Characterizing Social Marketing Behavior of E-commerce Celebrities and Predicting Their Value

With the rapid development of online social networks, marketing through online social platforms attracts a lot of attention. Recently, a special social marketing method is prevailing, i.e., e-commerce celebrities(ECs). ECs run their social network accounts to attract followers and then sell products to them directly. While the sales of ECs have dominated the e-commerce marketing in China, there is, however, a lack of accurate measurement and model about it. In this paper, we first conduct a large-scale cross-platform measurement on two of the biggest online social network platforms and e-commerce platforms in China, i.e., Sina Weibo and Taobao. We then characterize the typical behavioral patterns of ECs and build a machine learning model to quantitatively represent the relationship between the social network behavior and their product sale volumes. Experimental results show that we can accurately predict an EC's sale volume based on the 41 social network behavior features (F1 score can reach 0.83). Furthermore, we obtain the top-10 most important features that affect the sales. Our measurement and modeling results provide beneficial insights in understanding and optimizing social marketing for ECs.