Promotions play a crucial role in e-commerce platforms, and various cost structures are employed to drive user engagement. This paper focuses on promotions with response-dependent costs, where expenses are incurred only when a purchase is made. Such promotions include discounts and coupons. While existing uplift model approaches aim to address this challenge, these approaches often necessitate training multiple models, like meta-learners, or encounter complications when estimating profit due to zero-inflated values stemming from non-converted individuals with zero cost and profit. To address these challenges, we introduce Incremental Profit per Conversion (IPC), a novel uplift measure of promotional campaigns' efficiency in unit economics. Through a proposed response transformation, we demonstrate that IPC requires only converted data, its propensity, and a single model to be estimated. As a result, IPC resolves the issues mentioned above while mitigating the noise typically associated with the class imbalance in conversion datasets and biases arising from the many-to-one mapping between search and purchase data. Lastly, we validate the efficacy of our approach by presenting results obtained from a synthetic simulation of a discount coupon campaign.

翻译：促销活动在电子商务平台中扮演着关键角色，平台采用多种成本结构来驱动用户参与度。本文聚焦于响应依赖型成本的促销活动，这类促销仅在用户完成购买时产生费用，例如折扣券和优惠券。现有提升建模方法虽旨在解决这一挑战，但通常需要训练多个模型（如元学习器），或在估算利润时因非转化用户产生的零膨胀数据（零成本与零利润）而遇到复杂问题。为应对这些挑战，我们提出了基于转化的增量利润（IPC）——一种衡量促销活动在单位经济效率方面的新颖提升指标。通过所提出的响应转换方法，我们证明IPC仅需使用转化数据、其倾向性得分以及单一模型即可完成估算。因此，IPC不仅解决了上述问题，还缓解了转化数据集中因类别不平衡产生的噪声，以及搜索与购买数据多对一映射带来的偏差。最后，我们通过折扣券活动的合成模拟实验结果验证了该方法的有效性。