AI 기반 이커머스 티켓 자동 분류 시스템

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AI 프롬프트 목록

이커머스 티켓 분류

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자연어 처리 기술을 사용하여 이커머스 고객 티켓을 분석하고 의도를 식별하여 분류 및 우선순위 지정 자동화를 통해 응답 효율성과 서비스 품질을 향상시킵니다.

프롬프트 내용

복사

Analyze the provided e-commerce customer ticket text to identify its core intent, such as logistics inquiry, return or exchange request, product quality complaint, or price consultation. Categorize the ticket into the corresponding service department based on predefined business rules. Simultaneously, assess the urgency of the ticket; if it involves safety complaints or potential negative public opinion risks, mark it as high priority and recommend immediate human intervention. For routine inquiries, generate standardized response suggestions for customer service agents. The final output should include the classification label, urgency rating, and key information summary to ensure the customer service team can quickly understand customer needs and respond accurately.

복사

제공된 이커머스 고객 티켓 텍스트를 분석하여 물류 문의, 반품 또는 교환 요청, 제품 품질 불만, 가격 상담 등의 핵심 의도를 식별하십시오. 사전 정의된 비즈니스 규칙에 따라 해당 서비스 부서에 티켓을 분류하십시오. 동시에 티켓의 긴급성을 평가하십시오. 안전 관련 불만이나 잠재적인 부정적 여론 위험이 포함되는 경우 높은 우선순위로 표시하고 즉각적인 인간 개입을 권장하십시오. 일반적인 문의의 경우 고객 서비스 담당자를 위한 표준화된 응답 제안을 생성하십시오. 최종 출력에는 분류 라벨, 긴급성 등급 및 주요 정보 요약을 포함하여 고객 서비스 팀이 고객의 요구를 신속하게 이해하고 정확하게 대응할 수 있도록 하십시오.

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EC CS 자동 응답

Act as a senior e-commerce customer service expert. Based on the provided product knowledge base and the customer's specific inquiry, generate a professional, friendly, and accurate response. The reply must directly address the customer's questions regarding product specifications, logistics status, or after-sales policies. Avoid mechanical template language, ensure the tone is natural and fluent while matching the brand voice, and strictly adhere to factual information without fabricating details.

이커머스 CS 응답

Act as a senior e-commerce customer service expert. Generate a response for a customer complaining about a damaged item received during transit, who is upset and demanding a full refund plus compensation. Your reply must first sincerely apologize for the poor experience, then clearly state that you will immediately arrange a free replacement or full refund, and proactively cover the return shipping costs. Maintain a professional, gentle, yet firm tone, avoiding robotic phrases. Focus on resolving the practical issue quickly and rebuilding trust, ensuring the response is concise and aligns with brand service standards.

EC 티켓 분류

Analyze the following customer inquiry text from an e-commerce platform and determine its business category based on semantics, including return/exchange requests, logistics queries, product inquiries, or complaints/suggestions. Output only the classification label without explanation or additional notes to ensure accurate categorization for subsequent automated processing.

EC 티켓 분류

Please analyze the following raw customer inquiry text submitted via e-commerce channels and classify it into one of four categories: 'Logistics Inquiry', 'Return/Exchange Request', 'Product Quality Complaint', or 'Other'. When making the judgment, focus on keywords such as tracking numbers, damaged goods, size mismatches, or shipping delays mentioned by the customer, ignoring emotional expressions, and directly output the most matching category label along with a brief rationale, ensuring the classification result aligns with common handling processes in actual business scenarios.

EC 고객 서비스 답변

Act as a senior e-commerce customer service expert. Based on the user's provided order number, product name, and current logistics status, generate a professional and empathetic reply. If logistics are stalled, proactively provide inquiry channels and apologize; if delivered, guide the user to confirm receipt and invite a review. The reply must be concise, avoid robotic clichés, ensure a warm tone that addresses actual pain points, and output only the final reply text.

이커머스 티켓 분류기

Act as an intelligent customer service assistant for an e-commerce platform. Analyze the text of user-submitted inquiries to identify their core intent, such as return requests, logistics tracking, or product complaints. Classify each ticket into the appropriate department based on predefined business rules, and extract key entities like order numbers, product names, and specific issue descriptions to facilitate rapid follow-up by human agents.