Автоматическая классификация тикетов поддержки электронной коммерции с помощью ИИ

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Классификация тикетов поддержки

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Используйте ИИ для автоматической классификации тикетов поддержки клиентов электронной коммерции, чтобы повысить эффективность и качество обслуживания.

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Analyze the provided customer inquiry text and categorize it into one of four predefined classes: pre-sales consultation, after-sales service, logistics inquiry, or complaint/suggestion based on business semantics. If an order number is mentioned, extract it; if emotional expression is present, label the sentiment as positive, neutral, or negative. The output must include the classification result, key entity extraction, and sentiment label, ensuring the logic aligns with standard e-commerce customer service protocols without additional explanatory reasoning.

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Проанализируйте предоставленный текст запроса клиента и классифицируйте его в одну из четырех предопределенных категорий: предварительные консультации, послепродажное обслуживание, запросы по логистике или жалобы/предложения, основываясь на бизнес-семантике. Если упоминается номер заказа, извлеките его; если присутствует эмоциональное выражение, пометьте_sentiment как положительный, нейтральный или отрицательный. Вывод должен включать результат классификации, извлечение ключевых сущностей и метку_sentiment, обеспечивая логику, соответствующую стандартным протоколам обслуживания клиентов электронной коммерции, без дополнительного объяснения рассуждений.

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