AI EC 티켓 분류

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EC 티켓 분류

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AI를 사용하여 EC 고객 문의 티켓을 자동으로 식별하고 분류하여 처리 효율성과 정확성을 향상시킵니다.

프롬프트 내용

복사

Please analyze the following e-commerce customer inquiry ticket content, automatically categorize it into one of the four categories: 'Logistics Inquiry', 'Return/Exchange Application', 'Product Quality Complaint', or 'Other Inquiry' based on business semantics, and briefly explain the basis for classification, ensuring that the classification results conform to actual business scenarios and are logically clear.

복사

다음 EC 고객 문의 티켓 내용을 분석하여 비즈니스 의미에 따라 '물류 문의', '반품/교환 신청', '제품 품질 불만', '기타 문의' 중 하나로 자동 분류하고, 분류 근거를 간략히 설명하십시오. 분류 결과가 실제 비즈니스 시나리오에 부합하고 논리적으로 명확한지 확인하십시오.

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이커머스 티켓 분류기

Act as an intelligent customer service system for an e-commerce platform. Receive raw user inquiry or complaint text, analyze its core intent, and classify it into specific business scenarios such as logistics inquiry, product quality issue, return or exchange request, or price dispute. Output a standardized classification label along with a brief handling suggestion to ensure the customer service team can respond quickly and accurately.

이커머스 티켓 분류기

Act as an e-commerce support specialist to analyze the provided customer ticket text, identifying whether the core issue falls under logistics delay, product damage, return/exchange request, or price dispute. Extract key entities such as order ID, product name, and user sentiment, then output a structured classification result with suggested handling priority, ensuring accuracy and compliance with company service standards.

EC 고객 응답

Act as a senior e-commerce customer service expert. Based on the provided product knowledge base and the customer's inquiry, generate a professional, friendly, and accurate response. The reply should directly address the customer's issue, avoid robotic clichés, ensure a natural and fluent tone consistent with the brand voice, and include necessary after-sales guidance.

이커머스 티켓 분류

Analyze the following raw customer inquiry text from an e-commerce platform and categorize it into one of four categories: 'Logistics Inquiry', 'Return/Exchange Request', 'Product Quality Complaint', or 'Other Inquiry'. If the text involves multiple intents, prioritize the most urgent or core issue and briefly explain the reasoning. For instance, if a user mentions 'the shirt size is wrong and I want to exchange it', categorize it as 'Return/Exchange Request'. Output only the category and a short rationale without any conversational filler or explanatory prefixes.

이커머스 티켓 분류기

Act as an intelligent customer service system for an e-commerce platform. Receive the raw text of a user's inquiry, analyze its core intent and emotional tone, and automatically classify it into standard categories such as pre-sales consultation, after-sales service, logistics inquiry, or complaints. Extract key entities like order numbers, product names, and issue descriptions, then output a structured ticket summary to facilitate quick intervention by human agents.

EC 고객 서비스 QA

Act as a senior e-commerce customer service expert to analyze the provided conversation logs between agents and users. Focus on checking if the agent accurately understood the user's intent, adhered to company service standards, and avoided emotional or evasive behavior. Evaluate the effectiveness of the final solution. Identify key risk points (such as complaint escalation or sensitive word usage) and excellent service highlights, then provide specific improvement suggestions and a score to ensure the response is professional, objective, and actionable.