Toshiya Minami 
This workflow corresponds to n8n.io template #9809 — we link there as the canonical source.
This workflow follows the Agent → Google Sheets recipe pattern — see all workflows that pair these two integrations.
The workflow JSON
Copy or download the full n8n JSON below. Paste it into a new n8n workflow, add your credentials, activate. Full import guide →
{
"id": "hMRDyYoD0nvR4E5y",
"meta": {
"templateCredsSetupCompleted": false
},
"name": "Customer Survey Summary Generator",
"tags": [
"surveys",
"ai-analysis",
"slack",
"sheets"
],
"nodes": [
{
"id": "note1",
"name": "Workflow Overview",
"type": "n8n-nodes-base.stickyNote",
"position": [
-144,
224
],
"parameters": {
"color": 5,
"width": 350,
"height": 280,
"content": "## Customer Survey Summary Generator (Template)\n\nThis workflow groups customer survey responses by sentiment (positive / neutral / negative), then uses an AI agent to extract themes, insights, and actionable recommendations, and finally saves or shares a consolidated report.\n\n**What you get**\n- Automated daily run via Schedule Trigger\n- Clean grouping & batching for accurate AI analysis\n- Consolidated summary (themes / insights / recommendations)\n- Output to Google Sheets and Slack\n\n**No secrets included** \u2014 add your own credentials after import."
},
"typeVersion": 1
},
{
"id": "trigger1",
"name": "Daily Schedule Trigger",
"type": "n8n-nodes-base.scheduleTrigger",
"position": [
208,
816
],
"parameters": {
"rule": {
"interval": [
{
"triggerAtHour": 9
}
]
}
},
"typeVersion": 1.2
},
{
"id": "note2",
"name": "Data Source Note",
"type": "n8n-nodes-base.stickyNote",
"position": [
336,
224
],
"parameters": {
"color": 3,
"width": 300,
"height": 260,
"content": "## Setup: Data Source\n\nConnect your survey data source:\n- Google Sheets (responses table)\n- Database (SQL query)\n- CSV/other sources\n\n**How to configure (example: Google Sheets)**\n1. Add Google Sheets credentials (OAuth2) in n8n Credentials.\n2. Replace `YOUR_SHEET_ID` and `YOUR_SHEET_NAME` on both Sheets nodes.\n3. Make sure columns exist (e.g., `\u6e80\u8db3\u5ea6 (Rating)`, `\u81ea\u7531\u8a18\u8ff0\u30b3\u30e1\u30f3\u30c8 (Comment)`, `\u56de\u7b54\u65e5\u6642 (Timestamp)`)."
},
"typeVersion": 1
},
{
"id": "sheets1",
"name": "Get Survey Responses",
"type": "n8n-nodes-base.googleSheets",
"position": [
432,
816
],
"parameters": {
"options": {},
"sheetName": {
"__rl": true,
"mode": "name",
"value": "YOUR_SHEET_NAME"
},
"documentId": {
"__rl": true,
"mode": "id",
"value": "YOUR_SHEET_ID"
}
},
"credentials": {},
"typeVersion": 4.5
},
{
"id": "code1",
"name": "Group & Prepare Data",
"type": "n8n-nodes-base.code",
"position": [
656,
816
],
"parameters": {
"jsCode": "const items = $input.all();\n\n// Group responses by rating\nconst groupedByRating = {\n positive: [],\n neutral: [],\n negative: []\n};\n\nfor (const item of items) {\n // Google Sheet\u306e\u65e5\u672c\u8a9e\u5217\u540d\u3092\u6b63\u3057\u304f\u53c2\u7167\u3059\u308b\u3088\u3046\u306b\u4fee\u6b63\n const rating = parseInt(item.json[\"\u6e80\u8db3\u5ea6 (Rating)\"] || 0);\n const feedback = item.json[\"\u81ea\u7531\u8a18\u8ff0\u30b3\u30e1\u30f3\u30c8 (Comment)\"] || '';\n const date = item.json[\"\u56de\u7b54\u65e5\u6642 (Timestamp)\"] || new Date().toISOString();\n \n const response = {\n rating: rating,\n feedback: feedback,\n date: date\n };\n \n // --- \u3053\u3053\u304b\u3089\u4fee\u6b63 ---\n // 5\u6bb5\u968e\u8a55\u4fa1\u306b\u5408\u308f\u305b\u3066\u611f\u60c5\u3092\u5206\u985e\n if (rating >= 4) {\n // 4\u70b9\u4ee5\u4e0a\u306a\u3089 \"positive\"\n groupedByRating.positive.push(response);\n } else if (rating === 3) {\n // 3\u70b9\u306a\u3089 \"neutral\"\n groupedByRating.neutral.push(response);\n } else {\n // 3\u70b9\u672a\u6e80\u306a\u3089 \"negative\"\n groupedByRating.negative.push(response);\n }\n // --- \u3053\u3053\u307e\u3067\u4fee\u6b63 ---\n}\n\n// Prepare batches for AI processing\nconst batches = [];\n\nfor (const [sentiment, responses] of Object.entries(groupedByRating)) {\n if (responses.length > 0) {\n batches.push({\n sentiment: sentiment,\n count: responses.length,\n responses: responses.slice(0, 50) // 1\u30d0\u30c3\u30c1\u3042\u305f\u308a\u306e\u56de\u7b54\u6570\u309250\u306b\u5236\u9650\n });\n }\n}\n\nreturn batches;"
},
"typeVersion": 2
},
{
"id": "note3",
"name": "AI Processing Note",
"type": "n8n-nodes-base.stickyNote",
"position": [
736,
224
],
"parameters": {
"color": 4,
"width": 300,
"height": 260,
"content": "## Setup: AI Processing\n\nThe AI Agent analyzes each batch to:\n1. Identify recurring themes\n2. Extract key insights\n3. Propose actionable recommendations\n\n**Configure**\n- Add your preferred model credentials (e.g., OpenAI or OpenRouter) in n8n.\n- Keep the JSON-only output requirement for reliable downstream parsing."
},
"typeVersion": 1
},
{
"id": "agent1",
"name": "Analyze Survey Batch",
"type": "@n8n/n8n-nodes-langchain.agent",
"position": [
1104,
432
],
"parameters": {
"text": "=Analyze these {{ $json.sentiment }} customer survey responses and provide insights.\nNumber of responses: {{ $json.count }}\n\nResponses:\n{{ $json.responses.map(r => `Rating: ${r.rating}/5 - Feedback: \"${r.feedback}\"`).join('\\n\\n') }}\n\nPlease provide:\n1. Top 3-5 recurring themes\n2. Key insights and patterns\n3. Actionable recommendations for improvement\n\nIMPORTANT: Your entire response must be ONLY the JSON object, starting with { and ending with }. Do not include any other text, explanations, or markdown formatting.",
"options": {
"systemMessage": "# ROLE\nYou are an exceptional expert in customer feedback analysis.\n\n# CORE TASK\nYour mission is tao thoroughly analyze the provided batch of customer survey responses and extract strategic insights that drive business growth, not just mere summaries.\n\n# ANALYSIS PRINCIPLES\n1. **Deep Dive:** Go beyond surface-level keywords. Delve deep into the underlying causes and customer emotions behind the responses.\n2. **Thematization:** Identify recurring patterns and themes that appear across multiple responses. Support each theme with specific quotes or examples from the feedback.\n3. **Actionability:** Ensure your analysis leads to specific, measurable, and actionable business improvements. Avoid vague recommendations.\n\n# CRITICAL: OUTPUT FORMAT\nYour entire response MUST strictly adhere to the provided JSON schema. Do NOT include any explanatory text, conversational pleasantries, or markdown formatting. Your entire output must be a single, valid JSON object and nothing else."
},
"promptType": "define",
"hasOutputParser": true
},
"typeVersion": 2
},
{
"id": "parser1",
"name": "Structure Output",
"type": "@n8n/n8n-nodes-langchain.outputParserStructured",
"position": [
1248,
640
],
"parameters": {
"schemaType": "manual",
"inputSchema": "{\n \"type\": \"object\",\n \"properties\": {\n \"themes\": {\n \"type\": \"array\",\n \"items\": {\n \"type\": \"object\",\n \"properties\": {\n \"theme\": { \"type\": \"string\" },\n \"frequency\": { \"type\": \"string\" },\n \"examples\": { \"type\": \"array\", \"items\": { \"type\": \"string\" } }\n }\n },\n \"description\": \"Top recurring themes from the feedback\"\n },\n \"insights\": {\n \"type\": \"array\",\n \"items\": { \"type\": \"string\" },\n \"description\": \"Key insights and patterns identified\"\n },\n \"recommendations\": {\n \"type\": \"array\",\n \"items\": { \"type\": \"string\" },\n \"description\": \"Actionable recommendations for improvement\"\n },\n \"sentiment_summary\": {\n \"type\": \"string\",\n \"description\": \"Overall sentiment summary for this batch\"\n }\n },\n \"required\": [\"themes\", \"insights\", \"recommendations\", \"sentiment_summary\"]\n}"
},
"typeVersion": 1.2
},
{
"id": "code2",
"name": "Aggregate Results",
"type": "n8n-nodes-base.code",
"position": [
1200,
800
],
"parameters": {
"jsCode": "// Aggregate all AI analysis results\nconst allAnalyses = $input.all();\nconst timestamp = new Date().toISOString();\nconst totalResponses = allAnalyses.reduce((sum, item) => sum + (item.json.originalCount || 0), 0);\n\n// Combine all themes\nconst allThemes = [];\nconst themeMap = new Map();\n\nfor (const analysis of allAnalyses) {\n // --- \u3053\u3053\u304b\u3089\u4fee\u6b63 ---\n // \"output\" \u30ad\u30fc\u306e\u4e2d\u306b\u3042\u308b themes \u3092\u53c2\u7167\u3059\u308b\u3088\u3046\u306b\u5909\u66f4\n if (analysis.json.output && analysis.json.output.themes) {\n for (const theme of analysis.json.output.themes) {\n // --- \u3053\u3053\u307e\u3067\u4fee\u6b63 ---\n const key = theme.theme.toLowerCase();\n if (themeMap.has(key)) {\n const existing = themeMap.get(key);\n existing.examples = [...existing.examples, ...theme.examples];\n existing.frequency = 'Multiple mentions';\n } else {\n themeMap.set(key, { ...theme });\n }\n }\n }\n}\n\n// Convert map to array and sort by frequency\nconst consolidatedThemes = Array.from(themeMap.values())\n .sort((a, b) => b.examples.length - a.examples.length)\n .slice(0, 10);\n\n// Combine all insights and recommendations\nconst allInsights = [];\nconst allRecommendations = [];\nconst sentimentBreakdown = {};\n\nfor (const analysis of allAnalyses) {\n const sentiment = analysis.json.originalSentiment || 'unknown';\n sentimentBreakdown[sentiment] = analysis.json.originalCount || 0;\n \n // --- \u3053\u3053\u304b\u3089\u4fee\u6b63 ---\n // \"output\" \u30ad\u30fc\u306e\u4e2d\u306b\u3042\u308b insights \u3068 recommendations \u3092\u53c2\u7167\n if (analysis.json.output && analysis.json.output.insights) {\n allInsights.push(...analysis.json.output.insights);\n }\n if (analysis.json.output && analysis.json.output.recommendations) {\n allRecommendations.push(...analysis.json.output.recommendations);\n }\n // --- \u3053\u3053\u307e\u3067\u4fee\u6b63 ---\n}\n\n// Create final summary\nconst summary = {\n reportDate: timestamp,\n totalResponsesAnalyzed: totalResponses,\n sentimentBreakdown: sentimentBreakdown,\n topThemes: consolidatedThemes,\n keyInsights: [...new Set(allInsights)].slice(0, 10),\n priorityRecommendations: [...new Set(allRecommendations)].slice(0, 7),\n executiveSummary: `Analysis of ${totalResponses} customer survey responses revealed ${consolidatedThemes.length} key themes. Overall sentiment distribution: Positive (${sentimentBreakdown.positive || 0}), Neutral (${sentimentBreakdown.neutral || 0}), Negative (${sentimentBreakdown.negative || 0}).`\n};\n\nreturn [summary];"
},
"typeVersion": 2
},
{
"id": "note4",
"name": "Output Options Note",
"type": "n8n-nodes-base.stickyNote",
"position": [
1136,
160
],
"parameters": {
"color": 6,
"width": 300,
"height": 260,
"content": "## Output Options\n\nChoose one or more:\n1. Save the consolidated summary to Google Sheets\n2. Send a formatted report to Slack\n3. (Optional) Add CSV export or Database insert\n\n**Reminder**\n- Replace all placeholders (e.g., Sheet ID/Name, Slack Channel).\n- Add your own credentials in n8n after importing."
},
"typeVersion": 1
},
{
"id": "sheets2",
"name": "Save to Sheet",
"type": "n8n-nodes-base.googleSheets",
"position": [
1456,
992
],
"parameters": {
"columns": {
"value": {
"\u5831\u544a\u65e5": "={{ $json.reportDate }}",
"\u611f\u60c5\u306e\u5185\u8a33": "={{ $json.sentimentBreakdown }}",
"\u30c8\u30c3\u30d7\u30c6\u30fc\u30de": "={{ $json.topThemes }}",
"\u30ad\u30fc\u30a4\u30f3\u30b5\u30a4\u30c8": "={{ $json.keyInsights }}",
"\u512a\u5148\u5ea6\u63a8\u5968\u4e8b\u9805": "={{ $json.priorityRecommendations }}",
"\u5206\u6790\u3055\u308c\u305f\u5408\u8a08\u5fdc\u7b54\u6570": "={{ $json.totalResponsesAnalyzed }}",
"\u30a8\u30b0\u30bc\u30af\u30c6\u30a3\u30d6\u30b5\u30de\u30ea\u30fc```": "={{ $json.executiveSummary }}"
},
"schema": [
{
"id": "\u5831\u544a\u65e5",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "\u5831\u544a\u65e5",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "\u5206\u6790\u3055\u308c\u305f\u5408\u8a08\u5fdc\u7b54\u6570",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "\u5206\u6790\u3055\u308c\u305f\u5408\u8a08\u5fdc\u7b54\u6570",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "\u611f\u60c5\u306e\u5185\u8a33",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "\u611f\u60c5\u306e\u5185\u8a33",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "\u30c8\u30c3\u30d7\u30c6\u30fc\u30de",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "\u30c8\u30c3\u30d7\u30c6\u30fc\u30de",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "\u30ad\u30fc\u30a4\u30f3\u30b5\u30a4\u30c8",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "\u30ad\u30fc\u30a4\u30f3\u30b5\u30a4\u30c8",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "\u512a\u5148\u5ea6\u63a8\u5968\u4e8b\u9805",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "\u512a\u5148\u5ea6\u63a8\u5968\u4e8b\u9805",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "\u30a8\u30b0\u30bc\u30af\u30c6\u30a3\u30d6\u30b5\u30de\u30ea\u30fc```",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "\u30a8\u30b0\u30bc\u30af\u30c6\u30a3\u30d6\u30b5\u30de\u30ea\u30fc```",
"defaultMatch": false,
"canBeUsedToMatch": true
}
],
"mappingMode": "defineBelow",
"matchingColumns": [],
"attemptToConvertTypes": false,
"convertFieldsToString": false
},
"options": {},
"operation": "append",
"sheetName": {
"__rl": true,
"mode": "name",
"value": "YOUR_SHEET_NAME"
},
"documentId": {
"__rl": true,
"mode": "id",
"value": "YOUR_SHEET_ID"
}
},
"credentials": {},
"typeVersion": 4.5
},
{
"id": "note5",
"name": "Processing Note",
"type": "n8n-nodes-base.stickyNote",
"position": [
-80,
720
],
"parameters": {
"color": 2,
"width": 250,
"height": 204,
"content": "## Processing Logic\n\n- `Group & Prepare Data` splits responses into three buckets by rating (>=4 Positive, =3 Neutral, <3 Negative).\n- `Loop Over Batches` ensures the AI analyzes each bucket separately for better accuracy.\n- Results are aggregated into a single executive summary for sharing."
},
"typeVersion": 1
},
{
"id": "loop1",
"name": "Loop Over Batches",
"type": "n8n-nodes-base.splitInBatches",
"position": [
864,
816
],
"parameters": {
"options": {}
},
"typeVersion": 3
},
{
"id": "code3",
"name": "Add Metadata",
"type": "n8n-nodes-base.code",
"position": [
1456,
560
],
"parameters": {
"jsCode": "// Add metadata to each analysis result\nconst item = $input.first();\nconst loopData = $('Loop Over Batches').first();\n\nreturn {\n ...item.json,\n originalSentiment: loopData.json.sentiment,\n originalCount: loopData.json.count\n};"
},
"typeVersion": 2
},
{
"id": "9a64a877-3946-4c33-82c2-e802c98c30f8",
"name": "Send a message1",
"type": "n8n-nodes-base.slack",
"position": [
1456,
1184
],
"parameters": {
"text": "=*\u9867\u5ba2\u30a2\u30f3\u30b1\u30fc\u30c8\u5206\u6790\u30ec\u30dd\u30fc\u30c8*\n\n*\u5831\u544a\u65e5\u6642:* {{ DateTime.fromISO($json.reportDate).toFormat('yyyy\u5e74MM\u6708dd\u65e5 HH:mm') }}\n*\u5206\u6790\u5bfe\u8c61\u306e\u56de\u7b54\u6570:* {{ $json.totalResponsesAnalyzed }} \u4ef6\n---------------------------------\n\n*\u30a8\u30b0\u30bc\u30af\u30c6\u30a3\u30d6\u30b5\u30de\u30ea\u30fc*\n{{ $json.executiveSummary }}\n\n*\u611f\u60c5\u306e\u5185\u8a33*\n\u2022 \u30dd\u30b8\u30c6\u30a3\u30d6: {{ $json.sentimentBreakdown.positive || 0 }} \u4ef6\n\u2022 \u30cd\u30ac\u30c6\u30a3\u30d6: {{ $json.sentimentBreakdown.negative || 0 }} \u4ef6\n\u2022 \u4e2d\u7acb: {{ $json.sentimentBreakdown.neutral || 0 }} \u4ef6\n\n*\u30c8\u30c3\u30d7\u30c6\u30fc\u30de*\n{{ $json.topThemes.length > 0 ? '\\n> ' + $json.topThemes.map(item => item.theme).join('\\n> ') : '\u8a72\u5f53\u306a\u3057' }}\n\n*\u30ad\u30fc\u30a4\u30f3\u30b5\u30a4\u30c8*\n{{ $json.keyInsights.length > 0 ? '\\n> ' + $json.keyInsights.join('\\n> ') : '\u8a72\u5f53\u306a\u3057' }}\n\n*\u512a\u5148\u5ea6\u63a8\u5968\u4e8b\u9805*\n{{ $json.priorityRecommendations.length > 0 ? '\\n> ' + $json.priorityRecommendations.join('\\n> ') : '\u8a72\u5f53\u306a\u3057' }}\n\n---------------------------------\n_\u672c\u30e1\u30c3\u30bb\u30fc\u30b8\u306fn8n\u30ef\u30fc\u30af\u30d5\u30ed\u30fc\u306b\u3088\u3063\u3066\u81ea\u52d5\u9001\u4fe1\u3055\u308c\u307e\u3057\u305f\u3002_",
"select": "channel",
"channelId": {
"__rl": true,
"mode": "list",
"value": "YOUR_CHANNEL_ID",
"cachedResultName": "YOUR_CHANNEL_NAME"
},
"otherOptions": {},
"authentication": "oAuth2"
},
"credentials": {},
"typeVersion": 2.3
},
{
"id": "0c3ebcf9-2daf-4014-94c8-ae8a9c2a4244",
"name": "OpenRouter Chat Model",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenRouter",
"position": [
1104,
640
],
"parameters": {
"options": {
"responseFormat": "json_object"
}
},
"credentials": {},
"typeVersion": 1
}
],
"active": false,
"settings": {
"timezone": "Asia/Tokyo",
"errorWorkflow": "",
"executionOrder": "v1",
"saveManualExecutions": true,
"saveExecutionProgress": true,
"saveDataErrorExecution": "all",
"saveDataSuccessExecution": "all"
},
"versionId": "2657a174-a4b8-49e9-a6f8-d0979141c6be",
"connections": {
"Add Metadata": {
"main": [
[
{
"node": "Loop Over Batches",
"type": "main",
"index": 0
}
]
]
},
"Structure Output": {
"ai_outputParser": [
[
{
"node": "Analyze Survey Batch",
"type": "ai_outputParser",
"index": 0
}
]
]
},
"Aggregate Results": {
"main": [
[
{
"node": "Save to Sheet",
"type": "main",
"index": 0
},
{
"node": "Send a message1",
"type": "main",
"index": 0
}
]
]
},
"Loop Over Batches": {
"main": [
[
{
"node": "Aggregate Results",
"type": "main",
"index": 0
}
],
[
{
"node": "Analyze Survey Batch",
"type": "main",
"index": 0
}
]
]
},
"Analyze Survey Batch": {
"main": [
[
{
"node": "Add Metadata",
"type": "main",
"index": 0
}
]
]
},
"Get Survey Responses": {
"main": [
[
{
"node": "Group & Prepare Data",
"type": "main",
"index": 0
}
]
]
},
"Group & Prepare Data": {
"main": [
[
{
"node": "Loop Over Batches",
"type": "main",
"index": 0
}
]
]
},
"OpenRouter Chat Model": {
"ai_languageModel": [
[
{
"node": "Analyze Survey Batch",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Daily Schedule Trigger": {
"main": [
[
{
"node": "Get Survey Responses",
"type": "main",
"index": 0
}
]
]
}
}
}
Pro
For the full experience including quality scoring and batch install features for each workflow upgrade to Pro
About this workflow
Here’s a clean, English-only template description you can paste into the n8n “Description” field.
Source: https://n8n.io/workflows/9809/ — original creator credit. Request a take-down →
Related workflows
Workflows that share integrations, category, or trigger type with this one. All free to copy and import.
This workflow automates the discovery of new B2B service providers entering the market. It scrapes a specific category on Clutch.co weekly, standardizes the data using AI, and compares it against a hi
Output Parser Structured, OpenRouter Chat, Google Sheets +3
This workflow automates competitive intelligence by continuously monitoring competitor websites for pricing and feature updates. It scrapes current data using BrowserAct, compares it against historica
Google Sheets, N8N Nodes Browseract, Agent +3
This workflow automates Generative Engine Optimization (GEO) tracking by monitoring how your company appears in AI search results. It generates strategic queries, simulates searches on AI engines like
OpenRouter Chat, Output Parser Structured, Google Sheets +3
This workflow acts as an automated marketing analyst. It runs weekly to scrape competitor landing pages, compare them against historical data to detect changes in pricing, messaging, or offers, and de
OpenRouter Chat, Output Parser Structured, Slack +3
Created by: Peyton Leveillee Last updated: October 2025
OpenAI Chat, Google Sheets, HTTP Request +5