AI辅助电商客服自动分类

首页

AI 提示词列表

电商客服工单分类

${:__('collect %s',电商客服工单分类)}$

利用AI自动识别并分类电商客户咨询工单，提升处理效率与准确率。

提示内容

复制

Act as a data analysis assistant for an e-commerce customer service system. Read the current list of pending customer inquiry tickets, automatically categorize them into four major categories based on the nature of the issue: logistics inquiry, product quality, return/exchange application, or complaint/suggestion. Extract key entities such as order numbers, product SKUs, and user sentiment tendencies, and finally output structured classification results for subsequent manual review or automatic routing.

复制

请作为电商客服系统的数据分析助手，读取当前待处理的客户咨询工单列表，根据问题性质将其自动归类为物流查询、产品质量、退换货申请或投诉建议四大类，并提取关键实体如订单号、商品SKU及用户情绪倾向，最终输出结构化的分类结果以便后续人工复核或自动路由处理。

复制

分享点击复制链接

0/300

提交

推荐

电商客服工单分类

Analyze the following customer service message from an e-commerce platform and determine its business category. The possible categories are: Logistics Inquiry, Return/Exchange Request, Product Quality Issue, Price Dispute, or Other Inquiry. Output only the category name without any explanation or additional text. If the message cannot be clearly classified, output 'Other Inquiry'.

电商客服工单分类

Act as an intelligent customer service assistant for an e-commerce platform. Receive the raw text of a user's inquiry, analyze its core intent and urgency, and categorize it into one of four types: pre-sales consultation, after-sales rights protection, logistics inquiry, or complaint/suggestion. Extract key entities such as order numbers, product names, and problem descriptions. Finally, output a structured classification result and handling suggestions, ensuring the response is accurate and complies with corporate customer service standards.

电商客服工单分类

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.

电商客服工单分类

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.

电商客服工单分类

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.

电商客服工单分类

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.