Research on the Selection of the most Popular Product Categories in TikTok based on Linear Regression

Abstract

In the e-commerce industry, live streaming has become an increasingly popular way to promote and sell products. With the rise of social media platforms like Facebook, Instagram, and TikTok, more and more businesses are using live streaming to engage with their customers and boost sales. This study investigates the relationship between sales and the number of live viewers to find out the most popular partition for live sales of TikTok’s e-commerce by employing simple linear regression, deseasonalization, and variable transformation techniques to optimize the models. This study improves the accuracy and interpretability of regression models in the e-commerce industry, specifically focusing on the relationship between real-time viewership and Gross Merchandise Volume (GMV). The findings indicate that removing seasonality and applying log-to-log transformation provide more reliable and accurate models, ultimately helping businesses better understand and optimize their sales strategies.