Classificateur automatique de tickets de support

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Classificateur de tickets e-commerce

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Identifiez et classez automatiquement les tickets de demande d'information des utilisateurs reçus par le service client e-commerce basés sur la technologie de traitement du langage naturel pour améliorer l'efficacité de la réponse.

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Please analyze the following user inquiry text received by e-commerce customer service, determine its business category based on semantics, such as logistics inquiry, return and exchange application, product consultation, or complaint suggestion, and output the corresponding category label and confidence score for subsequent handling by corresponding specialists.

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Veuillez analyser le texte de demande d'information utilisateur suivant reçu par le service client e-commerce, déterminer sa catégorie commerciale en fonction de la sémantique, telle que l'enquête logistique, la demande de retour et d'échange, la consultation de produit ou la suggestion de plainte, et générer l'étiquette de catégorie correspondante et le score de confiance pour une prise en charge ultérieure par des spécialistes correspondants.

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Classification des tickets de support e-commerce

Analyze the following e-commerce support ticket text to identify the user's core request and emotional tone, then automatically categorize it into specific business modules such as logistics delay, product quality issue, return/refund application, or general inquiry, while extracting key entities like order ID, product name, and problem description for subsequent manual agent intervention.

Classification Billets Support

Please read the following customer service message submitted by the user, analyze its core request, and categorize it into one of the predefined ticket types: Logistics Inquiry, Return/Exchange Request, Product Quality Issue, Account Anomaly, or Other. Output only the classification label without explaining the reasoning process, ensuring accurate classification to assist subsequent manual handling.

QA Service Client EC

Act as a senior e-commerce customer service quality monitoring expert to analyze the provided customer-service dialogue records. Focus on checking if the customer service accurately understood the customer's needs, used standard polite language, effectively resolved complaints or inquiries, and avoided shirking responsibility or emotional responses. Identify key risk points in the dialogue, such as unfulfilled promises, incorrect information, or poor attitude, and provide specific improvement suggestions. The final output should include a dialogue quality score, a description of main issues, and an optimized reply example to ensure improved customer satisfaction and compliance.

Réponse Service Client EC

Act as a senior e-commerce customer service expert. Based on the provided order details and common after-sales issues, generate a professional, friendly, and efficient reply text. The reply must include confirmation of the order status, proactive answers to potential issues, and specific solution suggestions. Maintain a polite and empathetic tone, avoiding mechanical template language, ensuring the content is natural, fluent, and effectively resolves customer concerns.

Classificateur de tickets EC

Act as a senior customer service manager for an e-commerce platform. Analyze the incoming customer inquiry text, determine its business category based on semantics such as logistics inquiry, return or exchange application, product consultation, or complaint suggestion, and output a standardized classification label along with brief handling suggestions, ensuring the classification results are accurate and comply with platform standards.

Catégorisation des tickets de support

Analyze the following e-commerce support ticket text to identify its core issue type (e.g., logistics delay, damaged goods, refund request, usage inquiry), determine urgency, and recommend the most suitable handling department or solution. Output only the JSON formatted classification result, including ticket ID, issue category, urgency level (high/medium/low), and suggested action, without additional explanation.