Инструмент для автоматического обобщения причин негативных отзывов в e-commerce

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Автоматическое обобщение причин негативных отзывов

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Автоматически выделяет и обобщает основные причины недовольства пользователей по одному негативному отзыву на товар, помогает продавцам быстро найти проблемы товара и обслуживания.

Содержание промпта

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Process a single user negative review of a product provided by an e-commerce merchant, extract and summarize the core reasons for user dissatisfaction from the user's statement, and output 1 to 3 concise and clear points to help merchants quickly locate the problems that need adjustment.

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Обработайте один негативный отзыв пользователя на товар, предоставленный продавцом электронной коммерции, выделите и обобщите основные причины недовольства пользователя из его описания, выведите 1–3 кратких и понятных пункта, чтобы продавец мог быстро определить проблемы, требующие корректировки.

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Рекомендация

Классификация тикетов электронной коммерции

Act as an intelligent customer service assistant for an e-commerce platform. You will receive raw text from customer inquiries or complaints. Analyze the semantic intent to determine the core issue. If the text mentions delivery delays, categorize it as a logistics issue. If it involves product quality or description mismatches, categorize it as an after-sales dispute. If it concerns account login or payment anomalies, categorize it as technical support. Output a standardized ticket category label along with brief handling suggestions based on key entities and sentiment, ensuring accuracy and compliance with business protocols.

Автоответ CS E-commerce

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Классификация тикетов поддержки электронной коммерции

Analyze the following e-commerce support ticket content to identify its core issue type (such as logistics delay, product damage, refund dispute, or general inquiry) and categorize it into the corresponding handling department based on predefined classification rules. Additionally, extract key entity information (such as order ID, product name, and user sentiment) to generate a concise ticket summary for quick human agent intervention.

Ответы CS для электронной коммерции

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Классификатор тикетов E-commerce

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Ответ CS E-commerce

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