When and How to Leverage E-commerce Cart Targeting: The Relative and Moderated Effects of Scarcity and Price Incentives with a Two-Stage Field Experiment and Causal Forest Optimization

• 成果类型: Article
• 署名作者: Luo, Xueming; Lu, Xianghua; Li, Jing
• 署名单位: Pennsylvania Commonwealth System of Higher Education (PCSHE); Temple University; Fudan University; Nanjing University
• 刊物名称: INFORMATION SYSTEMS RESEARCH
• ISSN/ISSBN: 1047-7047
• DOI: 10.1287/isre.2019.0859
• 发表日期: 2019
• 页码: 1203-1227
• 关键词: EMPIRICAL-ANALYSIS; Resource scarcity; shopping goals; consumer price; promotion; IMPACT; product; MODEL; sales; uncertainty

摘要

The rise of online shopping cart-tracking technologies enables new opportunities for e-commerce cart targeting (ECT). However, practitioners might target shoppers who have short-listed products in their digital carts without fully considering how ECT designs interact with consumer mindsets in online shopping stages. This paper develops a conceptual model of ECT that addresses the question of when (with versus without carts) and how to target (scarcity versus price promotion). Our ECT model is grounded in the consumer goal stage theory of deliberative or implemental mindsets and supported by a two-stage field experiment involving more than 22,000 mobile users. The results indicate that ECT has a substantial impact on consumer purchases, inducing a 29.9% higher purchase rate than e-commerce targeting without carts. Moreover, this incremental impact is moderated: the ECT design with a price incentive amplifies the impact, but the same price incentive leads to ineffective e-commerce targeting without carts. By contrast, a scarcity message attenuates the impact but significantly boosts purchase responses to targeting without carts. Interestingly, the costless scarcity nudge is approximately 2.3 times more effective than the costly price incentive in the early shopping stage without carts, whereas a price incentive is 11.4 times more effective than the scarcity message in the late stage with carts. We also leverage a causal forest algorithm that can learn purchase response heterogeneity to develop a practical scheme of optimizing ECT. Our model and findings empower managers to prudently target consumer shopping interests embedded in digital carts to capitalize on new opportunities in e-commerce.