电商客服自动工单分类助手

首页

AI 提示词列表

电商客服工单分类

${:__('collect %s',电商客服工单分类)}$

基于自然语言处理技术，自动识别并分类电商客服收到的用户咨询与投诉工单，提升响应效率。

提示内容

复制

Please analyze the following customer message received by an e-commerce support team and determine its business category. The categories include: Logistics Inquiry, Return/Exchange Request, Product Quality Complaint, Price Dispute, and General Consultation. Read the user's input carefully, extract key information such as order numbers, product names, and problem descriptions, and then classify the message into one of the five predefined categories based on established standards. If the message involves multiple issues, select the most urgent or core issue for classification. Finally, output the result in JSON format, including the original message, the predicted category, and a confidence score, ensuring the classification is accurate and meets actual business needs.

复制

请分析以下电商客服收到的用户消息，判断其所属的业务类别。业务类别包括：物流查询、退换货申请、产品质量投诉、价格争议以及一般咨询。请仔细阅读用户输入的内容，提取关键信息如订单号、商品名称、问题描述等，然后根据预设的分类标准将消息归类到上述五个类别之一。如果消息涉及多个问题，请选择最紧急或最核心的问题进行分类。最后，请以JSON格式输出结果，包含原始消息、预测类别以及置信度分数，确保分类准确且符合实际业务场景的需求。

复制

分享点击复制链接

0/300

提交

推荐

电商客服智能回复

Act as a senior e-commerce customer service expert. Based on the user-provided order ID, product name, and current logistics status, generate a friendly, professional, and efficient reply text. The reply must include an apology for the logistics delay, a specific estimated delivery time, and a compensation plan (such as coupons or points). Ensure the language is natural and fluent, avoiding mechanical template feelings, with the goal of alleviating user anxiety and promoting repurchase.

电商客服工单分类

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 user intent, providing a brief explanation for the classification.

电商客服工单分类

Act as an intelligent customer service system for an e-commerce platform. You will receive raw user inquiry texts and must analyze their core intent to classify them into one of five predefined standard categories: Logistics Inquiry, Return/Exchange Request, Product Consultation, Complaint/Suggestion, or Account Issue. Carefully review the input, extract key entities such as order numbers, product names, or specific pain points, and ignore irrelevant pleasantries. Finally, output only a standardized JSON result containing the original text, the identified intent label, and a confidence score, ensuring accurate classification to support subsequent automated processing workflows.

电商客服智能回复

Act as a senior ecommerce customer service expert. Generate a professional, friendly, and accurate response based on the provided customer inquiry and product knowledge base. The reply must directly address the customer's issue, avoid mechanical template language, and maintain brand tone consistency to ensure the customer feels valued and understood.

电商客服智能回复

Act as a senior e-commerce customer service expert. Based on the provided product details (including name, price, specifications, stock status, and key selling points) and the user's specific inquiry, generate a natural, friendly, and professional reply text. The reply should directly address the user's concerns, accurately cite key product information, avoid mechanical template language or overly complex jargon, and ensure the tone aligns with brand standards to facilitate conversion.

电商客服智能回复

Act as a senior e-commerce customer service expert. Based on the provided order ID, current logistics status, and user's emotional keywords, generate a professional yet empathetic reply. The response must include a sincere apology for the delay, a specific estimated delivery time, and a small coupon as compensation. Maintain a friendly tone that solves the problem while avoiding robotic template language.