オプション
家 AIプロンプト一覧 AI EC商品キーワード生成ツール

EC商品キーワード生成ツール

{:__('collect %s',EC商品キーワード生成ツール)}
AI
34

アマゾンやアリエクスプレスなどの越境ECプラットフォーム向けに、商品の基本情報から検索流入に適した精密なロングテールキーワードリストを生成し、商品の露出を高めます。

プロンプト内容 コピー コピー

Based on the provided basic information of a cross-border e-commerce product, combined with common user search habits on mainstream platforms, generate 15 accurate long-tail keywords that fit search traffic. These keywords should cover different user search scenarios, have a length of 3 to 8 words, and match platform search preferences to help the product gain more exposure.

コピー コピー

提供された越境EC商品の基本情報をもとに、プラットフォームの一般的なユーザー検索習慣に合わせて、検索流入に適した精密なロングテールキーワードを15組生成してください。これらのキーワードはさまざまなユーザー検索シナリオをカバーし、単語数は3~8語の範囲に収め、プラットフォームの検索傾向に合わせることで、商品の露出を増やせるようにしてください。

コピー コピー
コメント (0)
0/300

おすすめ

ECサポートチケット分類
Analyze the following e-commerce customer support ticket text to identify the core issue and determine its business category. The content involves common scenarios such as damaged goods, delayed shipping, or refund requests. Extract key entities like order numbers, product names, and problem descriptions. Map the ticket to the most appropriate category based on predefined standards including logistics issues, product quality, after-sales service, and others. If the user expresses strong emotions or uses sensitive language, flag it as high priority. The final output should include the classification result, confidence score, and brief handling suggestions, ensuring clear logic that aligns with actual business operations to help the customer service team quickly triage and prioritize urgent cases.
ECチケット分類
Analyze the following raw customer inquiry text submitted via e-commerce channels and classify it into one of four categories: 'Logistics Inquiry', 'Return/Exchange Request', 'Product Quality Complaint', or 'Other Inquiry'. If the text does not clearly indicate a specific business scenario, assign it to 'Other Inquiry'. Output only the category label name without explaining the reasoning process or providing additional suggestions.
ECカスタマーサポート分類
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 sentiment, and categorize it into specific business scenarios such as pre-sales consultation, after-sales complaint, logistics inquiry, or return/exchange application. Extract key entities like order numbers, product names, and problem descriptions, then output a structured ticket summary to facilitate quick handling by human agents.
ECカスタマーサポートチケット分類
Analyze the following e-commerce support ticket text to identify the user's core request and emotional tone, then automatically categorize it into specific business modules such as logistics delay, product quality issue, return/refund application, or general inquiry, while extracting key entities like order ID, product name, and problem description for subsequent manual agent intervention.
ECカスタマーサポート分類
Please read the following customer service message submitted by the user, analyze its core request, and categorize it into one of the predefined ticket types: Logistics Inquiry, Return/Exchange Request, Product Quality Issue, Account Anomaly, or Other. Output only the classification label without explaining the reasoning process, ensuring accurate classification to assist subsequent manual handling.
ECカスタマーサポートQA
Act as a senior e-commerce customer service quality monitoring expert to analyze the provided customer-service dialogue records. Focus on checking if the customer service accurately understood the customer's needs, used standard polite language, effectively resolved complaints or inquiries, and avoided shirking responsibility or emotional responses. Identify key risk points in the dialogue, such as unfulfilled promises, incorrect information, or poor attitude, and provide specific improvement suggestions. The final output should include a dialogue quality score, a description of main issues, and an optimized reply example to ensure improved customer satisfaction and compliance.
OR