Automated E-commerce Support Ticket Classifier

Home

AI Prompt List

E-commerce Ticket Classifier

${:__('collect %s',E-commerce Ticket Classifier)}$

An AI tool that automatically categorizes customer service tickets in e-commerce based on natural language processing.

Prompt Content

Copy

Please analyze the following customer message received by an e-commerce support team and determine its business category. The categories include: Logistics Inquiry, Return/Exchange Request, Product Quality Complaint, Price Dispute, and General Consultation. Read the user's input carefully, extract key information such as order numbers, product names, and problem descriptions, and then classify the message into one of the five predefined categories based on established standards. If the message involves multiple issues, select the most urgent or core issue for classification. Finally, output the result in JSON format, including the original message, the predicted category, and a confidence score, ensuring the classification is accurate and meets actual business needs.

Copy

Share Click to copy the link

Comments (0)

0/300

Submit

Recommendation

E-commerce CS Reply

Act as a senior e-commerce customer service expert. Based on the user-provided order ID, product name, and current logistics status, generate a friendly, professional, and efficient reply text. The reply must include an apology for the logistics delay, a specific estimated delivery time, and a compensation plan (such as coupons or points). Ensure the language is natural and fluent, avoiding mechanical template feelings, with the goal of alleviating user anxiety and promoting repurchase.

E-commerce Ticket Classification

Analyze the following e-commerce customer service ticket content and classify it into one of four categories: 'Logistics Inquiry', 'Return/Exchange Request', 'Product Quality Complaint', or 'Other Inquiry' based on user intent, providing a brief explanation for the classification.

E-commerce Ticket Classifier

Act as an intelligent customer service system for an e-commerce platform. You will receive raw user inquiry texts and must analyze their core intent to classify them into one of five predefined standard categories: Logistics Inquiry, Return/Exchange Request, Product Consultation, Complaint/Suggestion, or Account Issue. Carefully review the input, extract key entities such as order numbers, product names, or specific pain points, and ignore irrelevant pleasantries. Finally, output only a standardized JSON result containing the original text, the identified intent label, and a confidence score, ensuring accurate classification to support subsequent automated processing workflows.

Ecommerce CS Auto Reply

Act as a senior ecommerce customer service expert. Generate a professional, friendly, and accurate response based on the provided customer inquiry and product knowledge base. The reply must directly address the customer's issue, avoid mechanical template language, and maintain brand tone consistency to ensure the customer feels valued and understood.

E-commerce CS Reply

Act as a senior e-commerce customer service expert. Based on the provided product details (including name, price, specifications, stock status, and key selling points) and the user's specific inquiry, generate a natural, friendly, and professional reply text. The reply should directly address the user's concerns, accurately cite key product information, avoid mechanical template language or overly complex jargon, and ensure the tone aligns with brand standards to facilitate conversion.

E-commerce CS Reply

Act as a senior e-commerce customer service expert. Based on the provided order ID, current logistics status, and user's emotional keywords, generate a professional yet empathetic reply. The response must include a sincere apology for the delay, a specific estimated delivery time, and a small coupon as compensation. Maintain a friendly tone that solves the problem while avoiding robotic template language.