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Classification des tickets e-commerce

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AI
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Utiliser le traitement du langage naturel pour analyser les tickets clients e-commerce, identifier l'intention et automatiser la catégorisation et la priorisation pour améliorer l'efficacité de réponse et la qualité de service.

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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. Categorize the ticket into the corresponding service department based on predefined business rules. Simultaneously, assess the urgency of the ticket; if it involves safety complaints or potential negative public opinion risks, mark it as high priority and recommend immediate human intervention. For routine inquiries, generate standardized response suggestions for customer service agents. The final output should include the classification label, urgency rating, and key information summary to ensure the customer service team can quickly understand customer needs and respond accurately.

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Analysez le texte du ticket client e-commerce fourni pour identifier son intention principale, telle qu'une demande de suivi logistique, une demande de retour ou d'échange, une plainte sur la qualité du produit ou une consultation sur les prix. Catégorisez le ticket dans le département de service correspondant en fonction des règles commerciales prédéfinies. Évaluez simultanément l'urgence du ticket ; s'il s'agit de plaintes pour sécurité ou de risques potentiels d'opinion publique négative, marquez-le comme haute priorité et recommandez une intervention humaine immédiate. Pour les demandes routinières, générez des suggestions de réponse standardisées pour les agents du service client. La sortie finale doit inclure l'étiquette de classification, le niveau d'urgence et le résumé des informations clés pour garantir que l'équipe du service client puisse rapidement comprendre les besoins des clients et y répondre avec précision.

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Recommandation

Classification Billets Support
Act as an intelligent customer service assistant for an e-commerce platform. Read the provided raw text of customer inquiries, analyze their core demands and emotional tone, and accurately classify them into one of the following categories: logistics inquiry, return or exchange application, product consultation, or complaint and suggestion. Extract key entities such as order numbers or product names, and finally generate a concise structured summary to facilitate quick follow-up by human customer service agents.
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Analyze the following e-commerce support ticket text to identify its core issue type (e.g., delayed shipping, damaged goods, refund dispute, pre-sales inquiry) and categorize it into the corresponding handling department (Logistics, Quality Control, Finance, or Pre-sales) based on predefined rules. If the ticket contains multiple issues, prioritize the most urgent or impactful one, and output the classification result along with a brief rationale.
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Act as an intelligent customer service assistant for an e-commerce platform. Receive raw user inquiry text, analyze its core intent and sentiment, and categorize it into standard classes such as pre-sales consultation, after-sales service, logistics inquiry, or complaints. Extract key entities like order numbers, product names, and issue descriptions, then output a structured classification result with handling suggestions for quick human agent intervention.
Classification des tickets e-commerce
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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