Автоматический классификатор тикетов поддержки

Дом

Список AI-промптов

Классификатор тикетов электронной коммерции

${:__('collect %s',Классификатор тикетов электронной коммерции)}$

Автоматически идентифицируйте и классифицируйте тикеты запросов пользователей, полученные службой поддержки электронной коммерции на основе технологии обработки естественного языка, чтобы повысить эффективность ответа.

Содержание промпта

Копировать

Please analyze the following user inquiry text received by e-commerce customer service, determine its business category based on semantics, such as logistics inquiry, return and exchange application, product consultation, or complaint suggestion, and output the corresponding category label and confidence score for subsequent handling by corresponding specialists.

Копировать

Пожалуйста, проанализируйте следующий текст запроса пользователя, полученный службой поддержки электронной коммерции, определите его бизнес-категорию на основе семантики, такую как логистический запрос, заявка на возврат и обмен, консультация по продукту или предложение по жалобе, и выведите соответствующую метку категории и оценку уверенности для последующей обработки соответствующими специалистами.

Копировать

Поделиться Нажмите, чтобы скопировать ссылку

Комментарии (0)

0/300

Представлять на рассмотрение

Рекомендация

Классификация тикетов поддержки электронной коммерции

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.

Классификация Тикетов

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.

QA службы поддержки EC

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.

Ответы службы поддержки EC

Act as a senior e-commerce customer service expert. Based on the provided order details and common after-sales issues, generate a professional, friendly, and efficient reply text. The reply must include confirmation of the order status, proactive answers to potential issues, and specific solution suggestions. Maintain a polite and empathetic tone, avoiding mechanical template language, ensuring the content is natural, fluent, and effectively resolves customer concerns.

Классификатор тикетов EC

Act as a senior customer service manager for an e-commerce platform. Analyze the incoming customer inquiry text, determine its business category based on semantics such as logistics inquiry, return or exchange application, product consultation, or complaint suggestion, and output a standardized classification label along with brief handling suggestions, ensuring the classification results are accurate and comply with platform standards.

Классификация тикетов поддержки

Analyze the following e-commerce support ticket text to identify its core issue type (e.g., logistics delay, damaged goods, refund request, usage inquiry), determine urgency, and recommend the most suitable handling department or solution. Output only the JSON formatted classification result, including ticket ID, issue category, urgency level (high/medium/low), and suggested action, without additional explanation.