Abstract:
After-sales service plays a vital role in customer satisfaction and long-term
business success, yet many organizations struggle with efficiently managing
product-related data and delivering personalized support. In the automotive and retail
sectors, customers often face challenges in finding the right spare parts or follow-up
products based on their past purchases. Existing systems typically lack intelligent
recommendation features and centralized management, which hinders customer
engagement and limits business insights.
To address these issues, this project introduces AIS (After Sales Information
System), a web-based product management platform designed to enhance the
post-purchase customer experience through personalized product recommendations. The
system integrates transactional data processing with a collaborative filtering algorithm to
recommend relevant spare parts or follow-up items tailored to each customer's purchase
history. AIS also includes a streamlined interface for managing product information, sales
data, and customer profiles, ensuring centralized control and improved decision-making.
Key features of AIS include structured product categorization, a hybrid
recommendation engine that combines collaborative and content-based logic, and
evaluation metrics to track algorithm accuracy. By offering intelligent suggestions and
simplifying data management, AIS aims to increase customer retention, optimize
inventory usage, and support data-driven after-sales strategies.
Ultimately, this platform bridges the gap between product usage and post-sale
engagement, empowering businesses to deliver proactive, personalized service while
unlocking valuable insights from customer behavior.
