Système de classification automatique des tickets par IA

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Classification des tickets support e-commerce

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Utiliser le traitement du langage naturel pour analyser les demandes et plaintes des utilisateurs, identifier automatiquement le type de problème et l'assigner au département concerné pour améliorer l'efficacité.

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Analyze the following customer message received by e-commerce support, identify if the core request falls under product inquiry, logistics tracking, return/exchange application, or complaint/suggestion, and assign the corresponding classification label to route the ticket to the appropriate business team for rapid and professional handling.

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Analysez le message suivant reçu par le service client e-commerce, identifiez si la demande principale relève d'une inquiry produit, d'un suivi logistique, d'une demande de retour ou d'une plainte, et attribuez l'étiquette de classification correspondante pour acheminer le ticket vers l'équipe commerciale appropriée afin d'assurer une réponse rapide et professionnelle.

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Classificateur de tickets e-commerce

Analyze the following e-commerce customer inquiry text, determine its business category based on semantics such as logistics query, return or exchange application, product consultation, or complaint suggestion, and output a standardized classification label along with brief handling suggestions, ensuring accurate classification that complies with corporate customer service standards.

Classificateur de Tickets E-commerce

Analyze the following customer service message received by an e-commerce platform and determine its business category. The possible categories are: Logistics Inquiry, Return/Exchange Request, Product Quality Complaint, Price Dispute, Account Issue, and Other. Output only the category name without any explanation. The customer message is: 'The item I received three days ago has damaged packaging and is missing accessories. I request a replacement or a refund.'

Réponse Service Client EC

Act as a senior e-commerce customer service expert. Based on the provided order number, current logistics status, and the user's emotional tone, generate a professional yet empathetic response. The reply must include a sincere apology for the logistics delay, an updated estimated delivery time, and a small coupon as compensation. Maintain a gentle but firm tone to defuse dissatisfaction and rebuild trust, avoiding robotic template language to ensure the response is natural, fluent, and consistent with brand values.

Classificateur de tickets e-commerce

Act as an intelligent customer service assistant for an e-commerce platform. Receive the raw text of a user's inquiry, analyze its core intent and emotional tone, and categorize it into standard classes such as pre-sales consultation, after-sales complaint, logistics inquiry, or return/exchange application. Extract key entities like order numbers, product names, and problem descriptions, and finally output a structured classification result with handling suggestions to enable manual agents to intervene quickly.

Classification Billets EC

Please analyze the following e-commerce customer service ticket content, classify it into one of four categories: 'Logistics Inquiry', 'Return/Exchange Request', 'Product Quality Complaint', or 'Other Inquiry' based on business semantics, and briefly explain the reasoning. If the ticket involves multiple intents, prioritize the most urgent or core issue. The output format must include the classification label and a short reason, ensuring the result is accurate and aligns with actual business scenarios.

Classification des tickets de support e-commerce

Analyze the provided e-commerce customer consultation text to identify the core request and determine its business category. If the user mentions damaged, missing, or malfunctioning items, categorize it as an after-sales issue. If it involves delayed shipping, logistics updates, or lost packages, categorize it as a logistics issue. If it asks about product specifications, usage tutorials, or compatibility, categorize it as a product inquiry. Ignore emotional expressions and extract key facts for precise classification, outputting the corresponding standard business label.