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Customer segmentation application based on K-Means
- 1 Southwestern University of Finance and Economics
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Abstract
Customer segmentation(CS) is a crucial aspect of customer relationship management, widely utilized by industries, banks, and consulting companies. However, the intricate data relationship between individuals presents significant challenges in customer segmentation research. Fortunately, machine learning has made remarkable progress in processing big data, and its exceptional performance has captivated the attention of business analytics researchers. Based on this, numerous customer segmentation methods based on machine learning have been proposed. This paper aims to review the papers published after 2010 on customer segmentation, and summarize the current status and importance of customer segmentation in implementing marketing strategies. Additionally, it introduces two primary types of customer segmentation scenarios, and summarizes the common combination of analysis models and machine learning algorithms in customer segmentation. Finally, the paper introduces a customer segmentation method based on k-means and provides a perspective on the future development of customer segmentation.
Keywords
RFM Analysis, Customer segmentation, K-Means Clustering
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Cite this article
Zhao,J. (2024). Customer segmentation application based on K-Means. Applied and Computational Engineering,47,242-247.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 4th International Conference on Signal Processing and Machine Learning
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