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LRFMV: An efficient customer segmentation model for superstores.

Rezwana Mahfuza1, Nafisa Islam1, Md Toyeb1

  • 1Dept. of Computer Science and Engineering, Brac University, Dhaka, Bangladesh.

Plos One
|December 20, 2022
PubMed
Summary
This summary is machine-generated.

The new Length, Recency, Frequency, Monetary, and Volume (LRFMV) model enhances customer segmentation by incorporating purchase volume. This model reveals a direct profit-quantity relationship, improving profit maximization for superstores.

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Area of Science:

  • Business Analytics
  • Machine Learning
  • Customer Relationship Management

Background:

  • The Recency, Frequency, and Monetary (RFM) model is a standard for customer segmentation and profit analysis.
  • The Length, Recency, Frequency, and Monetary (LRFM) model improved upon RFM for more precise customer grouping.
  • Previous models overlooked the relationship between profit and purchase quantity in superstores.

Purpose of the Study:

  • To introduce an efficient customer segmentation model, LRFMV, integrating purchase volume.
  • To investigate the direct relationship between profit and purchase quantity.
  • To compare the effectiveness of LRFMV against RFM and LRFM models for profit maximization.

Main Methods:

  • Developed the LRFMV model by adding a 'Volume' dimension to the LRFM model.
  • Utilized K-means, K-Medoids, and Mini Batch K-means for data clustering.
  • Applied unsupervised machine learning to analyze volume-profit correlations and customer traits via a Customer-Classification Matrix.

Main Results:

  • The LRFMV model demonstrated a clear profit-quantity relationship, previously unobserved.
  • K-means clustering was selected as the optimal algorithm for the superstore dataset within the LRFMV framework.
  • LRFMV identified precise customer segments with enhanced profit generation compared to RFM and LRFM.

Conclusions:

  • The LRFMV model offers superior customer segmentation for profit maximization in superstores.
  • Incorporating purchase volume is crucial for understanding and optimizing profit-quantity dynamics.
  • The study validates the efficacy of LRFMV in identifying profitable customer segments more accurately.