Smart Notification Optimization System for E-Commerce Applications

Abstract

With the rapid growth of online shopping platforms, communication between businesses and customers has become increasingly important. Notifications play a significant role in informing customers about discounts, offers, order updates, and personalized recommendations. However, many e-commerce platforms send large numbers of notifications without considering whether the information is relevant to the user. As a result, customers often experience notification fatigue, ignore messages, or disable notifications completely. The E-Commerce Smart Notification System is developed to address this challenge by providing a more intelligent and personalized notification mechanism. The system analyzes user behavior such as browsing activity, purchase history, product interests, and interaction frequency to determine the relevance of a notification before sending it. Instead of broadcasting the same message to all users, the system identifies which users are most likely to benefit from a specific notification. The application is developed using the Flask framework in Python, which provides a lightweight and efficient backend environment. User activity data is stored and processed using a relational database. A notification scoring algorithm evaluates customer engagement and assigns a relevance score to each notification event. When the score exceeds a predefined threshold, the system triggers an SMS notification through theTwilio API. The proposed solution helps reduce unnecessary notifications, improves customer engagement, and increases the likelihood of user interaction. By delivering timely and personalized communication, the system enhances customer satisfaction while helping businesses achieve better marketing performance. The architecture is designed to be scalable and can be extended in the future using machine learning models for advanced recommendation and prediction capabilities.