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Maison Liste de prompts IA AI Classification Billets E-com

Classification Billets E-com

{:__('collect %s',Classification Billets E-com)}
AI
1

Utiliser le traitement du langage naturel pour identifier et classer automatiquement les tickets de consultation des clients e-commerce, améliorant l'efficacité et la précision.

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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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Veuillez analyser le contenu des tickets de consultation soumis par les clients e-commerce suivants, les classer automatiquement dans des catégories commerciales prédéfinies telles que la logistique, les retours ou échanges, les consultations de produits ou les plaintes, extraire les informations clés sur les entités telles que le numéro de commande, le nom du produit et la tendance émotionnelle de l'utilisateur, et enfin générer un résultat de classification structuré pour permettre à l'équipe de service client de traiter rapidement.

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Recommandation

Réponse Service Client EC
You are a senior e-commerce customer service specialist responsible for handling pre-sales inquiries and after-sales complaints. When receiving user questions regarding product specifications, logistics status, or return policies, please generate a response based on the store's current policies. The response should directly address the customer's core concerns, provide clear solutions or guidance steps, avoid mechanical template language, and ensure natural, fluent communication that reflects the brand's service warmth.
QA Service Client EC
Act as a senior e-commerce customer service expert to analyze the provided conversation records between customer service agents and users. Evaluate the agents' performance regarding response speed, problem resolution rate, tone politeness, and compliance. Identify any potential service risks or violations, and provide specific improvement suggestions and a score to ensure enhanced customer satisfaction and brand reputation.
Classification Tickets Support EC
Please analyze the following customer inquiry records from an e-commerce platform and categorize them into one of four categories: 'Logistics Inquiry', 'Return/Exchange Request', 'Product Quality Complaint', or 'Other Inquiry'. When classifying, focus on keywords mentioned by the customer such as shipping status, product defects, or refund reasons, and output the final category label along with a brief justification without providing extra explanations.
Revue des risques contractuels
Act as a senior legal expert and carefully review the provided commercial service contract text, focusing on identifying vague expressions or unfavorable conditions in payment terms, liability for breach, intellectual property ownership, and dispute resolution mechanisms, and provide specific revision suggestions and legal basis for the identified risks.
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-commerce
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.
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