報告時間：2022年1月15日（星期六）14:30-16:00
報告平臺：騰訊會議 ID:249 346 028
報 告 人：Chenshuo Sun(孫辰碩) 博士
工作單位：美國紐約大學
舉辦單位：管理學院
報告簡介：
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.
報告人簡介：
Chenshuo Sun(孫辰朔)，紐約大學斯特恩商學院信息系統與信息管理專業博士。目前主要方向是結合機器學習與數理經濟學方法，研究數字經濟領域前沿問題，包括消費者路徑分析、數據價值、全渠道營銷、數字隱私與新興技術經濟學。研究成果發表在Information Systems Research，Transportation Research Part B等國際學術期刊。研究成果獲得2021年摩根大通AI博士生獎學金（J.P. Morgan Ph.D. Fellowship Award 2021）、2021年獲第42屆國際信息系統年會（ICIS，美國奧斯汀）最佳學生論文獎，2021年第20屆國際電子商務論壇（AIS SIGEBIZ Web，美國奧斯汀）Michael J. Shaw最佳論文獎，美國營銷科學院（MSI）2018-2020最佳工作論文提名。
