Ian Kerins 
This workflow corresponds to n8n.io template #10563 — we link there as the canonical source.
This workflow follows the Google Sheets → Slack 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": "r5wvaRaYTgvWjmmK",
"meta": {
"templateCredsSetupCompleted": true
},
"name": "Walmart Scraping with ScrapeOps",
"tags": [
{
"id": "tOnkaXwaFBiEoFQm",
"name": "price-tracking",
"createdAt": "2025-11-06T11:10:23.912Z",
"updatedAt": "2025-11-06T11:10:23.912Z"
},
{
"id": "yh7XX4GObvd3np2S",
"name": "google sheets",
"createdAt": "2025-11-06T11:10:29.979Z",
"updatedAt": "2025-11-06T11:10:29.979Z"
},
{
"id": "lZKSh2IoxHklnOUw",
"name": "ScrapeOps",
"createdAt": "2025-10-20T20:27:13.410Z",
"updatedAt": "2025-10-20T20:27:13.410Z"
},
{
"id": "tDTf2TyCxgErkuae",
"name": "walmart-scraping",
"createdAt": "2025-10-20T20:27:13.542Z",
"updatedAt": "2025-10-20T20:27:13.542Z"
}
],
"nodes": [
{
"id": "840b4f5f-5dec-45dc-88c7-9ccf5b7c42ff",
"name": "Sticky Note1",
"type": "n8n-nodes-base.stickyNote",
"position": [
-176,
176
],
"parameters": {
"width": 576,
"height": 480,
"content": "### \ud83d\uded2 Walmart Product Scraper\n\nThis workflow automatically scrapes product data from Walmart search results and saves it to Google Sheets for price tracking and analysis.\n\n### How it works\n1. **Schedule**: Runs automatically every 4 hours.\n2. **Search**: Defines a search term (default: \"Storage\").\n3. **Scrape**: Uses **ScrapeOps** to fetch and parse Walmart search results.\n4. **Process**: Cleans data and filters invalid items.\n5. **Save**: Appends to **Google Sheets** and notifies **Slack**.\n\n### Setup steps\n1. **ScrapeOps**: Add your free API key in the **[ScrapeOps](https://scrapeops.io/app/register/n8n)** node credentials.\n2. **Google Sheets**:\n - Duplicate this [Template Sheet](https://docs.google.com/spreadsheets/d/1WbIzWaXjuQeTxrn_C7PjGMNNibUyLIgY2hdeWh3gOLA/edit?gid=0#gid=0).\n - Connect your account and select the new sheet in the **Save to Google Sheets** node.\n3. **Slack**: Connect your account or remove the node."
},
"typeVersion": 1
},
{
"id": "78771e7f-9449-476a-bcf0-3532b45a76d0",
"name": "ScrapeOps Parser",
"type": "@scrapeops/n8n-nodes-scrapeops.ScrapeOps",
"position": [
1136,
424
],
"parameters": {
"apiType": "parserApi",
"parserUrl": "=https://www.walmart.com/search?q= {{ $('Set Search Parameters').item.json.search_term }}",
"parserHtml": "={{ $json }}",
"parserDomain": "walmart"
},
"credentials": {
"scrapeOpsApi": {
"name": "<your credential>"
}
},
"typeVersion": 1
},
{
"id": "87879598-0fdd-477a-aae8-3aa60efc47a3",
"name": "ScrapeOps Proxy",
"type": "@scrapeops/n8n-nodes-scrapeops.ScrapeOps",
"position": [
912,
424
],
"parameters": {
"url": "=https://www.walmart.com/search?q= {{ $json.search_term }}",
"advancedOptions": {}
},
"credentials": {
"scrapeOpsApi": {
"name": "<your credential>"
}
},
"typeVersion": 1
},
{
"id": "c65d4eea-6fcc-40fa-b046-20813e314f9f",
"name": "Add Metadata",
"type": "n8n-nodes-base.set",
"position": [
1360,
424
],
"parameters": {
"fields": {
"values": [
{
"name": "= timestamp",
"stringValue": "={{ new Date().toISOString() }}"
},
{
"name": "search_term",
"stringValue": "={{ $('Set Search Parameters').item.json.search_term }}"
},
{
"name": "=total_products_found",
"stringValue": "={{ $json.data.searchMetadata.totalResults || 0}}"
},
{
"name": "=products",
"stringValue": "={{ $json.data.products }}"
},
{
"name": "=url_scraped",
"stringValue": "={{ $json.url }}"
},
{
"name": "=data_coverage",
"stringValue": "={{ $json.data_coverage_percentage }}"
}
]
},
"options": {}
},
"typeVersion": 3.2
},
{
"id": "9635f959-cf12-460f-870c-582220fbb991",
"name": "Save to Google Sheets",
"type": "n8n-nodes-base.googleSheets",
"position": [
2256,
352
],
"parameters": {
"columns": {
"value": {
"saving": "={{\n $json.price.match(/was[^\\d]*(\\d+\\.\\d+)/i)\n ? Number(($json.price.match(/was[^\\d]*(\\d+\\.\\d+)/i) || [\"\",\"0\"])[1]) \n - Number(($json.price.match(/(?:current price|now)[^\\d]*(\\d+\\.\\d+)/i) || [\"\",\"0\"])[1])\n : 0\n}}\n",
"Image URL": "={{ $json.image_url }}",
"Product URL": "={{$json.product_url || $json.url }}",
"Search Term": "={{ $('Add Metadata').item.json.search_term }}",
"Is Sponsored": "={{ $json.is_sponsored }}",
"Product Name": "={{ $json.product_name || $json.title }}",
"Review Count": "={{ $json.review_count || $json.reviews}}",
"Price Display": "=${{ Number(($json.price.match(/(?:current price|now)[^\\d]*(\\d+\\.\\d+)/i) || [\"\",\"0\"])[1]) }}",
"Price Numeric": "={{ Number(($json.price.match(/(?:current price|now)[^\\d]*(\\d+\\.\\d+)/i) || [\"\",\"0\"])[1]) }}",
"Popularity Level": "={{ parseInt($json.review_count ) > 1000 ? 'High' : (parseInt($json.review_count || 0) > 100 ? 'Medium' : (parseInt($json.review_count || 0) > 10 ? 'Low' : 'Very Low')) }}",
"Scraped Timestamp": "={{ $json.scraped_at }}"
},
"schema": [
{
"id": "Product Name",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "Product Name",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Price Display",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "Price Display",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Price Numeric",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "Price Numeric",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Review Count",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "Review Count",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Popularity Level",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "Popularity Level",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Is Sponsored",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "Is Sponsored",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Product URL",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "Product URL",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Image URL",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "Image URL",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Search Term",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "Search Term",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Scraped Timestamp",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "Scraped Timestamp",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "saving",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "saving",
"defaultMatch": false,
"canBeUsedToMatch": true
}
],
"mappingMode": "defineBelow",
"matchingColumns": [],
"attemptToConvertTypes": false,
"convertFieldsToString": false
},
"options": {},
"operation": "append",
"sheetName": {
"__rl": true,
"mode": "list",
"value": 460852058,
"cachedResultUrl": "https://docs.google.com/spreadsheets/d/1WbIzWaXjuQeTxrn_C7PjGMNNibUyLIgY2hdeWh3gOLA/edit#gid=460852058",
"cachedResultName": "Sheet1"
},
"documentId": {
"__rl": true,
"mode": "url",
"value": "https://docs.google.com/spreadsheets/d/1WbIzWaXjuQeTxrn_C7PjGMNNibUyLIgY2hdeWh3gOLA/edit?gid=0#gid=0"
}
},
"credentials": {
"googleSheetsOAuth2Api": {
"name": "<your credential>"
}
},
"typeVersion": 4.4
},
{
"id": "338cf225-db7f-46dc-87bd-ed90b8931d0f",
"name": "Sticky Note",
"type": "n8n-nodes-base.stickyNote",
"position": [
416,
296
],
"parameters": {
"color": 7,
"width": 416,
"height": 288,
"content": "## 1. Setup & Config\nDefine your search keywords and schedule frequency."
},
"typeVersion": 1
},
{
"id": "537940b2-b718-4aa1-8e0d-d10a039f5750",
"name": "Sticky Note2",
"type": "n8n-nodes-base.stickyNote",
"position": [
872,
296
],
"parameters": {
"color": 7,
"width": 400,
"height": 288,
"content": "## 2. Scraping Pipeline\n[Fetches Walmart](https://scrapeops.io/docs/n8n/overview/) search results and parses the HTML using ScrapeOps."
},
"typeVersion": 1
},
{
"id": "5932945d-e0b8-42ba-a99e-e926d51a63e7",
"name": "Sticky Note3",
"type": "n8n-nodes-base.stickyNote",
"position": [
1320,
296
],
"parameters": {
"color": 7,
"width": 848,
"height": 288,
"content": "## 3. Data Processing\nStandardizes product data, splits arrays, and filters out invalid results."
},
"typeVersion": 1
},
{
"id": "135308a1-f69a-471f-93f7-5445528925a8",
"name": "Sticky Note4",
"type": "n8n-nodes-base.stickyNote",
"position": [
2184,
152
],
"parameters": {
"color": 7,
"width": 464,
"height": 432,
"content": "## 4. Storage & Alerts\nSaves valid data to Google Sheets and sends a summary to Slack."
},
"typeVersion": 1
},
{
"id": "cf41f4bf-605b-48a7-98ff-b72e347cf988",
"name": "Format Product Data",
"type": "n8n-nodes-base.set",
"position": [
1808,
424
],
"parameters": {
"fields": {
"values": [
{
"name": "product_name",
"stringValue": "={{ $json.name || $json.title || 'N/A' }}"
},
{
"name": "price",
"stringValue": "={{ $json.price || 'N/A' }}"
},
{
"name": "image_url",
"stringValue": "={{ $json.image?.url || $json.image || 'N/A' }}"
},
{
"name": "product_url",
"stringValue": "={{ $json.url || 'N/A' }}"
},
{
"name": "review_count",
"type": "numberValue",
"numberValue": "={{ $json.rating?.reviewCount || $json.reviews || 0 }}"
},
{
"name": "rating",
"stringValue": "={{ $json.rating?.ratingValue || $json.rating || 'N/A' }}"
},
{
"name": "saving",
"stringValue": "={{ $json.saving || 'N/A' }}"
},
{
"name": "is_sponsored",
"stringValue": "={{ $json.isSponsored || $json.sponsored || false }}"
},
{
"name": "=scraped_at",
"stringValue": "={{ $('Set Search Parameters').item.json.Month }}/{{ $('Set Search Parameters').item.json[\"Day of month\"] }}/{{ $('Set Search Parameters').item.json.Year }}"
},
{
"name": "search_term_used",
"stringValue": "={{ $('Add Metadata').item.json.search_term }}"
}
]
},
"options": {}
},
"typeVersion": 3.2
},
{
"id": "ece9febe-78af-47d6-a7a5-04c2428ae9df",
"name": "Split Products Array",
"type": "n8n-nodes-base.code",
"position": [
1584,
424
],
"parameters": {
"jsCode": "// Parse the products array and return individual product items\nconst inputData = $input.first().json;\n\n// Try multiple possible field names (with and without = prefix)\nconst productsArray = inputData['=products'] || inputData.products || inputData.data?.products || null;\n\n// Handle both string and array cases\nlet products;\nif (!productsArray) {\n return [];\n}\n\nif (typeof productsArray === 'string') {\n try {\n products = JSON.parse(productsArray);\n } catch (e) {\n return [];\n }\n} else {\n products = productsArray;\n}\n\n// Ensure products is an array\nif (!Array.isArray(products)) {\n return [];\n}\n\n// Filter out any null or undefined products\n// Note: Walmart products use 'name' field, not 'title'\nconst validProducts = products.filter(product => product && (product.name || product.title));\n\nreturn validProducts.map(product => ({ json: product }));"
},
"typeVersion": 2
},
{
"id": "e791c0f1-5c60-48a7-9166-a52feae77a34",
"name": "Set Search Parameters",
"type": "n8n-nodes-base.set",
"position": [
688,
424
],
"parameters": {
"fields": {
"values": [
{
"name": "search_term",
"stringValue": "Raspberry Pi"
}
]
},
"options": {}
},
"typeVersion": 3.2
},
{
"id": "433ab6ca-5e97-4fe4-8a9e-c4cc2e32be5f",
"name": "Schedule Trigger",
"type": "n8n-nodes-base.scheduleTrigger",
"position": [
464,
424
],
"parameters": {
"rule": {
"interval": [
{
"field": "hours",
"hoursInterval": 4
}
]
}
},
"typeVersion": 1.1
},
{
"id": "0f0a0663-34d9-4521-91ff-8e3b62e6a0c9",
"name": "Filter Valid Products",
"type": "n8n-nodes-base.if",
"position": [
2032,
424
],
"parameters": {
"options": {},
"conditions": {
"options": {
"version": 1,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"id": "valid_product_check",
"operator": {
"type": "string",
"operation": "notEquals"
},
"leftValue": "={{ $json.product_name }}",
"rightValue": "N/A"
},
{
"id": "valid_price_check",
"operator": {
"type": "string",
"operation": "notEquals"
},
"leftValue": "={{ $json.price }}",
"rightValue": "N/A"
}
]
}
},
"typeVersion": 2
},
{
"id": "a539163d-ee9f-4995-be57-aa7e0b45dc14",
"name": "Send Slack Summary",
"type": "n8n-nodes-base.slack",
"position": [
2480,
424
],
"parameters": {
"text": "\ud83d\uded2 Walmart Scrape Complete! | Results Summary: \u2022https://docs.google.com/spreadsheets/d/1WbIzWaXjuQeTxrn_C7PjGMNNibUyLIgY2hdeWh3gOLA/edit?gid=460852058#gid=460852058",
"select": "channel",
"channelId": {
"__rl": true,
"mode": "name",
"value": "#testnoor"
},
"otherOptions": {
"includeLinkToWorkflow": true
}
},
"credentials": {
"slackApi": {
"name": "<your credential>"
}
},
"executeOnce": true,
"typeVersion": 2.1
}
],
"active": false,
"settings": {
"executionOrder": "v1"
},
"versionId": "81faa070-d36e-4525-96e3-69d4865b9f64",
"connections": {
"Add Metadata": {
"main": [
[
{
"node": "Split Products Array",
"type": "main",
"index": 0
}
]
]
},
"ScrapeOps Proxy": {
"main": [
[
{
"node": "ScrapeOps Parser",
"type": "main",
"index": 0
}
]
]
},
"Schedule Trigger": {
"main": [
[
{
"node": "Set Search Parameters",
"type": "main",
"index": 0
}
]
]
},
"ScrapeOps Parser": {
"main": [
[
{
"node": "Add Metadata",
"type": "main",
"index": 0
}
]
]
},
"Format Product Data": {
"main": [
[
{
"node": "Filter Valid Products",
"type": "main",
"index": 0
}
]
]
},
"Split Products Array": {
"main": [
[
{
"node": "Format Product Data",
"type": "main",
"index": 0
}
]
]
},
"Filter Valid Products": {
"main": [
[
{
"node": "Save to Google Sheets",
"type": "main",
"index": 0
}
],
[
{
"node": "Send Slack Summary",
"type": "main",
"index": 0
}
]
]
},
"Save to Google Sheets": {
"main": [
[
{
"node": "Send Slack Summary",
"type": "main",
"index": 0
}
]
]
},
"Set Search Parameters": {
"main": [
[
{
"node": "ScrapeOps Proxy",
"type": "main",
"index": 0
}
]
]
}
}
}
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.
googleSheetsOAuth2ApiscrapeOpsApislackApi
Pro
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About this workflow
This n8n template automates Walmart product discovery and sends clean results to Google Sheets on a fixed schedule (default: every 4 hours). It uses ScrapeOps Proxy API for resilient page fetches (with JS render + scroll) and ScrapeOps Parser API for structured data extraction…
Source: https://n8n.io/workflows/10563/ — original creator credit. Request a take-down →
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