Outil de Catégorisation de Tickets de Support E-commerce par IA

Maison

Liste de prompts IA

Classificateur de Tickets E-commerce

${:__('collect %s',Classificateur de Tickets E-commerce)}$

Exploitez le traitement du langage naturel pour analyser les tickets de support client, catégorisant automatiquement les problèmes comme la logistique, les dommages ou les retours pour rationaliser le routage et améliorer l'efficacité.

Contenu du prompt

Copier

Act as an e-commerce support specialist to analyze the provided customer ticket text, identifying whether the core issue falls under logistics delay, product damage, return/exchange request, or price dispute. Extract key entities such as order ID, product name, and user sentiment, then output a structured classification result with suggested handling priority, ensuring accuracy and compliance with company service standards.

Copier

Agissez en tant qu'expert du support e-commerce pour analyser le texte du ticket client fourni, identifiant si le problème central relève d'un retard logistique, d'une détérioration du produit, d'une demande de retour/échange ou d'un litige de prix. Extrayez les entités clés telles que l'ID de commande, le nom du produit et la tendance émotionnelle de l'utilisateur, puis produisez un résultat de classification structuré avec une priorité de traitement suggérée, garantissant l'exactitude et la conformité aux normes de service de l'entreprise.

Copier

Partager Cliquez pour copier le lien

commentaires (0)

0/300

Soumettre

Recommandation

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.

Réponse Client E-com

Act as a senior e-commerce customer service expert. Based on the provided product knowledge base and the customer's inquiry, generate a professional, friendly, and accurate response. The reply should directly address the customer's issue, avoid robotic clichés, ensure a natural and fluent tone consistent with the brand voice, and include necessary after-sales guidance.

Classification Billets E-com

Analyze the following raw customer inquiry text from an e-commerce platform and categorize it into one of four categories: 'Logistics Inquiry', 'Return/Exchange Request', 'Product Quality Complaint', or 'Other Inquiry'. If the text involves multiple intents, prioritize the most urgent or core issue and briefly explain the reasoning. For instance, if a user mentions 'the shirt size is wrong and I want to exchange it', categorize it as 'Return/Exchange Request'. Output only the category and a short rationale without any conversational filler or explanatory prefixes.

Classificateur de Tickets E-commerce

Act as an intelligent customer service system for an e-commerce platform. Receive the raw text of a user's inquiry, analyze its core intent and emotional tone, and automatically classify it into standard categories 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 ticket summary to facilitate quick intervention by human agents.

QA Service Client EC

Act as a senior e-commerce customer service expert to analyze the provided conversation logs between agents and users. Focus on checking if the agent accurately understood the user's intent, adhered to company service standards, and avoided emotional or evasive behavior. Evaluate the effectiveness of the final solution. Identify key risk points (such as complaint escalation or sensitive word usage) and excellent service highlights, then provide specific improvement suggestions and a score to ensure the response is professional, objective, and actionable.