  Livestreaming commerce, a hybrid of e-commerce and self-media, has expanded
the broad spectrum of traditional sales performance determinants. To
investigate the factors that contribute to the success of livestreaming
commerce, we construct a longitudinal firm-level database with 19,175
observations, covering an entire livestreaming subsector. By comparing the
forecasting accuracy of eight machine learning models, we identify a random
forest model that provides the best prediction of gross merchandise volume
(GMV). Furthermore, we utilize explainable artificial intelligence to open the
black-box of machine learning model, discovering four new facts: 1) variables
representing the popularity of livestreaming events are crucial features in
predicting GMV. And voice attributes are more important than appearance; 2)
popularity is a major determinant of sales for female hosts, while vocal
aesthetics is more decisive for their male counterparts; 3) merits and
drawbacks of the voice are not equally valued in the livestreaming market; 4)
based on changes of comments, page views and likes, sales growth can be divided
into three stages. Finally, we innovatively propose a 3D-SHAP diagram that
demonstrates the relationship between predicting feature importance, target
variable, and its predictors. This diagram identifies bottlenecks for both
beginner and top livestreamers, providing insights into ways to optimize their
sales performance.
