AI 기반 이커머스 고객 문의 티켓 자동 분류 시스템

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AI 프롬프트 목록

이커머스 티켓 분류

${:__('collect %s',이커머스 티켓 분류)}$

자연어 처리 기술을 활용하여 이커머스 고객 문의 티켓을 자동으로 식별 및 분류하여 고객 서비스 응답 효율성과 정확도를 향상시킵니다.

프롬프트 내용

복사

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.

복사

다음 이커머스 고객이 제출한 문의 티켓 내용을 분석하고, 물류 조회, 반품 또는 교환 신청, 제품 상담, 불만 제안 등 사전 정의된 비즈니스 카테고리로 자동으로 분류하십시오. 또한 주문 번호, 제품 이름, 사용자 감정 경향성과 같은 주요 엔티티 정보를 추출하여 고객 서비스 팀이 신속하게 처리할 수 있도록 구조화된 분류 결과를 출력하십시오.

복사

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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.

EC 고객 서비스 QA

Act as a senior e-commerce customer service expert to analyze the provided conversation records between customer service agents and users. Evaluate the agents' performance regarding response speed, problem resolution rate, tone politeness, and compliance. Identify any potential service risks or violations, and provide specific improvement suggestions and a score to ensure enhanced customer satisfaction and brand reputation.

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

Act as a senior legal expert and carefully review the provided commercial service contract text, focusing on identifying vague expressions or unfavorable conditions in payment terms, liability for breach, intellectual property ownership, and dispute resolution mechanisms, and provide specific revision suggestions and legal basis for the identified risks.

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.

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.