Yanmin Li, Chao Meng, Jintao Tian, Zhengyang Fang, Huimin Cao
{"title":"数据驱动的客户网上购物行为分析和个性化营销策略","authors":"Yanmin Li, Chao Meng, Jintao Tian, Zhengyang Fang, Huimin Cao","doi":"10.4018/joeuc.346230","DOIUrl":null,"url":null,"abstract":"In today's highly competitive market environment, personalized marketing has become an important means for enterprises to gain competitive advantages. In order to better meet customer needs, companies need to accurately identify and classify customers to implement more refined market strategies. This study focuses on the customer classification problem. Based on several classic deep learning models, the BiLSTM-TabNet model is designed, and the Whale Optimization Algorithm (WOA) is introduced to further improve the model performance, thereby improving classification accuracy and practicality. Experimental results show that this model has achieved excellent performance on each data set, has higher accuracy and AUC value than the baseline method, and has advantages over other control models in comparative experiments. This research provides solid support for the implementation of personalized marketing strategies.","PeriodicalId":3,"journal":{"name":"ACS Applied Electronic Materials","volume":"53 8","pages":""},"PeriodicalIF":4.3000,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Data-Driven Customer Online Shopping Behavior Analysis and Personalized Marketing Strategy\",\"authors\":\"Yanmin Li, Chao Meng, Jintao Tian, Zhengyang Fang, Huimin Cao\",\"doi\":\"10.4018/joeuc.346230\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In today's highly competitive market environment, personalized marketing has become an important means for enterprises to gain competitive advantages. In order to better meet customer needs, companies need to accurately identify and classify customers to implement more refined market strategies. This study focuses on the customer classification problem. Based on several classic deep learning models, the BiLSTM-TabNet model is designed, and the Whale Optimization Algorithm (WOA) is introduced to further improve the model performance, thereby improving classification accuracy and practicality. Experimental results show that this model has achieved excellent performance on each data set, has higher accuracy and AUC value than the baseline method, and has advantages over other control models in comparative experiments. This research provides solid support for the implementation of personalized marketing strategies.\",\"PeriodicalId\":3,\"journal\":{\"name\":\"ACS Applied Electronic Materials\",\"volume\":\"53 8\",\"pages\":\"\"},\"PeriodicalIF\":4.3000,\"publicationDate\":\"2024-07-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACS Applied Electronic Materials\",\"FirstCategoryId\":\"91\",\"ListUrlMain\":\"https://doi.org/10.4018/joeuc.346230\",\"RegionNum\":3,\"RegionCategory\":\"材料科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Applied Electronic Materials","FirstCategoryId":"91","ListUrlMain":"https://doi.org/10.4018/joeuc.346230","RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
Data-Driven Customer Online Shopping Behavior Analysis and Personalized Marketing Strategy
In today's highly competitive market environment, personalized marketing has become an important means for enterprises to gain competitive advantages. In order to better meet customer needs, companies need to accurately identify and classify customers to implement more refined market strategies. This study focuses on the customer classification problem. Based on several classic deep learning models, the BiLSTM-TabNet model is designed, and the Whale Optimization Algorithm (WOA) is introduced to further improve the model performance, thereby improving classification accuracy and practicality. Experimental results show that this model has achieved excellent performance on each data set, has higher accuracy and AUC value than the baseline method, and has advantages over other control models in comparative experiments. This research provides solid support for the implementation of personalized marketing strategies.
期刊介绍:
ACS Applied Electronic Materials is an interdisciplinary journal publishing original research covering all aspects of electronic materials. The journal is devoted to reports of new and original experimental and theoretical research of an applied nature that integrate knowledge in the areas of materials science, engineering, optics, physics, and chemistry into important applications of electronic materials. Sample research topics that span the journal's scope are inorganic, organic, ionic and polymeric materials with properties that include conducting, semiconducting, superconducting, insulating, dielectric, magnetic, optoelectronic, piezoelectric, ferroelectric and thermoelectric.
Indexed/Abstracted:
Web of Science SCIE
Scopus
CAS
INSPEC
Portico