Lrfs : Online Shopper's Behaviour - Based Efficient Customer Segmentation Model

• Author(s):

  Jahnavi Settipalli

• Keywords:

  Customer Segmentation, Unsupervised Learning, K-Means, K-Medoids, PCA, T-SNE, Autoencoder, Google Analytics

• 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.

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Other Details

• Paper id:

  IJSARTV12I4105126

• Published in:

  Volume: 12 Issue: 4 April 2026

• Publication Date:

  2026-04-23

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