 ML 应用可能帮助企业获得并保持市场优势的各种方法,还有很长的路要走。本研究系统地评估了学术和企业文献,提出了基于机器学习的营销用例分类法。作者发现了 11 种常见的使用案例,它们分为四个不同的组别,反映了机器学习在营销中的核心应用领域:购物者基础知识、消费体验、决策和财务影响。文献通过讨论分类法发现的重复模式,并提供了分析和扩展分类法的分析结构,突出了对研究人员和营销人员的实际意义。