Access Journal - Access to Science, Business, Innovation in the digital economy最新文献

筛选
英文 中文
Consumer behavior clustering of food retail chains by machine learning algorithms 基于机器学习算法的食品零售链消费者行为聚类
Access Journal - Access to Science, Business, Innovation in the digital economy Pub Date : 1900-01-01 DOI: 10.46656/access.2021.2.3(3)
O. Liashenko, T. Kravets, Matvii Prokopenko
{"title":"Consumer behavior clustering of food retail chains by machine learning algorithms","authors":"O. Liashenko, T. Kravets, Matvii Prokopenko","doi":"10.46656/access.2021.2.3(3)","DOIUrl":"https://doi.org/10.46656/access.2021.2.3(3)","url":null,"abstract":"Analysis of the behavior of an economic agent is one of the central themes of microeconomics. Right now, with the comprehensive increase in the amount of data and the expansion of the computing capabilities of personal computers, there is a need to implement methods of behavioral economics in the study of human behavior. In the course of this study, a survey was created aimed at identification of patterns of behavior of the modern consumer according to his selection criteria stores and reactions to questions based on Behavioral Economics theorems. Clustering the obtained results were performed using machine learning algorithms, after which the Random Forest classification algorithm was trained. According to the results of Silhouette analysis, K-means clusters were selected as the main ones for further modeling. TSNE algorithms, hierarchical and spectral analysis were used for additional visual representation. This study offers a tool for classifying customer preferences and analyzing current industry trends. A tool has been created to classify consumers of food retail chains in order to improve their \"buyer's journey\" and better understand their needs. The created tool for clustering and classification by machine learning methods can be used in business processes. To improve the result, it is necessary to choose a more representative sample, because used in this study consists of an average rationally thinking and knowledgeable individuals, which cannot be said of the average consumer not only among the older generation but also among the younger. Therefore, the next directions in the study may be to identify new ones behavioral trends in other industries; deepening understanding of food retail; use of geodata to improve analysis, etc. Potentially attractive the direction may be to add an assessment of the impact of network advertising on behavior consumers through semantics analysis and image recognition.","PeriodicalId":176153,"journal":{"name":"Access Journal - Access to Science, Business, Innovation in the digital economy","volume":"290 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129822506","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
0
×
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×
相关产品
×
本文献相关产品
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:604180095
Book学术官方微信