Customer Segmentation and Classification in E-Commerce Platforms

In recent years, e-commerce platforms have experienced rapid growth, leading to increased competition and a strong need for customer-oriented services. Traditional e-commerce systems provide uniform offers to all users without considering individual purchasing behavior, resulting in reduced customer satisfaction. This project proposes a customer segmentation and classification system for ecommerce platforms using a rule-based data mining approach. The system is developed using the .NET framework (C#) as the front end and SQL Server as the back end. It records customer purchase history automatically and analyzes buying behavior based on frequency and total expenditure. Customers are classified into Gold, Silver, and Platinum categories, and personalized discounts are provided accordingly. This approach enhances customer satisfaction, reduces manual effort, and improves overall business efficiency.