Data-Driven Real-time Coupon Allocation in the Online Platform

TL;DR

This paper presents a real-time, data-driven coupon allocation system for online platforms that uses machine learning, optimization, and control techniques to maximize revenue and demand while respecting marketing budgets, demonstrated through large-scale deployment.

Contribution

The study introduces an integrated framework combining ML-based CVR prediction, a Lagrangian Dual optimization algorithm, and a control loop for real-time coupon allocation, with successful large-scale deployment.

Findings

Outperforms traditional coupon strategies in increasing conversion rates.

Achieves higher revenue and profit in large-scale field experiments.

Successfully deployed to over 100 million users, generating significant profit.

Abstract

Traditionally, firms have offered coupons to customer groups at predetermined discount rates. However, advancements in machine learning and the availability of abundant customer data now enable platforms to provide real-time customized coupons to individuals. In this study, we partner with Meituan, a leading shopping platform, to develop a real-time, end-to-end coupon allocation system that is fast and effective in stimulating demand while adhering to marketing budgets when faced with uncertain traffic from a diverse customer base. Leveraging comprehensive customer and product features, we estimate Conversion Rates (CVR) under various coupon values and employ isotonic regression to ensure the monotonicity of predicted CVRs with respect to coupon value. Using calibrated CVR predictions as input, we propose a Lagrangian Dual-based algorithm that efficiently determines optimal coupon values for each arriving customer within 50 milliseconds. We theoretically and numerically investigate the model performance under parameter misspecifications and apply a control loop to adapt to real-time updated information, thereby better adhering to the marketing budget. Finally, we demonstrate through large-scale field experiments and observational data that our proposed coupon allocation algorithm outperforms traditional approaches in terms of both higher conversion rates and increased revenue. As of May 2024, Meituan has implemented our framework to distribute coupons to over 100 million users across more than 110 major cities in China, resulting in an additional CNY 8 million in annual profit. We demonstrate how to integrate a machine learning prediction model for estimating customer CVR, a Lagrangian Dual-based coupon value optimizer, and a control system to achieve real-time coupon delivery while dynamically adapting to random customer arrival patterns.