Research on a new retail business model of energy e-commerce based on clustering analysis
Authors
Hongda Gao1, Huifang Cao2, *, Jianhui Chen3, Rui Liu4
1Beijing University of Posts and Telecommunications, Beijing, China
2Chinese Academy of Fiscal Sciences, Beijing, China
3China North Standardization Center, Beijing, China
4State Grid Energy Research Institute CO., LTD, Beijing, China
*Corresponding author.
Corresponding Author
Huifang Cao
Available Online 9 October 2023.
- DOI
- 10.2991/978-94-6463-262-0_67How to use a DOI?
- Keywords
- big data; user portrait; business model; precision marketing
- Abstract
After the Internet has gradually entered the era of big data, our lives have gradually entered the information age, which has changed and reshaped the behavior of enterprises and consumers. In this paper, a user portrait clustering model based on big data is proposed to implement business model design for specific groups after clustering, target potential user groups for active marketing, and promote actual purchase behavior. The research results provide a certain reference for precision marketing of relevant industries and enterprises.
- Copyright
- © 2024 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Hongda Gao AU - Huifang Cao AU - Jianhui Chen AU - Rui Liu PY - 2023 DA - 2023/10/09 TI - Research on a new retail business model of energy e-commerce based on clustering analysis BT - Proceedings of the 3rd International Conference on Management Science and Software Engineering (ICMSSE 2023) PB - Atlantis Press SP - 642 EP - 649 SN - 2589-4943 UR - https://doi.org/10.2991/978-94-6463-262-0_67 DO - 10.2991/978-94-6463-262-0_67 ID - Gao2023 ER -