Automatische Bewertung und Priorisierung von B2B-Verkaufsleads im Stapel

Heim

KI-Prompt-Liste

Bewertungs- und Filtertool für B2B-Verkaufsleads

${:__('collect %s',Bewertungs- und Filtertool für B2B-Verkaufsleads)}$

Bewertet unbearbeitete B2B-Verkaufsleads über mehrere Dimensionen und sortiert sie nach Priorität, damit das Vertriebsteam zuerst hochwertige Interessenten bearbeitet

Prompt-Inhalt

Kopieren

You will use provided basic information of B2B sales leads to assign a 0-100 qualification score to each lead based on four dimensions: company size, business requirement matching, procurement budget availability and decision-making cycle, then sort all leads from highest to lowest score and output a clear prioritized lead list with no extra redundant notes.

Kopieren

Sie nutzen die bereitgestellten Basisdaten zu B2B-Verkaufsleads, um jedem Lead eine Qualifikationsnote von 0 bis 100 nach vier Dimensionen zuzuweisen: Unternehmensgröße, Übereinstimmung der Geschäftsanforderungen, Verfügbarkeit des Beschaffungsbudgets und Entscheidungszyklus. Anschließend sortieren Sie alle Leads von höchster nach niedrigster Note und geben eine klare priorisierte Lead-Liste ohne zusätzliche überflüssige Anmerkungen aus.

Kopieren

Teilen Klicken Sie, um den Link zu kopieren

Kommentare (1)

0/300

Einreichen

GeorgeMartinez

24. August 2026 01:26:37 MESZ

B2Bの見込み客スコアリングツール、会社規模と要件のマッチ度、予算、意思決定サイクルの4項目でスコアを出してくれるのは便利。ただ結果だけでなく少し理由も教えてくれたら、優先順位の判断がもっとしやすいかもね😅

Empfehlung

E-Commerce Ticket-Klassifizierung

Analyze the provided customer support ticket text from an e-commerce platform and determine its primary intent. Classify the ticket into one of four categories: 'Logistics Inquiry', 'Return/Exchange Request', 'Product Quality Complaint', or 'Other Inquiry'. If multiple issues are present, prioritize the most urgent or the last mentioned issue. Output only the category label without any additional explanation.

E-Com Support Ticket Klassifizierung

Analyze the following customer message received by e-commerce support, determine its business category based on semantics such as return/exchange inquiry, logistics query, product quality complaint, or account issue, and output the corresponding category label along with a brief handling suggestion, ensuring accurate classification that aligns with customer service standards.

E-Commerce-Ticket-Klassifizierer

Analyze the following customer service message from an e-commerce platform and determine its business category. The possible categories are: Logistics Inquiry, Return/Exchange Request, Product Quality Complaint, Price Dispute, Account Issue, and Other. Output only the category name without any explanation. The customer message is: 'The package I received three days ago shows as delivered, but I have not received any physical item. The logistics information is stuck at the transit center. Please verify this and expedite a reshipment or refund.'

E-Com Ticket-Klassifizierer

EC Ticket-Klassifizierung

Act as a data analysis assistant for an e-commerce customer service system. Read the current list of pending customer inquiry tickets, automatically categorize them into four major categories based on the nature of the issue: logistics inquiry, product quality, return/exchange application, or complaint/suggestion. Extract key entities such as order numbers, product SKUs, and user sentiment tendencies, and finally output structured classification results for subsequent manual review or automatic routing.

EC-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 urgency, and categorize it into one of four types: pre-sales consultation, after-sales rights protection, logistics inquiry, or complaint/suggestion. Extract key entities such as order numbers, product names, and problem descriptions. Finally, output a structured classification result and handling suggestions, ensuring the response is accurate and complies with corporate customer service standards.