Strategic Implementation of Deep Learning in AI and Soft Computing to Transform and Enhance Customer Service Excellence

In today's dynamic business environment, effective customer service interactions play a pivotal role in maintaining customer satisfaction and loyalty. To meet the evolving needs of customers, organizations are increasingly turning to advanced artificial intelligence (AI) technologies for sentiment analysis and response generation. In order to improve the comprehension and handling of consumer sentiments in customer service interactions, this study suggests a holistic architecture that combines GPT for response generation with BERT-based sentiment analysis. The procedure starts with thorough data collecting and proceeds to preprocess the dataset in order to get it ready for model training. Transfer learning methods are employed to choose and train suitable models, such as GPT for response generation and BERT for sentiment analysis. The model's performance is then fine-tuned for the particular purpose of customer service interaction analysis and response production. To evaluate the model's performance, evaluation measures including accuracy, precision, recall, and F1-score are used. Validation testing on different datasets is then conducted to make sure the model could be applied to authentic situations. Significant gains in response times, resolution rates, and general customer satisfaction are shown by the framework. The framework demonstrates its capacity to produce accurate sentiment predictions with an astounding accuracy rate of 98.5%, which can improve customer service interactions and encourage long-term client loyalty. As an outcome, the study indicates that in today's cutthroat business environment, AI-driven solutions have the capacity to significantly improve consumer experiences and boost operational efficiency.