Sistema de Clasificación Automática de Tickets de E-commerce con IA

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Clasificación de Tickets E-com

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Utilizar el procesamiento del lenguaje natural para identificar y clasificar automáticamente los tickets de consulta de clientes de e-commerce, mejorando la eficiencia y precisión.

Contenido del prompt

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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.

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Analice el contenido de los siguientes tickets de consulta enviados por clientes de e-commerce, clasifíquelos automáticamente en categorías comerciales predefinidas como consulta logística, solicitud de devolución o cambio, consulta de productos o quejas, extraiga información clave sobre entidades como el número de pedido, el nombre del producto y la tendencia emocional del usuario, y finalmente genere un resultado de clasificación estructurado para que el equipo de servicio al cliente pueda manejarlo rápidamente.

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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.

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Act as an intelligent customer service system for an e-commerce platform. Receive raw user inquiry text, analyze its core intent and emotional tone, and accurately classify it into specific business categories such as pre-sales consultation, after-sales complaint, logistics inquiry, or return/exchange application. Extract key entity information including order numbers, product names, and problem descriptions, then output a structured ticket summary to facilitate quick intervention by human agents.