Автоматическая категоризация тикетов поддержки электронной коммерции с помощью ИИ

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Классификатор тикетов поддержки

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Автоматическое определение и классификация тикетов послепродажного обслуживания электронной коммерции с использованием обработки естественного языка для повышения эффективности и удовлетворенности клиентов.

Содержание промпта

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Analyze the following e-commerce after-sales customer inquiry text and determine its business category based on semantics, including logistics inquiry, return/exchange application, product quality complaint, invoice issuance, and others. Output only the classification label without explaining the reasoning process, ensuring accuracy and compliance with business standards.

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Проанализируйте следующий текст запроса клиента послепродажного обслуживания электронной коммерции и определите его бизнес-категорию на основе семантики, включая запрос о логистике, заявку на возврат/обмен, жалобу на качество продукта, выпуск счета-фактуры и другие. Выводите только метку классификации без объяснения процесса рассуждения, обеспечивая точность и соответствие бизнес-стандартам.

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Рекомендация

Классификация Тикетов

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.

Ответы службы поддержки EC

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.

QA Клиентская Служба

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.

Классификация тикетов поддержки EC

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.

Обзор рисков договора

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.

Классификация тикетов поддержки

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.