Long-Tail Keyword Generator für E-Commerce Produktlisten

Heim

KI-Prompt-Liste

E-Commerce Produkt Keyword Generator

${:__('collect %s',E-Commerce Produkt Keyword Generator)}$

Generiert genaue Long-Tail-Suchbegriffe für grenzüberschreitende E-Commerce-Produkte anhand von Basisdaten, um die Produktpräsenz zu steigern.

Prompt-Inhalt

Kopieren

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.

Kopieren

Basierend auf den bereitgestellten Basisdaten eines grenzüberschreitenden E-Commerce-Produkts und unter Berücksichtigung gängiger Nutzersuchgewohnheiten auf der Plattform generieren Sie 15 genaue Long-Tail-Schlüsselwörter, die für Suchverkehr geeignet sind. Diese Schlüsselwörter müssen verschiedene Nutzersuchszenarien abdecken, 3 bis 8 Wörter umfassen und den Suchpräferenzen der Plattform entsprechen, um dem Produkt mehr Präsenz zu verschaffen.

Kopieren

Teilen Klicken Sie, um den Link zu kopieren

Kommentare (0)

0/300

Einreichen

Empfehlung

E-Commerce-Ticket-Klassifizierung

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.

E-Commerce-Ticket-Klassifizierer

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.

E-Commerce-Ticket-Klassifizierer

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.

E-Commerce-Support-Ticket-Klassifizierung

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.

E-Commerce Ticket-Klassifizierung

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-Kundenservice-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.