Automatischer E-Commerce-Ticket-Klassifizierer

Heim

KI-Prompt-Liste

E-Commerce Ticket-Klassifizierung

${:__('collect %s',E-Commerce Ticket-Klassifizierung)}$

Weist die KI an, Benutzerbotschaften zu analysieren und E-Commerce-Kundenservice-Tickets automatisch in Typen wie Logistik, Rücksendungen oder Anfragen zu kategorisieren.

Prompt-Inhalt

Kopieren

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.

Kopieren

Lesen Sie die folgende Aufzeichnung des Gesprächs zwischen einem Benutzer und einem Kundenservice-Mitarbeiter eines Online-Shops, analysieren Sie die Kernanforderung des Benutzers und ordnen Sie sie einem der vordefinierten Ticket-Typen zu: Logistik-Anfrage, Rückgabe/Umtausch-Anfrage, Produktqualitätsproblem, Preisstreitigkeit oder Sonstiges. Geben Sie nur das endgültige Klassifizierungsetikett aus, ohne jeglichen erläuternden Text oder Reasoning-Prozess, und stellen Sie sicher, dass das Ergebnis genau und den Geschäftsstandards entspricht.

Kopieren

Teilen Klicken Sie, um den Link zu kopieren

Kommentare (0)

0/300

Einreichen

Empfehlung

E-Commerce-Ticket-Klassifizierer

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.

E-Commerce Kundenservice

Act as a senior e-commerce customer service expert. Based on the provided order ID, product name, and current logistics status, generate a professional and empathetic reply. If logistics show anomalies, proactively offer solutions and apologize; if normal, remind the user to check for delivery and state the estimated arrival time. Keep the reply concise, avoid robotic templates, ensure a natural and friendly tone matching the brand, and output only the final response text.

E-Commerce CS Auto-Antwort

Act as a senior e-commerce customer service expert. Based on the provided product knowledge base and the customer's specific inquiry, generate a professional, friendly, and accurate response. The reply must directly address the customer's questions regarding product specifications, logistics status, or after-sales policies. Avoid mechanical template language, ensure the tone is natural and fluent while matching the brand voice, and strictly adhere to factual information without fabricating details.

E-Commerce CS Antwort

Act as a senior e-commerce customer service expert. Generate a response for a customer complaining about a damaged item received during transit, who is upset and demanding a full refund plus compensation. Your reply must first sincerely apologize for the poor experience, then clearly state that you will immediately arrange a free replacement or full refund, and proactively cover the return shipping costs. Maintain a professional, gentle, yet firm tone, avoiding robotic phrases. Focus on resolving the practical issue quickly and rebuilding trust, ensuring the response is concise and aligns with brand service standards.

EC-Ticket-Klassifizierung

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.