| Issue |
SHS Web Conf.
Volume 181, 2024
2023 International Conference on Digital Economy and Business Administration (ICDEBA 2023)
|
|
|---|---|---|
| Article Number | 04006 | |
| Number of page(s) | 8 | |
| Section | Digital Transformation and Emerging Technologies | |
| DOI | https://doi.org/10.1051/shsconf/202418104006 | |
| Published online | 17 January 2024 | |
Research on the Selection of the most Popular Product Categories in TikTok based on Linear Regression
Business Analysis, Macau University of Science and Technology, 999078, Macau, China
* Corresponding author: 2009853gb011045@student.must.edu.mo
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.
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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