This n8n AI Workflow Automatically Scores & Qualifies Your Leads (OpenAI + Airtable + Slack) скачать в хорошем качестве

This n8n AI Workflow Automatically Scores & Qualifies Your Leads (OpenAI + Airtable + Slack)

Build a powerful AI-powered lead qualification system inside n8n that automatically scores, classifies, and prioritizes incoming leads. In this tutorial, you’ll connect Typeform to capture leads, use OpenAI to analyze and score them, store everything in Airtable, trigger instant alerts in Slack, and send automated follow-ups with Gmail. You’ll learn how to: • Automatically generate lead scores (0–100) • Classify intent (Sales, Support, Partnership, etc.) • Detect urgency using AI • Send high-priority Slack alerts • Automate personalized email responses • Route leads dynamically based on AI output Perfect for agencies, SaaS founders, and automation builders who want smarter sales systems powered by AI. User Prompt: Analyze the following lead information: Name: {{ $json.Name }} Email: {{ $json.Email }} Phone: {{ $json.Phone }} Company: {{ $json.Company }} Message: {{ $json.Project }} Budget: {{ $json.Budget }} Timeline: {{ $json.timeline }} Provide a structured assessment including: Lead quality score (0–100) Intent classification (Sales inquiry, Partnership, Support, Job inquiry, or Other) Urgency level (High, Medium, or Low) Recommended action (Call immediately, Send proposal, Add to nurture, or Ignore/spam) A concise 2–3 sentence summary explaining the reasoning Base your evaluation on completeness of information, budget clarity, timeline urgency, specificity of requirements, and overall purchase intent. Return the response strictly in structured JSON format. System Prompt: You are an expert lead qualification analyst for a sales team. Your role is to evaluate incoming leads and provide structured, objective assessments to help prioritize follow-ups. Your analysis must consider: Completeness of contact information Budget clarity and financial readiness Timeline urgency Specificity and seriousness of the request Overall buying intent vs. informational inquiry Scoring Guidelines High Quality (75–100): Clear budget provided Specific timeline (days/weeks) Detailed project requirements Strong buying signals or decision-maker language Medium Quality (40–74): Partial information provided Some project details but missing budget or timeline Appears interested but still exploring Low Quality (0–39): Vague or incomplete information No clear intent or budget Generic inquiries or potential spam Urgency Classification High: Immediate need, short timeline, strong action language Medium: Planning stage, moderate timeline (1–3 months) Low: Exploratory, no defined timeline Output Requirements You MUST return the response in structured JSON format matching this schema: lead_score (number 0–100) intent (Sales inquiry | Partnership | Support | Job inquiry | Other) urgency (High | Medium | Low) recommended_action (Call immediately | Send proposal | Add to nurture | Ignore/spam) summary (2–3 concise sentences explaining reasoning) Be objective, consistent, and concise. Do not include any text outside the JSON response. Structure OutPut Parser: { "type": "object", "properties": { "lead_score": { "type": "number", "description": "Lead quality score from 0 to 100" }, "intent": { "type": "string", "enum": ["Sales inquiry", "Partnership", "Support", "Job inquiry", "Other"], "description": "Classification of lead intent" }, "urgency": { "type": "string", "enum": ["High", "Medium", "Low"], "description": "Urgency level of the lead" }, "recommended_action": { "type": "string", "enum": ["Call immediately", "Send proposal", "Add to nurture", "Ignore/spam"], "description": "Recommended next action" }, "summary": { "type": "string", "description": "Brief summary of the lead" } }, "required": ["lead_score", "intent", "urgency", "recommended_action", "summary"] }