Order Notification Innovation: Development of a Telegram Chatbot Integration System to Enhance Online Store Efficiency
Abstract
The rapid growth of e-commerce has revolutionized online business operations, requiring faster, more accurate, and automated order management processes. A common challenge for online stores delays or inaccuracies in order notifications, which can disrupt workflow, reduce customer satisfaction, and increase administrative workload. This study aims to develop, implement, and evaluate a Telegram-based chatbot notification system that acts as an automated communication tool for delivering real-time order updates. The system was deployed on the Hasanatul Store platform through an experimental approach that included system design, bot creation, integration, testing, and performance assessment. Integration allows for automated retrieval of transaction data and instant delivery of order status updates via Telegram. Black box testing was performed to verify the system’s functionality across various order scenarios, including pending and successful payments. Results show that the chatbot system greatly enhances operational efficiency by reducing manual tasks, minimizing notification delays, and decreasing the chance of human error. Customer feedback also indicates improved experiences due to prompt responses and clearer communication. Additionally, the system is reliable and scalable, making it suitable for online stores with varying transaction volumes. The novelty of this study lies in conceptualizing the Telegram chatbot as an operational notification middleware that enables event-driven monitoring of online store transactions, rather than merely functioning as a conversational customer service tool. This research offers practical insights into chatbot-based automation for e-commerce and highlights its potential to improve service quality, reduce operational obstacles, and support data-driven decision-making. Future research could explore incorporating artificial intelligence to enhance personalization, conversational responsiveness, and adaptability to diverse customer needs.