The Comparative Performance of Online Referral Channels in E-Commerce
使用向量自回归模型分析大型点击流数据，量化搜索引擎、社交媒体和第三方网站三种推荐渠道对销售和转化率的相对效果及相互依赖关系，发现社交媒体推荐对转化率的即时和累积影响最强。
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.