Connecting Customers and Merchants Offline: Experimental Evidence from the Commercialization of Last-mile Stations at Alibaba

• 成果类型: Article
• 署名作者: Han, Brian Rongqing; Sun, Tianshu; Chu, Leon Yang; Wu, Lixia
• 署名单位: University of Illinois System; University of Illinois Urbana-Champaign
• 刊物名称: MIS QUARTERLY
• ISSN/ISSBN: 0276-7783
• DOI: 10.25300/MISQ/2022/16674
• 发表日期: 2024
• 页码: 1157-1192
• 关键词: electronic commerce; online; ECONOMICS; INFORMATION; software; IMPACT; MARKET; identification; externalities; acquisition

摘要

Many e-commerce platforms have established extensive networks of stations as their last-mile logistics infrastructure. This study investigates how this last-mile infrastructure may serve as an offline platform to connect customers and merchants in the physical world by leveraging walk-in traffic (organic interaction) and prompting interested customers through online intervention (induced interaction). Using free sample distribution as an example, we designed two large-scale studies in collaboration with Alibaba-an observational study across 1,032 stations and a randomized field experiment among 189,019 customers- to examine the causal effects of organic and induced interactions on customers'subsequent online purchases from the focal brands, respectively. We found that induced interaction drives significantly more online sales than organic interaction. Under induced interaction, the online intervention effectively increases the number of free samples distributed. Nevertheless, the more significant increase in online sales for induced claimers is not simply due to more free samples distributed but because the induced customers are more interested and more likely to purchase. We identified this phenomenon as a screening mechanism that facilitates an advantageous selection of customers claiming the samples. Customers willing to pay the additional travel cost to claim samples are more likely to purchase the focal brands afterward. Finally, we developed a customized targeting framework using a generalized random forest model to enhance the effectiveness of induced interaction at the last-mile stations. Our study raises a key insight that the foot-in-the-door traffic in an omnichannel environment can be fundamentally different depending on whether the offline customers are driven from the online channel.