The Comparative Performance of Online Referral Channels in E-Commerce

• 成果类型: Article
• 署名作者: Duan, Wenjing; Zhang, Jie
• 署名单位: George Washington University; University of Texas System; University of Texas Arlington
• 刊物名称: JOURNAL OF MANAGEMENT INFORMATION SYSTEMS
• ISSN/ISSBN: 0742-1222
• DOI: 10.1080/07421222.2021.1962598
• 发表日期: 2021
• 页码: 828-854
• 关键词: User-Generated Content; Social media; search; consumer; attribution; IMPACT; MODEL; strategies; spillover; frequency

摘要

The means by which e-commerce websites can reach and track online customers have expanded enormously through the use of various digital marketing referral channels. However, evaluating comparative effectiveness and return on investment (ROI) across different referral channels remain difficult undertakings for many companies. This study aims to contribute to this line of investigation by quantifying the relative effectiveness, the dynamics, and the interdependencies among three types of major online referral channels: search engines, social media, and third-party websites. To this end, we employ the vector autoregressive (VARX) model on a large-scale clickstream dataset and have the following findings. Though search engine referrals demonstrate strong impact on sales, our results show that social media referrals have the strongest immediate and cumulative effects on e-commerce websites' conversion rates. Our results also demonstrate the synergies and interdependencies across these channels. This study contributes to the multi-channel analytics literature and sheds new light to digital marketing managers on assessing the cumulative impact and the economic value of online referral channels.