报告时间：2022年1月15日（星期六）14:30-16:00

报告平台：腾讯会议 ID:249 346 028

报 告 人：Chenshuo Sun(孙辰硕) 博士

工作单位：美国纽约大学

举办单位：管理学院

报告简介：

The proliferation of omnichannel practices and emerging technologies opens up new opportunities for companies to collect voluminous data across multiple channels. This study examines whether leveraging omnichannel data can lead to, statistically and economically, significantly better predictions on consumers’ online path-to-purchase journeys, given the intrinsic fluidity in and heterogeneity brought forth by the digital transformation of traditional marketing. Using an omnichannel data set that captures consumers’ online behavior in terms of their website browsing trajectories and their offline behavior in terms of physical location trajectories, we predict consumers’ future path-to-purchase journeys based on their historical omnichannel behaviors. Using a state-of-the-art deep-learning algorithm, we find that using omnichannel data can significantly improve our model’s predictive power. The lift curve analysis reveals that the omnichannel model outperforms the corresponding single-channel model by 7.38%. This enhanced predictive power benefits various heterogeneous online firms, regardless of their size, offline presence, mobile app availability, or whether they are selling single- or multicategory products. Using an illustrative example of targeted marketing, we further quantify the economic value of the improved predictive power using a cost-revenue analysis. Our paper contributes to the emerging literature on omnichannel marketing and sheds light on the inherent dynamics and fluidity in consumers’ online path-to-purchase journeys.

报告人简介：

Chenshuo Sun(孙辰朔)，纽约大学斯特恩商学院信息系统与信息管理专业博士。目前主要方向是结合机器学习与数理经济学方法，研究数字经济领域前沿问题，包括消费者路径分析、数据价值、全渠道营销、数字隐私与新兴技术经济学。研究成果发表在Information Systems Research，Transportation Research Part B等国际学术期刊。研究成果获得2021年摩根大通AI博士生奖学金（J.P. Morgan Ph.D. Fellowship Award 2021）、2021年获第42届国际信息系统年会（ICIS，美国奥斯汀）最佳学生论文奖，2021年第20届国际电子商务论坛（AIS SIGEBIZ Web，美国奥斯汀）Michael J.  Shaw最佳论文奖，美国营销科学院（MSI）2018-2020最佳工作论文提名。