Auto E-commerce Support Ticket Classifier

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E-commerce Ticket Classifier

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Automatically identify and categorize user inquiries and complaints in e-commerce customer service tickets using NLP to improve response efficiency.

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Analyze the following user message received by e-commerce customer service and determine its business category. The categories include: Logistics Inquiry, Return/Exchange Request, Product Quality Complaint, Price Dispute, Account Issue, and Others. Output only the classification result without explanation. For example, if the user says 'My package hasn't arrived after three days', classify it as Logistics Inquiry; if the user says 'The clothes shrank after washing', classify it as Product Quality Complaint. Accurately classify the input text.

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E-commerce Ticket Categorization

Analyze the following e-commerce support ticket text to identify its core issue type (e.g., delayed shipping, damaged goods, refund dispute, pre-sales inquiry) and categorize it into the corresponding handling department (Logistics, Quality Control, Finance, or Pre-sales) based on predefined rules. If the ticket contains multiple issues, prioritize the most urgent or impactful one, and output the classification result along with a brief rationale.

E-commerce Ticket Classifier

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Analyze the provided e-commerce customer ticket text to identify its core intent, such as logistics inquiry, return or exchange request, product quality complaint, or price consultation. Classify the ticket into the corresponding customer service queue based on predefined business rules. Simultaneously, assess the urgency of the ticket; if it involves safety complaints or significant public opinion risks, mark it as high priority and recommend immediate manual intervention. For routine inquiries, extract key entities (e.g., order number, product SKU, problem description) and generate a concise summary to help customer service agents quickly understand the context. The final output should include the classification label, priority level, list of key entities, and a summary of no more than 50 words, ensuring accuracy and facilitating subsequent processing.

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