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": "Template - SERP Analysis (Serper)",
"nodes": [
{
"parameters": {
"formTitle": "Keyword Research Form",
"formFields": {
"values": [
{
"fieldLabel": "Focus Keyword",
"requiredField": true
},
{
"fieldLabel": "Country",
"fieldType": "dropdown",
"fieldOptions": {
"values": [
{
"option": "de"
},
{
"option": "us"
}
]
}
},
{
"fieldLabel": "hl",
"fieldType": "dropdown",
"fieldOptions": {
"values": [
{
"option": "de"
},
{
"option": "en"
}
]
}
}
]
},
"options": {}
},
"type": "n8n-nodes-base.formTrigger",
"typeVersion": 2.2,
"position": [
-1340,
1080
],
"id": "349ba094-8f87-4bcb-83f1-d286e7b96f76",
"name": "On form submission"
},
{
"parameters": {
"url": "https://google.serper.dev/search",
"authentication": "genericCredentialType",
"genericAuthType": "httpHeaderAuth",
"sendQuery": true,
"queryParameters": {
"parameters": [
{
"name": "q",
"value": "={{ $json['Focus Keyword']}}"
},
{
"name": "gl",
"value": "={{ $json.Country }}"
},
{
"name": "device",
"value": "mobile"
},
{
"name": "exclude",
"value": "[\"video\", \"image\", \"news\", \"places\"]"
},
{
"name": "hl",
"value": "={{ $json.hl }}"
}
]
},
"sendHeaders": true,
"headerParameters": {
"parameters": [
{
"name": "Content-Type",
"value": "application/json"
}
]
},
"options": {}
},
"type": "n8n-nodes-base.httpRequest",
"typeVersion": 4.2,
"position": [
-1120,
980
],
"id": "0262df51-ba80-46db-bf76-03417617fe44",
"name": "get_mobile_serp_results",
"credentials": {
"httpHeaderAuth": {
"name": "<your credential>"
}
}
},
{
"parameters": {
"fieldToSplitOut": "faqs",
"options": {}
},
"type": "n8n-nodes-base.splitOut",
"typeVersion": 1,
"position": [
-180,
900
],
"id": "fd37ed1e-149b-49bb-a1fc-345d642997f2",
"name": "get_questions"
},
{
"parameters": {
"operation": "append",
"documentId": {
"__rl": true,
"value": "1Wdynwg7WmGldyWOd8r-mf9VjnCHJQtHosxJskNLfb2A",
"mode": "list",
"cachedResultName": "SERP Analysis (Serper)",
"cachedResultUrl": ""
},
"sheetName": {
"__rl": true,
"value": 954676522,
"mode": "list",
"cachedResultName": "FAQs",
"cachedResultUrl": ""
},
"columns": {
"mappingMode": "defineBelow",
"value": {
"question": "={{ $json.question }}",
"snippet": "={{ $json.snippet }}",
"title": "={{ $json.title }}",
"link": "={{ $json.link }}"
},
"matchingColumns": [],
"schema": [
{
"id": "question",
"displayName": "question",
"required": false,
"defaultMatch": false,
"display": true,
"type": "string",
"canBeUsedToMatch": true
},
{
"id": "title",
"displayName": "title",
"required": false,
"defaultMatch": false,
"display": true,
"type": "string",
"canBeUsedToMatch": true
},
{
"id": "link",
"displayName": "link",
"required": false,
"defaultMatch": false,
"display": true,
"type": "string",
"canBeUsedToMatch": true
},
{
"id": "snippet",
"displayName": "snippet",
"required": false,
"defaultMatch": false,
"display": true,
"type": "string",
"canBeUsedToMatch": true
}
],
"attemptToConvertTypes": false,
"convertFieldsToString": false
},
"options": {}
},
"type": "n8n-nodes-base.googleSheets",
"typeVersion": 4.5,
"position": [
260,
900
],
"id": "6b533b68-e380-4fc7-8286-d7741751847e",
"name": "insert_faqs",
"alwaysOutputData": true,
"credentials": {
"googleSheetsOAuth2Api": {
"name": "<your credential>"
}
},
"onError": "continueRegularOutput"
},
{
"parameters": {
"url": "https://google.serper.dev/search",
"authentication": "genericCredentialType",
"genericAuthType": "httpHeaderAuth",
"sendQuery": true,
"queryParameters": {
"parameters": [
{
"name": "q",
"value": "={{ $json['Focus Keyword']}}"
},
{
"name": "gl",
"value": "={{ $json.Country }}"
},
{
"name": "device",
"value": "desktop"
},
{
"name": "hl",
"value": "={{ $json.hl }}"
}
]
},
"sendHeaders": true,
"headerParameters": {
"parameters": [
{
"name": "Content-Type",
"value": "application/json"
}
]
},
"options": {}
},
"type": "n8n-nodes-base.httpRequest",
"typeVersion": 4.2,
"position": [
-1120,
1180
],
"id": "df178d34-0af1-46dd-a542-ac49a619c543",
"name": "get_desktop_serp_results",
"credentials": {
"httpHeaderAuth": {
"name": "<your credential>"
}
}
},
{
"parameters": {
"fieldToSplitOut": "related_searches",
"options": {}
},
"type": "n8n-nodes-base.splitOut",
"typeVersion": 1,
"position": [
-200,
500
],
"id": "5140a96e-5086-40ef-b357-fcd535e8a4f0",
"name": "get_queries"
},
{
"parameters": {
"operation": "append",
"documentId": {
"__rl": true,
"value": "1Wdynwg7WmGldyWOd8r-mf9VjnCHJQtHosxJskNLfb2A",
"mode": "list",
"cachedResultName": "SERP Analysis (Serper)",
"cachedResultUrl": ""
},
"sheetName": {
"__rl": true,
"value": 857390152,
"mode": "list",
"cachedResultName": "Related Searches",
"cachedResultUrl": ""
},
"columns": {
"mappingMode": "defineBelow",
"value": {
"query": "={{ $json.query }}"
},
"matchingColumns": [
"query"
],
"schema": [
{
"id": "query",
"displayName": "query",
"required": false,
"defaultMatch": false,
"display": true,
"type": "string",
"canBeUsedToMatch": true
}
],
"attemptToConvertTypes": false,
"convertFieldsToString": false
},
"options": {}
},
"type": "n8n-nodes-base.googleSheets",
"typeVersion": 4.5,
"position": [
240,
500
],
"id": "7811c41b-ed61-49ca-903c-b121ffea7f3b",
"name": "insert_related_searches",
"alwaysOutputData": true,
"credentials": {
"googleSheetsOAuth2Api": {
"name": "<your credential>"
}
},
"onError": "continueRegularOutput"
},
{
"parameters": {
"assignments": {
"assignments": [
{
"id": "0e112e59-ad43-4321-9de0-d1d502f98266",
"name": "faqs",
"value": "={{ $json.peopleAlsoAsk }}",
"type": "array"
}
]
},
"options": {}
},
"type": "n8n-nodes-base.set",
"typeVersion": 3.4,
"position": [
-680,
900
],
"id": "623722a5-a1f6-470d-b9c9-d48272641bb5",
"name": "get_faq_mobile",
"alwaysOutputData": true,
"onError": "continueRegularOutput"
},
{
"parameters": {
"assignments": {
"assignments": [
{
"id": "fcf16964-061d-4c9e-82c6-954e769e899c",
"name": "organic_results",
"value": "={{ $json.organic }}",
"type": "array"
},
{
"id": "44fba0e5-686c-4475-89dd-66f711bf910c",
"name": "search_parameters.device",
"value": "={{ $json.searchParameters.device }}",
"type": "string"
}
]
},
"options": {}
},
"type": "n8n-nodes-base.set",
"typeVersion": 3.4,
"position": [
-680,
1160
],
"id": "d08f09c1-8f87-4a8e-9ed7-88b8ace602cd",
"name": "get_organic_results_mobile"
},
{
"parameters": {
"assignments": {
"assignments": [
{
"id": "e76a9070-4782-4a54-84b6-bce1eb50023d",
"name": "organic_results",
"value": "={{ $json.organic }}",
"type": "array"
},
{
"id": "64fffe61-bb13-47e6-9555-c8adb9c3762d",
"name": "search_parameters.device",
"value": "={{ $json.searchParameters.device }}",
"type": "string"
}
]
},
"options": {}
},
"type": "n8n-nodes-base.set",
"typeVersion": 3.4,
"position": [
-680,
1360
],
"id": "fd37ffb2-175d-43be-b3eb-a1a1bc0bec3e",
"name": "get_organic_results_desktop"
},
{
"parameters": {
"assignments": {
"assignments": [
{
"id": "0e112e59-ad43-4321-9de0-d1d502f98266",
"name": "faqs",
"value": "={{ $json.peopleAlsoAsk }}",
"type": "array"
}
]
},
"options": {}
},
"type": "n8n-nodes-base.set",
"typeVersion": 3.4,
"position": [
-680,
1560
],
"id": "d3c39dcf-a921-434b-bbe9-2ba0e2af3f2b",
"name": "get_faq_desktop",
"alwaysOutputData": true,
"onError": "continueRegularOutput"
},
{
"parameters": {
"content": "# Insert Related Searches\n",
"height": 380,
"width": 960,
"color": 4
},
"type": "n8n-nodes-base.stickyNote",
"typeVersion": 1,
"position": [
-460,
400
],
"id": "55b418ae-b85e-48ce-b5ab-7c6e8f6bd237",
"name": "Sticky Note"
},
{
"parameters": {
"content": "# Insert FAQs",
"height": 380,
"width": 960,
"color": 4
},
"type": "n8n-nodes-base.stickyNote",
"typeVersion": 1,
"position": [
-460,
800
],
"id": "19e037a0-0e0d-4b1c-a0a2-5d4f169a5298",
"name": "Sticky Note1"
},
{
"parameters": {
"content": "# Get Data",
"height": 1560,
"width": 900
},
"type": "n8n-nodes-base.stickyNote",
"typeVersion": 1,
"position": [
-1400,
400
],
"id": "17460b8b-cd05-4396-9c7b-280c1007ca3c",
"name": "Sticky Note3"
},
{
"parameters": {
"content": "# SERP Analysis\n",
"height": 760,
"width": 2180,
"color": 5
},
"type": "n8n-nodes-base.stickyNote",
"typeVersion": 1,
"position": [
-460,
1200
],
"id": "b4ac485c-2dc6-40e2-b373-46d320ef3901",
"name": "Sticky Note4"
},
{
"parameters": {
"content": "## Workflow Instructions\n#### Requirements\nTo use this workflow, you will need accounts and API access for the following services:\n- **Google Sheets**\n- **Serper Dev**\n- **OpenAI**\n#### Google Spreadsheet Setup\nCreate a Google Spreadsheet with **three sheets** named exactly as follows:\n1. **SERP Analysis** \n Columns (in order): \n `position`, `title`, `link`, `snippet`, `short_summary`, `potential_focus_keyword`, `relevant_long_tail_keywords`, `unigrams`, `bigrams`, `trigrams`\n2. **FAQs** \n Columns (in order): \n `question`, `title`, `link`, `snippet`\n3. **Related Searches** \n Columns (in order): \n `query`\n> **Tip:** Make sure the sheet names and column headers match exactly, including capitalization and spacing, to ensure the workflow functions correctly.\n#### Getting Started\n1. **Set up your Google Spreadsheet** as described above.\n2. **Configure your API credentials** for Serper and OpenAI within n8n.\n3. **Import the workflow** into your n8n instance.\n4. **Update any required variables or nodes** in the workflow to point to your specific spreadsheet and API keys.\n5. **Run the workflow** and monitor the results in your Google Sheets.\n#### Support & Contact\nDeveloped by **Marvomatic**\n- [Website](https://marvomatic.com)\n- [TikTok](https://www.tiktok.com/@marvomatic)\n- [YouTube](https://www.youtube.com/@marvomatic)\n\nFor business inquiries, email: [hello@marvomatic.com](mailto:hello@marvomatic.com)",
"height": 780,
"width": 1180
},
"type": "n8n-nodes-base.stickyNote",
"typeVersion": 1,
"position": [
540,
400
],
"id": "5ef92013-20dd-4f6a-bc7c-230065b9972e",
"name": "Sticky Note5"
},
{
"parameters": {
"jsCode": "// Create an array to hold the cleaned items\nconst cleanedArray = [];\n\n// Loop through all input items\nfor (const item of $input.all()) {\n // Create a copy of the item's JSON\n const cleanedItem = {...item.json};\n \n // Filter organic results to remove any with youtube.com in the link\n if (cleanedItem.organic) {\n cleanedItem.organic = cleanedItem.organic.filter(result => \n !result.link.includes('youtube.com')\n );\n \n // Also filter any sitelinks that might contain youtube.com\n for (const result of cleanedItem.organic) {\n if (result.sitelinks) {\n result.sitelinks = result.sitelinks.filter(sitelink => \n !sitelink.link.includes('youtube.com')\n );\n }\n }\n }\n \n // Filter related searches if needed\n if (cleanedItem.relatedSearches) {\n cleanedItem.relatedSearches = cleanedItem.relatedSearches.filter(search => \n !search.query.includes('youtube.com')\n );\n }\n \n // Add the cleaned item to our array\n cleanedArray.push(cleanedItem);\n}\n\n// Return the cleaned array\nreturn cleanedArray;"
},
"type": "n8n-nodes-base.code",
"typeVersion": 2,
"position": [
-920,
980
],
"id": "6db2c1a6-0b73-447d-b5bc-caf2503a5e3d",
"name": "clean_results_mobile"
},
{
"parameters": {
"jsCode": "// Create an array to hold the cleaned items\nconst cleanedArray = [];\n\n// Loop through all input items\nfor (const item of $input.all()) {\n // Create a copy of the item's JSON\n const cleanedItem = {...item.json};\n \n // Filter organic results to remove any with youtube.com in the link\n if (cleanedItem.organic) {\n cleanedItem.organic = cleanedItem.organic.filter(result => \n !result.link.includes('youtube.com')\n );\n \n // Also filter any sitelinks that might contain youtube.com\n for (const result of cleanedItem.organic) {\n if (result.sitelinks) {\n result.sitelinks = result.sitelinks.filter(sitelink => \n !sitelink.link.includes('youtube.com')\n );\n }\n }\n }\n \n // Filter related searches if needed\n if (cleanedItem.relatedSearches) {\n cleanedItem.relatedSearches = cleanedItem.relatedSearches.filter(search => \n !search.query.includes('youtube.com')\n );\n }\n \n // Add the cleaned item to our array\n cleanedArray.push(cleanedItem);\n}\n\n// Return the cleaned array\nreturn cleanedArray;"
},
"type": "n8n-nodes-base.code",
"typeVersion": 2,
"position": [
-920,
1180
],
"id": "a74656ba-5010-47fa-b0af-5d1362ec21c7",
"name": "clean_results_desktop"
},
{
"parameters": {
"assignments": {
"assignments": [
{
"id": "0e112e59-ad43-4321-9de0-d1d502f98266",
"name": "related_searches",
"value": "={{ $json.relatedSearches }}",
"type": "array"
}
]
},
"options": {}
},
"type": "n8n-nodes-base.set",
"typeVersion": 3.4,
"position": [
-680,
500
],
"id": "80407282-3e1f-4299-acf5-46208a88dc3a",
"name": "get_related_searches_mobile"
},
{
"parameters": {
"assignments": {
"assignments": [
{
"id": "0e112e59-ad43-4321-9de0-d1d502f98266",
"name": "related_searches",
"value": "={{ $json.relatedSearches }}",
"type": "array"
}
]
},
"options": {}
},
"type": "n8n-nodes-base.set",
"typeVersion": 3.4,
"position": [
-680,
1760
],
"id": "95d4baed-a22f-40d7-aaa8-07f2927cc83f",
"name": "get_related_searches_desktop"
},
{
"parameters": {
"maxItems": 3
},
"type": "n8n-nodes-base.limit",
"typeVersion": 1,
"position": [
-400,
1360
],
"id": "7731c85d-1eee-4358-9596-8ec3352dbc6a",
"name": "top_3_by_mobile"
},
{
"parameters": {
"maxItems": 3
},
"type": "n8n-nodes-base.limit",
"typeVersion": 1,
"position": [
-400,
1560
],
"id": "46a784cc-9522-449e-8401-9b8624311cca",
"name": "top_3_by_desktop"
},
{
"parameters": {},
"type": "n8n-nodes-base.merge",
"typeVersion": 3,
"position": [
-180,
1360
],
"id": "307fc94b-5d8f-489d-933d-68d219625ad2",
"name": "get_urls_to_crawl"
},
{
"parameters": {
"fieldToSplitOut": "organic_results",
"options": {}
},
"type": "n8n-nodes-base.splitOut",
"typeVersion": 1,
"position": [
40,
1360
],
"id": "0f11d76a-f581-4ca2-9e74-07f2ed377d63",
"name": "Split Out"
},
{
"parameters": {
"compare": "selectedFields",
"fieldsToCompare": "link",
"options": {}
},
"type": "n8n-nodes-base.removeDuplicates",
"typeVersion": 2,
"position": [
260,
1360
],
"id": "7aa14696-7e34-4f83-a55f-50e3f6de2a7c",
"name": "Remove Duplicates"
},
{
"parameters": {
"maxItems": 5
},
"type": "n8n-nodes-base.limit",
"typeVersion": 1,
"position": [
500,
1360
],
"id": "42822b6c-1853-4124-bd48-63926e8c961a",
"name": "Limit"
},
{
"parameters": {
"fieldToSplitOut": "link, snippet, position, title",
"options": {}
},
"type": "n8n-nodes-base.splitOut",
"typeVersion": 1,
"position": [
700,
1360
],
"id": "c333051f-5237-43c6-a05d-672a600bd5e3",
"name": "get_links"
},
{
"parameters": {
"assignments": {
"assignments": [
{
"id": "4eec2c04-b519-4c06-a911-bb7780b776b5",
"name": "title",
"value": "={{ $json.metadata.title }}",
"type": "string"
},
{
"id": "0eac8d67-34d9-4809-92fc-69f47381e104",
"name": "description",
"value": "={{ $json.metadata.description }}",
"type": "string"
},
{
"id": "58b74b11-74f8-42e3-b68f-a2aa4c380578",
"name": "url",
"value": "={{ $json.metadata['og:url']}}",
"type": "string"
},
{
"id": "f57680c0-48ed-492f-a1d9-508a0c996198",
"name": "content",
"value": "={{ $json.markdown }}",
"type": "string"
}
]
},
"options": {}
},
"type": "n8n-nodes-base.set",
"typeVersion": 3.4,
"position": [
800,
1580
],
"id": "9c18cb01-e226-48d9-beb9-fa0cdc02ae29",
"name": "get_metadata"
},
{
"parameters": {
"operation": "append",
"documentId": {
"__rl": true,
"value": "1Wdynwg7WmGldyWOd8r-mf9VjnCHJQtHosxJskNLfb2A",
"mode": "list",
"cachedResultName": "SERP Analysis (Serper)",
"cachedResultUrl": ""
},
"sheetName": {
"__rl": true,
"value": "gid=0",
"mode": "list",
"cachedResultName": "SERP Analysis",
"cachedResultUrl": ""
},
"columns": {
"mappingMode": "defineBelow",
"value": {
"position": "={{ $('Loop Over Items').item.json.position }}",
"title": "={{ $('Loop Over Items').item.json.title }}",
"link": "={{ $('Loop Over Items').item.json.link }}",
"snippet": "={{ $('Loop Over Items').item.json.snippet }}",
"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: \"\" }}",
"relevant_long_tail_keywords": "={{ $json.message.content.relevant_long_tail_keywords ? $json.message.content.relevant_long_tail_keywords: \"\" }}",
"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: \"\" }}",
"short_summary": "={{ $json.message.content.short_summary ? $json.message.content.short_summary: \"\" }}"
},
"matchingColumns": [],
"schema": [
{
"id": "position",
"displayName": "position",
"required": false,
"defaultMatch": false,
"display": true,
"type": "string",
"canBeUsedToMatch": true
},
{
"id": "title",
"displayName": "title",
"required": false,
"defaultMatch": false,
"display": true,
"type": "string",
"canBeUsedToMatch": true
},
{
"id": "link",
"displayName": "link",
"required": false,
"defaultMatch": false,
"display": true,
"type": "string",
"canBeUsedToMatch": true
},
{
"id": "snippet",
"displayName": "snippet",
"required": false,
"defaultMatch": false,
"display": true,
"type": "string",
"canBeUsedToMatch": true
},
{
"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": [
1460,
1580
],
"id": "6c9c8793-2d05-4897-b7c5-3f2a8c31e2bf",
"name": "insert_serp_analysis",
"credentials": {
"googleSheetsOAuth2Api": {
"name": "<your credential>"
}
}
},
{
"parameters": {
"options": {}
},
"type": "n8n-nodes-base.splitInBatches",
"typeVersion": 3,
"position": [
1120,
1360
],
"id": "ddab64b7-027e-4ed9-b5d2-3c5cd08aa7f7",
"name": "Loop Over Items"
},
{
"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": {
"temperature": 0
}
},
"type": "@n8n/n8n-nodes-langchain.openAi",
"typeVersion": 1.8,
"position": [
1140,
1580
],
"id": "09bf9148-59a2-4a15-ba1e-b93a04e366a6",
"name": "Analyze Content",
"credentials": {
"openAiApi": {
"name": "<your credential>"
}
}
},
{
"parameters": {},
"type": "n8n-nodes-base.merge",
"typeVersion": 3,
"position": [
-420,
500
],
"id": "3770a01b-51c7-4799-a4dc-bccd53c36f76",
"name": "merge_related_searches"
},
{
"parameters": {},
"type": "n8n-nodes-base.merge",
"typeVersion": 3,
"position": [
-400,
900
],
"id": "2833526f-c8e7-4962-9404-1c88d589d030",
"name": "merge_faqs"
},
{
"parameters": {
"compare": "selectedFields",
"fieldsToCompare": "query",
"options": {}
},
"type": "n8n-nodes-base.removeDuplicates",
"typeVersion": 2,
"position": [
20,
500
],
"id": "c0f3b915-532f-4c2d-8e50-33bc5b3cc44c",
"name": "remove_duplicates_in_related_searches"
},
{
"parameters": {
"compare": "selectedFields",
"fieldsToCompare": "faqs",
"options": {}
},
"type": "n8n-nodes-base.removeDuplicates",
"typeVersion": 2,
"position": [
40,
900
],
"id": "60b88dd3-d1af-451d-8eca-e244f6fdc38e",
"name": "removed_duplicates_in_faqs"
},
{
"parameters": {
"method": "POST",
"url": "https://scrape.serper.dev",
"authentication": "genericCredentialType",
"genericAuthType": "httpHeaderAuth",
"sendQuery": true,
"queryParameters": {
"parameters": [
{
"name": "url",
"value": "={{ $json.link }}"
},
{
"name": "includeMarkdown",
"value": "true"
}
]
},
"sendHeaders": true,
"headerParameters": {
"parameters": [
{
"name": "Content-Type",
"value": "application/json"
}
]
},
"options": {}
},
"type": "n8n-nodes-base.httpRequest",
"typeVersion": 4.2,
"position": [
580,
1580
],
"id": "d3de725a-c32d-44e3-9925-b97aa291689e",
"name": "scrape_url",
"credentials": {
"httpHeaderAuth": {
"name": "<your credential>"
}
}
}
],
"connections": {
"On form submission": {
"main": [
[
{
"node": "get_mobile_serp_results",
"type": "main",
"index": 0
},
{
"node": "get_desktop_serp_results",
"type": "main",
"index": 0
}
]
]
},
"get_mobile_serp_results": {
"main": [
[
{
"node": "clean_results_mobile",
"type": "main",
"index": 0
}
]
]
},
"get_questions": {
"main": [
[
{
"node": "removed_duplicates_in_faqs",
"type": "main",
"index": 0
}
]
]
},
"get_desktop_serp_results": {
"main": [
[
{
"node": "clean_results_desktop",
"type": "main",
"index": 0
}
]
]
},
"get_queries": {
"main": [
[
{
"node": "remove_duplicates_in_related_searches",
"type": "main",
"index": 0
}
]
]
},
"get_faq_mobile": {
"main": [
[
{
"node": "merge_faqs",
"type": "main",
"index": 0
}
]
]
},
"get_organic_results_mobile": {
"main": [
[
{
"node": "top_3_by_mobile",
"type": "main",
"index": 0
}
]
]
},
"get_organic_results_desktop": {
"main": [
[
{
"node": "top_3_by_desktop",
"type": "main",
"index": 0
}
]
]
},
"get_faq_desktop": {
"main": [
[
{
"node": "merge_faqs",
"type": "main",
"index": 1
}
]
]
},
"clean_results_mobile": {
"main": [
[
{
"node": "get_related_searches_mobile",
"type": "main",
"index": 0
},
{
"node": "get_organic_results_mobile",
"type": "main",
"index": 0
},
{
"node": "get_faq_mobile",
"type": "main",
"index": 0
}
]
]
},
"clean_results_desktop": {
"main": [
[
{
"node": "get_organic_results_desktop",
"type": "main",
"index": 0
},
{
"node": "get_faq_desktop",
"type": "main",
"index": 0
},
{
"node": "get_related_searches_desktop",
"type": "main",
"index": 0
}
]
]
},
"get_related_searches_mobile": {
"main": [
[
{
"node": "merge_related_searches",
"type": "main",
"index": 0
}
]
]
},
"get_related_searches_desktop": {
"main": [
[
{
"node": "merge_related_searches",
"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
}
]
]
},
"Split Out": {
"main": [
[
{
"node": "Remove Duplicates",
"type": "main",
"index": 0
}
]
]
},
"Remove Duplicates": {
"main": [
[
{
"node": "Limit",
"type": "main",
"index": 0
}
]
]
},
"Limit": {
"main": [
[
{
"node": "get_links",
"type": "main",
"index": 0
}
]
]
},
"get_links": {
"main": [
[
{
"node": "Loop Over Items",
"type": "main",
"index": 0
}
]
]
},
"get_metadata": {
"main": [
[
{
"node": "Analyze Content",
"type": "main",
"index": 0
}
]
]
},
"insert_serp_analysis": {
"main": [
[
{
"node": "Loop Over Items",
"type": "main",
"index": 0
}
]
]
},
"Loop Over Items": {
"main": [
[],
[
{
"node": "scrape_url",
"type": "main",
"index": 0
}
]
]
},
"Analyze Content": {
"main": [
[
{
"node": "insert_serp_analysis",
"type": "main",
"index": 0
}
]
]
},
"merge_related_searches": {
"main": [
[
{
"node": "get_queries",
"type": "main",
"index": 0
}
]
]
},
"merge_faqs": {
"main": [
[
{
"node": "get_questions",
"type": "main",
"index": 0
}
]
]
},
"remove_duplicates_in_related_searches": {
"main": [
[
{
"node": "insert_related_searches",
"type": "main",
"index": 0
}
]
]
},
"removed_duplicates_in_faqs": {
"main": [
[
{
"node": "insert_faqs",
"type": "main",
"index": 0
}
]
]
},
"scrape_url": {
"main": [
[
{
"node": "get_metadata",
"type": "main",
"index": 0
}
]
]
}
},
"active": false,
"settings": {
"executionOrder": "v1"
},
"versionId": "906cd459-23f1-4cad-bd4e-0c2e68b0c01d",
"meta": {
"templateCredsSetupCompleted": true
},
"id": "GC4Ynw42APmu86h8",
"tags": [
{
"createdAt": "2025-03-02T12:05:53.836Z",
"updatedAt": "2025-03-02T12:05:53.836Z",
"id": "utwtHTzeqUdktrxJ",
"name": "SEO"
},
{
"createdAt": "2025-03-29T14:05:42.024Z",
"updatedAt": "2025-03-29T14:05:42.024Z",
"id": "FTx17qhhXpcrm35X",
"name": "Template"
}
]
}
Credentials you'll need
Each integration node will prompt for credentials when you import. We strip credential IDs before publishing — you'll add your own.
googleSheetsOAuth2ApihttpHeaderAuthopenAiApi
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 insights by analysing search engine results pages for any keyword you submit via a simple form, empowering marketers and SEO specialists to uncover competitor strategies and content opportunities without manual effort. It fetches both mobile and desktop results using Serper's API, extracts related searches and FAQs, then leverages OpenAI to generate actionable summaries before logging everything into Google Sheets for easy review. The pivotal step involves OpenAI's intelligent processing of raw SERP data into tailored recommendations, saving hours of sifting through results.
Use this when conducting quick keyword research or auditing campaign performance, especially for e-commerce sites tracking seasonal trends. Avoid it for high-volume, real-time monitoring, as it's optimised for on-demand analysis rather than continuous scraping. Common variations include swapping OpenAI for custom AI prompts to focus on specific metrics like local search rankings or integrating additional tools for backlink extraction.
About this workflow
Template - SERP Analysis (Serper). Uses formTrigger, httpRequest, googleSheets, openAi. Event-driven trigger; 36 nodes.
Source: https://github.com/Marvomatic/n8n-templates/blob/main/serp-analysis/serp_analysis_serper.json — original creator credit. Request a take-down →
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