AI 기반 전자상거래 고객 지원 티켓 태그링

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AI 프롬프트 목록

고객 지원 티켓 분류

${:__('collect %s',고객 지원 티켓 분류)}$

자연어 처리를 활용하여 전자상거래 플랫폼에서 사용자가 제출한 애프터세일 문의 및 불만 티켓을 의미론적으로 분석합니다. 물류 지연, 제품 손상, 환불 분쟁과 같은 문제 유형을 자동으로 식별하고 주요 엔티티를 추출하여 이후 수동 개입 또는 자동화된 워크플로우를 위한 구조화된 데이터를 제공함으로써 고객 서비스 대응 효율성과 사용자 만족도를 향상시킵니다.

프롬프트 내용

복사

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.

복사

다음 전자상거래 애프터세일 티켓 텍스트를 분석하여 물류 문제, 제품 품질 문제, 애프터세일 서비스 태도 또는 환불 분쟁 등 문제 카테고리를 식별하십시오. 주문 번호, 제품 이름, 사용자 요청, 감정 경향성과 같은 주요 엔티티를 추출하십시오. 마지막으로, 고객 서비스 팀이 신속하게 대응할 수 있도록 구조화된 분류 결과와 권장 처리 우선순위를 출력하십시오.

복사

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이커머스 티켓 분류기

Act as an intelligent customer service system for an e-commerce platform. Receive raw user inquiry text, analyze its core intent and emotional tone, and accurately classify it into specific business categories such as pre-sales consultation, after-sales complaint, logistics inquiry, or return/exchange application. Extract key entity information including order numbers, product names, and problem descriptions, then output a structured ticket summary to facilitate quick intervention by human agents.

EC 티켓 분류

Please analyze the following user customer service conversation records from an e-commerce platform, determine the business category based on the content such as logistics inquiry, return or exchange application, product quality complaint, or account anomaly, extract key information like order number, problem description, and user sentiment, and finally output a structured ticket classification result for subsequent manual or automated processing.

EC 티켓 분류

Please analyze the content of the following user customer service tickets from an e-commerce platform, determine which category their core request belongs to based on the conversation context (logistics inquiry, return/exchange application, product quality complaint, or price consultation), and output the corresponding category label along with a brief reason.

고객 지원 티켓 분류

Analyze the following e-commerce support ticket content, identify its core issue type, including logistics delay, product damage, refund request, or inquiry, and route it to the logistics, quality inspection, or finance department based on preset rules. Extract key entities such as order number, product name, and user sentiment, and finally output a structured classification result and suggested handling plan to ensure accurate classification and compliance with business standards.

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.

EC 티켓 분류

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.