Enhancing Customer Experience in E-Commerce with NLP-Driven Chatbots and Virtual Assistants

E-commerce growth suggests that customer care services need to be smart, flexible, and sensitive to customers’ feelings. Intent detection is done with BERT, combined with GPT-2 for creating responses in a human-like style while using sentiment-aware attention mechanisms. Collaborative filtering and reinforcement learning are used in the recommendation engine to encourage users to stay engaged, feel satisfied, and convert. All the dialogue in this system is from an English, Spanish, and Hindi training set of 120,000 turns, and the system is improved with contrastive loss. With AWS Lambda and Dialogflow CX, deployment takes place in real-time and remains fast even when there are many concurrent users. 94.5% accuracy, 93.0% F1-score, 89.7% query resolution rate and 150 ms average response time are shown by the evaluation results. Satisfaction with customers increased to 4.7 out of 5 and at the same time, calls handled by agents declined from 75% to just 20%. By offering scalable talks, personal choices and emotion awareness, this framework can greatly boost users’ satisfaction and make the business operations more efficient in online shopping.