Customer Support Enhancement in E-Commerce with Retrieval-Augmented Generation

Abstract

In the rapidly evolving e-commerce industry, customer support is a critical component in ensuring customer satisfaction and business success. Traditional customer service models, often relying on static response templates and limited agent knowledge, struggle to meet the diverse and complex needs of customers. This paper explores the integration of Retrieval-Augmented Generation (RAG) as a solution for enhancing customer support in e-commerce platforms. RAG combines the strengths of information retrieval and generative language models to provide dynamic, contextually relevant, and personalized responses. The retrieval mechanism allows the model to pull from a vast knowledge base of product information, user histories, and common queries, while the generative component tailors the responses based on the unique context of each interaction. This hybrid approach aims to reduce response times, improve accuracy, and foster more natural customer interactions. By leveraging RAG, e-commerce platforms can handle a higher volume of customer inquiries while maintaining high-quality, consistent service. Additionally, this model supports multi-turn conversations, making it particularly suited for complex or nuanced customer queries. The paper evaluates the potential impact of RAG on various facets of customer service, including customer satisfaction, operational efficiency, and the scalability of support systems. In conclusion, the adoption of RAG offers a transformative approach to e-commerce customer support, creating a more responsive, adaptive, and personalized experience for users