Boost Sales with AI Personal Shopper & WooCommerce Integration
Streamline product discovery and reduce customer inquiry response times by over 60%, boosting sales and customer satisfaction.
Manually assisting customers with product searches and general store inquiries is time-consuming for e-commerce businesses. This workflow deploys an intelligent AI personal shopper that instantly guides customers to products and answers questions, significantly improving customer experience and conversion rates.
Documentation
AI Personal Shopper for WooCommerce with RAG
This n8n workflow creates a sophisticated AI assistant designed for WooCommerce stores. It intelligently guides customers through product discovery and provides instant answers to store-related questions, enhancing the online shopping experience and improving sales efficiency.
Key Features
- AI-powered product search: Users describe what they're looking for, and the AI extracts parameters to search your WooCommerce catalog.
- Contextual store information: Leverages RAG (Retrieval Augmented Generation) to answer questions about store hours, location, and policies from your documents.
- Continuous conversation memory: Maintains context across chat sessions for a personalized and natural interaction.
- Dynamic query optimization: Automatically extracts keywords, price ranges, and SKUs from natural language for precise product filtering.
- Easy knowledge base integration: Ingests documents from Google Drive into a Qdrant vector store for robust information retrieval.
How It Works
1. Customer Inquiry: A customer sends a chat message, triggering the workflow. 2. Intent Analysis: An AI-powered Information Extractor analyzes the message to determine if the user is searching for a product or asking a general question. 3. Product Search: If a product search is detected, the AI Agent queries your WooCommerce store with extracted details like keywords, price ranges, and SKUs. 4. General Inquiry (RAG): For general store questions (e.g., opening hours), the AI Agent utilizes a RAG tool, which queries a Qdrant vector store containing your store's information. 5. Contextual Responses: The AI Agent synthesizes responses, incorporating chat history for a seamless conversational flow. 6. Knowledge Base Setup (Initial): A separate branch of the workflow initializes your RAG system by clearing the Qdrant database, fetching documents from Google Drive, splitting them, creating embeddings, and storing them in Qdrant.