HumationHuman + Automation
Back to Catalog

Analyze customer feedback with Azure GPT-4, Jira tasks & Outlook reports from Monday.com

Rahul JoshiRahul Joshi
85 views
2/3/2026
System TasksCronSchedulerAutomationShell Command
Official Page

Description

Automate customer feedback analysis and action planning by integrating Monday.com, Azure OpenAI, Jira, Google Sheets, and Outlook. This workflow classifies customer feedback with AI, calculates business impact, creates Jira tasks for high-priority issues, and sends weekly performance summaries — turning raw feedback into actionable insights. 💬📈🤖

What This Template Does

  • Step 1: Triggers automatically every Monday at 9:00 AM to fetch new customer feedback from Monday.com. ⏰
  • Step 2: Normalizes and structures data into consistent fields (title, account, ARR, NPS, etc.). 🧩
  • Step 3: Uses Azure OpenAI GPT-4 to classify sentiment and identify feedback themes (e.g., “UI Design,” “App Crash”). 🧠
  • Step 4: Calculates a business impact score based on ARR, NPS delta, and sentiment weightings. ⚙️
  • Step 5: Creates Jira tasks for high-impact feedback items for product or engineering follow-up. 🎫
  • Step 6: Logs all feedback and impact scores into Google Sheets for analytics dashboards. 📊
  • Step 7: Generates a professional HTML report summarizing metrics, wins, and risks, then emails it via Outlook. 📧
  • Step 8: Sends automated error-alert emails if any node fails during execution. 🚨

Key Benefits

✅ Converts qualitative feedback into measurable business intelligence ✅ Identifies critical customer issues automatically using AI ✅ Reduces manual effort in triaging and prioritizing feedback ✅ Creates real-time visibility for product and CX teams ✅ Provides weekly executive summaries and performance insights

Features

  • Weekly scheduled trigger (every Monday 9 AM)
  • Monday.com data fetching and field normalization
  • Azure OpenAI GPT-4-based sentiment and theme detection
  • Impact scoring combining ARR + NPS + sentiment weighting
  • Jira issue creation with context-rich descriptions
  • Google Sheets logging for dashboards and historical records
  • Outlook HTML email reports for leadership visibility
  • Automated Gmail error-notification system

Requirements

  • Monday.com API credentials with board access
  • Azure OpenAI GPT-4 API credentials
  • Jira Software Cloud API credentials
  • Google Sheets OAuth2 credentials with edit permissions
  • Microsoft Outlook OAuth2 credentials for email delivery
  • Gmail OAuth2 credentials for error alerting

Target Audience

  • Product and CX teams analyzing customer sentiment
  • SaaS businesses tracking post-implementation feedback
  • Customer-success and support operations teams
  • Product managers prioritizing improvements based on impact
  • Leadership teams monitoring customer health and satisfaction

Step-by-Step Setup Instructions

1️⃣ Connect Monday.com API and update your boardId and groupId. 2️⃣ Configure Azure OpenAI GPT-4 credentials for the AI classifier. 3️⃣ Set Jira project ID and issue type for ticket creation. 4️⃣ Link Google Sheets and replace YOUR_SHEET_ID. 5️⃣ Connect Outlook OAuth2 and add recipient email for reports. 6️⃣ Configure Gmail OAuth2 for error alerts. 7️⃣ Adjust the cron expression (0 9 * * 1) to fit your timezone. 8️⃣ Test the workflow end-to-end with sample data. 9️⃣ Enable automation for seamless weekly feedback intelligence. ✅

Analyze Customer Feedback with Azure GPT-4, Jira Tasks & Outlook Reports from monday.com

This n8n workflow automates the process of analyzing customer feedback from monday.com, generating insights using Azure OpenAI's GPT-4, creating Jira tasks for actionable feedback, and sending summary reports via Outlook. It also includes robust error handling to ensure smooth operation.

What it does

This workflow streamlines your customer feedback loop by:

  1. Triggering on a Schedule: The workflow starts at predefined intervals (e.g., daily, weekly) to check for new feedback.
  2. Fetching Customer Feedback: It retrieves customer feedback data from a specified Google Sheet.
  3. Processing Feedback with AI: It uses an AI Agent powered by Azure OpenAI's GPT-4 to analyze the feedback. A Structured Output Parser ensures the AI's response is in a usable format.
  4. Creating Jira Tasks: Based on the AI analysis, it creates new tasks in Jira for feedback that requires action or further investigation.
  5. Generating Summary Reports: It compiles a summary of the analyzed feedback and any created Jira tasks.
  6. Sending Outlook Reports: The generated summary is then sent as an email report via Microsoft Outlook to relevant stakeholders.
  7. Error Handling: If any part of the workflow fails, an Error Trigger node is activated, allowing for custom error notification or logging (though the specific error handling actions are not defined in the provided JSON).

Prerequisites/Requirements

To use this workflow, you will need:

  • n8n Instance: A running n8n instance.
  • Google Sheets Account: To store and retrieve customer feedback.
  • Azure OpenAI Service: An Azure OpenAI deployment with access to a GPT-4 chat model.
  • Jira Software Account: To create and manage tasks.
  • Microsoft Outlook Account: To send email reports.
  • monday.com Account: (Implied by directory name, but not explicitly used in the provided JSON for data retrieval or modification. It's present as an app node, suggesting it might be used in a more complete version of the workflow.)
  • n8n Credentials: Configured credentials for Google Sheets, Azure OpenAI, Jira Software, and Microsoft Outlook.

Setup/Usage

  1. Import the Workflow: Import the provided JSON into your n8n instance.
  2. Configure Credentials:
    • Set up your Google Sheets credential to access the spreadsheet containing customer feedback.
    • Configure your Azure OpenAI Chat Model credential with your Azure OpenAI endpoint and API key, ensuring it points to a GPT-4 deployment.
    • Set up your Jira Software credential.
    • Configure your Microsoft Outlook credential.
    • (Optional) If you intend to use monday.com, set up your Monday.com credential.
  3. Customize Nodes:
    • Schedule Trigger (ID: 839): Adjust the schedule to your desired frequency for checking feedback.
    • Google Sheets (ID: 18): Configure this node to read data from your specific customer feedback spreadsheet and sheet.
    • AI Agent (ID: 1119): Review and adjust the agent's prompt and tools to accurately analyze your customer feedback.
    • Structured Output Parser (ID: 1179): Ensure the parser is configured to extract the desired structured information from the AI's output.
    • Jira Software (ID: 77): Customize the "Create" operation to map the AI-generated insights to appropriate Jira fields (e.g., summary, description, assignee, project).
    • Edit Fields (Set) (ID: 38): This node can be used to format or combine data before sending it to other services.
    • Microsoft Outlook (ID: 433): Configure the "Send Email" operation with the recipient list, subject, and email body, using data from previous nodes for the report content.
    • Code (ID: 834): This node allows for custom JavaScript logic. Review its code to understand or modify its functionality.
  4. Activate the Workflow: Once configured, activate the workflow to start automating your customer feedback analysis and reporting.

Related Templates

Track competitor SEO keywords with Decodo + GPT-4.1-mini + Google Sheets

This workflow automates competitor keyword research using OpenAI LLM and Decodo for intelligent web scraping. Who this is for SEO specialists, content strategists, and growth marketers who want to automate keyword research and competitive intelligence. Marketing analysts managing multiple clients or websites who need consistent SEO tracking without manual data pulls. Agencies or automation engineers using Google Sheets as an SEO data dashboard for keyword monitoring and reporting. What problem this workflow solves Tracking competitor keywords manually is slow and inconsistent. Most SEO tools provide limited API access or lack contextual keyword analysis. This workflow solves that by: Automatically scraping any competitor’s webpage with Decodo. Using OpenAI GPT-4.1-mini to interpret keyword intent, density, and semantic focus. Storing structured keyword insights directly in Google Sheets for ongoing tracking and trend analysis. What this workflow does Trigger — Manually start the workflow or schedule it to run periodically. Input Setup — Define the website URL and target country (e.g., https://dev.to, france). Data Scraping (Decodo) — Fetch competitor web content and metadata. Keyword Analysis (OpenAI GPT-4.1-mini) Extract primary and secondary keywords. Identify focus topics and semantic entities. Generate a keyword density summary and SEO strength score. Recommend optimization and internal linking opportunities. Data Structuring — Clean and convert GPT output into JSON format. Data Storage (Google Sheets) — Append structured keyword data to a Google Sheet for long-term tracking. Setup Prerequisites If you are new to Decode, please signup on this link visit.decodo.com n8n account with workflow editor access Decodo API credentials OpenAI API key Google Sheets account connected via OAuth2 Make sure to install the Decodo Community node. Create a Google Sheet Add columns for: primarykeywords, seostrengthscore, keyworddensity_summary, etc. Share with your n8n Google account. Connect Credentials Add credentials for: Decodo API credentials - You need to register, login and obtain the Basic Authentication Token via Decodo Dashboard OpenAI API (for GPT-4o-mini) Google Sheets OAuth2 Configure Input Fields Edit the “Set Input Fields” node to set your target site and region. Run the Workflow Click Execute Workflow in n8n. View structured results in your connected Google Sheet. How to customize this workflow Track Multiple Competitors → Use a Google Sheet or CSV list of URLs; loop through them using the Split In Batches node. Add Language Detection → Add a Gemini or GPT node before keyword analysis to detect content language and adjust prompts. Enhance the SEO Report → Expand the GPT prompt to include backlink insights, metadata optimization, or readability checks. Integrate Visualization → Connect your Google Sheet to Looker Studio for SEO performance dashboards. Schedule Auto-Runs → Use the Cron Node to run weekly or monthly for competitor keyword refreshes. Summary This workflow automates competitor keyword research using: Decodo for intelligent web scraping OpenAI GPT-4.1-mini for keyword and SEO analysis Google Sheets for live tracking and reporting It’s a complete AI-powered SEO intelligence pipeline ideal for teams that want actionable insights on keyword gaps, optimization opportunities, and content focus trends, without relying on expensive SEO SaaS tools.

Ranjan DailataBy Ranjan Dailata
161

Generate song lyrics and music from text prompts using OpenAI and Fal.ai Minimax

Spark your creativity instantly in any chat—turn a simple prompt like "heartbreak ballad" into original, full-length lyrics and a professional AI-generated music track, all without leaving your conversation. 📋 What This Template Does This chat-triggered workflow harnesses AI to generate detailed, genre-matched song lyrics (at least 600 characters) from user messages, then queues them for music synthesis via Fal.ai's minimax-music model. It polls asynchronously until the track is ready, delivering lyrics and audio URL back in chat. Crafts original, structured lyrics with verses, choruses, and bridges using OpenAI Submits to Fal.ai for melody, instrumentation, and vocals aligned to the style Handles long-running generations with smart looping and status checks Returns complete song package (lyrics + audio link) for seamless sharing 🔧 Prerequisites n8n account (self-hosted or cloud with chat integration enabled) OpenAI account with API access for GPT models Fal.ai account for AI music generation 🔑 Required Credentials OpenAI API Setup Go to platform.openai.com → API keys (sidebar) Click "Create new secret key" → Name it (e.g., "n8n Songwriter") Copy the key and add to n8n as "OpenAI API" credential type Test by sending a simple chat completion request Fal.ai HTTP Header Auth Setup Sign up at fal.ai → Dashboard → API Keys Generate a new API key → Copy it In n8n, create "HTTP Header Auth" credential: Name="Fal.ai", Header Name="Authorization", Header Value="Key [Your API Key]" Test with a simple GET to their queue endpoint (e.g., /status) ⚙️ Configuration Steps Import the workflow JSON into your n8n instance Assign OpenAI API credentials to the "OpenAI Chat Model" node Assign Fal.ai HTTP Header Auth to the "Generate Music Track", "Check Generation Status", and "Fetch Final Result" nodes Activate the workflow—chat trigger will appear in your n8n chat interface Test by messaging: "Create an upbeat pop song about road trips" 🎯 Use Cases Content Creators: YouTubers generating custom jingles for videos on the fly, streamlining production from idea to audio export Educators: Music teachers using chat prompts to create era-specific folk tunes for classroom discussions, fostering interactive learning Gift Personalization: Friends crafting anniversary R&B tracks from shared memories via quick chats, delivering emotional audio surprises Artist Brainstorming: Songwriters prototyping hip-hop beats in real-time during sessions, accelerating collaboration and iteration ⚠️ Troubleshooting Invalid JSON from AI Agent: Ensure the system prompt stresses valid JSON; test the agent standalone with a sample query Music Generation Fails (401/403): Verify Fal.ai API key has minimax-music access; check usage quotas in dashboard Status Polling Loops Indefinitely: Bump wait time to 45-60s for complex tracks; inspect fal.ai queue logs for bottlenecks Lyrics Under 600 Characters: Tweak agent prompt to enforce fuller structures like [V1][C][V2][B][C]; verify output length in executions

Daniel NkenchoBy Daniel Nkencho
601

Automate invoice processing with OCR, GPT-4 & Salesforce opportunity creation

PDF Invoice Extractor (AI) End-to-end pipeline: Watch Drive ➜ Download PDF ➜ OCR text ➜ AI normalize to JSON ➜ Upsert Buyer (Account) ➜ Create Opportunity ➜ Map Products ➜ Create OLI via Composite API ➜ Archive to OneDrive. --- Node by node (what it does & key setup) 1) Google Drive Trigger Purpose: Fire when a new file appears in a specific Google Drive folder. Key settings: Event: fileCreated Folder ID: google drive folder id Polling: everyMinute Creds: googleDriveOAuth2Api Output: Metadata { id, name, ... } for the new file. --- 2) Download File From Google Purpose: Get the file binary for processing and archiving. Key settings: Operation: download File ID: ={{ $json.id }} Creds: googleDriveOAuth2Api Output: Binary (default key: data) and original metadata. --- 3) Extract from File Purpose: Extract text from PDF (OCR as needed) for AI parsing. Key settings: Operation: pdf OCR: enable for scanned PDFs (in options) Output: JSON with OCR text at {{ $json.text }}. --- 4) Message a model (AI JSON Extractor) Purpose: Convert OCR text into strict normalized JSON array (invoice schema). Key settings: Node: @n8n/n8n-nodes-langchain.openAi Model: gpt-4.1 (or gpt-4.1-mini) Message role: system (the strict prompt; references {{ $json.text }}) jsonOutput: true Creds: openAiApi Output (per item): $.message.content → the parsed JSON (ensure it’s an array). --- 5) Create or update an account (Salesforce) Purpose: Upsert Buyer as Account using an external ID. Key settings: Resource: account Operation: upsert External Id Field: taxid_c External Id Value: ={{ $json.message.content.buyer.tax_id }} Name: ={{ $json.message.content.buyer.name }} Creds: salesforceOAuth2Api Output: Account record (captures Id) for downstream Opportunity. --- 6) Create an opportunity (Salesforce) Purpose: Create Opportunity linked to the Buyer (Account). Key settings: Resource: opportunity Name: ={{ $('Message a model').item.json.message.content.invoice.code }} Close Date: ={{ $('Message a model').item.json.message.content.invoice.issue_date }} Stage: Closed Won Amount: ={{ $('Message a model').item.json.message.content.summary.grand_total }} AccountId: ={{ $json.id }} (from Upsert Account output) Creds: salesforceOAuth2Api Output: Opportunity Id for OLI creation. --- 7) Build SOQL (Code / JS) Purpose: Collect unique product codes from AI JSON and build a SOQL query for PricebookEntry by Pricebook2Id. Key settings: pricebook2Id (hardcoded in script): e.g., 01sxxxxxxxxxxxxxxx Source lines: $('Message a model').first().json.message.content.products Output: { soql, codes } --- 8) Query PricebookEntries (Salesforce) Purpose: Fetch PricebookEntry.Id for each Product2.ProductCode. Key settings: Resource: search Query: ={{ $json.soql }} Creds: salesforceOAuth2Api Output: Items with Id, Product2.ProductCode (used for mapping). --- 9) Code in JavaScript (Build OLI payloads) Purpose: Join lines with PBE results and Opportunity Id ➜ build OpportunityLineItem payloads. Inputs: OpportunityId: ={{ $('Create an opportunity').first().json.id }} Lines: ={{ $('Message a model').first().json.message.content.products }} PBE rows: from previous node items Output: { body: { allOrNone:false, records:[{ OpportunityLineItem... }] } } Notes: Converts discount_total ➜ per-unit if needed (currently commented for standard pricing). Throws on missing PBE mapping or empty lines. --- 10) Create Opportunity Line Items (HTTP Request) Purpose: Bulk create OLIs via Salesforce Composite API. Key settings: Method: POST URL: https://<your-instance>.my.salesforce.com/services/data/v65.0/composite/sobjects Auth: salesforceOAuth2Api (predefined credential) Body (JSON): ={{ $json.body }} Output: Composite API results (per-record statuses). --- 11) Update File to One Drive Purpose: Archive the original PDF in OneDrive. Key settings: Operation: upload File Name: ={{ $json.name }} Parent Folder ID: onedrive folder id Binary Data: true (from the Download node) Creds: microsoftOneDriveOAuth2Api Output: Uploaded file metadata. --- Data flow (wiring) Google Drive Trigger → Download File From Google Download File From Google → Extract from File → Update File to One Drive Extract from File → Message a model Message a model → Create or update an account Create or update an account → Create an opportunity Create an opportunity → Build SOQL Build SOQL → Query PricebookEntries Query PricebookEntries → Code in JavaScript Code in JavaScript → Create Opportunity Line Items --- Quick setup checklist 🔐 Credentials: Connect Google Drive, OneDrive, Salesforce, OpenAI. 📂 IDs: Drive Folder ID (watch) OneDrive Parent Folder ID (archive) Salesforce Pricebook2Id (in the JS SOQL builder) 🧠 AI Prompt: Use the strict system prompt; jsonOutput = true. 🧾 Field mappings: Buyer tax id/name → Account upsert fields Invoice code/date/amount → Opportunity fields Product name must equal your Product2.ProductCode in SF. ✅ Test: Drop a sample PDF → verify: AI returns array JSON only Account/Opportunity created OLI records created PDF archived to OneDrive --- Notes & best practices If PDFs are scans, enable OCR in Extract from File. If AI returns non-JSON, keep “Return only a JSON array” as the last line of the prompt and keep jsonOutput enabled. Consider adding validation on parsing.warnings to gate Salesforce writes. For discounts/taxes in OLI: Standard OLI fields don’t support per-line discount amounts directly; model them in UnitPrice or custom fields. Replace the Composite API URL with your org’s domain or use the Salesforce node’s Bulk Upsert for simplicity.

Le NguyenBy Le Nguyen
942