A two-stage prediction model based on behavior mining in livestream e-commerce

Abstract

Livestream e-commerce has been developing at a tremendous pace in recent years. On livestream platforms, such as Douyin, a retailer attracts viewers into the live room through short video advertising, and then streamers promote and sell products in real time. In such a scenario, an accurate prediction of traffic and sales plays an essential role in operation management, including live content strategy and inventory control. However, complex behaviors (follow, share, comment, etc.) of users and long conversion paths (from seeing the advertisement to entering the live room, and to purchasing the goods) lead to poor performance of traditional prediction methods. Additionally, few studies focus on advertising information in evaluating live room performance. Therefore, we propose a two-stage learning model for traffic and sales prediction based on behavior mining, which combines marketing models and deep learning methods. In the first stage, we integrate user behaviors before getting into the live room with short video advertising data for traffic prediction. In the second stage, based on the traditional marketing model, AIDA (Attention-Interest-Desire-Action), we design a funnel convolutional neural network (FCNN) to learn sophisticated behaviors in the live room in both time and behavior orientations, and take the predicted traffic volume as the auxiliary information for sales prediction. Extensive experiments on real-world datasets from Douyin illustrate the efficacy of our proposed method, which shows the value of fusing marketing models with deep learning techniques. Furthermore, the in-depth analysis provides practical insights into user behaviors for livestream e-commerce.