Abstract
The rise of live-streaming e-commerce has attracted the wide participation of online influencers, brands, and retailers. Live streamers offer a fresh shopping experience to consumers through broadcasting product demonstrations and communicating with them. This study characterizes the streamers’ behavior and explores the key drivers of live-streaming e-commerce success as measured by gross merchandise value (GMV) and fan growth. We employ both machine learning and econometric methods to analyze a unique dataset of 55,096 shows by the top 1,000 live streamers on Alibaba’s live streaming platform. We identify three distinct clusters. The most important differentiating features include a live streamer’s platform affiliation and product category. Selling more products and spending more time on each product in a live-streaming show are two factors driving both GMV and fan growth. We also discover that a large fan base does not always help, as the positive effect of fan base only exists conditionally.
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