24 Track Satisfaction Scores
ๆ่ฟฐ
ๅ็ฑป
๐ข Marketing๐ค AI & Machine Learning
ไฝฟ็จ็่็น
n8n-nodes-base.setn8n-nodes-base.coden8n-nodes-base.stickyNoten8n-nodes-base.stickyNoten8n-nodes-base.stickyNoten8n-nodes-base.stickyNoten8n-nodes-base.stickyNoten8n-nodes-base.stickyNoten8n-nodes-base.googleSheetsn8n-nodes-mcp.mcpClientTool
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่ดน
ๆต่ง้0
ๆๅๆดๆฐ11/28/2025
workflow.json
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"name": "24 Track Satisfaction Scores",
"tags": [],
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"name": "โฐ Run Weekly NPS Tracker",
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"text": "=Extract Customer reviews, Star ratings (1 to 5 stars), Comments (optional for deeper insight) and Date of review from the following url\n\nURL: {{ $json.url }}",
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"jsCode": "const reviews = items[0].json.output; // The agent output you provided\nlet promoters = 0;\nlet passives = 0;\nlet detractors = 0;\n\nfor (const review of reviews) {\n const starRating = review.rating;\n\n // Convert 1โ5 stars to 0โ10 scale\n const score = starRating * 2;\n\n // Categorize into NPS groups\n if (score >= 9) {\n promoters += 1;\n } else if (score >= 7) {\n passives += 1;\n } else {\n detractors += 1;\n }\n}\n\n// NPS Calculation\nconst total = promoters + passives + detractors;\nconst nps = total > 0 ? ((promoters - detractors) / total) * 100 : 0;\n\nreturn [\n {\n json: {\n totalResponses: total,\n promoters,\n passives,\n detractors,\n nps: Math.round(nps),\n message: `NPS Score is ${Math.round(nps)} based on ${total} responses.`\n }\n }\n];\n"
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"NPS": "={{ $json.nps }}",
"Passive": "={{ $json.passives }}",
"summary": "={{ $json.message }}",
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"Promoters": "={{ $json.promoters }}",
"Total Responses": "={{ $json.totalResponses }}"
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"content": "## ๐ถ **Section 1: Set the Target Survey Page**\n\n### ๐งฉ Nodes:\n\n* `โฐ Run Weekly NPS Tracker` *(Schedule Trigger)*\n* `โ๏ธ Set Survey Page URL` *(Edit Fields)*\n\n### ๐ What This Does:\n\nThis section controls **when** and **where** to begin.\n\n* The **Schedule Trigger** node is set to run automatically โ for example, **every Monday at 10 AM** โ to check the latest customer satisfaction reviews from a specific website like Trustpilot.\n* The **Edit Fields** node allows you to define or update the **target survey/review page URL** (e.g., Shopifyโs Trustpilot page). You can also set any other optional config like number of reviews, date range, etc.\n\n### ๐ง Why It Matters:\n\n* Allows **automation** instead of manual monitoring.\n* Keeps your system flexible โ you can easily change the source URL without editing the rest of the workflow.\n\n---\n\n"
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"content": "## ๐ค **Section 2: Scrape Reviews Using AI Agent**\n\n### ๐งฉ Nodes:\n\n* `๐ง Scrape Reviews with Agent (MCP)` *(AI Agent)*\n\n * ๐ `๐ฏ Prompt & Guide Agent` *(OpenAI Chat Model)*\n * ๐ `Execute Web Scrape (Bright Data)` *(MCP Client)*\n * ๐ฆ `Parse Agent Response` *(Structured Output Parser)*\n\n### ๐ What This Does:\n\nThis is where the **magic** happens โ using AI + Bright Data to fetch live reviews.\n\n* The **AI Agent** launches a **headless browser** via Bright Data's Mobile Carrier Proxy (MCP), mimicking a real user to visit the review site.\n* Itโs guided by an OpenAI model that understands natural prompts, like:\n *โExtract customer ratings, comments, and dates from Trustpilotโ.*\n* After scraping, the **Structured Output Parser** turns unstructured HTML into clean JSON data โ a list of reviews like:\n\n ```json\n { \"rating\": 2, \"comment\": \"...\" }\n ```\n\n### ๐ง Why It Matters:\n\n* Many review sites use JavaScript โ Bright Data MCP bypasses anti-bot detection.\n* No need to write complex scraping code โ just describe what you want in plain English.\n* This is your **data foundation** โ reliable, structured, fresh.\n\n---\n\n"
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"content": "## ๐ **Section 3: Analyze & Log NPS Results**\n\n### ๐งฉ Nodes:\n\n* `๐ Calculate NPS from Ratings` *(Code Function)*\n* `๐ Log NPS to Google Sheet` *(Google Sheets)*\n\n### ๐ What This Does:\n\nThis is your **data analysis and reporting** layer.\n\n* The **Function node** receives the scraped review data, calculates **Net Promoter Score (NPS)** by:\n\n * Mapping 1โ5 star reviews into a 0โ10 scale.\n * Categorizing users as:\n\n * ๐ **Promoters** (9โ10)\n * ๐ **Passives** (7โ8)\n * ๐ **Detractors** (0โ6)\n * Calculating:\n **NPS = (% Promoters โ % Detractors) ร 100**\n\n* The **Google Sheets node** appends this NPS result to a spreadsheet with columns like:\n\n ```\n Date | Total Responses | Promoters | Passives | Detractors | NPS Score\n ```\n\n\n"
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"content": "=======================================\n WORKFLOW ASSISTANCE\n=======================================\nFor any questions or support, please contact:\n [email protected]\n\nExplore more tips and tutorials here:\n - YouTube: https://www.youtube.com/@YaronBeen/videos\n - LinkedIn: https://www.linkedin.com/in/yaronbeen/\n=======================================\n"
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"content": "# ๐ **Track Satisfaction Scores Workflow**\n\n### โจ *Automatically scrape survey reviews, calculate Net Promoter Score (NPS), and log results to Google Sheets for leadership insights.*\n\n---\n\n## ๐ถ **Section 1: Set the Target Survey Page**\n\n### ๐งฉ Nodes:\n\n* `โฐ Run Weekly NPS Tracker` *(Schedule Trigger)*\n* `โ๏ธ Set Survey Page URL` *(Edit Fields)*\n\n### ๐ What This Does:\n\nThis section controls **when** and **where** to begin.\n\n* The **Schedule Trigger** node is set to run automatically โ for example, **every Monday at 10 AM** โ to check the latest customer satisfaction reviews from a specific website like Trustpilot.\n* The **Edit Fields** node allows you to define or update the **target survey/review page URL** (e.g., Shopifyโs Trustpilot page). You can also set any other optional config like number of reviews, date range, etc.\n\n### ๐ง Why It Matters:\n\n* Allows **automation** instead of manual monitoring.\n* Keeps your system flexible โ you can easily change the source URL without editing the rest of the workflow.\n\n---\n\n## ๐ค **Section 2: Scrape Reviews Using AI Agent**\n\n### ๐งฉ Nodes:\n\n* `๐ง Scrape Reviews with Agent (MCP)` *(AI Agent)*\n\n * ๐ `๐ฏ Prompt & Guide Agent` *(OpenAI Chat Model)*\n * ๐ `Execute Web Scrape (Bright Data)` *(MCP Client)*\n * ๐ฆ `Parse Agent Response` *(Structured Output Parser)*\n\n### ๐ What This Does:\n\nThis is where the **magic** happens โ using AI + Bright Data to fetch live reviews.\n\n* The **AI Agent** launches a **headless browser** via Bright Data's Mobile Carrier Proxy (MCP), mimicking a real user to visit the review site.\n* Itโs guided by an OpenAI model that understands natural prompts, like:\n *โExtract customer ratings, comments, and dates from Trustpilotโ.*\n* After scraping, the **Structured Output Parser** turns unstructured HTML into clean JSON data โ a list of reviews like:\n\n ```json\n { \"rating\": 2, \"comment\": \"...\" }\n ```\n\n### ๐ง Why It Matters:\n\n* Many review sites use JavaScript โ Bright Data MCP bypasses anti-bot detection.\n* No need to write complex scraping code โ just describe what you want in plain English.\n* This is your **data foundation** โ reliable, structured, fresh.\n\n---\n\n## ๐ **Section 3: Analyze & Log NPS Results**\n\n### ๐งฉ Nodes:\n\n* `๐ Calculate NPS from Ratings` *(Code Function)*\n* `๐ Log NPS to Google Sheet` *(Google Sheets)*\n\n### ๐ What This Does:\n\nThis is your **data analysis and reporting** layer.\n\n* The **Function node** receives the scraped review data, calculates **Net Promoter Score (NPS)** by:\n\n * Mapping 1โ5 star reviews into a 0โ10 scale.\n * Categorizing users as:\n\n * ๐ **Promoters** (9โ10)\n * ๐ **Passives** (7โ8)\n * ๐ **Detractors** (0โ6)\n * Calculating:\n **NPS = (% Promoters โ % Detractors) ร 100**\n\n* The **Google Sheets node** appends this NPS result to a spreadsheet with columns like:\n\n ```\n Date | Total Responses | Promoters | Passives | Detractors | NPS Score\n ```\n\n### ๐ง Why It Matters:\n\n* Helps your leadership team **track trends** over time.\n* Spot dips in customer satisfaction before they turn into churn.\n* All automated โ no spreadsheets, no manual scraping, just insights.\n\n---\n\n## ๐ Final Output Example:\n\n| Date | Responses | Promoters | Detractors | NPS |\n| ---------- | --------- | --------- | ---------- | ---- |\n| 2025-07-06 | 4 | 0 | 4 | -100 |\n\n๐ก Imagine your manager getting this **every Monday morning** โ it's like having a customer success analyst on autopilot!\n\n---\n\n## โ
Takeaways for Beginners\n\n| ๐ง Feature | ๐ Benefit |\n| ---------- | ---------------------------------------------------------- |\n| No-code | You didnโt write a single scraper โ just configured blocks |\n| Dynamic | URLs, prompts, and responses are editable |\n| Scalable | You can run this for 1 site or 100 with minor tweaks |\n| Actionable | Leadership gets real-time feedback in sheets or Slack |\n\n---\n\n"
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"content": "## Iโll receive a tiny commission if you join Bright Data through this linkโthanks for fueling more free content!\n\n### https://get.brightdata.com/1tndi4600b25"
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