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

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

Author

Listed:
  • Jiahao Huang

    (Dongguan University of Technology)

  • Pao-Min Tu

    (Dongguan University of Technology)

  • Zhicheng Liu

    (Dongguan University of Technology)

  • Weisen Song

    (Dongguan University of Technology)

  • Lijie Li

    (Dongguan University of Technology)

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.

Suggested Citation

  • Jiahao Huang & Pao-Min Tu & Zhicheng Liu & Weisen Song & Lijie Li, 2024. "Applying K-Means Clustering for User Profiling in Retail: A Department Store Case Study," Advances in Economics, Business and Management Research, in: Suhaiza Hanim Binti Dato Mohamad Zailani & Kosga Yagapparaj & Norhayati Zakuan (ed.), Proceedings of the 2023 4th International Conference on Management Science and Engineering Management (ICMSEM 2023), pages 1718-1725, Springer.
    • Handle: RePEc:spr:advbcp:978-94-6463-256-9_175
    DOI: 10.2991/978-94-6463-256-9_175
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