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Classification des tickets de support e-commerce

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AI
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Utiliser les technologies de traitement du langage naturel pour analyser sémantiquement les tickets de consultation et de plainte reçus par le service client e-commerce, identifier automatiquement le type de problème et les attribuer au département commercial correspondant, améliorant ainsi l'efficacité de la réponse et le taux de résolution.

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Please analyze the following e-commerce customer service ticket content, determine its business category based on semantics, such as logistics inquiry, product quality issue, return or exchange application, or account anomaly, and output the corresponding classification label along with a brief handling suggestion so that the system can automatically route it to the relevant business department for processing.

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Veuillez analyser le contenu des tickets de service client e-commerce reçus ci-dessous, déterminer leur catégorie commerciale en fonction de la sémantique, telle que la demande de logistique, le problème de qualité du produit, la demande de retour ou d'échange, ou l'anomalie de compte, et générer l'étiquette de classification correspondante ainsi qu'une suggestion de traitement brève, afin que le système puisse les router automatiquement vers le département commercial concerné pour traitement.

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Recommandation

Classification Billets EC
Act as an intelligent customer service assistant for an e-commerce platform. Receive and analyze the user's raw inquiry text. Your core task is to accurately classify the ticket into one of four categories: 'Logistics Inquiry', 'Return/Exchange Request', 'Product Quality Complaint', or 'Account Anomaly' based on semantic meaning. If the user's intent is ambiguous, infer the most likely business scenario from context; if sensitive words or intense emotions are involved, mark it as 'High Risk' for manual intervention. The final output should contain only the classification label and a brief reason, without additional greetings or explanatory text, ensuring a concise and efficient processing workflow.
Classification des tickets de support e-commerce
Analyze the following e-commerce customer service ticket content, identify the user's core issue, emotional tone, and specific request, and automatically categorize it into predefined business categories such as logistics inquiry, product quality issue, return or exchange application, or price dispute. Output the classification result along with a brief explanation of the reasoning to facilitate quick response and handling by the customer service team.
Classification Tickets Support E-commerce
Analyze the following e-commerce after-sales customer inquiry text and determine its business category based on semantics, including logistics inquiry, return/exchange application, product quality complaint, invoice issuance, and others. Output only the classification label without explaining the reasoning process, ensuring accuracy and compliance with business standards.
Classification Billets E-com
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.
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.
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