Fuzzy Q-Learning-Based Multimodal Chatbot for Enhanced Customer Service in Electric Power Marketing

Due to the advancement of technology, most of the applications have shifted to online platforms, which is an unbelievable fact. Electric power marketing is one of the critical domains, and it has recently moved to this online platform due to the increasing number of digital habits and devices. Today, most customers mainly depend on online platforms regarding service complaints, bill enquiries, payment processing, raising queries, etc. This digital transition creates a volume of customer queries that is impossible to handle by manual staff. Further, vast volumes of data lead to time-consuming issues, dissatisfaction, and customer delays. To solve this problem, many existing research authors suggest using advanced online robot systems. However, these bots still face issues in handling various types of communication. Most traditional bots depend on voice or text inputs, making them less effective when dealing with multiple customer needs. Additionally, one of the main disadvantages of using this traditional bot is its unadaptable capacity, which means these bots are not able to respond to varying customer needs and complex queries. To address these drawbacks, we developed the novel multimodal FQL (Fuzzy Q Learning) chat, which combines the strength of fuzzy logic and Q learning techniques. Fuzzy logic is one of the AI techniques that can effectively deal with uncertain and incomplete information. In comparison, Q learning allows the system to learn and improve continuously. This combined technology makes the suggested FQL chatbot understand and respond more naturally to customer interactions. Also, it can analyse past customer service data from electric power companies. The experiments with these suggested bots were performed on online platforms like Taobao and Jindong’s everyday services and transactions. The results of the suggested bot show notable improvements. It solved payment-related query issues with an accuracy of 98.12% and other challengeable queries with an improvement of 94.15%. By handling the multiple ways of communication, this FQL Bot is not only an effective contribution to the customers. Still, it is also considered a great benefit to electric power companies that can deal with customers in different locations and use varying modes of communication.