Система автоматической классификации тикетов

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Классификация тикетов поддержки

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Использование обработки естественного языка для анализа запросов и жалоб пользователей, автоматическое определение типа проблемы и назначение соответствующему отделу для повышения эффективности.

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Analyze the following customer message received by e-commerce support, identify if the core request falls under product inquiry, logistics tracking, return/exchange application, or complaint/suggestion, and assign the corresponding classification label to route the ticket to the appropriate business team for rapid and professional handling.

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Проанализируйте следующее сообщение, полученное службой поддержки электронной коммерции, определите, относится ли основная просьба к запросу о продукте, отслеживанию логистики, возврату/обмену или жалобе, и присвойте соответствующую метку классификации для маршрутизации тикета в соответствующую бизнес-команду для обеспечения быстрой и профессиональной реакции.

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Классификатор тикетов электронной коммерции

Analyze the following e-commerce customer inquiry text, determine its business category based on semantics such as logistics query, return or exchange application, product consultation, or complaint suggestion, and output a standardized classification label along with brief handling suggestions, ensuring accurate classification that complies with corporate customer service standards.

Классификатор тикетов E-commerce

Analyze the following customer service message received by an e-commerce platform and determine its business category. The possible categories are: Logistics Inquiry, Return/Exchange Request, Product Quality Complaint, Price Dispute, Account Issue, and Other. Output only the category name without any explanation. The customer message is: 'The item I received three days ago has damaged packaging and is missing accessories. I request a replacement or a refund.'

Ответ службы поддержки EC

Act as a senior e-commerce customer service expert. Based on the provided order number, current logistics status, and the user's emotional tone, generate a professional yet empathetic response. The reply must include a sincere apology for the logistics delay, an updated estimated delivery time, and a small coupon as compensation. Maintain a gentle but firm tone to defuse dissatisfaction and rebuild trust, avoiding robotic template language to ensure the response is natural, fluent, and consistent with brand values.

Классификатор тикетов электронной коммерции

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 emotional tone, and categorize it into standard classes such as pre-sales consultation, after-sales complaint, logistics inquiry, or return/exchange application. Extract key entities like order numbers, product names, and problem descriptions, and finally output a structured classification result with handling suggestions to enable manual agents to intervene quickly.

Классификация тикетов EC

Please analyze the following e-commerce customer service ticket content, 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 reasoning. If the ticket involves multiple intents, prioritize the most urgent or core issue. The output format must include the classification label and a short reason, ensuring the result is accurate and aligns with actual business scenarios.

Классификация тикетов электронной коммерции

Analyze the provided e-commerce customer consultation text to identify the core request and determine its business category. If the user mentions damaged, missing, or malfunctioning items, categorize it as an after-sales issue. If it involves delayed shipping, logistics updates, or lost packages, categorize it as a logistics issue. If it asks about product specifications, usage tutorials, or compatibility, categorize it as a product inquiry. Ignore emotional expressions and extract key facts for precise classification, outputting the corresponding standard business label.