Sales promotions, as short-term incentives to stimulate product purchases, play a pivotal role in modern e-commerce marketing strategies. During promotional events, user behavior patterns exhibit distinct characteristics compared to regular periods. In the pre-promotion phase, users typically engage in product search and browsing without immediate purchases, adding items to carts in anticipation of promotional discounts. This behavior leads to delayed conversions, resulting in significantly lower conversion rates (CVR) before the promotion day. Although existing research has made progress in CVR prediction for promotion days using historical data, it largely overlooks the critical pre-promotion period. And delayed feedback modeling has been extensively studied, current approaches fail to account for the unique distribution shifts in conversion behavior before promotional events, where delayed conversions predominantly occur on the promotion day rather than over continuous time windows. To address these limitations, we propose the Counterfactual Multi-task Delayed Conversion Model (CM-DCM), which leverages historical pre-promotion data to enhance CVR prediction for both delayed and direct conversions. Our model incorporates three key innovations: (i) A multi-task architecture that jointly models direct and delayed conversions using historical pre-promotion data; (ii) A personalized user behavior gating module to mitigate data sparsity issues during brief pre-promotion periods; (iii) A counterfactual causal approach to model the transition probability from add-to-cart (ATC) to delayed conversion. Extensive experiments demonstrate that CM-DCM outperforms baselines in pre-promotion scenarios. Online A/B tests during major promotional events showed significant improvements in advertising revenue, delayed conversion GMV, and overall GMV, validating the effectiveness of our approach.

翻译：促销活动作为刺激产品购买的短期激励，在现代电商营销策略中发挥着关键作用。在促销活动期间，用户行为模式相比常规时期呈现出显著差异。在预售阶段，用户通常进行商品搜索与浏览但不立即购买，而是将商品加入购物车以期待促销折扣。这种行为导致转化延迟，使得促销日前转化率（CVR）显著偏低。尽管现有研究已利用历史数据在促销日CVR预测方面取得进展，但很大程度上忽视了关键的预售期。同时，尽管延迟反馈建模已被广泛研究，现有方法未能应对促销事件前转化行为的独特分布偏移——延迟转化主要发生在促销日而非连续时间窗口。针对这些限制，我们提出反事实多任务延迟转化模型（CM-DCM），利用历史预售数据增强对延迟转化与直接转化的CVR预测。该模型包含三项核心创新：（i）多任务架构，通过历史预售数据联合建模直接转化与延迟转化；（ii）个性化用户行为门控模块，缓解短暂预售期的数据稀疏问题；（iii）反事实因果方法，建模从加购（ATC）到延迟转化的转移概率。大量实验表明，CM-DCM在预售场景中优于基线模型。在大型促销活动期间的在线A/B测试显示，广告收入、延迟转化GMV及整体GMV均显著提升，验证了本方法的有效性。