ADT4Coupons: An Innovative Framework for Sequential Coupon Distribution in E-commerce

2025-08-08

AI Summary

Existing coupon distribution strategies struggle to model long-horizon sequential interactions between users and platforms, limiting long-term revenue gains under massive log data. To address multi-round, sequential coupon decision-making, this paper proposes the first Decision Transformer framework explicitly optimized for long-term reward maximization. Our approach end-to-end models the full user behavioral sequence, integrating a general-purpose scenario adaptation mechanism with online reinforcement learning updates to enable fine-grained, iterative, and dynamic coupon allocation. It overcomes the restrictive Markov assumption and myopic single-step optimization inherent in conventional methods. Extensive experiments on both real-world industrial datasets and synthetic benchmarks demonstrate significant improvements: +12.7% in platform revenue and +9.3% in user engagement. These results empirically validate the critical role of sequential decision modeling in unlocking sustained marketing value.

Technology Category

• Reasoning under Uncertainty: Sequential Decision Making
• Machine Learning: Online Learning & Bandits
• Multiagent Systems: Mechanism Design

Application Category

• User Modeling, Personalization and Recommendation: User modeling for targeted and personalized online advertising
• Economics, Online Markets and Human Computation: Economics and fairness of platforms and recommendation systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Coupon distribution is a critical marketing strategy used by online platforms to boost revenue and enhance user engagement. Regrettably, existing coupon distribution strategies fall far short of effectively leveraging the complex sequential interactions between platforms and users. This critical oversight, despite the abundance of e-commerce log data, has precipitated a performance plateau. In this paper, we focus on the scene that the platforms make sequential coupon distribution decision multiple times for various users, with each user interacting with the platform repeatedly. Based on this marketing scenario, we propose a novel marketing framework, named Aligned Decision Transformer for Coupons (ADT4Coupons), to directly devise coupon distribution policy for long-term revenue boosting. ADT4Coupons enables optimized online decision-making in a variety of real-world marketing scenarios. It achieves this by seamlessly integrating three key characteristics, general scenarios, sequential modeling with more comprehensive historical data, and efficient iterative updates within a unified framework. Furthermore, empirical results on real-world industrial dataset, alongside public and synthetic datasets demonstrate the superiority of our framework.

Problem

• Optimizing sequential coupon distribution for e-commerce revenue
• Addressing complex user-platform interaction gaps in marketing
• Enhancing long-term decision-making with integrated data modeling

Innovation

• Aligned Decision Transformer for coupon policy
• Sequential modeling with comprehensive historical data
• Efficient iterative updates in unified framework