Proceedings of the 2023 4th International Conference on Management Science and Engineering Management (ICMSEM 2023)

Applying K-Means Clustering for User Profiling in Retail: A Department Store Case Study

Authors
Jiahao Huang1, Pao-Min Tu1, *, Zhicheng Liu1, Weisen Song1, Lijie Li1
1Dongguan University of Technology, Dongguan, Guangdong, China
*Corresponding author. Email: paomin.tu@dgut.edu.cn
Corresponding Author
Pao-Min Tu
Available Online 9 October 2023.
DOI
10.2991/978-94-6463-256-9_175How to use a DOI?
Keywords
k-means; user profiling; Calinski-Harabasz index; department store
Abstract

In the face of intensifying market competition, department stores are increasingly focused on understanding consumer characteristics and behaviors, as well as evaluating their value. User profiling emerges as a crucial method for comprehending customer needs and preferences, enabling the development of targeted marketing strategies to enhance customer loyalty and improve user experience. This study employs the k-means clustering algorithm for user profiling in department stores. By utilizing the Calinski-Harabasz index and the elbow method, users are grouped based on three features, resulting in optimal clustering and the division of users into four distinct clusters. Each cluster represents a unique user profile, reflecting diverse characteristics and behaviors. User profiling facilitates the understanding of target customer segments, thereby enabling the implementation of effective personalized marketing strategies. Additionally, it promotes the integration of online and offline experiences and facilitates the prediction of future demand trends. The advancements in big data and artificial intelligence technologies make user profiling an essential tool in the retail industry.

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.

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Volume Title
Proceedings of the 2023 4th International Conference on Management Science and Engineering Management (ICMSEM 2023)
Series
Advances in Economics, Business and Management Research
Publication Date
9 October 2023
ISBN
10.2991/978-94-6463-256-9_175
ISSN
2352-5428
DOI
10.2991/978-94-6463-256-9_175How to use a DOI?
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  - Jiahao Huang
AU  - Pao-Min Tu
AU  - Zhicheng Liu
AU  - Weisen Song
AU  - Lijie Li
PY  - 2023
DA  - 2023/10/09
TI  - Applying K-Means Clustering for User Profiling in Retail: A Department Store Case Study
BT  - Proceedings of the 2023 4th International Conference on Management Science and Engineering Management (ICMSEM 2023)
PB  - Atlantis Press
SP  - 1718
EP  - 1725
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-256-9_175
DO  - 10.2991/978-94-6463-256-9_175
ID  - Huang2023
ER  -