報(bào)告時(shí)間：2022年1月15日（星期六）14:30-16:00

報(bào)告平臺(tái)：騰訊會(huì)議 ID:249 346 028

報(bào) 告 人：Chenshuo Sun(孫辰碩) 博士

工作單位：美國(guó)紐約大學(xué)

舉辦單位：管理學(xué)院

報(bào)告簡(jiǎn)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(bào)告人簡(jiǎn)介：

Chenshuo Sun(孫辰朔)，紐約大學(xué)斯特恩商學(xué)院信息系統(tǒng)與信息管理專(zhuān)業(yè)博士。目前主要方向是結(jié)合機(jī)器學(xué)習(xí)與數(shù)理經(jīng)濟(jì)學(xué)方法，研究數(shù)字經(jīng)濟(jì)領(lǐng)域前沿問(wèn)題，包括消費(fèi)者路徑分析、數(shù)據(jù)價(jià)值、全渠道營(yíng)銷(xiāo)、數(shù)字隱私與新興技術(shù)經(jīng)濟(jì)學(xué)。研究成果發(fā)表在Information Systems Research，Transportation Research Part B等國(guó)際學(xué)術(shù)期刊。研究成果獲得2021年摩根大通AI博士生獎(jiǎng)學(xué)金（J.P. Morgan Ph.D. Fellowship Award 2021）、2021年獲第42屆國(guó)際信息系統(tǒng)年會(huì)（ICIS，美國(guó)奧斯?。┳罴褜W(xué)生論文獎(jiǎng)，2021年第20屆國(guó)際電子商務(wù)論壇（AIS SIGEBIZ Web，美國(guó)奧斯汀）Michael J.  Shaw最佳論文獎(jiǎng)，美國(guó)營(yíng)銷(xiāo)科學(xué)院（MSI）2018-2020最佳工作論文提名。