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This paper introduces Counterfactual Multi-task Delayed Conversion Model (CM-DCM) to improve conversion rate (CVR) prediction during the pre-promotion phase in e-commerce, addressing the challenge of delayed conversions. CM-DCM employs a multi-task architecture to jointly model direct and delayed conversions, a personalized user behavior gating module to handle data sparsity, and a counterfactual causal approach to model add-to-cart to delayed conversion transitions. Experiments and online A/B tests demonstrate CM-DCM's superiority over baselines, leading to significant improvements in advertising revenue and overall GMV.
Predicting pre-promotion conversions in e-commerce gets a boost with a new model that understands how users "window shop" before sales actually start.
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.
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