KI-gestütztes System zur automatischen Klassifizierung von E-Commerce-Tickets

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E-Commerce Ticket-Klassifizierung

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Nutzung von Natural Language Processing zur automatischen Identifizierung und Klassifizierung von E-Commerce-Kundenanfragen, um Effizienz und Genauigkeit zu verbessern.

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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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Analysieren Sie den Inhalt der folgenden von E-Commerce-Kunden eingereichten Beratungs-Tickets, klassifizieren Sie sie automatisch in vordefinierte Geschäftskategorien wie Logistikabfragen, Rückgabe- oder Umtauschanträge, Produktberatung oder Beschwerden, extrahieren Sie Schlüsselinformationen zu Entitäten wie Bestellnummer, Produktname und Benutzerstimmung und geben Sie schließlich ein strukturiertes Klassifizierungsergebnis aus, damit das Kundenservice-Team schnell handeln kann.

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EC-Kundenservice-Antwort

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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.

E-Commerce Ticket-Klassifizierer

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.