ИИ-сортировка тикетов поддержки электронной коммерции

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Категоризация тикетов электронной коммерции

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Использование обработки естественного языка для анализа тикетов поддержки, автоматической идентификации типов проблем и маршрутизации в соответствующий отдел для повышения эффективности и удовлетворенности.

Содержание промпта

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Analyze the following e-commerce support ticket text to identify its core issue type (e.g., delayed shipping, damaged goods, refund dispute, pre-sales inquiry) and categorize it into the corresponding handling department (Logistics, Quality Control, Finance, or Pre-sales) based on predefined rules. If the ticket contains multiple issues, prioritize the most urgent or impactful one, and output the classification result along with a brief rationale.

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Проанализируйте текст следующего тикета поддержки электронной коммерции, чтобы определить его основной тип проблемы (например, задержка доставки, повреждение товара, спор о возврате средств, предпродажный запрос) и классифицировать его в соответствующий отдел обработки (Логистика, Контроль качества, Финансы или Предпродажная группа) в соответствии с заранее определенными правилами. Если тикет содержит несколько проблем, приоритизируйте самую срочную или наиболее влияющую, и выведите результат классификации вместе с кратким обоснованием.

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Рекомендация

Классификация Тикетов

Analyze the following customer inquiry text from an e-commerce platform and determine its business category based on semantics, including return/exchange requests, logistics queries, product inquiries, or complaints/suggestions. Output only the classification label without explanation or additional notes to ensure accurate categorization for subsequent automated processing.

Классификация тикетов EC

Please 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'. When making the judgment, focus on keywords such as tracking numbers, damaged goods, size mismatches, or shipping delays mentioned by the customer, ignoring emotional expressions, and directly output the most matching category label along with a brief rationale, ensuring the classification result aligns with common handling processes in actual business scenarios.

Ответ службы поддержки EC

Act as a senior e-commerce customer service expert. Based on the user's provided order number, product name, and current logistics status, generate a professional and empathetic reply. If logistics are stalled, proactively provide inquiry channels and apologize; if delivered, guide the user to confirm receipt and invite a review. The reply must be concise, avoid robotic clichés, ensure a warm tone that addresses actual pain points, and output only the final reply text.

Классификатор тикетов E-commerce

Act as an intelligent customer service assistant for an e-commerce platform. Analyze the text of user-submitted inquiries to identify their core intent, such as return requests, logistics tracking, or product complaints. Classify each ticket into the appropriate department based on predefined business rules, and extract key entities like order numbers, product names, and specific issue descriptions to facilitate rapid follow-up by human agents.

Классификатор тикетов электронной коммерции

Act as an intelligent customer service assistant for an e-commerce platform. Receive raw user consultation or complaint text, analyze its core intent, and categorize it into specific business categories such as logistics inquiry, product quality issue, return or exchange application, or price dispute. Extract key entities like order numbers, product names, and user sentiment. Finally, output the classification results and brief handling suggestions in a structured format, ensuring accuracy and compliance with customer service standards.

Классификация Тикетов

Act as an intelligent customer service assistant for an e-commerce platform. Read the provided raw text of customer inquiries, analyze their core demands and emotional tone, and accurately classify them into one of the following categories: logistics inquiry, return or exchange application, product consultation, or complaint and suggestion. Extract key entities such as order numbers or product names, and finally generate a concise structured summary to facilitate quick follow-up by human customer service agents.