WhatsApp Chatbot for E-Commerce Customer Support
Narinder Rana
Description: A microservice-based chatbot for an e-commerce platform that provides customer support, order tracking, product recommendations, and more through WhatsApp.
Tech Stack: Java, Spring Boot, Spring Cloud, Twilio API (for WhatsApp integration), MySQL, Kafka (for async messaging), Redis, Docker.
Key Features:
Customer Queries & Support:
Handle customer inquiries regarding product availability, order status, and return policies.
Integrate FAQs to provide instant answers to common queries.
Product Recommendations:
Provide personalized product suggestions based on customer preferences and purchase history.
Implement a recommendation engine to analyze user activity and suggest relevant products.
Order Management:
Retrieve order status by integrating the chatbot with order microservice.
Allow customers to view and manage their orders, including tracking shipment, modifying orders, or initiating returns.
Cart Management:
Enable customers to add items to their cart, remove items, or get cart suggestions.
Integration with payment gateway for payment options directly through the chatbot.
Real-time Notifications:
Send notifications for order confirmation, dispatch details, and delivery status updates.
Notify customers about promotions, new products, and other marketing campaigns.
Chat History:
Keep a log of customer interactions, allowing them to refer back to previous chats.
Store chat history in MySQL, with sensitive data encrypted.
Integration Details:
WhatsApp API Integration:
Use Twilio's WhatsApp API or Meta's Cloud API for WhatsApp integration.
Create a microservice called WhatsAppBotService to manage all interactions with the WhatsApp Business API.
Implement message templates for common responses to improve response time.
Kafka for Asynchronous Processing:
Use Kafka for messaging between microservices to handle events such as order confirmation or customer queries.
Kafka topics for sending notifications or order status updates to customers.
Customer Data and Session Management:
Use Redis to store active user sessions to ensure the context of the conversation is maintained throughout interactions.
Each customer query and response is tracked to understand context better (session-based responses).
Challenges & Solutions:
Concurrency and Scalability:
Optimized chatbot for handling multiple simultaneous user interactions by deploying the WhatsAppBotService in a containerized environment using Docker and Kubernetes.
Security:
Ensure secure communication between the chatbot and the backend services with SSL.
Encrypt sensitive user data and follow WhatsApp's guidelines to protect customer information.
Natural Language Processing (NLP):
Integrate an NLP service like Google Dialogflow or Rasa to provide better understanding and response to customer messages.
Enhanced the chatbot’s capabilities to handle natural language requests effectively, ensuring better user satisfaction.
