Ассистент автоклассификации тикетов

Дом

Список AI-промптов

Классификация тикетов EC

${:__('collect %s',Классификация тикетов EC)}$

Автоматически идентифицируйте и классифицируйте тикеты обращений и жалоб службы поддержки клиентов электронной коммерции с использованием технологий обработки естественного языка для повышения эффективности ответа.

Содержание промпта

Копировать

Please analyze the following user customer service conversation records from an e-commerce platform, determine the business category based on the content such as logistics inquiry, return or exchange application, product quality complaint, or account anomaly, extract key information like order number, problem description, and user sentiment, and finally output a structured ticket classification result for subsequent manual or automated processing.

Копировать

Пожалуйста, проанализируйте следующие записи разговоров службы поддержки пользователей с платформы электронной коммерции, определите бизнес-категорию на основе содержания, такую как логистический запрос, заявка на возврат или обмен, жалоба на качество продукта или аномалия учетной записи, извлеките ключевую информацию, такую как номер заказа, описание проблемы и тенденция настроения пользователя, и в конечном итоге выведите структурированный результат классификации тикета для последующей ручной или автоматизированной обработки.

Копировать

Поделиться Нажмите, чтобы скопировать ссылку

Комментарии (0)

0/300

Представлять на рассмотрение

Рекомендация

Классификация тикетов EC

Please analyze the content of the following user customer service tickets from an e-commerce platform, determine which category their core request belongs to based on the conversation context (logistics inquiry, return/exchange application, product quality complaint, or price consultation), and output the corresponding category label along with a brief reason.

Классификация Тикетов

Analyze the following e-commerce support ticket content, identify its core issue type, including logistics delay, product damage, refund request, or inquiry, and route it to the logistics, quality inspection, or finance department based on preset rules. Extract key entities such as order number, product name, and user sentiment, and finally output a structured classification result and suggested handling plan to ensure accurate classification and compliance with business standards.

Классификатор тикетов e-commerce

Act as an intelligent customer service system for an e-commerce platform. Receive raw user inquiry or complaint text, analyze its core intent, and classify it into specific business scenarios such as logistics inquiry, product quality issue, return or exchange request, or price dispute. Output a standardized classification label along with a brief handling suggestion to ensure the customer service team can respond quickly and accurately.

Классификация Тикетов

Please analyze the following e-commerce customer inquiry ticket content, automatically categorize it into one of the four categories: 'Logistics Inquiry', 'Return/Exchange Application', 'Product Quality Complaint', or 'Other Inquiry' based on business semantics, and briefly explain the basis for classification, ensuring that the classification results conform to actual business scenarios and are logically clear.

Классификатор тикетов E-commerce

Act as an e-commerce support specialist to analyze the provided customer ticket text, identifying whether the core issue falls under logistics delay, product damage, return/exchange request, or price dispute. Extract key entities such as order ID, product name, and user sentiment, then output a structured classification result with suggested handling priority, ensuring accuracy and compliance with company service standards.

Ответ Клиенту E-com

Act as a senior e-commerce customer service expert. Based on the provided product knowledge base and the customer's inquiry, generate a professional, friendly, and accurate response. The reply should directly address the customer's issue, avoid robotic clichés, ensure a natural and fluent tone consistent with the brand voice, and include necessary after-sales guidance.