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

Jiahao Huang (), Pao-Min Tu (), Zhicheng Liu (), Weisen Song () and Lijie Li ()
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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

A chapter in Proceedings of the 2023 4th International Conference on Management Science and Engineering Management (ICMSEM 2023), 2024, pp 1718-1725 from Springer

Abstract: 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.

Keywords: k-means; user profiling; Calinski-Harabasz index; department store (search for similar items in EconPapers)
Date: 2024
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DOI: 10.2991/978-94-6463-256-9_175

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