option
Maison Liste de prompts IA AI Classification Tickets E-commerce

Classification Tickets E-commerce

{:__('collect %s',Classification Tickets E-commerce)}
AI
0

Identifiez et catégorisez automatiquement les tickets de service client e-commerce via le NLP pour améliorer l'efficacité.

Contenu du prompt Copier Copier

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.

Copier Copier

Agissez en tant que système de service client intelligent pour une plateforme de commerce électronique. Recevez le texte brut des demandes des utilisateurs, analysez leur intention principale et leur ton émotionnel, et classez-les avec précision dans des catégories commerciales spécifiques telles que la consultation avant-vente, la plainte après-vente, la demande de logistique ou la demande de retour/échange. Extrayez les informations clés sur les entités, y compris les numéros de commande, les noms de produits et les descriptions des problèmes, puis produisez un résumé de ticket structuré pour faciliter une intervention rapide par les agents humains.

Copier Copier
commentaires (0)
0/300

Recommandation

Classification des tickets de support
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.
Classification Tickets E-com
Please analyze the following user customer service conversation records from an e-commerce platform, determine the business category based on the content such as logistics inquiry, return or exchange application, product quality complaint, or account anomaly, extract key information like order number, problem description, and user sentiment, and finally output a structured ticket classification result for subsequent manual or automated processing.
Classification Tickets EC
Please analyze the content of the following user customer service tickets from an e-commerce platform, determine which category their core request belongs to based on the conversation context (logistics inquiry, return/exchange application, product quality complaint, or price consultation), and output the corresponding category label along with a brief reason.
Classification Billets Support
Analyze the following e-commerce support ticket content, identify its core issue type, including logistics delay, product damage, refund request, or inquiry, and route it to the logistics, quality inspection, or finance department based on preset rules. Extract key entities such as order number, product name, and user sentiment, and finally output a structured classification result and suggested handling plan to ensure accurate classification and compliance with business standards.
Classificateur de tickets e-commerce
Act as an intelligent customer service system for an e-commerce platform. Receive raw user inquiry or complaint text, analyze its core intent, and classify it into specific business scenarios such as logistics inquiry, product quality issue, return or exchange request, or price dispute. Output a standardized classification label along with a brief handling suggestion to ensure the customer service team can respond quickly and accurately.
Classification Billets EC
Please analyze the following e-commerce customer inquiry ticket content, automatically categorize it into one of the four categories: 'Logistics Inquiry', 'Return/Exchange Application', 'Product Quality Complaint', or 'Other Inquiry' based on business semantics, and briefly explain the basis for classification, ensuring that the classification results conform to actual business scenarios and are logically clear.
OR