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Research on Segmenting E-Commerce Customer through an Improved K-Medoids Clustering Algorithm.

Zengyuan Wu¹, Lingmin Jin¹, Jiali Zhao¹

¹College of Economics and Management, China Jiliang University, No. 258, Xueyuan Street, Hangzhou, Zhejiang 310018, China.

Computational Intelligence and Neuroscience

|June 28, 2022

Summary

This summary is machine-generated.

This study introduces an improved Recency, Frequency, and Money (RFM) model and K-medoids algorithm for e-commerce customer segmentation. The enhanced methods improve segmentation accuracy and efficiency by optimizing feature selection and clustering.

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Area of Science:

Data Science
Machine Learning
E-commerce Analytics

Background:

Traditional clustering algorithms face limitations in feature selection and overall clustering effectiveness.
Existing methods for e-commerce customer segmentation often lack precision and efficiency.

Purpose of the Study:

To enhance e-commerce customer segmentation by proposing an improved Recency, Frequency, and Money (RFM) model and an optimized K-medoids algorithm.
To address the shortcomings of traditional clustering techniques in feature selection and clustering outcomes.

Main Methods:

An improved RFM model was developed by incorporating two additional customer consumption behavior features.
An optimized K-medoids algorithm was implemented, utilizing the Calinski-Harabasz (CH) index to determine the optimal number of clusters and refining centroid selection to mitigate noise and outliers.

Main Results:

Empirical validation using e-commerce platform data demonstrated the effectiveness of the proposed approach.
The improved K-medoids algorithm significantly enhanced the efficiency and accuracy of customer segmentation.

Conclusions:

The developed improved RFM model and K-medoids algorithm offer a superior method for e-commerce customer segmentation.
This research contributes to more effective data-driven customer relationship management in the e-commerce sector.