AI 中文总结
针对存在处理不依从和多重中介变量的零膨胀纵向数据，研究人员开发贝叶斯因果中介框架，分析促销邮件活动，发现增值激励优于价格折扣，并设计了优化预期购买量的个性化邮件策略。
AI 中文摘要
评估数字营销活动是否有效是设计有效客户互动策略的核心。我们分析了美国某零售商开展的大规模纵向促销邮件活动，以评估免费配送等增值激励措施与传统价格折扣对客户购买行为的影响。该分析因邮件未打开导致的不依从、多个纵向中介变量、零膨胀中介变量及购买结果而变得复杂。为应对这些挑战，我们开发了基于富集狄利克雷过程混合模型的贝叶斯因果中介框架，并使用可扩展的G-计算算法估计因果估计量。研究表明，忽略邮件打开行为的分析会大幅低估估计效应；增值激励措施始终优于价格折扣，产生更高的估计潜在购买金额，且收益随时间累积；我们设计了个性化的顺序邮件策略，以优化观测数据中的预期购买数量。
英文摘要
Understanding whether a digital marketing campaign is effective is central to designing effective customer engagement strategies. We analyze a large-scale, longitudinal promotional email campaign conducted by a U.S.\ retailer to evaluate how value-added incentives, such as free shipping, compare with traditional price discounts in influencing customer purchasing behavior. The analysis is complicated by non-compliance, due to not opening emails, multiple longitudinal mediators, and zero-inflated mediators and purchase outcomes. To address these challenges, we develop a Bayesian causal mediation framework based on enriched Dirichlet process mixture models and estimate the causal estimands using a scalable G-computation algorithm. We show that analyses ignoring email-opening behavior substantially attenuate estimated effects. Value-added incentives consistently outperform price discounts, yielding higher estimated potential purchase amounts, with benefits accumulating over time. We design an individualized sequential emailing strategy that optimizes expected purchase count in the observed data.
