Automatischer E-Commerce-Support-Ticket-Klassifizierer

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E-Commerce-Ticket-Klassifizierer

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Ein KI-Tool, das Support-Tickets basierend auf dem Verständnis der natürlichen Sprache automatisch kategorisiert, um die Antworteffizienz zu verbessern.

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Analyze the following customer message received by e-commerce support, determine its business category based on semantics, such as logistics inquiry, return or exchange application, product quality complaint, or price consultation, and output the corresponding category label.

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Analysieren Sie die folgende vom E-Commerce-Support erhaltene Kundenmeldung, bestimmen Sie ihre Geschäftskategorie basierend auf der Semantik, wie z. B. Logistikanfrage, Rückgabe- oder Austauschbeantragung, Produktqualitätsbeschwerde oder Preiskonsultation, und geben Sie das entsprechende Kategorielabel aus.

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E-Commerce Ticket-Klassifizierung

Read the following conversation between a user and an e-commerce customer service representative, analyze the core intent, and classify it into one of the predefined ticket types: Logistics Inquiry, Return/Exchange Request, Product Quality Issue, Price Dispute, or Other. Output only the final classification label without any explanatory text or reasoning process, ensuring the result is accurate and compliant with business standards.

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Analyze the following customer inquiry text from an e-commerce platform and determine its business category based on semantics, including return/exchange requests, logistics queries, product inquiries, or complaints/suggestions. Output only the classification label without explanation or additional notes to ensure accurate categorization for subsequent automated processing.

EC-Ticket-Klassifizierung

Please analyze the following raw customer inquiry text submitted via e-commerce channels and classify it into one of four categories: 'Logistics Inquiry', 'Return/Exchange Request', 'Product Quality Complaint', or 'Other'. When making the judgment, focus on keywords such as tracking numbers, damaged goods, size mismatches, or shipping delays mentioned by the customer, ignoring emotional expressions, and directly output the most matching category label along with a brief rationale, ensuring the classification result aligns with common handling processes in actual business scenarios.