ECサポートチケット自動分類システム

家

AIプロンプト一覧

ECサポートチケット分類

${:__('collect %s',ECサポートチケット分類)}$

自然言語処理を用いて、ECサイトの顧客サポートチケットを自動的に分類するAIツール。

プロンプト内容

コピー

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.

コピー

以下のECサポートチームが受信した顧客メッセージを分析し、そのビジネスカテゴリを特定してください。カテゴリには、物流問い合わせ、返品・交換リクエスト、製品品質に関する苦情、価格に関する紛争、および一般的な相談が含まれます。ユーザーの入力を注意深く読み、注文番号、商品名、問題の説明などの关键情報を抽出し、確立された基準に基づいて、5つの定義されたカテゴリのいずれかにメッセージを分類してください。メッセージに複数の問題が含まれている場合は、最も緊急または中核的な問題を選択して分類してください。最後に、元のメッセージ、予測されたカテゴリ、および信頼度スコアを含むJSON形式で結果を出力し、分類が正確であり、実際のビジネスニーズを満たしていることを確認してください。

コピー

共有リンクをコピーするにはクリック

コメント (0)

0/300

送信

おすすめ

ECカスタマー対応返信

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.

ECカスタマーサポートチケット分類

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.

ECサポートチケット分類

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.

ECカスタマーサポート自動返信

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.

ECカスタマー対応返信

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.

ECカスタマー対応返信

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.