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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

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Customer Analysis Using Machine Learning-Based Classification Algorithms for Effective Segmentation Using Recency,

Asmat Ullah¹, Muhammad Ismail Mohmand¹, Hameed Hussain²

¹Department of Computer Science, Brains Institute, Peshawar 25000, Pakistan.

Sensors (Basel, Switzerland)

|March 30, 2023

Summary

This summary is machine-generated.

This study introduces a novel Recency, Frequency, Monetary, and Time (RFMT) model for e-commerce customer segmentation. It compares multiple clustering algorithms to identify distinct customer groups for improved marketing strategies.

Keywords:

Calinsky–Harabasz Davies–Bouldin Dunn index Gaussian agglomerative customer segmentation dbscan k-means recency silhouette

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Area of Science:

Data Science
Machine Learning
E-commerce Analytics

Background:

Customer segmentation is crucial for business competitiveness.
Existing Recency, Frequency, Monetary, and Time (RFMT) models have limitations in analyzing data characteristics.
The need for advanced algorithms to refine customer segmentation in e-commerce.

Purpose of the Study:

To propose a novel RFMT model for enhanced customer segmentation.
To compare the effectiveness of k-means, Gaussian, DBSCAN, and agglomerative algorithms.
To identify distinct customer clusters for strategic marketing improvements.

Main Methods:

Application of k-means, Gaussian, DBSCAN, and agglomerative clustering algorithms on an e-commerce dataset.
Utilized cluster validation metrics: elbow, dendrogram, silhouette, Calinski-Harabasz, Davies-Bouldin, and Dunn index.
Employed a majority voting technique to determine the optimal number of clusters.

Main Results:

Identification of three distinct customer clusters using the novel RFMT model.
Successful segmentation across product categories, time periods (year, fiscal year, month), transaction status, and seasons.
Demonstrated the efficacy of the majority voting technique in selecting stable clusters.

Conclusions:

The proposed RFMT model offers a robust approach to customer segmentation.
Effective segmentation enables retailers to enhance customer relationships and targeted marketing.
This methodology provides actionable insights for e-commerce business strategies.