{"title":"Predicting livestream shopping success","authors":"Zekun Liu, Weiqing Zhang, Xiao Liu, Eitan Muller, Feiyu Xiong","doi":"10.1016/j.ijresmar.2025.12.002","DOIUrl":null,"url":null,"abstract":"<div><div>Despite the popularity of the livestream shopping industry—projected to surpass $830 billion in China and $68 billion in the U.S. by 2026—research on identifying the factors that drive success in this industry remains scarce, primarily because of the complexity of this environment. In this study, we use data from Taobao Live, the largest livestream shopping platform, to predict the success of sellers hosting livestream sessions. We develop a scoring model using a comprehensive set of livestream strategies and metrics, including session strategies, product assortment strategies, livestream content metrics, and engagement metrics. We find that incorporating session and assortment strategies and livestream content and engagement metrics improves the accuracy of sales performance predictions by 25.6%. Additionally, we identify the key strategies and metrics that are most predictive of sales performance. The proposed predictive framework not only helps in identifying promising sellers but also enhances the understanding of the complex dynamics of the livestream shopping ecosystem, providing valuable insights for stakeholders such as investors, lenders, suppliers, and platform operators.</div></div>","PeriodicalId":48298,"journal":{"name":"International Journal of Research in Marketing","volume":"43 3","pages":"Pages 866-883"},"PeriodicalIF":8.7000,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Research in Marketing","FirstCategoryId":"91","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0167811625001168","RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/12/28 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"BUSINESS","Score":null,"Total":0}
引用次数: 0
Abstract
Despite the popularity of the livestream shopping industry—projected to surpass $830 billion in China and $68 billion in the U.S. by 2026—research on identifying the factors that drive success in this industry remains scarce, primarily because of the complexity of this environment. In this study, we use data from Taobao Live, the largest livestream shopping platform, to predict the success of sellers hosting livestream sessions. We develop a scoring model using a comprehensive set of livestream strategies and metrics, including session strategies, product assortment strategies, livestream content metrics, and engagement metrics. We find that incorporating session and assortment strategies and livestream content and engagement metrics improves the accuracy of sales performance predictions by 25.6%. Additionally, we identify the key strategies and metrics that are most predictive of sales performance. The proposed predictive framework not only helps in identifying promising sellers but also enhances the understanding of the complex dynamics of the livestream shopping ecosystem, providing valuable insights for stakeholders such as investors, lenders, suppliers, and platform operators.
期刊介绍:
The International Journal of Research in Marketing is an international, double-blind peer-reviewed journal for marketing academics and practitioners. Building on a great tradition of global marketing scholarship, IJRM aims to contribute substantially to the field of marketing research by providing a high-quality medium for the dissemination of new marketing knowledge and methods. Among IJRM targeted audience are marketing scholars, practitioners (e.g., marketing research and consulting professionals) and other interested groups and individuals.