OPTIMIZING CUSTOMER SUPPORT WITH AI CHATBOTS: A CASE STUDY

Abstract

This article researches the effectiveness of implementing AI chatbots to optimize customer support (Customer Support) using the example of a waste collection business. The classification of chatbots is considered, and examples of popular AI solutions, such as Zendesk Answer Bot and LivePerson, are provided for analyzing ready-made systems in customer service. The key areas of project implementation for developing a chatbot for a waste collection company are described in detail, including data collection and analysis, optimization of garbage collection routes, and management of the vehicle fleet. The work highlights the intelligent technologies used to create the chatbot, particularly machine learning for data analysis and forecasting, natural language processing (NLP) for understanding queries, and GPS integration for logistics optimization. The main stages of chatbot development and training are considered, and the importance of its integration with CRM and other corporate systems to ensure seamless data exchange and improve service quality is emphasized. The advantages of using AI chatbots are analyzed, including improved customer interaction, optimization of logistics processes, and reduction of negative environmental impact. The specifics of the developed chatbot for the waste collection company are outlined: goals and tasks were defined, the Microsoft Bot Framework platform was chosen, and the architecture, user interface, and knowledge base were developed. Training and testing of the system were carried out, and its integration with CRM for accessing customer data and updating information, with a GPS monitoring system for tracking garbage trucks and informing customers, as well as with notification and waste accounting systems, is envisaged. Currently, the project is at the stage of implementation and monitoring, which includes integration into the real environment and analysis of effectiveness. Prospects for further research are identified, in particular, forecasting waste volumes, developing more complex interaction scenarios, and studying ethical aspects.