A multimodal analytics framework for product sales prediction with the reputation of anchors in live streaming e-commerce

Abstract

Insufficient sales prediction easily leads to the untimely dispatch of supply and causes inventory problems, resulting in a loss of profits for merchants. Previous sales prediction research was mainly based on traditional e-commerce platforms that cannot be directly applied to live streaming e-commerce, which adds many important elements. The main contribution of our research is the design of a multimodal analytics framework for product sales prediction in live streaming e-commerce. In the proposed framework, we explore the influence of anchor reputation on product sales and innovatively consider both historical and real-time reputation signals. In addition, to better extract valuable information for real-time signals, we propose an A-tiFSR model to extract features from product text and images and design a fine-grained analysis method to mine danmaku data. We conduct our experiments based on a real-world dataset collected from Douyin live streaming. The experimental results demonstrate the effectiveness of the constructed multimodal reputation signals of anchors on product sales prediction. The management implication of our research is that merchants and anchors should pay more attention to their multidimensional reputation. The proposed sales prediction framework can help merchants predict sales more accurately and optimize inventory policy and marketing strategies. This study thus supports the more effective development of live streaming e-commerce markets.