Identifying Consumer Behavioral Patterns in Livestreaming E-Commerce

Abstract

Probabilistic selling, where consumers purchase a product without knowing its exact identity, has traditionally served as a strategy for price discrimination and inventory clearance. One related growing trend is the global sale of blind boxes, where companies such as Pop Mart expand on this strategy through unique designs and emotional engagement, oftentimes through livestreams. As a result, many shoppers now turn to livestream shopping, where influencers unbox blind boxes in real time for their audiences. This study examines the effects of livestream unboxing on viewer spontaneous purchasing decisions using a direct analysis of livestream data. Preliminary testing included comparing seven different audio transcription models for potential use, with OpenAI’s Whisper Large V2 Model achieving the highest accuracy. Additionally, custom words that the model failed to initially identify, such as common names of blind boxes, were added to initial prompting to increase the accuracy of the model. Livestream videos from a leading Chinese livestreaming platform were then converted to audio transcriptions, and phrase and emotion pairs were subsequently identified through a combination of human tagging and Natural Language Processing (NLP) models. Analysis on these phrases, in combination with sales and engagement data, suggest that phrases identified as urgent or persuasive correlate to spikes in viewer purchases. Overall, these results support the work of previous studies that identified a positive correlation between influencer and buyer consumption choices.