SACO: Sequence-Aware Constrained Optimization Framework for Coupon Distribution in E-commerce

TL;DR

This paper introduces SACO, a novel framework for sequential coupon distribution in e-commerce that leverages historical data and iterative optimization to improve long-term revenue and user engagement.

Contribution

The paper presents SACO, a new sequence-aware constrained optimization framework that effectively models sequential interactions and enhances coupon distribution strategies.

Findings

SACO outperforms existing methods on real-world datasets.

The framework improves long-term revenue and user engagement.

Empirical results validate the effectiveness of SACO.

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 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.