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 scenario, we propose a novel marketing framework, named \textbf{S}equence-\textbf{A}ware \textbf{C}onstrained \textbf{O}ptimization (SACO) framework, to directly devise coupon distribution policy for long-term revenue boosting. SACO framework 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.

翻译：优惠券分发是线上平台用于提升收入和增强用户参与度的关键营销策略。遗憾的是，现有优惠券分发策略远未有效利用平台与用户之间复杂的序列交互。尽管存在丰富的电商日志数据，这一关键疏忽已导致性能提升陷入停滞。本文聚焦于平台为不同用户多次进行序列化优惠券分发决策、且每个用户与平台重复交互的场景。基于此场景，我们提出一种新颖的营销框架——**序列感知约束优化（SACO）框架**，以直接设计提升长期收入的优惠券分发策略。SACO框架通过将三个关键特性——通用场景、基于更全面历史数据的序列建模以及高效迭代更新——无缝集成于统一框架中，从而能够在多种现实营销场景中实现优化的在线决策。此外，在真实工业数据集以及公开与合成数据集上的实证结果均证明了本框架的优越性。