Multi-Channel Sellers Traffic Allocation in Large-scale E-commerce Promotion

Abstract

Large-scale online promotions, such as Double 11 and Black Friday, are of great value to e-commerce platforms nowadays. Traditional methods are not successful when we aim to maximize global Gross Merchandise Volume (GMV) in the promotion scenarios due to three limitations. The first is that the GMV of sellers varies significantly from daily scenarios to promotions. Second, these methods do not consider explosive demands in promotions, so that a consumer may fail to purchase some popular items due to sellers' limited capacities. Third, the traffic distribution over sellers presents divergence in different channels, thus rendering the performance of the traditional single-channel methods far from optimal in creating commercial values. To address these problems, we design a Multi-Channel Sellers Traffic Allocation (MCSTA) optimization model to obtain optimal page view (PV) distribution concerning global GMV. Then we propose a general constrained non-smooth convex optimization solution with a Multi-Objective Shortest Distance (MOSD) hyperparameter tuning method to solve MCSTA. This is the first work to systematically address this issue in the scenario of large-scale online promotions. The empirical results show that MCSTA achieves significant improvement of GMV by 1.1% based on A/B test during Alibaba's Global Shopping Festival, one of the world's largest online sales events. Furthermore, we deploy MCSTA in other popular scenarios, including everyday promotion and video live stream service, to showcase that MCSTA can be widely applied in e-commerce and online entertainment services.