Customer Analysis Using Machine Learning-Based Classification Algorithms for Effective Segmentation Using Recency, Frequency, Monetary, and Time

Author:

Ullah Asmat1,Mohmand Muhammad Ismail1,Hussain Hameed2ORCID,Johar Sumaira1ORCID,Khan Inayat3,Ahmad Shafiq4ORCID,Mahmoud Haitham A.4ORCID,Huda Shamsul5ORCID

Affiliation:

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

2. Department of Computer Science, University of Buner, Buner 19290, Pakistan

3. Department of Computer Science, University of Engineering and Technology, Mardan 23200, Pakistan

4. Industrial Engineering Department, College of Engineering, King Saud University, P.O. Box 800, Riyadh 11421, Saudi Arabia

5. School of Information Technology, Deakin University, Burwood, VIC 3128, Australia

Abstract

Customer segmentation has been a hot topic for decades, and the competition among businesses makes it more challenging. The recently introduced Recency, Frequency, Monetary, and Time (RFMT) model used an agglomerative algorithm for segmentation and a dendrogram for clustering, which solved the problem. However, there is still room for a single algorithm to analyze the data’s characteristics. The proposed novel approach model RFMT analyzed Pakistan’s largest e-commerce dataset by introducing k-means, Gaussian, and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) beside agglomerative algorithms for segmentation. The cluster is determined through different cluster factor analysis methods, i.e., elbow, dendrogram, silhouette, Calinsky–Harabasz, Davies–Bouldin, and Dunn index. They finally elected a stable and distinctive cluster using the state-of-the-art majority voting (mode version) technique, which resulted in three different clusters. Besides all the segmentation, i.e., product categories, year-wise, fiscal year-wise, and month-wise, the approach also includes the transaction status and seasons-wise segmentation. This segmentation will help the retailer improve customer relationships, implement good strategies, and improve targeted marketing.

Funder

King Saud University through Researchers Supporting Project

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry

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