AI 기반 전자상거래 고객 지원 티켓 자동 분류

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전자상거래 고객 지원 티켓 분류

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AI를 활용하여 전자상거래 고객 지원 티켓을 자동으로 분류하여 대응 효율성과 서비스 품질을 향상시킵니다.

프롬프트 내용

복사

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.

복사

제공된 고객 문의 텍스트를 분석하여 비즈니스 의미에 따라 사전 판매 상담, 사후 서비스, 물류 문의 또는 불만/제안의 네 가지 미리 정의된 카테고리 중 하나로 분류하십시오. 주문 번호가 언급된 경우 이를 추출하고, 감정 표현이 포함된 경우 감정을 긍정, 중립 또는 부정으로 레이블 지정하십시오. 출력에는 분류 결과, 주요 엔티티 추출 및 감정 레이블을 포함해야 하며, 추가적인 설명적 추론 없이 표준 전자상거래 고객 서비스 프로토콜과 일치하는 논리를 보장해야 합니다.

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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.

EC 고객 지원 티켓 분류

Analyze the following customer service message received by an e-commerce platform 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 category name without explanation. For instance, if the user says 'My package hasn't moved for three days', classify it as Logistics Inquiry; if the user says 'The clothes faded after washing', classify it as Product Quality Complaint. Accurately categorize the input text.

이커머스 티켓 분류기

Act as a senior e-commerce customer service operations expert. Analyze the provided customer inquiry text and categorize it into one of four types: 'Logistics Inquiry', 'Return/Exchange Request', 'Product Quality Complaint', or 'Other Inquiry'. Briefly explain the reasoning behind your classification based on the key intent of the message.

EC CS 응답

Act as a senior e-commerce customer service expert. Draft a response to a customer complaining about a minor scratch on their received item, who is visibly upset. Your reply must start with a sincere apology acknowledging the issue, then offer two specific solutions (e.g., partial refund or free replacement) for the customer to choose from. Conclude with gratitude and a commitment to improvement. Maintain a professional, gentle, yet firm tone, avoiding robotic phrases, ensuring the customer feels valued and respected to effectively resolve the conflict and protect the brand image.

EC 고객 서비스 응답

Act as a senior e-commerce customer service expert. Based on the customer inquiry: 'My order #12345 shows shipped but logistics info hasn't updated for three days, I'm anxious,' and considering the order status is 'In Transit,' estimated delivery is '2023-10-15,' and current logistics node is 'East China Distribution Center,' generate a professional, reassuring, and solution-oriented reply. The response must include an apology for the delay, an explanation of the current logistics status, a specific estimated time for the next update, and proactively offer a compensation plan if delivery is not met, maintaining a friendly yet professional tone without using robotic template language.

EC 티켓 분류기

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.