AI EC 티켓 분류

집

AI 프롬프트 목록

EC 티켓 분류

${:__('collect %s',EC 티켓 분류)}$

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

프롬프트 내용

복사

Act as a data analysis assistant for an e-commerce customer service system. Read the current list of pending customer inquiry tickets, automatically categorize them into four major categories based on the nature of the issue: logistics inquiry, product quality, return/exchange application, or complaint/suggestion. Extract key entities such as order numbers, product SKUs, and user sentiment tendencies, and finally output structured classification results for subsequent manual review or automatic routing.

복사

EC 고객 서비스 시스템의 데이터 분석 어시스턴트 역할을 하세요. 보류 중인 고객 문의 티켓 목록을 읽고 문제의 성격에 따라 물류 문의, 제품 품질, 반품/교환 신청 또는 불만/제안의 네 가지 주요 카테고리로 자동으로 분류하세요. 주문 번호, 제품 SKU, 사용자 감정 경향과 같은 주요 엔티티를 추출한 후 후속 수동 검토 또는 자동 라우팅을 위해 구조화된 분류 결과를 출력하세요.

복사

공유 링크를 복사하려면 클릭

의견 (0)

0/300

제출하다

추천

이커머스 티켓 분류기

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'.

EC 티켓 분류기

Act as an intelligent customer service assistant for an e-commerce platform. Receive the raw text of a user's inquiry, analyze its core intent and urgency, and categorize it into one of four types: pre-sales consultation, after-sales rights protection, logistics inquiry, or complaint/suggestion. Extract key entities such as order numbers, product names, and problem descriptions. Finally, output a structured classification result and handling suggestions, ensuring the response is accurate and complies with corporate customer service standards.

이커머스 티켓 분류기

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.