This workflow follows the Emailsend → Google Sheets recipe pattern — see all workflows that pair these two integrations.
The workflow JSON
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{
"name": "Data Analytics Dashboard with AI Insights",
"nodes": [
{
"parameters": {
"rule": {
"interval": [
{
"field": "cronExpression",
"expression": "0 8 * * 1"
}
]
}
},
"id": "da1a2b3c-1111-2222-3333-444455556666",
"name": "Weekly Schedule",
"type": "n8n-nodes-base.scheduleTrigger",
"typeVersion": 1.1,
"position": [
250,
300
]
},
{
"parameters": {
"operation": "getAll",
"documentId": {
"__rl": true,
"value": "YOUR_GOOGLE_SHEET_ID",
"mode": "id"
},
"sheetName": {
"__rl": true,
"value": "SalesData",
"mode": "name"
},
"options": {}
},
"id": "da2b3c4d-2222-3333-4444-555566667777",
"name": "Fetch Sales Data",
"type": "n8n-nodes-base.googleSheets",
"typeVersion": 4,
"position": [
480,
200
]
},
{
"parameters": {
"operation": "getAll",
"documentId": {
"__rl": true,
"value": "YOUR_GOOGLE_SHEET_ID",
"mode": "id"
},
"sheetName": {
"__rl": true,
"value": "MarketingData",
"mode": "name"
},
"options": {}
},
"id": "da3c4d5e-3333-4444-555566667777",
"name": "Fetch Marketing Data",
"type": "n8n-nodes-base.googleSheets",
"typeVersion": 4,
"position": [
480,
400
]
},
{
"parameters": {
"url": "https://api.example.com/analytics/metrics",
"options": {}
},
"id": "da4d5e6f-4444-5555-6666-777788889999",
"name": "Fetch External Metrics",
"type": "n8n-nodes-base.httpRequest",
"typeVersion": 4.1,
"position": [
480,
600
]
},
{
"parameters": {
"jsCode": "// Data Processing & KPI Calculation\nconst salesData = $input.first().json;\nconst marketingData = $('Fetch Marketing Data').first().json;\nconst externalMetrics = $('Fetch External Metrics').first().json;\n\n// Calculate KPIs\nconst totalRevenue = salesData.reduce((sum, row) => sum + parseFloat(row.revenue || 0), 0);\nconst totalOrders = salesData.length;\nconst avgOrderValue = totalRevenue / totalOrders;\nconst conversionRate = (marketingData.conversions / marketingData.visitors * 100).toFixed(2);\nconst customerAcquisitionCost = marketingData.spend / marketingData.conversions;\nconst roi = ((totalRevenue - marketingData.spend) / marketingData.spend * 100).toFixed(2);\n\nreturn [{\n json: {\n kpis: {\n total_revenue: totalRevenue,\n total_orders: totalOrders,\n avg_order_value: avgOrderValue.toFixed(2),\n conversion_rate: conversionRate,\n customer_acquisition_cost: customerAcquisitionCost.toFixed(2),\n roi: roi,\n period: 'Weekly'\n },\n sales_data: salesData,\n marketing_data: marketingData,\n external_metrics: externalMetrics\n }\n}];"
},
"id": "da5e6f7a-5555-6666-7777-888899990000",
"name": "Calculate KPIs",
"type": "n8n-nodes-base.code",
"typeVersion": 2,
"position": [
710,
300
]
},
{
"parameters": {
"modelId": {
"__rl": true,
"value": "gpt-4",
"mode": "id"
},
"messages": {
"values": [
{
"content": "=Analyze this business data and provide insights:\n\nKPIs:\n- Total Revenue: ${{ $json.kpis.total_revenue }}\n- Total Orders: {{ $json.kpis.total_orders }}\n- Avg Order Value: ${{ $json.kpis.avg_order_value }}\n- Conversion Rate: {{ $json.kpis.conversion_rate }}%\n- Customer Acquisition Cost: ${{ $json.kpis.customer_acquisition_cost }}\n- ROI: {{ $json.kpis.roi }}%\n\nReturn JSON with:\n- trends (array of 3 key trends detected)\n- anomalies (array of unusual patterns)\n- insights (array of 5 actionable insights)\n- recommendations (array of 3 strategic recommendations)\n- forecast (predicted revenue for next week)\n- risk_level (low, medium, high)\n- executive_summary (2-3 sentence summary)\n\nReturn only JSON."
}
]
},
"options": {
"temperature": 0.4
}
},
"id": "da6f7a8b-6666-7777-8888-999900001111",
"name": "AI Data Analysis",
"type": "@n8n/n8n-nodes-langchain.openAi",
"typeVersion": 1.3,
"position": [
940,
300
]
},
{
"parameters": {
"conditions": {
"string": [
{
"value1": "={{ JSON.parse($json.message.content).risk_level }}",
"value2": "high"
}
]
}
},
"id": "da7a8b9c-7777-8888-9999-000011112222",
"name": "Check Risk Level",
"type": "n8n-nodes-base.if",
"typeVersion": 1,
"position": [
1170,
300
]
},
{
"parameters": {
"chatId": "YOUR_TELEGRAM_CHAT_ID",
"text": "=\ud83d\udea8 HIGH RISK ALERT - Weekly Analytics Report\n\nRisk Level: {{ JSON.parse($('AI Data Analysis').item.json.message.content).risk_level }}\n\nExecutive Summary:\n{{ JSON.parse($('AI Data Analysis').item.json.message.content).executive_summary }}\n\nKey Metrics:\n- Revenue: ${{ $('Calculate KPIs').item.json.kpis.total_revenue }}\n- ROI: {{ $('Calculate KPIs').item.json.kpis.roi }}%\n- Conversion Rate: {{ $('Calculate KPIs').item.json.kpis.conversion_rate }}%\n\n\u26a1 Immediate review required!",
"additionalFields": {
"parse_mode": "Markdown"
}
},
"id": "da8b9c0d-8888-9999-0000-111122223333",
"name": "High Risk Alert",
"type": "n8n-nodes-base.telegram",
"typeVersion": 1.1,
"position": [
1400,
200
]
},
{
"parameters": {
"channel": "#analytics-reports",
"text": "=\ud83d\udcca Weekly Analytics Report\n\nPeriod: {{ $('Calculate KPIs').item.json.kpis.period }}\n\nKey Metrics:\n\ud83d\udcb0 Revenue: ${{ $('Calculate KPIs').item.json.kpis.total_revenue }}\n\ud83d\udce6 Orders: {{ $('Calculate KPIs').item.json.kpis.total_orders }}\n Avg Order: ${{ $('Calculate KPIs').item.json.kpis.avg_order_value }}\n Conversion: {{ $('Calculate KPIs').item.json.kpis.conversion_rate }}%\n\ud83d\udcb8 CAC: ${{ $('Calculate KPIs').item.json.kpis.customer_acquisition_cost }}\n\ud83d\udcc8 ROI: {{ $('Calculate KPIs').item.json.kpis.roi }}%\n\nRisk Level: {{ JSON.parse($('AI Data Analysis').item.json.message.content).risk_level }}\n\nForecast Next Week: ${{ JSON.parse($('AI Data Analysis').item.json.message.content).forecast }}\n\n\ud83d\udcdd Executive Summary:\n{{ JSON.parse($('AI Data Analysis').item.json.message.content).executive_summary }}",
"additionalFields": {
"parse_mode": "Markdown"
}
},
"id": "da9c0d1e-9999-0000-1111-222233334444",
"name": "Slack Report",
"type": "n8n-nodes-base.slack",
"typeVersion": 2.1,
"position": [
1400,
400
]
},
{
"parameters": {
"fromEmail": "analytics@yourcompany.com",
"toEmail": "management@yourcompany.com",
"subject": "=Weekly Analytics Report - {{ $now.toFormat('yyyy-MM-dd') }}",
"emailType": "text",
"message": "=Dear Management Team,\n\nPlease find the weekly analytics report below:\n\nKEY METRICS:\n- Total Revenue: ${{ $('Calculate KPIs').item.json.kpis.total_revenue }}\n- Total Orders: {{ $('Calculate KPIs').item.json.kpis.total_orders }}\n- Average Order Value: ${{ $('Calculate KPIs').item.json.kpis.avg_order_value }}\n- Conversion Rate: {{ $('Calculate KPIs').item.json.kpis.conversion_rate }}%\n- Customer Acquisition Cost: ${{ $('Calculate KPIs').item.json.kpis.customer_acquisition_cost }}\n- ROI: {{ $('Calculate KPIs').item.json.kpis.roi }}%\n\nAI INSIGHTS:\n{{ JSON.parse($('AI Data Analysis').item.json.message.content).insights.join('\n- ') }}\n\nRECOMMENDATIONS:\n{{ JSON.parse($('AI Data Analysis').item.json.message.content).recommendations.join('\n- ') }}\n\nNEXT WEEK FORECAST: ${{ JSON.parse($('AI Data Analysis').item.json.message.content).forecast }}\n\nBest regards,\nAnalytics Team",
"options": {}
},
"id": "da0d1e2f-0000-1111-2222-333344445555",
"name": "Email Report",
"type": "n8n-nodes-base.emailSend",
"typeVersion": 2,
"position": [
1630,
400
]
},
{
"parameters": {
"operation": "append",
"documentId": {
"__rl": true,
"value": "YOUR_GOOGLE_SHEET_ID",
"mode": "id"
},
"sheetName": {
"__rl": true,
"value": "AnalyticsDashboard",
"mode": "name"
},
"columns": {
"mappingMode": "defineBelow",
"value": {
"week": "={{ $now.toFormat('yyyy-Www') }}",
"total_revenue": "={{ $('Calculate KPIs').item.json.kpis.total_revenue }}",
"total_orders": "={{ $('Calculate KPIs').item.json.kpis.total_orders }}",
"avg_order_value": "={{ $('Calculate KPIs').item.json.kpis.avg_order_value }}",
"conversion_rate": "={{ $('Calculate KPIs').item.json.kpis.conversion_rate }}",
"cac": "={{ $('Calculate KPIs').item.json.kpis.customer_acquisition_cost }}",
"roi": "={{ $('Calculate KPIs').item.json.kpis.roi }}",
"risk_level": "={{ JSON.parse($('AI Data Analysis').item.json.message.content).risk_level }}",
"forecast": "={{ JSON.parse($('AI Data Analysis').item.json.message.content).forecast }}",
"trends": "={{ JSON.stringify(JSON.parse($('AI Data Analysis').item.json.message.content).trends) }}",
"insights": "={{ JSON.stringify(JSON.parse($('AI Data Analysis').item.json.message.content).insights) }}",
"generated_at": "={{ $now.toISO() }}"
},
"schema": []
},
"options": {}
},
"id": "da1e2f3a-1111-2222-3333-444455556666",
"name": "Save to Dashboard",
"type": "n8n-nodes-base.googleSheets",
"typeVersion": 4,
"position": [
1860,
300
]
}
],
"connections": {
"Weekly Schedule": {
"main": [
[
{
"node": "Fetch Sales Data",
"type": "main",
"index": 0
},
{
"node": "Fetch Marketing Data",
"type": "main",
"index": 0
},
{
"node": "Fetch External Metrics",
"type": "main",
"index": 0
}
]
]
},
"Fetch Sales Data": {
"main": [
[
{
"node": "Calculate KPIs",
"type": "main",
"index": 0
}
]
]
},
"Fetch Marketing Data": {
"main": [
[
{
"node": "Calculate KPIs",
"type": "main",
"index": 0
}
]
]
},
"Fetch External Metrics": {
"main": [
[
{
"node": "Calculate KPIs",
"type": "main",
"index": 0
}
]
]
},
"Calculate KPIs": {
"main": [
[
{
"node": "AI Data Analysis",
"type": "main",
"index": 0
}
]
]
},
"AI Data Analysis": {
"main": [
[
{
"node": "Check Risk Level",
"type": "main",
"index": 0
}
]
]
},
"Check Risk Level": {
"main": [
[
{
"node": "High Risk Alert",
"type": "main",
"index": 0
}
],
[
{
"node": "Slack Report",
"type": "main",
"index": 0
}
]
]
},
"High Risk Alert": {
"main": [
[
{
"node": "Slack Report",
"type": "main",
"index": 0
}
]
]
},
"Slack Report": {
"main": [
[
{
"node": "Email Report",
"type": "main",
"index": 0
}
]
]
},
"Email Report": {
"main": [
[
{
"node": "Save to Dashboard",
"type": "main",
"index": 0
}
]
]
}
},
"settings": {
"executionOrder": "v1"
}
}
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About this workflow
Data Analytics Dashboard with AI Insights. Uses googleSheets, httpRequest, openAi, telegram. Scheduled trigger; 11 nodes.
Source: https://github.com/kooroosh1363/agentic-automation-lab/blob/main/10-data-analytics-dashboard/workflow.json — original creator credit. Request a take-down →
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