Customer Segmentation in User Behavior Analysis: A Comparative Study of Clustering Algorithms

Yingze Liu

doi:10.54097/hbem.v21i.14758

Authors

Yingze Liu

DOI:

https://doi.org/10.54097/hbem.v21i.14758

Keywords:

Customer segmentation; user behavior analysis; clustering algorithms; K-means; hierarchical clustering, DBSCAN.

Abstract

A thorough understanding of customer behavior patterns is still essential for corporate success in today's digital environment. To segment clients in-depth, this study used three distinct clustering algorithms: k-means, hierarchical clustering, and dbscan. By carefully examining the age, yearly income, and consumption score This study goes beyond conventional views and creates a comprehensive and distinct perspective when disclosing numerous consumer attributes using data derived from the mall consumer Segmentation Data set. It offers insightful information that can be used to adjust a company's marketing plan. The strategic application of customer insights is redefined in this study, empowering stakeholders to decide wisely and improve market performance. The road to greater competitiveness and relevance in a developing market segment is mapped using real-world data and powerful clustering techniques. Based on research of this dataset, it was discovered that Dbscan performed best on this dataset with unequal density. It highlights the usefulness of these algorithms in today's complicated business world.

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