Option
Heim KI-Prompt-Liste AI E-Commerce-Ticket-Klassifizierer

E-Commerce-Ticket-Klassifizierer

{:__('collect %s',E-Commerce-Ticket-Klassifizierer)}
AI
1

Ein KI-Tool, das E-Commerce-Kundenservice-Tickets automatisch klassifiziert, um die Effizienz und Genauigkeit zu verbessern.

Prompt-Inhalt Kopieren Kopieren

Please analyze the following e-commerce customer service ticket content and classify it into one of four categories: 'Logistics Inquiry', 'Return/Exchange Request', 'Product Quality Complaint', or 'Other Inquiry' based on predefined business rules. If the ticket involves multiple issues, prioritize the most urgent or core issue category. Ensure the classification result is accurate and aligns with actual business scenarios to enable the customer service team to respond quickly.

Kopieren Kopieren

Bitte analysieren Sie den Inhalt des folgenden E-Commerce-Kundenservice-Tickets und klassifizieren Sie es basierend auf vordefinierten Geschäftsregeln in eine der vier Kategorien: 'Logistikanfrage', 'Rückgabe/Umtauschantrag', 'Produktqualitätsbeschwerde' oder 'Sonstige Anfrage'. Wenn das Ticket mehrere Probleme beinhaltet, priorisieren Sie die Kategorie des dringendsten oder Kernproblems. Stellen Sie sicher, dass das Klassifizierungsergebnis genau ist und mit den tatsächlichen Geschäftsszenarien übereinstimmt, damit das Kundenserviceteam schnell reagieren kann.

Kopieren Kopieren
Kommentare (0)
0/300

Empfehlung

E-Commerce-Ticket-Klassifizierung
Please analyze the following e-commerce customer inquiry ticket content, automatically classify it into one of four categories: 'Logistics Inquiry', 'Return/Exchange Request', 'Product Quality Complaint', or 'Other Inquiry' based on business semantics, and briefly explain the classification basis to enable the customer service team to respond and handle it quickly.
E-Commerce-Support-Ticket-Klassifizierung
Analyze the provided customer inquiry text and categorize it into one of four predefined classes: pre-sales consultation, after-sales service, logistics inquiry, or complaint/suggestion based on business semantics. If an order number is mentioned, extract it; if emotional expression is present, label the sentiment as positive, neutral, or negative. The output must include the classification result, key entity extraction, and sentiment label, ensuring the logic aligns with standard e-commerce customer service protocols without additional explanatory reasoning.
EC-Ticket-Klassifizierer
Act as an intelligent customer service assistant for an e-commerce platform. Receive raw user inquiry text, analyze its core intent and emotional tone, and categorize it into standard classes such as pre-sales consultation, after-sales rights protection, logistics inquiry, or complaint suggestion. Extract key entities like order numbers, product names, and problem descriptions, and finally output structured classification results and handling suggestions for quick human agent intervention.
E-Commerce Ticket-Klassifizierung
Analyze the following customer service message received by an e-commerce platform and determine its business category. The categories include: Logistics Inquiry, Return/Exchange Request, Product Quality Complaint, Price Dispute, Account Issue, and Others. Output only the category name without explanation. For instance, if the user says 'My package hasn't moved for three days', classify it as Logistics Inquiry; if the user says 'The clothes faded after washing', classify it as Product Quality Complaint. Accurately categorize the input text.
E-Commerce-Ticket-Klassifizierer
Act as a senior e-commerce customer service operations expert. Analyze the provided customer inquiry text and categorize it into one of four types: 'Logistics Inquiry', 'Return/Exchange Request', 'Product Quality Complaint', or 'Other Inquiry'. Briefly explain the reasoning behind your classification based on the key intent of the message.
E-Commerce CS Antwort
Act as a senior e-commerce customer service expert. Draft a response to a customer complaining about a minor scratch on their received item, who is visibly upset. Your reply must start with a sincere apology acknowledging the issue, then offer two specific solutions (e.g., partial refund or free replacement) for the customer to choose from. Conclude with gratitude and a commitment to improvement. Maintain a professional, gentle, yet firm tone, avoiding robotic phrases, ensuring the customer feels valued and respected to effectively resolve the conflict and protect the brand image.
OR