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 Sequence-Aware Constrained Optimization (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.

E-hailing platforms have become an important component of public transportation in recent years. The supply (online drivers) and demand (passenger requests) are intrinsically imbalanced because of the pattern of human behavior, especially in time and locations such as peak hours and train stations. Hence, how to balance supply and demand is one of the key problems to satisfy passengers and drivers and increase social welfare. As an intuitive and effective approach to address this problem, driver repositioning has been employed by some real-world e-hailing platforms. In this paper, we describe a novel framework of driver repositioning system, which meets various requirements in practical situations, including robust driver experience satisfaction and multi-driver collaboration. We introduce an effective and user-friendly driver interaction design called “driver repositioning task”. A novel modularized algorithm is developed to generate the repositioning tasks in real time. To our knowledge, this is the first industry-level application of driver repositioning. We evaluate the proposed method in real-world experiments, achieving a 2% improvement of driver income. Our framework has been fully deployed in the online system of DiDi Chuxing and serves millions of drivers on a daily basis.