Boosting e-commerce sales with live streaming: the power of barrages

Real-time barrage during e-commerce live streaming introduces a new way to predict online product sales. The primary objective of this research is to quantitatively assess how the count and length of barrages affect onine product sales during e-commerce live streams. By focusing on TikTok as a case study, this study aims to uncover the effect of the count and length of barrages on sales performance, thereby providing strategic insights for optimizing user engagement and improving sales outcomes in e-commerce live streaming. Particularly, we focus on barrage count and length while incorporating several control variables like the number of live-streaming likes and new followers. Based on the Elaboration Likelihood Model (ELM), we presented three models and employed Python to collect barrage data from TikTok and perform statistical analysis, regression analysis, and robustness test. The results demonstrate an inverted U-shaped effect of barrage count and length on online product sales. Furthermore, we discovered that the inverted U-shaped curve reaches its turning point with increased barrage count and length. These findings offer fresh insights into e-commerce live streaming, upon which we provide further suggestions for advancing e-commerce live streaming.