집
이커머스 티켓 분류기
AI
2
2
물류, 반품, 품질, 가격 또는 기타 문의에 자동으로 분류하는 AI 도구입니다.
프롬프트 내용
복사
복사
Analyze the following customer service message from an e-commerce platform and determine its business category. The possible categories are: Logistics Inquiry, Return/Exchange Request, Product Quality Issue, Price Dispute, or Other Inquiry. Output only the category name without any explanation or additional text. If the message cannot be clearly classified, output 'Other Inquiry'.
복사
다음 이커머스 플랫폼으로부터의 고객 서비스 메시지를 분석하고 비즈니스 카테고리를 결정하십시오. 가능한 카테고리는 물류 문의, 반품/교환 요청, 제품 품질 문제, 가격 분쟁 또는 기타 문의입니다. 설명이나 추가 텍스트 없이 카테고리 이름만 출력하십시오. 메시지를 명확하게 분류할 수 없는 경우 '기타 문의'를 출력하십시오.
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추천
EC 티켓 분류
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Please 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 predefined business rules. If the ticket involves multiple issues, prioritize the most urgent or core issue category. Ensure the classification result is accurate and aligns with actual business scenarios to enable the customer service team to respond quickly.
이커머스 티켓 분류
Please analyze the following e-commerce customer inquiry ticket content, automatically classify it into one of four categories: 'Logistics Inquiry', 'Return/Exchange Request', 'Product Quality Complaint', or 'Other Inquiry' based on business semantics, and briefly explain the classification basis to enable the customer service team to respond and handle it quickly.
전자상거래 고객 지원 티켓 분류
Analyze the provided customer inquiry text and categorize it into one of four predefined classes: pre-sales consultation, after-sales service, logistics inquiry, or complaint/suggestion based on business semantics. If an order number is mentioned, extract it; if emotional expression is present, label the sentiment as positive, neutral, or negative. The output must include the classification result, key entity extraction, and sentiment label, ensuring the logic aligns with standard e-commerce customer service protocols without additional explanatory reasoning.
EC 티켓 분류기
Act as an intelligent customer service assistant for an e-commerce platform. Receive raw user inquiry text, analyze its core intent and emotional tone, and categorize it into standard classes such as pre-sales consultation, after-sales rights protection, logistics inquiry, or complaint suggestion. Extract key entities like order numbers, product names, and problem descriptions, and finally output structured classification results and handling suggestions for quick human agent intervention.
