关键词: 直播电商, 主播, 声音特征, 信号理论, 音频挖掘

Abstract: Live streaming e-commerce is an emerging and popular mode of online retail, where products are introduced and promoted through live streaming. Based on the signal theory and the non-verbal cue theory, this paper utilizes audio mining techniques to analyze the live streaming and related sales data from Taobao Live and investigates the influence of streamer’s voice characteristics (specifically pitch, loudness, and speech rate) on sales performance. The results show that pitch, loudness, and speech speed have a significant and robust inverted U-shaped effect on live streaming sales. Additionally, the rhythm of background music positively moderates this relationship between speech rate and sales performance. The matching of background music tempo and speech rate for a specific product category enhances sales performance. This paper further explores different influences of voice characteristics during the two-stage live streaming. The results show that the inverted U-shaped effect of speech rate is insignificant in the first stage of product explanation but becomes significant in the subsequent stage focused on convincing purchase. Overall, this paper provides a new direction for improving the sales performance of live streaming e-commerce. The findings may help live streaming e-commerce merchants and streamers to improve their online performance through strategic use of voice and background music.

Key words: live streaming e-commerce, streamer, voice characteristics, signal theory, audio mining