Counterfactual Multi-task Learning for Delayed Conversion Modeling in E-commerce Sales Pre-Promotion

2026-04-23

AI Summary

This study addresses the challenge of significant user conversion delays during pre-promotion periods, where behavioral distribution shifts and data sparsity severely degrade the performance of conventional prediction models. To tackle these issues, the authors propose the Causal Multi-task Delayed Conversion Model (CM-DCM), which jointly models immediate and delayed conversions through a multi-task architecture. The model incorporates a personalized behavior gating mechanism to mitigate data sparsity and leverages counterfactual causal inference to capture the dynamic transition from add-to-cart actions to delayed conversions. Extensive experiments demonstrate that CM-DCM substantially outperforms existing baselines in pre-promotion scenarios. Online A/B tests further confirm its practical efficacy, yielding significant improvements in ad revenue, delayed-conversion GMV, and overall GMV.

Technology Category

• Machine Learning: Causal Learning
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior

Application Category

• User Modeling, Personalization and Recommendation: User modeling for targeted and personalized online advertising
• Web Mining and Content Analysis: Large pretrained models with web data
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

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.

Problem

• delayed conversion
• pre-promotion
• conversion rate prediction
• distribution shift
• e-commerce

Innovation

• counterfactual learning
• multi-task learning
• delayed conversion
• e-commerce promotion
• causal inference