Система автоматической классификации тикетов электронной коммерции на базе ИИ

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Классификация тикетов электронной коммерции

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Используйте обработку естественного языка для анализа тикетов клиентов электронной коммерции, выявления намерений и приоритетов для автоматической маршрутизации, повышения эффективности и удовлетворенности.

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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.

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Проанализируйте предоставленный текст тикета клиента электронной коммерции, чтобы определить его основную цель, такую как запрос о статусе доставки, запрос на возврат или обмен, жалоба на качество продукта или консультация по ценам. Классифицируйте тикет в соответствующую очередь обслуживания клиентов на основе заранее определенных бизнес-правил. Одновременно оцените срочность тикета; если он касается жалоб на безопасность или значительных рисков общественного мнения, отметьте его как высокий приоритет и порекомендуйте немедленное ручное вмешательство. Для рутинных запросов извлеките ключевые сущности (например, номер заказа, артикул товара, описание проблемы) и создайте краткое резюме, чтобы помочь агентам службы поддержки быстро понять контекст. Итоговый вывод должен включать метку классификации, уровень приоритета, список ключевых сущностей и резюме не более 50 слов, обеспечивая точность и облегчая последующую обработку.

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