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집 AI 프롬프트 목록 AI 이커머스 티켓 분류기

이커머스 티켓 분류기

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자연어 처리 기술을 사용하여 이커머스 애프터서비스 문의 티켓을 자동으로 식별 및 분류하여 응답 효율성과 고객 만족도를 향상시킵니다.

프롬프트 내용 복사 복사

Analyze the following e-commerce after-sales customer inquiry text and determine its business category based on semantics, including logistics inquiry, return/exchange application, product quality complaint, invoice issuance, and others. Output only the classification label without explaining the reasoning process, ensuring accuracy and compliance with business standards.

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다음 이커머스 애프터서비스 고객 문의 텍스트를 분석하여 물류 조회, 반품/교환 신청, 제품 품질 불만, 청구서 발행 및 기타를 포함하는 의미 기반 비즈니스 카테고리를 결정하십시오. 추론 과정에 대한 설명 없이 분류 라벨만 출력하고 정확성과 비즈니스 표준 준수를 보장하십시오.

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이커머스 티켓 분류
Analyze the following e-commerce customer inquiry ticket content, automatically classify it into predefined business categories such as logistics inquiry, return or exchange application, product consultation, or complaint suggestion, extract key entity information including order number, product name, and user sentiment, and finally output a structured classification result for the customer service team to handle quickly.
EC 고객 서비스 응답
You are a senior e-commerce customer service specialist responsible for handling pre-sales inquiries and after-sales complaints. When receiving user questions regarding product specifications, logistics status, or return policies, please generate a response based on the store's current policies. The response should directly address the customer's core concerns, provide clear solutions or guidance steps, avoid mechanical template language, and ensure natural, fluent communication that reflects the brand's service warmth.
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EC 고객 지원 티켓 분류
Please analyze the following customer inquiry records from an e-commerce platform and categorize them into one of four categories: 'Logistics Inquiry', 'Return/Exchange Request', 'Product Quality Complaint', or 'Other Inquiry'. When classifying, focus on keywords mentioned by the customer such as shipping status, product defects, or refund reasons, and output the final category label along with a brief justification without providing extra explanations.
계약 위험 검토
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고객 지원 티켓 분류
Analyze the following e-commerce after-sales ticket text to identify its problem category, including logistics issues, product quality issues, after-sales service attitude, or refund disputes. Extract key entities such as order numbers, product names, user requests, and sentiment tendencies. Finally, output a structured classification result and suggested handling priority to enable the customer service team to respond quickly.
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