{"workflow":{"id":12674,"name":"Send AI revenue forecast alerts from HubSpot via Gmail, Slack and Sheets","views":48,"recentViews":0,"totalViews":48,"createdAt":"2026-01-13T07:16:33.202Z","description":"## How it works\nThis workflow automatically generates an AI-powered revenue forecast whenever a new deal is created in HubSpot. It collects all active deals, standardizes key sales data, and sends it to an AI model for forecasting and risk analysis. The AI produces best, likely, and worst-case revenue scenarios along with actionable insights. Results are shared with stakeholders via Slack and Email and stored in Google Sheets for tracking.\n\n## Step-by-step\n- **Step 1 : Collect & prepare HubSpot deals**\n - **HubSpot Trigger** – Starts the workflow when a new deal is created in HubSpot.\n - **Get many deals** – Fetches all active deals from the sales pipeline.\n - **Format HubSpot Data** – Cleans and standardizes deal fields like amount, stage, probability, and region.\n - **Loop Over Items** – Iterates through formatted deals to prepare them for AI analysis.\n\n- **Step 2 : Generate & distribute AI forecast**\n - **AI Revenue Forecast & Risk Analysis** – Sends pipeline data to the AI model to generate revenue forecasts and insights.\n - **Groq Chat Model** – Powers the AI analysis and produces structured forecasting output.\n - **Format AI response** – Extracts key metrics, risks, and recommendations from the AI response.\n - **Send a message (Gmail)** – Emails the revenue forecast report to stakeholders.\n - **Send a message (Slack)** – Posts the forecast summary to a selected Slack channel.\n - **Append row in sheet** – Logs forecast data and insights into Google Sheets.\n - **Wait** – Adds a controlled pause before looping or completing the workflow.\n\n## Why use this?\n- Get real-time revenue forecasts triggered directly by CRM activity.\n- Reduce manual pipeline analysis and reporting effort.\n- Identify high-risk deals early with AI-driven insights.\n- Keep leadership aligned through automated Slack and Email updates.\n- Maintain a historical forecast log for audits and performance tracking.\n","workflow":{"meta":{"instanceId":"c91c5b6efe2709e07c37996245857ac5d863d575d07e0072127351337c204c40","templateCredsSetupCompleted":true},"nodes":[{"id":"8ac7bc5d-c409-44b2-8176-24ecbd2ae969","name":"Groq Chat Model1","type":"@n8n/n8n-nodes-langchain.lmChatGroq","position":[18496,6112],"parameters":{"model":"llama-3.3-70b-versatile","options":{}},"typeVersion":1},{"id":"f6b4adc7-0709-4058-8ff6-9107ccfbef6b","name":"Send a message2","type":"n8n-nodes-base.gmail","position":[19088,5856],"webhookId":"gmail-webhook-id","parameters":{"sendTo":"your-email","message":"={{ $json.email_data.full_report }}","options":{},"subject":"Revenue Forecast Summary Data"},"typeVersion":2.2},{"id":"911581aa-7453-44ca-b5c6-db1a6ce8efd7","name":"Send a message3","type":"n8n-nodes-base.slack","position":[19088,6064],"webhookId":"YOUR_SLACK_WEBHOOK_ID","parameters":{"text":"={{ $json.email_data.full_report }}","select":"channel","channelId":{"__rl":true,"mode":"list","value":"gmail-id","cachedResultName":"all-n8ntest"},"otherOptions":{},"authentication":"oAuth2"},"typeVersion":2.4},{"id":"5bcf9c46-80b9-4c46-999e-ab02c2743f9b","name":"Loop Over Items1","type":"n8n-nodes-base.splitInBatches","position":[18352,5936],"parameters":{"options":{}},"typeVersion":3},{"id":"08c06ca5-dfaf-4b10-ae33-df0fb3e2cc6d","name":"Wait1","type":"n8n-nodes-base.wait","position":[19472,6048],"webhookId":"YOUR_WAIT_WEBHOOK_ID","parameters":{},"typeVersion":1.1},{"id":"ed54b5d9-678f-4a17-8b56-d96cc7dda6f8","name":"Format AI response1","type":"n8n-nodes-base.code","position":[18864,5952],"parameters":{"jsCode":"// Extract AI response text\nconst aiOutput = $input.first().json.output;\n\n// Helper function to extract section content\nfunction extractSection(text, sectionTitle) {\n const regex = new RegExp(`## ${sectionTitle}([\\\\s\\\\S]*?)(?=##|$)`, 'i');\n const match = text.match(regex);\n return match ? match[1].trim() : 'Not available';\n}\n\n// Helper function to extract bullet points\nfunction extractBullets(text) {\n const bullets = text.match(/[\\*\\+\\-]\\s+\\*\\*(.+?)\\*\\*/g) || [];\n return bullets.map(b => b.replace(/[\\*\\+\\-]\\s+\\*\\*(.+?)\\*\\*/, '$1')).join(', ');\n}\n\n// Helper function to clean markdown formatting\nfunction cleanText(text) {\n return text\n .replace(/\\*\\*/g, '') // Remove bold markers\n .replace(/\\*/g, '') // Remove italic markers\n .replace(/\\t/g, '') // Remove tabs\n .replace(/\\n\\n+/g, '\\n') // Remove multiple newlines\n .trim();\n}\n\n// Extract revenue figures\nfunction extractRevenue(text) {\n const bestCase = text.match(/Best Case:\\s*₹?([\\d,]+)/i);\n const likelyCase = text.match(/Likely Case:\\s*₹?([\\d,]+)/i);\n const worstCase = text.match(/Worst Case:\\s*₹?([\\d,]+)/i);\n const weighted = text.match(/Weighted Pipeline Value:\\s*₹?([\\d,]+)/i);\n \n return {\n best_case: bestCase ? `₹${bestCase[1]}` : '₹0',\n likely_case: likelyCase ? `₹${likelyCase[1]}` : '₹0',\n worst_case: worstCase ? `₹${worstCase[1]}` : '₹0',\n weighted_value: weighted ? `₹${weighted[1]}` : '₹0'\n };\n}\n\n// Extract high-risk deals\nfunction extractHighRiskDeals(text) {\n const dealName = text.match(/\\*\\*Deal Name:\\*\\*\\s*(.+)/);\n const dealAmount = text.match(/\\*\\*Deal Amount:\\*\\*\\s*₹?([\\d,]+)/);\n const riskFactors = text.match(/\\*\\*Risk Factors:\\*\\*([\\s\\S]*?)(?=\\*\\*Recommended|$)/);\n \n let riskText = '';\n if (dealName && dealAmount) {\n riskText = `${dealName[1]} (₹${dealAmount[1]})`;\n if (riskFactors) {\n const factors = riskFactors[1].match(/\\+\\s+(.+)/g);\n if (factors) {\n riskText += ' - ' + factors.map(f => f.replace(/\\+\\s+/, '')).join('; ');\n }\n }\n }\n return riskText || 'No high-risk deals identified';\n}\n\n// Extract top recommendations\nfunction extractRecommendations(text) {\n const recommendations = text.match(/\\*\\*Top 3 Actions.*?:\\*\\*([\\s\\S]*?)(?=\\*\\*Deals Requiring|$)/);\n if (recommendations) {\n const actions = recommendations[1].match(/\\d+\\.\\s+\\*\\*(.+?)\\*\\*/g);\n if (actions) {\n return actions.map((a, i) => {\n const title = a.match(/\\*\\*(.+?)\\*\\*/)[1];\n const desc = recommendations[1].match(new RegExp(`${i + 1}\\\\.\\\\s+\\\\*\\\\*${title}\\\\*\\\\*:?\\\\s*(.+?)(?=\\\\n\\\\s*\\\\d+\\\\.|$)`, 's'));\n return `${i + 1}. ${title}${desc ? ': ' + cleanText(desc[1]) : ''}`;\n }).join('\\n');\n }\n }\n return 'No specific recommendations';\n}\n\n// Extract revenue drivers\nfunction extractRevenueDrivers(text) {\n const section = extractSection(aiOutput, 'Revenue Drivers Analysis');\n const keyDeals = section.match(/\\*\\*Key Deals:\\*\\*\\s*(.+)/);\n const trends = section.match(/\\*\\*Revenue Trends:\\*\\*\\s*(.+)/);\n \n let drivers = '';\n if (trends) drivers += cleanText(trends[1]) + '. ';\n if (keyDeals) drivers += 'Key deals: ' + cleanText(keyDeals[1]);\n \n return drivers || 'Insufficient data for analysis';\n}\n\n// Parse all sections\nconst revenue = extractRevenue(aiOutput);\nconst highRiskDeals = extractHighRiskDeals(extractSection(aiOutput, 'High-Risk Deals'));\nconst revenueDrivers = extractRevenueDrivers(aiOutput);\nconst recommendations = extractRecommendations(aiOutput);\n\n// Format for Google Sheets\nconst formattedData = {\n timestamp: new Date().toLocaleString('en-IN', { timeZone: 'Asia/Kolkata' }),\n forecast_period: new Date().toLocaleString('en-IN', { month: 'long', year: 'numeric' }),\n expected_revenue: revenue.likely_case,\n best_case: revenue.best_case,\n worst_case: revenue.worst_case,\n weighted_pipeline: revenue.weighted_value,\n high_risk_deals: highRiskDeals,\n revenue_drivers: revenueDrivers,\n recommendations: recommendations,\n full_ai_response: cleanText(aiOutput),\n report_sent_to: 'user@example.com, user@example.com'\n};\n\n// Format for Email HTML (clean sections)\nconst emailData = {\n timestamp: formattedData.timestamp,\n forecast_summary: `\n

Expected Revenue: ${revenue.likely_case}

\n

Best Case: ${revenue.best_case}

\n

Worst Case: ${revenue.worst_case}

\n

Weighted Pipeline: ${revenue.weighted_value}

\n `,\n high_risk_section: `\n

${highRiskDeals}

\n `,\n revenue_drivers_section: `\n

${revenueDrivers}

\n `,\n recommendations_section: `\n 
${recommendations}
\n `,\n full_report: aiOutput.replace(/\\n/g, '
').replace(/\\*\\*(.+?)\\*\\*/g, '$1')\n};\n\n// Return both formats\nreturn [\n {\n json: {\n // For Google Sheets\n sheets_data: formattedData,\n \n // For Email\n email_data: emailData,\n \n // Raw data for debugging\n raw_output: aiOutput\n }\n }\n];"},"typeVersion":2},{"id":"75c2b1e5-d2c2-4867-93e7-41d81f3db16a","name":"Append row in sheet","type":"n8n-nodes-base.googleSheets","position":[19248,5952],"parameters":{"columns":{"value":{"Status":"Email Sent","Best_Case":"={{ $json.sheets_data.best_case }}","Time Stamp":"={{ $json.sheets_data.timestamp }}","Worst_Case":"={{ $json.sheets_data.worst_case }}","Report_Sent_To":"={{ $json.sheets_data.report_sent_to }}","Forecast_Period":"={{ $json.sheets_data.forecast_period }}","High_Risk_Deals":"={{ $json.sheets_data.high_risk_deals }}","Recommendations":"={{ $json.sheets_data.recommendations }}","Revenue_Drivers":"={{ $json.sheets_data.revenue_drivers }}","Expected_Revenue":"={{ $json.sheets_data.expected_revenue }}","Full_AI_Response":"={{ $json.sheets_data.full_ai_response }}","Weighted_Pipeline":"={{ $json.sheets_data.weighted_pipeline }}"},"schema":[{"id":"Time Stamp","type":"string","display":true,"required":false,"displayName":"Time Stamp","defaultMatch":false,"canBeUsedToMatch":true},{"id":"Forecast_Period","type":"string","display":true,"required":false,"displayName":"Forecast_Period","defaultMatch":false,"canBeUsedToMatch":true},{"id":"Expected_Revenue","type":"string","display":true,"required":false,"displayName":"Expected_Revenue","defaultMatch":false,"canBeUsedToMatch":true},{"id":"Best_Case","type":"string","display":true,"required":false,"displayName":"Best_Case","defaultMatch":false,"canBeUsedToMatch":true},{"id":"Worst_Case","type":"string","display":true,"required":false,"displayName":"Worst_Case","defaultMatch":false,"canBeUsedToMatch":true},{"id":"Weighted_Pipeline","type":"string","display":true,"required":false,"displayName":"Weighted_Pipeline","defaultMatch":false,"canBeUsedToMatch":true},{"id":"High_Risk_Deals","type":"string","display":true,"required":false,"displayName":"High_Risk_Deals","defaultMatch":false,"canBeUsedToMatch":true},{"id":"Revenue_Drivers","type":"string","display":true,"required":false,"displayName":"Revenue_Drivers","defaultMatch":false,"canBeUsedToMatch":true},{"id":"Recommendations","type":"string","display":true,"required":false,"displayName":"Recommendations","defaultMatch":false,"canBeUsedToMatch":true},{"id":"Full_AI_Response","type":"string","display":true,"required":false,"displayName":"Full_AI_Response","defaultMatch":false,"canBeUsedToMatch":true},{"id":"Report_Sent_To","type":"string","display":true,"required":false,"displayName":"Report_Sent_To","defaultMatch":false,"canBeUsedToMatch":true},{"id":"Status","type":"string","display":true,"required":false,"displayName":"Status","defaultMatch":false,"canBeUsedToMatch":true}],"mappingMode":"defineBelow","matchingColumns":[],"attemptToConvertTypes":false,"convertFieldsToString":false},"options":{},"operation":"append","sheetName":{"__rl":true,"mode":"list","value":1485545756,"cachedResultUrl":"sheet-url","cachedResultName":"sheet-name"},"documentId":{"__rl":true,"mode":"list","value":"sheet-value","cachedResultUrl":"sheet-url","cachedResultName":"sheet-name"}},"typeVersion":4.7},{"id":"ad3b35be-95a2-4fa5-813a-62910b68b002","name":"HubSpot Trigger1","type":"n8n-nodes-base.hubspotTrigger","position":[17808,5936],"webhookId":"hubspot-webhhok-id","parameters":{"eventsUi":{"eventValues":[{"name":"deal.creation"}]},"additionalFields":{}},"typeVersion":1},{"id":"9686ae09-74f9-4f99-b92a-457b83f1cfaa","name":"Get many deals1","type":"n8n-nodes-base.hubspot","position":[17984,5936],"parameters":{"filters":{},"resource":"deal","operation":"getAll"},"typeVersion":2.2},{"id":"fe6d16bc-d256-4c3f-9945-7be037564dda","name":"Format Hubspot Data1","type":"n8n-nodes-base.code","position":[18160,5936],"parameters":{"jsCode":"// Helper: map region to currency\nfunction getCurrency(region) {\n const map = {\n \"India\": \"INR\",\n \"USA\": \"USD\",\n \"United States\": \"USD\",\n \"UK\": \"GBP\",\n \"United Kingdom\": \"GBP\",\n \"UAE\": \"AED\"\n };\n return map[region] || \"USD\"; // default\n}\n\nreturn items.map(item => {\n const deal = item.json.properties || {};\n\n // Extract values safely\n const dealName = deal.dealname || \"Unknown Deal\";\n const dealAmount = Number(deal.amount || 0);\n\n const stage = deal.dealstage || \"Unknown\";\n const closeDate = deal.closedate\n ? new Date(Number(deal.closedate)).toISOString().split(\"T\")[0]\n : null;\n\n const probability = deal.hs_probability\n ? Math.round(Number(deal.hs_probability) * 100)\n : 0;\n\n const owner = deal.hubspot_owner_id || \"Unassigned\";\n\n const region =\n deal.country ||\n deal.region ||\n deal.hs_country_region ||\n \"Unknown\";\n\n const currency = getCurrency(region);\n\n return {\n json: {\n \"Deal Name\": dealName,\n \"Customer Name\": deal.associatedcompanyname || \"N/A\",\n \"Deal Amount\": dealAmount,\n \"Currency\": currency,\n \"Stage\": stage,\n \"Close Date\": closeDate,\n \"Probability (%)\": probability,\n \"Sales Owner\": owner,\n \"Region\": region\n }\n };\n});\n"},"typeVersion":2},{"id":"4da571ca-aae7-4c74-b2a4-b19bf50da20a","name":"AI Revenue Forecast & Risk Analysis1","type":"@n8n/n8n-nodes-langchain.agent","position":[18560,5952],"parameters":{"text":"=Analyze the following sales pipeline data and generate a revenue forecast report:\n\n**Current Date:** {{$now.format('YYYY-MM-DD')}}\n**Forecast Period:** Next 30 days\n\n**Sales Pipeline Data:**\n{{JSON.stringify($json)}}\n\nPlease provide:\n\n1. **Revenue Forecast Summary**\n - Expected revenue (best case, likely case, worst case)\n - Weighted pipeline value\n - Conversion rate trends\n\n2. **High-Risk Deals** (identify deals that may slip or not close)\n - Deal name, amount, and risk factors\n - Recommended actions to mitigate risks\n\n3. **Revenue Drivers Analysis**\n - Why is revenue increasing or decreasing compared to last period?\n - Which deals are moving the needle?\n - Pipeline health assessment\n\n4. **Key Recommendations**\n - Top 3 actions for sales leadership\n - Deals requiring immediate attention\n\nFormat your response in clear sections with bullet points for easy reading.","options":{"systemMessage":"=You are an expert Revenue Forecasting AI specialized in sales pipeline analysis. Your role is to:\n\n1. Analyze sales deal data (amount, stage, close date, probability)\n2. Generate accurate revenue forecasts for the next month/quarter\n3. Identify high-risk deals that may not close\n4. Explain key revenue drivers (why revenue is increasing or decreasing)\n5. Provide actionable insights for sales leadership\n\n**Output Requirements:**\n- Be concise and executive-ready\n- Use clear, business-friendly language\n- Highlight critical risks and opportunities\n- Provide specific deal-level recommendations\n- Format responses in structured sections\n\n**Tone:** Professional, data-driven, and action-oriented"},"promptType":"define"},"typeVersion":3},{"id":"cdf2afe5-3f49-4c3f-a508-ee0570f81a18","name":"Sticky Note3","type":"n8n-nodes-base.stickyNote","position":[17136,5648],"parameters":{"width":640,"height":688,"content":"## AI Revenue Forecast Generator\n\nThis workflow automatically generates an AI-powered revenue forecast whenever a new deal is created in HubSpot. It analyzes the entire sales pipeline to estimate expected revenue, identify high-risk deals, and highlight key revenue drivers. The goal is to give sales leaders a clear, executive-ready view of pipeline health and upcoming revenue.\n\n### How it works\n- Triggers when a deal is created in HubSpot \n- Fetches all active deals from the pipeline \n- Cleans and standardizes deal data (amount, stage, probability, region) \n- Sends structured data to an AI model for forecasting and risk analysis \n- Generates best, likely, and worst-case revenue scenarios \n- Shares the report via Slack and Email \n- Logs all insights in Google Sheets for tracking and audits \n\n### Setup steps\n1. Connect your HubSpot account and enable the Deal Created trigger \n2. Ensure required deal properties are available \n3. Connect your AI provider (Groq / LLM) \n4. Connect Slack and choose a channel \n5. Connect Gmail for email delivery \n6. Connect Google Sheets for data logging \n7. Turn the workflow ON and test by creating a deal "},"typeVersion":1},{"id":"d1c68d2b-7f47-4d6c-a8ce-81381ecd368b","name":"Sticky Note4","type":"n8n-nodes-base.stickyNote","position":[17792,5648],"parameters":{"color":7,"width":688,"height":688,"content":"## Step 1 – Collect & prepare HubSpot deals \nTriggers on deal creation, fetches all active deals, and formats key fields for AI analysis.\n"},"typeVersion":1},{"id":"73e70ef7-057a-45dc-9ce1-27986c76a405","name":"Sticky Note5","type":"n8n-nodes-base.stickyNote","position":[18496,5648],"parameters":{"color":7,"width":1136,"height":688,"content":"## Step 2 – Generate & distribute AI forecast \nAI generates revenue forecasts and risk insights, sends them to Slack and Email, and logs them in Google Sheets."},"typeVersion":1}],"pinData":{},"connections":{"Wait1":{"main":[[{"node":"Loop Over Items1","type":"main","index":0}]]},"Get many deals1":{"main":[[{"node":"Format Hubspot Data1","type":"main","index":0}]]},"Groq Chat Model1":{"ai_languageModel":[[{"node":"AI Revenue Forecast & Risk Analysis1","type":"ai_languageModel","index":0}]]},"HubSpot Trigger1":{"main":[[{"node":"Get many deals1","type":"main","index":0}]]},"Loop Over Items1":{"main":[[],[{"node":"AI Revenue Forecast & Risk Analysis1","type":"main","index":0}]]},"Append row in sheet":{"main":[[{"node":"Wait1","type":"main","index":0}]]},"Format AI response1":{"main":[[{"node":"Send a message2","type":"main","index":0},{"node":"Append row in sheet","type":"main","index":0},{"node":"Send a message3","type":"main","index":0}]]},"Format Hubspot Data1":{"main":[[{"node":"Loop Over Items1","type":"main","index":0}]]},"AI Revenue Forecast & Risk Analysis1":{"main":[[{"node":"Format AI response1","type":"main","index":0}]]}}},"lastUpdatedBy":1,"workflowInfo":{"nodeCount":14,"nodeTypes":{"n8n-nodes-base.code":{"count":2},"n8n-nodes-base.wait":{"count":1},"n8n-nodes-base.gmail":{"count":1},"n8n-nodes-base.slack":{"count":1},"n8n-nodes-base.hubspot":{"count":1},"n8n-nodes-base.stickyNote":{"count":3},"n8n-nodes-base.googleSheets":{"count":1},"n8n-nodes-base.hubspotTrigger":{"count":1},"n8n-nodes-base.splitInBatches":{"count":1},"@n8n/n8n-nodes-langchain.agent":{"count":1},"@n8n/n8n-nodes-langchain.lmChatGroq":{"count":1}}},"status":"published","readyToDemo":null,"user":{"name":"Avkash Kakdiya","username":"itechnotion","bio":"🚀 Founder of iTechNotion — we build custom AI-powered automation workflows for startups, agencies, and founders.\n💡 Specializing in agentic AI systems, content automation, sales funnels, and digital workers.\n🔧 14+ years in tech | Building scalable no-code/low-code solutions using n8n, OpenAI, and other API-first tools.\n📬 Let’s automate what slows you down.","verified":true,"links":["https://calendly.com/itechnotion_sales/schedule-your-expert-consultation-for-automation"],"avatar":"https://gravatar.com/avatar/cd18cea4647ff1df4cb154c7d172ca67dcf656f09a3f1ffece5646296d1822d5?r=pg&d=retro&size=200"},"nodes":[{"id":18,"icon":"file:googleSheets.svg","name":"n8n-nodes-base.googleSheets","codex":{"data":{"alias":["CSV","Sheet","Spreadsheet","GS"],"resources":{"generic":[{"url":"https://n8n.io/blog/love-at-first-sight-ricardos-n8n-journey/","icon":"❤️","label":"Love at first sight: Ricardo’s n8n journey"},{"url":"https://n8n.io/blog/why-business-process-automation-with-n8n-can-change-your-daily-life/","icon":"🧬","label":"Why business process automation with n8n can change your daily life"},{"url":"https://n8n.io/blog/automatically-adding-expense-receipts-to-google-sheets-with-telegram-mindee-twilio-and-n8n/","icon":"🧾","label":"Automatically Adding Expense Receipts to Google Sheets with Telegram, Mindee, Twilio, and n8n"},{"url":"https://n8n.io/blog/supercharging-your-conference-registration-process-with-n8n/","icon":"🎫","label":"Supercharging your conference registration process with n8n"},{"url":"https://n8n.io/blog/creating-triggers-for-n8n-workflows-using-polling/","icon":"⏲","label":"Creating triggers for n8n workflows using polling"},{"url":"https://n8n.io/blog/no-code-ecommerce-workflow-automations/","icon":"store","label":"6 e-commerce workflows to power up your Shopify s"},{"url":"https://n8n.io/blog/migrating-community-metrics-to-orbit-using-n8n/","icon":"📈","label":"Migrating Community Metrics to Orbit using n8n"},{"url":"https://n8n.io/blog/automate-google-apps-for-productivity/","icon":"💡","label":"15 Google apps you can combine and automate to increase productivity"},{"url":"https://n8n.io/blog/your-business-doesnt-need-you-to-operate/","icon":" 🖥️","label":"Hey founders! 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