Автоматическая классификация тикетов поддержки

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Классификация тикетов поддержки

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Используйте ИИ для автоматической идентификации и классификации тикетов запросов пользователей, полученных службой поддержки электронной коммерции, чтобы повысить эффективность обработки.

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Please analyze the content of the user inquiry tickets received by the following e-commerce customer service, automatically classify them into categories such as logistics inquiry, return and exchange application, product quality complaint, or general consultation based on the nature of the problem, and briefly explain the basis for classification so that subsequent manual customer service can respond and handle quickly.

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Пожалуйста, проанализируйте содержание тикетов запросов пользователей, полученных следующей службой поддержки электронной коммерции, автоматически классифицируйте их по категориям, таким как логистический запрос, заявка на возврат и обмен, жалоба на качество продукта или общая консультация, в зависимости от характера проблемы, и кратко объясните основу классификации, чтобы последующий ручной клиентский сервис мог быстро реагировать и обрабатывать.

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Рекомендация

Классификатор тикетов E-commerce

Act as an intelligent customer service assistant for an e-commerce platform. Receive raw user inquiry text, analyze its core intent and sentiment, and categorize it into standard classes such as pre-sales consultation, after-sales service, logistics inquiry, or complaints. Extract key entities like order numbers, product names, and issue descriptions, then output a structured classification result with handling suggestions for quick human agent intervention.

Классификация тикетов электронной коммерции

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. Classify the ticket into the corresponding customer service queue based on predefined business rules. Simultaneously, assess the urgency of the ticket; if it involves safety complaints or significant public opinion risks, mark it as high priority and recommend immediate manual intervention. For routine inquiries, extract key entities (e.g., order number, product SKU, problem description) and generate a concise summary to help customer service agents quickly understand the context. The final output should include the classification label, priority level, list of key entities, and a summary of no more than 50 words, ensuring accuracy and facilitating subsequent processing.

Классификация тикетов

Act as part of an e-commerce customer service system. Analyze the incoming customer inquiry text and automatically categorize it into one of four categories: pre-sales consultation, after-sales service, logistics inquiry, or complaint/suggestion. Output the corresponding category label and confidence score to facilitate routing to the appropriate handling team.

Классификатор тикетов E-commerce

Analyze the provided e-commerce customer ticket text to identify its core intent, such as return inquiries, logistics queries, or quality complaints, and categorize it into the corresponding processing queue based on predefined business rules, while assessing urgency to assist human agents in prioritizing high-urgency issues.

Автоответ CS для E-commerce

Act as a senior e-commerce customer service expert. Generate a professional, friendly, and efficient response based on the user's specific inquiry. The reply must accurately address the user's issue while maintaining brand tone consistency, avoiding mechanical template language, and ensuring the response is natural, fluent, and aligned with actual business scenarios.

Классификация тикетов электронной коммерции

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.