Qingbo Hu, Sihong Xie, Jiawei Zhang, Qiang Zhu, Songtao Guo, Philip S. Yu

WWW 2016


Nowadays, a modern e-commerce company may have both online sales and offline sales departments. Normally, online sales attempt to sell in small quantities to individual customers through broadcasting a large amount of emails or promotion codes, which heavily rely on the designed backend algorithms. Offline sales, on the other hand, try to sell in much larger quantities to enterprise customers through contacts initiated by sales representatives, which are more costly compared to online sales. Unlike many previous research works focusing on machine learning algorithms to support online sales, this paper introduces an approach that utilizes heterogenous social networks to improve the effectiveness of offline sales. More specifically, we propose a two-phase framework, HeteroSales, which first constructs a company-to-company graph, a.k.a. Company Homophily Graph (CHG), from semantics based meta-path learning, and then adopts label propagation on the graph to predict promising companies that we may successfully close an offline deal with. Based on the statistical analysis on the world’s largest professional social network, LinkedIn, we demonstrate interesting discoveries showing that not all the social connections in a heterogeneous social network are useful in this task. In other words, some proper data preprocessing is essential to ensure the effectiveness of offline sales. Finally, through the experiments on LinkedIn social network data and third-party offline sales records, we demonstrate the power of HereroSales to identify potential enterprise customers in offline sales.