Proceedings of the 2022 International Conference on Social Sciences and Humanities and Arts (SSHA 2022)

Analysis of User Portraits in the Cosmetics Industry

Authors
Fangzhou Wang
Accounting, Hangzhou Dianzi University, Hangzhou, Zhejiang, China
Corresponding author. Email: wangfangzhou0219@163.com
Corresponding Author
Fangzhou Wang
Available Online 8 April 2022.
DOI
10.2991/assehr.k.220401.187How to use a DOI?
Keywords
cosmetics; user portrait; cluster analysis; precision marketing
Abstract

With the development of big data, the use of computer technology to collect and analyze Internet data, form user portraits, and extract the characteristic tags of target users is conducive to the realization of precision marketing for enterprises. This paper selects the cosmetics industry for data analysis and constructs user portraits. The first is to review cosmetics research and user portrait research, then to collect and analyze the data of the cosmetics industry, establish user portraits, and finally put forward the significance of precision marketing based on the analysis results.

Copyright
© 2022 The Authors. Published by Atlantis Press SARL.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license.

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Volume Title
Proceedings of the 2022 International Conference on Social Sciences and Humanities and Arts (SSHA 2022)
Series
Advances in Social Science, Education and Humanities Research
Publication Date
8 April 2022
ISBN
978-94-6239-560-2
ISSN
2352-5398
DOI
10.2991/assehr.k.220401.187How to use a DOI?
Copyright
© 2022 The Authors. Published by Atlantis Press SARL.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license.

Cite this article

TY  - CONF
AU  - Fangzhou Wang
PY  - 2022
DA  - 2022/04/08
TI  - Analysis of User Portraits in the Cosmetics Industry
BT  - Proceedings of the 2022 International Conference on Social Sciences and Humanities and Arts (SSHA 2022)
PB  - Atlantis Press
SP  - 973
EP  - 977
SN  - 2352-5398
UR  - https://doi.org/10.2991/assehr.k.220401.187
DO  - 10.2991/assehr.k.220401.187
ID  - Wang2022
ER  -