Impact Factor
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Volume 12 Issue 07
July 2026
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Abstract
Online Shopping Has Become A Dominant Platform In Modern Digital Commerce. Understanding Customer Behavior Is Essential For Improving Marketing Strategies And Increasing Revenue. This Paper Proposes An Enhanced Customer Segmentation Model Named LRFS, Which Extends The Traditional LRF Framework By Introducing A New Component Called Staying Rate. The Staying Rate Represents The Relationship Between User Engagement And Revenue Generation. The Model Utilizes Unsupervised Machine Learning Techniques Including K-Means And K-Medoids Clustering Along With Dimensionality Reduction Methods Such As PCA, T-SNE, And Autoencoder. Comparative Analysis Is Performed Between LR, LF, LRF, And The Proposed LRFS Model. Experimental Results Show That LRFS Provides More Accurate And Meaningful Customer Segmentation, Helping Businesses Identify High-value Customers And Improve Decision-making Strategies.
Keywords
Paper ID
IJSARTV12I4105126
Publication Date
April 23, 2026
Research Area
Artificial Intelligence And Data Science