AI活用ECカスタマーサポートチケット分類

家

AIプロンプト一覧

ECサポートチケット分類

${:__('collect %s',ECサポートチケット分類)}$

自然言語処理技術を用いて、ECプラットフォームのユーザーからのアフターサービス相談や苦情チケットを分析し、意図を認識して自動的にビジネス分類タグを割り当て、カスタマーサービスの応答効率とデータの正確性を向上させます。

プロンプト内容

コピー

Analyze the following e-commerce support ticket text to identify the user's core request and emotional tone, then categorize it into the most matching predefined business category (such as logistics delay, product damage, refund request, or inquiry query) while extracting key entities like order ID, product name, and problem description, finally outputting a standardized JSON result for downstream automated processing.

コピー

以下のECアフターサービスチケットテキストを分析し、ユーザーの核心的な要求と感情の傾向を特定し、物流の遅延、商品の破損、返金申請、問い合わせなどの事前定義されたビジネス分類体系に基づいて最も一致するカテゴリに分類してください。同時に、注文番号、商品名、問題の説明などの主要なエンティティ情報を抽出し、後続の自動化処理フローの呼び出しのために標準化されたJSON形式の結果を出力してください。

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.

ECカスタマーサポートチケット

Act as an intelligent customer service assistant for an e-commerce platform. Analyze the user's after-sales inquiry text to identify core intents such as returns, exchanges, or logistics queries. Based on the current order status and platform policies, generate a friendly and professional reply draft that directly addresses the user's concerns and guides them to the next steps.

ECカスタマーサポートチケット分類

Act as part of an e-commerce customer service system. Analyze the user's raw inquiry text and classify it into one of four categories: pre-sales consultation, after-sales service, logistics query, or complaint/suggestion based on its semantic intent. Output the corresponding category label and a brief justification to ensure accurate classification that assists human agents in handling tickets quickly.