This workflow follows the Form Trigger → 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 →
{
"name": "SERP Analysis Template",
"nodes": [
{
"parameters": {
"formTitle": "Keyword Research Form",
"formFields": {
"values": [
{
"fieldLabel": "Focus Keyword",
"requiredField": true
},
{
"fieldLabel": "Country",
"fieldType": "dropdown",
"fieldOptions": {
"values": [
{
"option": "de"
},
{
"option": "us"
},
{}
]
}
}
]
},
"options": {}
},
"type": "n8n-nodes-base.formTrigger",
"typeVersion": 2.2,
"position": [
-880,
940
],
"id": "b93168f8-109d-4014-9ec8-661086b9c4ad",
"name": "On form submission"
},
{
"parameters": {
"url": "https://serpapi.com/search",
"authentication": "predefinedCredentialType",
"nodeCredentialType": "serpApi",
"sendQuery": true,
"queryParameters": {
"parameters": [
{
"name": "q",
"value": "={{ $json['Focus Keyword'] }}"
},
{
"name": "gl",
"value": "={{ $json.Country }}"
},
{
"name": "device",
"value": "DESKTOP"
}
]
},
"options": {}
},
"type": "n8n-nodes-base.httpRequest",
"typeVersion": 4.2,
"position": [
-660,
1040
],
"id": "67331a54-782c-4e11-a3c8-5441d65ded74",
"name": "get_desktop_serp_results"
},
{
"parameters": {
"url": "https://serpapi.com/search",
"authentication": "predefinedCredentialType",
"nodeCredentialType": "serpApi",
"sendQuery": true,
"queryParameters": {
"parameters": [
{
"name": "q",
"value": "={{ $json['Focus Keyword'] }}"
},
{
"name": "gl",
"value": "={{ $json.Country }}"
},
{
"name": "device",
"value": "mobile"
}
]
},
"options": {}
},
"type": "n8n-nodes-base.httpRequest",
"typeVersion": 4.2,
"position": [
-660,
820
],
"id": "990a8da4-2021-4663-8416-09dbfafbe98c",
"name": "get_mobile_serp_results"
},
{
"parameters": {
"assignments": {
"assignments": [
{
"id": "fcf16964-061d-4c9e-82c6-954e769e899c",
"name": "organic_results",
"value": "={{ $json.organic_results }}",
"type": "array"
},
{
"id": "44fba0e5-686c-4475-89dd-66f711bf910c",
"name": "search_parameters.device",
"value": "={{ $json.search_parameters.device }}",
"type": "string"
}
]
},
"options": {}
},
"type": "n8n-nodes-base.set",
"typeVersion": 3.4,
"position": [
-440,
820
],
"id": "f9875911-5b24-4584-b161-a5d0a4978fb1",
"name": "get_organic_results_by_mobile"
},
{
"parameters": {
"assignments": {
"assignments": [
{
"id": "e76a9070-4782-4a54-84b6-bce1eb50023d",
"name": "organic_results",
"value": "={{ $json.organic_results }}",
"type": "array"
},
{
"id": "64fffe61-bb13-47e6-9555-c8adb9c3762d",
"name": "search_parameters.device",
"value": "={{ $json.search_parameters.device }}",
"type": "string"
}
]
},
"options": {}
},
"type": "n8n-nodes-base.set",
"typeVersion": 3.4,
"position": [
-460,
1040
],
"id": "97f9f45d-c6a7-47fd-9377-ab0261f673b5",
"name": "get_organic_results_by_desktop"
},
{
"parameters": {
"assignments": {
"assignments": [
{
"id": "0e112e59-ad43-4321-9de0-d1d502f98266",
"name": "related_questions",
"value": "={{ $json.related_questions }}",
"type": "array"
}
]
},
"options": {}
},
"type": "n8n-nodes-base.set",
"typeVersion": 3.4,
"position": [
-440,
640
],
"id": "228b96fa-9d26-474c-8105-af5d565beb34",
"name": "get_faq_by_mobile"
},
{
"parameters": {
"assignments": {
"assignments": [
{
"id": "0e112e59-ad43-4321-9de0-d1d502f98266",
"name": "related_questions",
"value": "={{ $json.related_questions }}",
"type": "array"
}
]
},
"options": {}
},
"type": "n8n-nodes-base.set",
"typeVersion": 3.4,
"position": [
-460,
1260
],
"id": "40066e50-cfe3-4fff-8afb-8661d75a0592",
"name": "get_faq_by_desktop"
},
{
"parameters": {
"maxItems": 3
},
"type": "n8n-nodes-base.limit",
"typeVersion": 1,
"position": [
-220,
820
],
"id": "ee22525b-fb1d-492e-8c6c-65665c705ca7",
"name": "top_3_by_mobile"
},
{
"parameters": {
"maxItems": 3
},
"type": "n8n-nodes-base.limit",
"typeVersion": 1,
"position": [
-240,
1040
],
"id": "18964edb-4e1c-46f5-9a84-eefcc5ab2119",
"name": "top_3_by_desktop"
},
{
"parameters": {},
"type": "n8n-nodes-base.merge",
"typeVersion": 3,
"position": [
40,
780
],
"id": "f07e0e80-7ab1-4631-8d12-0eabb82dbc4d",
"name": "get_urls_to_crawl"
},
{
"parameters": {
"options": {
"reset": false
}
},
"type": "n8n-nodes-base.splitInBatches",
"typeVersion": 3,
"position": [
40,
1080
],
"id": "21ae683d-b280-4250-9fd9-53cf4fceaacd",
"name": "Loop Over Items"
},
{
"parameters": {
"method": "POST",
"url": "http://crawl4ai:11235/crawl",
"authentication": "genericCredentialType",
"genericAuthType": "httpHeaderAuth",
"sendBody": true,
"bodyParameters": {
"parameters": [
{
"name": "urls",
"value": "={{ $json.link }}"
}
]
},
"options": {}
},
"type": "n8n-nodes-base.httpRequest",
"typeVersion": 4.2,
"position": [
320,
1100
],
"id": "8f051515-5b35-49b7-bb09-7da492daf249",
"name": "get_page_content"
},
{
"parameters": {
"url": "=http://crawl4ai:11235/task/{{ $json.task_id }}",
"authentication": "genericCredentialType",
"genericAuthType": "httpHeaderAuth",
"options": {}
},
"type": "n8n-nodes-base.httpRequest",
"typeVersion": 4.2,
"position": [
740,
1100
],
"id": "07f77190-2241-41aa-b731-4049dd1274c4",
"name": "get_status"
},
{
"parameters": {
"conditions": {
"options": {
"caseSensitive": true,
"leftValue": "",
"typeValidation": "strict",
"version": 2
},
"conditions": [
{
"id": "100015ac-f73c-406d-acd5-49a41d60111a",
"leftValue": "={{ $json.status }}",
"rightValue": "completed",
"operator": {
"type": "string",
"operation": "equals",
"name": "filter.operator.equals"
}
}
],
"combinator": "and"
},
"options": {}
},
"type": "n8n-nodes-base.if",
"typeVersion": 2.2,
"position": [
940,
1100
],
"id": "c0e6afbf-12a1-4d25-b335-dc0111fe1480",
"name": "If"
},
{
"parameters": {
"assignments": {
"assignments": [
{
"id": "4eec2c04-b519-4c06-a911-bb7780b776b5",
"name": "result.metadata.title",
"value": "={{ $('get_status').item.json.result.metadata.title }}",
"type": "string"
},
{
"id": "0eac8d67-34d9-4809-92fc-69f47381e104",
"name": "result.metadata.description",
"value": "={{ $('get_status').item.json.result.metadata.description }}",
"type": "string"
},
{
"id": "58b74b11-74f8-42e3-b68f-a2aa4c380578",
"name": "result.metadata['og:url']",
"value": "={{ $('get_status').item.json.result.metadata['og:url'] }}",
"type": "string"
},
{
"id": "f57680c0-48ed-492f-a1d9-508a0c996198",
"name": "result.cleaned_html",
"value": "={{ $('get_status').item.json.result.cleaned_html }}",
"type": "string"
}
]
},
"options": {}
},
"type": "n8n-nodes-base.set",
"typeVersion": 3.4,
"position": [
1160,
1040
],
"id": "c097144a-cc69-4061-a720-a1f94d91ab3b",
"name": "get_metadata"
},
{
"parameters": {
"html": "={{ $json.result.cleaned_html }}",
"destinationKey": "content",
"options": {}
},
"type": "n8n-nodes-base.markdown",
"typeVersion": 1,
"position": [
1400,
1200
],
"id": "52102ba3-90b5-4ff7-b368-469c0c79baff",
"name": "get_content_as_markdown"
},
{
"parameters": {},
"type": "n8n-nodes-base.wait",
"typeVersion": 1.1,
"position": [
540,
1100
],
"id": "0e696748-e2cb-4767-b532-83eb429f2906",
"name": "wait_5_sec_0"
},
{
"parameters": {
"fieldToSplitOut": "organic_results",
"options": {}
},
"type": "n8n-nodes-base.splitOut",
"typeVersion": 1,
"position": [
200,
780
],
"id": "5380bee4-e5ed-4022-9771-6858fe1af7ea",
"name": "Split Out"
},
{
"parameters": {
"compare": "selectedFields",
"fieldsToCompare": "link",
"options": {}
},
"type": "n8n-nodes-base.removeDuplicates",
"typeVersion": 2,
"position": [
520,
780
],
"id": "ef611ef3-164c-4fed-81d2-e724af8438a2",
"name": "Remove Duplicates"
},
{
"parameters": {
"modelId": {
"__rl": true,
"value": "gpt-4o-mini",
"mode": "list",
"cachedResultName": "GPT-4O-MINI"
},
"messages": {
"values": [
{
"content": "You are a SEO-specialist and your task is to analyze the articles from our competitors. The ultimate goal is to write an article that is better than the competitors article. Apart from that, you are familiar with markdown and you are able to extract only relevant content.",
"role": "system"
},
{
"content": "=Your task is to analyze the following <article> = {{ $json.content }} and provide me the following information:\n- Short Summary\n- Potential Focus Keyword\n- Relevant long-tail keywords\n- N-Gram-Analysis\n"
}
]
},
"jsonOutput": true,
"options": {}
},
"type": "@n8n/n8n-nodes-langchain.openAi",
"typeVersion": 1.8,
"position": [
1620,
1200
],
"id": "97b7b8c9-855c-4d83-a06f-3ff07f6213b6",
"name": "OpenAI"
},
{
"parameters": {
"maxItems": 5
},
"type": "n8n-nodes-base.limit",
"typeVersion": 1,
"position": [
360,
780
],
"id": "f55ec09b-332d-416e-aad5-e824dea44efd",
"name": "Limit"
},
{
"parameters": {
"content": "# Get SERPs by Keyword and Country",
"height": 840,
"width": 840
},
"type": "n8n-nodes-base.stickyNote",
"typeVersion": 1,
"position": [
-920,
580
],
"id": "2bf16ade-d73d-463b-9d72-8084e92b90ec",
"name": "Sticky Note"
},
{
"parameters": {
"content": "# Prepare SERPs for Crawling and Analysis",
"height": 400,
"width": 820,
"color": 6
},
"type": "n8n-nodes-base.stickyNote",
"typeVersion": 1,
"position": [
-60,
580
],
"id": "5767bdeb-11b2-45aa-9edb-56aaf4665dd2",
"name": "Sticky Note1"
},
{
"parameters": {
"content": "# Crawl Pages with Crawl4ai",
"height": 420,
"width": 2240,
"color": 4
},
"type": "n8n-nodes-base.stickyNote",
"typeVersion": 1,
"position": [
-60,
1000
],
"id": "c5414411-56e7-4307-ab8c-a1f2abf852e9",
"name": "Sticky Note2"
},
{
"parameters": {
"content": "# Save Results to Google Sheets",
"height": 920,
"width": 1020
},
"type": "n8n-nodes-base.stickyNote",
"typeVersion": 1,
"position": [
3060,
-360
],
"id": "04fe31ac-9da9-4814-a493-a90473942ed9",
"name": "Sticky Note3"
},
{
"parameters": {
"content": "### Google Sheets\n\nCreate a sheet called \"SERPs\" with the following columns:\n- position\n- title\n- link\n- snippet\n- short_summary\n- potential_focus_keyword\n- relevant_long_tail_keywords\n- unigrams\n- bigrams\n- trigrams",
"width": 400
},
"type": "n8n-nodes-base.stickyNote",
"typeVersion": 1,
"position": [
1760,
1020
],
"id": "4d2a7420-a20e-4c62-a0cd-8563c72ef2cc",
"name": "Sticky Note4"
},
{
"parameters": {
"content": "### Limit\n\nSet a limit when testing the workflow to save computing costs and token usage.",
"height": 120,
"width": 220
},
"type": "n8n-nodes-base.stickyNote",
"typeVersion": 1,
"position": [
360,
640
],
"id": "5b6f6a69-fe05-4a8a-82a0-73bb799e2276",
"name": "Sticky Note5"
},
{
"parameters": {},
"type": "n8n-nodes-base.merge",
"typeVersion": 3,
"position": [
920,
680
],
"id": "e73f5aac-00c8-4e5c-b6b1-d05cd18d452f",
"name": "merge_faq"
},
{
"parameters": {
"fieldToSplitOut": "related_questions",
"options": {}
},
"type": "n8n-nodes-base.splitOut",
"typeVersion": 1,
"position": [
1140,
680
],
"id": "f024e1c0-1b30-457e-8a76-e216ddab21ac",
"name": "get_questions"
},
{
"parameters": {
"compare": "selectedFields",
"fieldsToCompare": "question",
"options": {}
},
"type": "n8n-nodes-base.removeDuplicates",
"typeVersion": 2,
"position": [
1360,
680
],
"id": "ffa44fad-d363-40db-b94d-8fbd16edab19",
"name": "remove_duplicates_in_questions"
},
{
"parameters": {
"options": {}
},
"type": "n8n-nodes-base.splitInBatches",
"typeVersion": 3,
"position": [
1580,
680
],
"id": "4a3183c1-a045-4d8a-aae6-9fde63d5500e",
"name": "loop_over_questions"
},
{
"parameters": {
"operation": "append",
"documentId": {
"__rl": true,
"value": "1h9qtlFg5vYVWAvY6iUliFzHQaOAyDQ4kboutM6Zlj6w",
"mode": "list",
"cachedResultName": "Keyword Research",
"cachedResultUrl": "https://docs.google.com/spreadsheets/d/1h9qtlFg5vYVWAvY6iUliFzHQaOAyDQ4kboutM6Zlj6w/edit?usp=drivesdk"
},
"sheetName": {
"__rl": true,
"value": 738525081,
"mode": "list",
"cachedResultName": "FAQs",
"cachedResultUrl": "https://docs.google.com/spreadsheets/d/1h9qtlFg5vYVWAvY6iUliFzHQaOAyDQ4kboutM6Zlj6w/edit#gid=738525081"
},
"columns": {
"mappingMode": "defineBelow",
"value": {
"question": "={{ $json.question }}",
"title": "={{ $json.title }}",
"link": "={{ $json.link }}"
},
"matchingColumns": [],
"schema": [
{
"id": "question",
"displayName": "question",
"required": false,
"defaultMatch": false,
"display": true,
"type": "string",
"canBeUsedToMatch": true,
"removed": false
},
{
"id": "title",
"displayName": "title",
"required": false,
"defaultMatch": false,
"display": true,
"type": "string",
"canBeUsedToMatch": true,
"removed": false
},
{
"id": "link",
"displayName": "link",
"required": false,
"defaultMatch": false,
"display": true,
"type": "string",
"canBeUsedToMatch": true,
"removed": false
}
],
"attemptToConvertTypes": false,
"convertFieldsToString": false
},
"options": {}
},
"type": "n8n-nodes-base.googleSheets",
"typeVersion": 4.5,
"position": [
1900,
780
],
"id": "7527a6f0-1908-4a91-a1a2-704ba5602c36",
"name": "insert_faqs"
},
{
"parameters": {
"operation": "append",
"documentId": {
"__rl": true,
"value": "1h9qtlFg5vYVWAvY6iUliFzHQaOAyDQ4kboutM6Zlj6w",
"mode": "list",
"cachedResultName": "Keyword Research",
"cachedResultUrl": "https://docs.google.com/spreadsheets/d/1h9qtlFg5vYVWAvY6iUliFzHQaOAyDQ4kboutM6Zlj6w/edit?usp=drivesdk"
},
"sheetName": {
"__rl": true,
"value": "gid=0",
"mode": "list",
"cachedResultName": "SERPs",
"cachedResultUrl": "https://docs.google.com/spreadsheets/d/1h9qtlFg5vYVWAvY6iUliFzHQaOAyDQ4kboutM6Zlj6w/edit#gid=0"
},
"columns": {
"mappingMode": "defineBelow",
"value": {
"short_summary": "={{ $json.message.content.short_summary ? $json.message.content.short_summary: \"\" }}",
"potential_focus_keyword": "={{ $json.message.content.potential_focus_keyword ? $json.message.content.potential_focus_keyword: \"\" }}",
"unigrams": "={{ $json.message.content.n_gram_analysis.unigrams ? $json.message.content.n_gram_analysis.unigrams: \"\" }}",
"bigrams": "={{ $json.message.content.n_gram_analysis.bigrams ? $json.message.content.n_gram_analysis.bigrams: \"\" }}",
"trigrams": "={{ $json.message.content.n_gram_analysis.trigrams ? $json.message.content.n_gram_analysis.trigrams: \"\" }}",
"relevant_long_tail_keywords": "={{ $json.message.content.relevant_long_tail_keywords ? $json.message.content.relevant_long_tail_keywords: \"\" }}",
"title": "={{ $('Loop Over Items').item.json.title }}",
"link": "={{ $('Loop Over Items').item.json.link }}",
"snippet": "={{ $('Loop Over Items').item.json.snippet }}",
"position": "={{ $('Loop Over Items').item.json.position }}"
},
"matchingColumns": [],
"schema": [
{
"id": "position",
"displayName": "position",
"required": false,
"defaultMatch": false,
"display": true,
"type": "string",
"canBeUsedToMatch": true,
"removed": false
},
{
"id": "title",
"displayName": "title",
"required": false,
"defaultMatch": false,
"display": true,
"type": "string",
"canBeUsedToMatch": true,
"removed": false
},
{
"id": "link",
"displayName": "link",
"required": false,
"defaultMatch": false,
"display": true,
"type": "string",
"canBeUsedToMatch": true,
"removed": false
},
{
"id": "snippet",
"displayName": "snippet",
"required": false,
"defaultMatch": false,
"display": true,
"type": "string",
"canBeUsedToMatch": true,
"removed": false
},
{
"id": "short_summary",
"displayName": "short_summary",
"required": false,
"defaultMatch": false,
"display": true,
"type": "string",
"canBeUsedToMatch": true
},
{
"id": "potential_focus_keyword",
"displayName": "potential_focus_keyword",
"required": false,
"defaultMatch": false,
"display": true,
"type": "string",
"canBeUsedToMatch": true
},
{
"id": "relevant_long_tail_keywords",
"displayName": "relevant_long_tail_keywords",
"required": false,
"defaultMatch": false,
"display": true,
"type": "string",
"canBeUsedToMatch": true
},
{
"id": "unigrams",
"displayName": "unigrams",
"required": false,
"defaultMatch": false,
"display": true,
"type": "string",
"canBeUsedToMatch": true
},
{
"id": "bigrams",
"displayName": "bigrams",
"required": false,
"defaultMatch": false,
"display": true,
"type": "string",
"canBeUsedToMatch": true
},
{
"id": "trigrams",
"displayName": "trigrams",
"required": false,
"defaultMatch": false,
"display": true,
"type": "string",
"canBeUsedToMatch": true
}
],
"attemptToConvertTypes": false,
"convertFieldsToString": false
},
"options": {}
},
"type": "n8n-nodes-base.googleSheets",
"typeVersion": 4.5,
"position": [
1960,
1200
],
"id": "77f6f438-83ad-4e99-aec0-52e006fd3f49",
"name": "insert_serp_analysis"
},
{
"parameters": {
"content": "# GET FAQs",
"height": 400,
"width": 1400,
"color": 5
},
"type": "n8n-nodes-base.stickyNote",
"typeVersion": 1,
"position": [
780,
580
],
"id": "7bdc4d1c-9987-401b-8a23-0e44a052c541",
"name": "Sticky Note6"
},
{
"parameters": {
"content": "### Google Sheets\n\nCreate a sheet called \"FAQs\" with the following columns:\n- question\n- title\n- link",
"width": 260
},
"type": "n8n-nodes-base.stickyNote",
"typeVersion": 1,
"position": [
1880,
600
],
"id": "f0770b44-c147-4f04-9623-802beb9f7dcb",
"name": "Sticky Note7"
}
],
"connections": {
"On form submission": {
"main": [
[
{
"node": "get_desktop_serp_results",
"type": "main",
"index": 0
},
{
"node": "get_mobile_serp_results",
"type": "main",
"index": 0
}
]
]
},
"get_mobile_serp_results": {
"main": [
[
{
"node": "get_organic_results_by_mobile",
"type": "main",
"index": 0
},
{
"node": "get_faq_by_mobile",
"type": "main",
"index": 0
}
]
]
},
"get_desktop_serp_results": {
"main": [
[
{
"node": "get_organic_results_by_desktop",
"type": "main",
"index": 0
},
{
"node": "get_faq_by_desktop",
"type": "main",
"index": 0
}
]
]
},
"get_faq_by_mobile": {
"main": [
[
{
"node": "merge_faq",
"type": "main",
"index": 0
}
]
]
},
"get_organic_results_by_mobile": {
"main": [
[
{
"node": "top_3_by_mobile",
"type": "main",
"index": 0
}
]
]
},
"get_organic_results_by_desktop": {
"main": [
[
{
"node": "top_3_by_desktop",
"type": "main",
"index": 0
}
]
]
},
"get_faq_by_desktop": {
"main": [
[
{
"node": "merge_faq",
"type": "main",
"index": 1
}
]
]
},
"top_3_by_mobile": {
"main": [
[
{
"node": "get_urls_to_crawl",
"type": "main",
"index": 0
}
]
]
},
"top_3_by_desktop": {
"main": [
[
{
"node": "get_urls_to_crawl",
"type": "main",
"index": 1
}
]
]
},
"get_urls_to_crawl": {
"main": [
[
{
"node": "Split Out",
"type": "main",
"index": 0
}
]
]
},
"Loop Over Items": {
"main": [
[],
[
{
"node": "get_page_content",
"type": "main",
"index": 0
}
]
]
},
"get_page_content": {
"main": [
[
{
"node": "wait_5_sec_0",
"type": "main",
"index": 0
}
]
]
},
"get_status": {
"main": [
[
{
"node": "If",
"type": "main",
"index": 0
}
]
]
},
"If": {
"main": [
[
{
"node": "get_metadata",
"type": "main",
"index": 0
}
],
[
{
"node": "wait_5_sec_0",
"type": "main",
"index": 0
}
]
]
},
"get_metadata": {
"main": [
[
{
"node": "get_content_as_markdown",
"type": "main",
"index": 0
}
]
]
},
"wait_5_sec_0": {
"main": [
[
{
"node": "get_status",
"type": "main",
"index": 0
}
]
]
},
"get_content_as_markdown": {
"main": [
[
{
"node": "OpenAI",
"type": "main",
"index": 0
}
]
]
},
"Split Out": {
"main": [
[
{
"node": "Limit",
"type": "main",
"index": 0
}
]
]
},
"Remove Duplicates": {
"main": [
[
{
"node": "Loop Over Items",
"type": "main",
"index": 0
}
]
]
},
"Limit": {
"main": [
[
{
"node": "Remove Duplicates",
"type": "main",
"index": 0
}
]
]
},
"OpenAI": {
"main": [
[
{
"node": "insert_serp_analysis",
"type": "main",
"index": 0
}
]
]
},
"merge_faq": {
"main": [
[
{
"node": "get_questions",
"type": "main",
"index": 0
}
]
]
},
"get_questions": {
"main": [
[
{
"node": "remove_duplicates_in_questions",
"type": "main",
"index": 0
}
]
]
},
"remove_duplicates_in_questions": {
"main": [
[
{
"node": "loop_over_questions",
"type": "main",
"index": 0
}
]
]
},
"loop_over_questions": {
"main": [
[],
[
{
"node": "insert_faqs",
"type": "main",
"index": 0
}
]
]
},
"insert_faqs": {
"main": [
[
{
"node": "loop_over_questions",
"type": "main",
"index": 0
}
]
]
},
"insert_serp_analysis": {
"main": [
[
{
"node": "Loop Over Items",
"type": "main",
"index": 0
}
]
]
}
},
"active": false,
"settings": {
"executionOrder": "v1"
},
"versionId": "b43516de-b4c7-4038-b4f6-83294815796d",
"meta": {
"templateCredsSetupCompleted": true
},
"id": "9lW9vKY54sMpIvZC",
"tags": [
{
"createdAt": "2025-03-02T12:05:53.836Z",
"updatedAt": "2025-03-02T12:05:53.836Z",
"id": "utwtHTzeqUdktrxJ",
"name": "SEO"
}
]
}
Pro
For the full experience including quality scoring and batch install features for each workflow upgrade to Pro
How this works
This workflow delivers instant SERP analysis to reveal search engine insights without manual effort, saving hours of research for marketers and SEO specialists. It processes a submitted keyword through desktop and mobile queries, extracts key elements like organic results and FAQs, and leverages OpenAI to generate a concise report highlighting opportunities and competitors. The core step involves parallel HTTP requests to fetch real-time SERP data, followed by AI-driven summarisation, before appending everything to Google Sheets for easy tracking and sharing.
Use this when analysing ad-hoc keywords for content strategy or competitor benchmarking, especially in fast-paced campaigns needing quick mobile-desktop comparisons. Avoid it for high-volume, automated monitoring better suited to scheduled tools like Google Search Console integrations. Common variations include adding email notifications for reports or expanding OpenAI prompts to include sentiment analysis on featured snippets.
About this workflow
SERP Analysis Template. Uses formTrigger, httpRequest, openAi, googleSheets. Event-driven trigger; 35 nodes.
Source: https://github.com/Marvomatic/n8n-templates/blob/main/serp-analysis/serp_analysis_deprecated.json — 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.
Template - SERP Analysis (Serper). Uses formTrigger, httpRequest, googleSheets, openAi. Event-driven trigger; 36 nodes.
Form Trigger, HTTP Request, Google Sheets +1
Data-driven SEO Optimization. Uses formTrigger, httpRequest, googleDrive, googleSheets. Event-driven trigger; 26 nodes.
Form Trigger, HTTP Request, Google Drive +3
Cold Email Live Build. Uses formTrigger, httpRequest, googleSheets, splitInBatches. Event-driven trigger; 9 nodes.
Form Trigger, HTTP Request, Google Sheets +1
Converttofile Http. Uses lmChatOpenAi, googleSheets, httpRequest, openAi. Event-driven trigger; 18 nodes.
OpenAI Chat, Google Sheets, HTTP Request +3
Limit Code. Uses stickyNote, manualTrigger, splitInBatches, googleSheets. Event-driven trigger; 14 nodes.
Google Sheets, OpenAI, HTTP Request