Abstract :

Customer Segmentation Is A Vital Strategy In Modern E-commerce That Enables Businesses To Better Understand Their Customers, Deliver Personalized Experiences, And Optimize Marketing Strategies. This Project Presents A Machine Learning-based Approach To Customer Segmentation Using The K-Means Clustering Algorithm On A Synthetic E-commerce Dataset Containing Demographic, Geographic, Behavioral, And Campaign Interaction Features. The Dataset Consists Of 10,000 Customer Records With 42 Attributes, Providing A Comprehensive View Of Customer Profiles. The Methodology Includes Data Preprocessing, Exploratory Data Analysis (EDA), And Feature Engineering To Prepare The Data For Clustering. The Elbow Method Is Used To Determine The Optimal Number Of Clusters, Resulting In The Identification Of Four Distinct Customer Segments. Each Segment Is Analyzed Based On Key Characteristics Such As Age, Annual Income, Total Spending, Family Size, And Purchasing Behavior. The Segmentation Provides Valuable Insights That Can Be Used For Targeted Marketing, Personalized Recommendations, And Improving Customer Retention Strategies. The Results Demonstrate That Unsupervised Machine Learning Techniques Like K-Means Clustering Are Highly Effective In Identifying Meaningful Customer Groups From Complex Datasets. This Approach Can Further Support Advanced Applications Such As Customer Lifetime Value Analysis, Churn Prediction, And Campaign Optimization.

Published:

06-4-2026

Issue:

Vol. 26 No. 4 (2026)

Page Nos:

1284-1292

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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