Move a Nextcloud folder file by file
Description: This template facilitates the transfer of a folder, along with all its files and subfolders, within a Nextcloud instance. The Nextcloud user must have access to both the source and destination folders. While Nextcloud allows folder movement, complications may arise when dealing with external storage that has rate limits. This workflow ensures the individual transfer of each file to avoid exceeding rate limits, particularly useful for setups involving external storage with rate limitations. How it works: Identify all files and subfolders within the specified source folder. Recursive search within subfolders for additional files. Replicate the folder structure in the target folder. Individually move each identified file to the corresponding location in the target folder. Set up steps: Set Nextcloud credentials for all Nextcloud nodes involved in the process. -Edit the trigger settings. Detailed instructions can be found within the respective trigger configuration. Initiate the workflow to commence the folder transfer process. Help If you need assistance with applying this template, feel free to reach out to me. You can find additional information about me and my services here. => https://nicokowalczyk.de/links I have also produced a video where I explain the workflow and provide an example. You can find this video over here. https://youtu.be/K1kmGQjRk Cheers. Nico Kowalczyk
By Nico KowalczykManage Slack channel and users automatically
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By Harshil AgrawalAuto-generate AI videos with Gemini, KIE AI Sora-2 & Blotato (Multi-Platform)
Overview This n8n template automates the entire process of generating short-form AI videos and publishing them across multiple social media platforms. It combines Google Gemini for structured prompt creation, KIE AI for video generation, and Blotato for centralized publishing. The result is a fully automated content pipeline ideal for creators, marketers, agencies, or anyone who wants consistent, hands-free content generation. This workflow is especially useful for short-video creators, meme pages, educational creators, UGC teams, auto-posting accounts, and brands who want to maintain high-frequency posting without manual effort. --- Good to Know API costs: KIE AI generates videos using paid tokens/credits. Prices vary based on model, duration, and resolution (check KIE AI pricing). Google Gemini model restrictions: Certain Gemini models are geo-limited. If you receive “model not found,” the model may not be available in your region. Blotato publishing: Blotato supports many platforms: YouTube, Instagram, Facebook, LinkedIn, TikTok, X, Bluesky, and more. Platform availability depends on your Blotato setup. Runtime considerations: Video generation can take time (10–60 seconds+, depending on the complexity). Self-hosted requirement: This workflow uses a community node (Blotato). Community nodes do not run on n8n Cloud. A self-hosted instance is required. --- How It Works Scheduler Trigger Defines how frequently new videos should be created (e.g., every 12 hours). Random Template Selector A JavaScript node generates a random number to choose from multiple creative prompt templates. AI Agent (Google Gemini) Gemini generates a JSON object containing: A short title A human-readable video description A detailed text-to-video prompt The Structured Output Parser ensures strict JSON shape. Video Generation with KIE AI The prompt is sent to KIE AI’s video generation API. KIE AI creates a synthetic AI video based on the description and your chosen parameters (aspect ratio, frames, watermark removal, etc.). Polling & Retrieval The workflow waits until the video is fully rendered, then fetches the final video URL. Media Upload to Blotato The generated video is uploaded into Blotato’s media storage for publishing. Automatic Posting to Social Platforms Blotato distributes the video to all connected platforms. Examples include: YouTube Instagram Facebook LinkedIn Bluesky TikTok X Any platform supported by your Blotato account This results in a fully automated “idea → video → upload → publish” pipeline. --- How to Use Start by testing the workflow manually to verify video generation and posting. Adjust the Scheduler Trigger to fit your posting frequency. Add your API credentials for: Google Gemini KIE AI Blotato Ensure your Blotato account has social channels connected. Edit or expand the prompt templates for your content niche: Comedy clips Educational videos Product demos Storytelling Pet videos Motivational content The more template prompts you add, the more diverse your automated videos will be. --- Requirements Google Gemini API Key Used for generating structured titles, descriptions, and video prompts. KIE AI API key Required for creating the actual AI-generated video. Blotato account Required for uploading media and automatically posting to platforms. Self-hosted n8n instance Needed because Blotato uses a community node, which n8n Cloud does not support. --- Limitations KIE AI models may output inconsistent results if prompts are vague. High-frequency scheduling may consume API credits quickly. Some platforms (e.g., TikTok or Facebook Pages) may require additional permissions or account linking steps in Blotato. Video rendering time varies depending on prompt complexity. --- Customization Ideas Add more prompt templates to increase variety. Swap Gemini for an LLM of your choice (OpenAI, Claude, etc.). Add a Telegram, Discord, or Slack notification once posting is complete. Store all generated titles, descriptions, and video URLs in: Google Sheets Notion Airtable Supabase Add multi-language support using a translation node. Add an approval step where videos go to your team before publishing. Add analytics logging (impressions, views, etc.) using Blotato or another service. --- Troubleshooting Video not generating? Check if your KIE AI model accepts your chosen parameters. Model not found? Switch to a supported Gemini model for your region. Publishing fails? Ensure Blotato platform accounts are authenticated. Workflow stops early? Increase the wait timeout before polling KIE AI. --- This template is designed for easy setup and high flexibility. All technical details, configuration steps, and workflow logic are already included in sticky notes inside the workflow. Once configured, this pipeline becomes a hands-free AI-powered content engine capable of generating and publishing content at scale.
By Amit KumarAutomated email inquiry processing & routing with Gmail and Gemini AI
This automated n8n workflow processes any inquiry emails using AI-powered intelligence to determine customer intent and provide appropriate responses. The system analyzes incoming emails, performs availability checks or direct booking processing, and sends personalized responses based on the customer's specific requirements across any industry vertical. Good to Know Uses Google Gemini Chat Model for intelligent email analysis and response generation Automatically detects customer intent (availability check vs direct booking request) Includes conditional routing for different response types based on AI analysis Integrates with external booking systems through HTTP requests Provides seamless email automation with personalized customer communication How It Works Gmail Trigger: Initiates the workflow upon receiving a new email. AI Agent: Analyzes the email content to determine the customer's intent (availability check or direct booking). Code: Parses the JSON output from the AI Agent. Wait for Data - Ensures proper data synchronization before proceeding with conditional logic If: Routes the workflow based on the detected intent. Gmail Nodes: Sends appropriate responses or forwards booking details. How to Use Import workflow into n8n Configure Gmail API credentials for email monitoring and sending Set up Google Gemini Chat Model API access Customize AI prompts based on your industry and booking requirements Test with sample inquiry emails to verify intent detection accuracy Configure external booking system integration if needed Monitor email processing and response quality Requirements Gmail API credentials Google Gemini Chat Model API access Email monitoring and sending permissions Optional: External booking system API integration Customizing This Workflow Modify AI prompts for different industries (hotels, restaurants, services, appointments) Adjust conditional logic based on specific business requirements Configure custom email templates for various response scenarios Add integration with CRM or booking management systems Set up additional data processing nodes for complex booking workflows Implement custom validation rules for booking requests
By Oneclick AI Squad📊 Token Estim8r UI – visualize token usage analytics dashboard in n8n
📊 Visualize all your AI Token Usage analytics Dashboard using a single n8n Workflow --- Artwork Generated with ✨ ideoGener8r n8n workflow template --- Token Estim8r UI is the premium version of our token tracking solution for n8n users who want real-time insight into AI model usage and exact cost per execution — all in a beautifully designed analytics dashboard. We've done the work with over 4000 lines of code for you to simply add a pre-configured HTTP Request node to the end of any workflow you want to track, and Token Estim8r UI will handle the rest: collecting data, analyzing token usage, calculating model costs, and feeding everything into a clean UI with charts, graphs, and summaries. --- 🖼️ What the Dashboard Looks Like --- 🙋♂️ Who is this for? This workflow is perfect for: AI engineers Automation specialists Business analysts Teams using OpenAI, Anthropic, Claude, or any token-based LLM If you’re managing API budgets or optimizing prompt performance, this tool provides immediate visibility into where tokens (and money) are going. --- 😌 What problem does this solve? n8n makes it easy to build powerful workflows — but it doesn’t natively track OpenAI token usage or cost. Without that visibility, it’s impossible to: Know what each automation is costing Spot inefficiencies in prompt construction Track cost trends over time Token Estim8r UI solves that with zero manual logging. --- ⚙️ What this workflow does Fetches detailed execution logs from n8n Extracts prompt/completion token usage per model/tool Optionally retrieves live pricing or use preset pricing as of 4/2025 Calculates total cost per run Sends data to a backend for aggregation Displays a full-featured analytics dashboard with: Total tokens, cost, and usage trends Most used models/tools Workflow-model correlations Cost breakdowns and comparisons Workflow usage over time Auto-complete workflow search with filtering by ID or name Filter by date or workflow (single or all workflows) Built in image server Sortable & searchable data table of filtered results with: Prompt & completion token breakdown Cost calculations Workflow name + direct link to the workflow Link to exact execution in n8n --- 🛠️ How Setup Works Import the Token Estim8r UI workflow into your n8n instance Deploy the included dashboard (React/Next.js app, hosted or self-hosted) Configure Google Sheets or your preferred backend (e.g., Supabase, Airtable) Copy the prebuilt HTTP Request node into the end of any n8n workflow Run your workflow — data is captured, aggregated and stored automatically in Google Sheets 🎉 --- 🔄 What Makes This Better than the simple version? The simple version logs to Google Sheets only. This premium UI version adds: Full analytics dashboard Cost aggregation across all workflows Graphs, filters, and model/tool comparisons --- 🔧 Customization Ideas Switch backend to Supabase or Firebase Add alerts via Slack when costs exceed thresholds Build weekly token cost summaries Track model performance across teams Add filters by user/session/timeframe --- 🧠 Why Users Love It "Token Estim8r UI is exactly what I needed to take control of my AI costs inside n8n. It’s plug and play — and the dashboard makes optimization easy." – Beta user, AI Ops Lead 😐 If you're serious about building with AI in n8n, Token Estim8r UI gives you the visibility to scale confidently. 🚀
By RealSimple Solutions🛠️ GitLab tool MCP server 💪 all 18 operations
Need help? Want access to this workflow + many more paid workflows + live Q&A sessions with a top verified n8n creator? Join the community Complete MCP server exposing all GitLab Tool operations to AI agents. Zero configuration needed - all 18 operations pre-built. ⚡ Quick Setup Import this workflow into your n8n instance Activate the workflow to start your MCP server Copy the webhook URL from the MCP trigger node Connect AI agents using the MCP URL 🔧 How it Works • MCP Trigger: Serves as your server endpoint for AI agent requests • Tool Nodes: Pre-configured for every GitLab Tool operation • AI Expressions: Automatically populate parameters via $fromAI() placeholders • Native Integration: Uses official n8n GitLab Tool tool with full error handling 📋 Available Operations (18 total) Every possible GitLab Tool operation is included: 📄 File (5 operations) • Create a file • Delete a file • Edit a file • Get a file • List files 🐛 Issue (5 operations) • Create an issue • Create a comment on an issue • Edit an issue • Get an issue • Lock an issue 🚀 Release (5 operations) • Create a release • Delete a release • Get a release • Get many releases • Update a release 📦 Repository (2 operations) • Get a repository • Get issues of a repository 👤 User (1 operations) • Get a user's repositories 🤖 AI Integration Parameter Handling: AI agents automatically provide values for: • Resource IDs and identifiers • Search queries and filters • Content and data payloads • Configuration options Response Format: Native GitLab Tool API responses with full data structure Error Handling: Built-in n8n error management and retry logic 💡 Usage Examples Connect this MCP server to any AI agent or workflow: • Claude Desktop: Add MCP server URL to configuration • Custom AI Apps: Use MCP URL as tool endpoint • Other n8n Workflows: Call MCP tools from any workflow • API Integration: Direct HTTP calls to MCP endpoints ✨ Benefits • Complete Coverage: Every GitLab Tool operation available • Zero Setup: No parameter mapping or configuration needed • AI-Ready: Built-in $fromAI() expressions for all parameters • Production Ready: Native n8n error handling and logging • Extensible: Easily modify or add custom logic > 🆓 Free for community use! Ready to deploy in under 2 minutes.
By David AshbyExtract structured data from D&B company reports with GPT-4o
Pull a Dun & Bradstreet Business Information Report (PDF) by DUNS, convert the response into a binary PDF file, extract readable text, and use OpenAI to return a clean, flat JSON with only the key fields you care about (e.g., report date, Paydex, viability score, credit limit). Includes Sticky Notes for quick setup help and guidance. --- ✅ What this template does Requests a D&B report (PDF) for a specific DUNS via HTTP Converts the API response into a binary PDF file Extracts the text from the PDF for analysis Uses OpenAI with a Structured Output Parser to return a flat JSON Designed to be extended to Sheets, databases, or CRMs --- 🧩 How it works (node-by-node) Manual Trigger — Runs the workflow on demand ("When clicking 'Execute workflow'"). D&B Report (HTTP Request) — Calls the D&B Reports API for a Business Information Report (PDF). Convert to PDF File (Convert to File) — Turns the D&B response payload into a binary PDF. Extract Binary (Extract from File) — Extracts text content from the PDF. OpenAI Chat Model — Provides the language model context for the analyzer. Analyze PDF (AI Agent) — Reads the extracted text and applies strict rules for a flat JSON output. Structured Output (AI Structured Output Parser) — Enforces a schema and validates/auto-fixes the JSON shape. (Optional) Get Bearer Token (HTTP Request) — Template guidance for OAuth token retrieval (shown as disabled; included for reference if you prefer Bearer flows). --- 🛠️ Setup instructions (from the JSON) 1) D&B Report (HTTP Request) Auth: Header Auth (use an n8n HTTP Header Auth credential) URL: https://plus.dnb.com/v1/reports/duns/804735132?productId=birstd&inLanguage=en-US&reportFormat=PDF&orderReason=6332&tradeUp=hq&customerReference=customer%20reference%20text Headers: Accept: application/json Credential Example: D&B (HTTP Header Auth) > Put your Authorization: Bearer <token> header inside this credential, not directly in the node. 2) Convert to PDF File (Convert to File) Operation: toBinary Source Property: contents[0].contentObject > This takes the PDF content from the D&B API response and converts it to a binary file for downstream nodes. 3) Extract Binary (Extract from File) Operation: pdf > Produces a text field with the extracted PDF content, ready for AI analysis. 4) OpenAI Model(s) OpenAI Chat Model Model: gpt-4o (as configured in the JSON) Credential: Your stored OpenAI API credential (do not hardcode keys) Wiring: Connect OpenAI Chat Model as ai_languageModel to Analyze PDF Connect another OpenAI Chat Model (also gpt-4o) as ai_languageModel to Structured Output 5) Analyze PDF (AI Agent) Prompt Type: define Text: ={{ $json.text }} System Message (rules): You are a precision extractor. Read the provided business report PDF and return only a single flat JSON object with the fields below. No arrays/lists. No prose. If a value is missing, output null. Dates: YYYY-MM-DD. Numbers: plain numerics (no commas or $). Prefer most recent or highest-level overall values if multiple are shown. Never include arrays, nested structures, or text outside of the JSON object. 6) Structured Output (AI Structured Output Parser) JSON Schema Example: json { "report_date": "", "company_name": "", "duns": "", "dnbratingoverall": "", "compositecreditappraisal": "", "viability_score": "", "portfoliocomparisonscore": "", "paydex_3mo": "", "paydex_24mo": "", "creditlimitconservative": "" } Auto Fix: enabled Wiring: Connect as ai_outputParser to Analyze PDF 7) (Optional) Get Bearer Token (HTTP Request) — Disabled example If you prefer fetching tokens dynamically: Auth: Basic Auth (D&B username/password) Method: POST URL: https://plus.dnb.com/v3/token Body Parameters: granttype = clientcredentials Headers: Accept: application/json Downstream usage: Set header Authorization: Bearer {{$json["access_token"]}} in subsequent calls. > In this template, the D&B Report node uses Header Auth credential instead. Use one strategy consistently (credentials are recommended for security). --- 🧠 Output schema (flat JSON) The analyzer + parser return a single flat object like: json { "report_date": "2024-12-31", "company_name": "Example Corp", "duns": "123456789", "dnbratingoverall": "5A2", "compositecreditappraisal": "Fair", "viability_score": "3", "portfoliocomparisonscore": "2", "paydex_3mo": "80", "paydex_24mo": "78", "creditlimitconservative": "25000" } --- 🧪 Test flow Click Execute workflow (Manual Trigger). Confirm D&B Report returns the PDF response. Check Convert to PDF File for a binary file. Verify Extract from File produces a text field. Inspect Analyze PDF → Structured Output for valid JSON. --- 🔐 Security notes Do not hardcode tokens in nodes; use Credentials (HTTP Header Auth or Basic Auth). Restrict who can execute the workflow if it's accessible from outside your network. Avoid storing sensitive payloads in logs; mask tokens/headers. --- 🧩 Customize Map the structured JSON to Google Sheets, Postgres/BigQuery, or a CRM. Extend the schema with additional fields (e.g., number of employees, HQ address) — keep it flat. Add validation (Set/IF nodes) to ensure required fields exist before writing downstream. --- 🩹 Troubleshooting Missing PDF text? Ensure Convert to File source property is contents[0].contentObject. Unauthorized from D&B? Refresh/verify token; confirm Header Auth credential contains Authorization: Bearer <token>. Parser errors? Keep the agent output short and flat; the Structured Output node will auto-fix minor issues. Different DUNS/product? Update the D&B Report URL query params (duns, productId, etc.). --- 🗒️ Sticky Notes (included) Overview: "Fetch D&B Company Report (PDF) → Convert → Extract → Summarize to Structured JSON (n8n)" Setup snippets for Data Blocks (optional) and Auth flow --- 📬 Contact Need help customizing this (e.g., routing the PDF to Drive, mapping JSON to your CRM, or expanding the schema)? 📧 robert@ynteractive.com 🔗 https://www.linkedin.com/in/robert-breen-29429625/ 🌐 https://ynteractive.com
By Robert BreenTransform hotel guest feedback with GPT-4 sentiment analysis & service recovery
Transform guest complaints into loyalty opportunities - achieving 60% reduction in negative reviews, 85% faster service recovery, and turning dissatisfied guests into brand advocates through AI-powered sentiment analysis and automated response workflows. What This Workflow Does Revolutionizes hotel guest experience management with AI-driven sentiment analysis and proactive service recovery: 📝 Real-Time Feedback Capture - Jotform collects guest feedback during their stay, not after checkout 🤖 AI Sentiment Analysis - GPT-4 analyzes feedback across sentiment, urgency, and reputation impact 🚨 Instant Escalation - Critical issues immediately alert managers via email and Slack 💝 Personalized Recovery Offers - AI generates custom compensation (upgrades, discounts, credits) 📧 Automated Guest Communication - Sends recovery offers to unhappy guests, thank-you notes to satisfied ones 🎫 PMS Integration - Creates tickets in your Property Management System with full context ⭐ Review Generation - Encourages happy guests to share experiences on Google, TripAdvisor, Booking.com 📊 Complete Analytics - Tracks all feedback with sentiment scores for trend analysis 🎯 Proactive Prevention - Resolves issues before guests post negative online reviews 💰 ROI Tracking - Measures service recovery effectiveness and guest satisfaction improvements Key Features AI Guest Experience Analyst: GPT-4 analyzes feedback across 10+ dimensions including sentiment scoring, urgency classification, and reputation impact assessment Intelligent Routing: Automatically escalates critical/high-urgency issues to hotel management within minutes Personalized Service Recovery: AI generates tailored compensation offers based on issue severity, guest emotional state, and long-term value potential Multi-Channel Alerts: Instant notifications via Gmail and Slack ensure no critical feedback is missed Sentiment Scoring: 0-100 numerical sentiment scores enable data-driven trend analysis Category Detection: AI identifies issue categories (cleanliness, staff, amenities, noise, etc.) Reputation Impact Assessment: Predicts likelihood of negative online review (low/medium/high) Recovery Action Suggestions: AI recommends specific steps to resolve each guest concern Positive Feedback Amplification: Automatically requests online reviews from satisfied guests with incentivized return offers Property Management Integration: Creates structured tickets with all AI insights for staff follow-up Complete Audit Trail: Google Sheets logging enables performance tracking and staff training insights Cost Optimization: AI balances recovery offer value against long-term guest lifetime value Perfect For Boutique Hotels: 20-100 rooms requiring personalized guest experience management Hotel Chains: Multi-property operations standardizing service recovery protocols Resorts: Large properties with multiple service areas (spa, dining, housekeeping, etc.) Business Hotels: Corporate-focused properties prioritizing fast issue resolution Vacation Rentals: Airbnb management companies handling guest communications at scale Hostels: Budget accommodations building reputation through responsive service Extended Stay Properties: Long-term guest relationships requiring proactive care Conference Centers: Event venues managing large groups and critical feedback What You'll Need Required Integrations Jotform - Guest feedback form (free tier works) Create your form for free on Jotform using this link OpenAI API - GPT-4 for AI sentiment analysis (~$0.10-0.30 per feedback) Gmail - Automated notifications to managers and guests Google Sheets - Feedback database and analytics dashboard Optional Integrations Slack - Real-time alerts to management team Property Management System - Automated ticket creation (via API) Quick Start Import Template - Copy JSON and import into n8n Add OpenAI Credentials - Set up OpenAI API key (GPT-4 recommended for best results) Create Jotform Guest Feedback Form: Guest Name (q3_guestName) Guest Email (q4_guestEmail) Room Number (q5_roomNumber) Stay Dates (q6_stayDates) Overall Rating 1-5 (q7_overallRating) Feedback Comments (q8_feedbackComments) Service Area (q9_serviceArea) Create your form for free on Jotform using this link Configure Gmail - Add Gmail OAuth2 credentials (same credential for all 3 Gmail nodes) Setup Google Sheets: Create spreadsheet with "Guest Feedback Analytics" sheet Replace YOURGOOGLESHEET_ID in workflow Columns: timestamp, submissionId, guestName, roomNumber, stayDates, overallRating, serviceArea, sentiment, sentimentScore, urgencyLevel, keyIssues, categories, reputationImpact, recoveryOfferSent, feedbackText Configure PMS Integration (Optional): Add your PMS API endpoint URL Set up HTTP authentication credentials Setup Slack Webhook (Optional): Create Slack incoming webhook Replace YOUR/SLACK/WEBHOOK in workflow Customize Email Addresses: Update hotel.manager@yourhotel.com Update guestrelations@yourhotel.com Update review site URLs in positive feedback email Test Workflow - Submit test feedback through Jotform Go Live - Share feedback form link with guests (QR codes in rooms, checkout emails, etc.) Customization Options Service Recovery Tiers: Adjust compensation levels based on issue severity and guest value Auto-Approval Thresholds: Set limits for automatic vs manager-approved recovery offers AI Prompt Tuning: Customize sentiment analysis criteria for your brand standards Multi-Language Support: Add translation nodes for international guests Guest Segmentation: VIP guests receive premium recovery offers Timing Rules: Different workflows for during-stay vs post-checkout feedback Review Platform Integration: Direct API connections to TripAdvisor, Google Reviews Staff Training Alerts: Route feedback to specific department managers Competitive Analysis: Track sentiment vs competitor properties Seasonal Adjustments: Higher compensation during peak season to retain bookings Loyalty Program Integration: Award points as part of service recovery Follow-Up Sequences: Automated check-ins after issue resolution Expected Results 60% reduction in negative online reviews - Proactive resolution before guests post publicly 85% faster service recovery - Automated workflows vs manual monitoring 40% increase in repeat bookings - Effective recovery turns complainers into loyalists 95% manager response rate - Instant alerts ensure nothing falls through cracks 3x increase in positive review requests - Automated outreach to satisfied guests 75% cost reduction in review management - Less time fighting bad reviews 90% guest satisfaction with recovery - Personalized, immediate responses 100% feedback tracking - Complete audit trail for quality improvement 50% improvement in staff training - Data-driven insights on recurring issues 30% reduction in compensation costs - AI optimizes offer value vs actual resolution Use Cases Luxury Resort (200 Rooms) Scenario: Guest in oceanview suite complains about noisy pool area disrupting afternoon nap. Rating: 2/5. Feedback submitted at 2:47 PM during stay. AI Analysis: Sentiment = negative (35/100), Urgency = high, Impact = high reputation risk. Key issue: noise disturbance. Category: amenities/environment. Automated Response: 2:48 PM: Hotel manager receives urgent email and Slack alert 2:49 PM: AI generates recovery offer: complimentary room upgrade to quiet wing + $100 spa credit + late checkout 2:52 PM: Manager reviews AI recommendation, approves via phone 3:00 PM: Guest receives personalized apology email with upgrade offer 3:15 PM: Guest accepts, moves to premium suite 3:45 PM: Manager personally visits guest with welcome amenity Next Day: Guest updates internal feedback to 5/5 Result: $200 recovery cost prevents $5,000+ in future lost bookings from negative review. Guest becomes repeat customer, books 3 more stays over next year. Business Hotel (80 Rooms) Scenario: Corporate traveler rates stay 5/5, praises front desk staff professionalism and fast WiFi. Checkout feedback at 7:23 AM. AI Analysis: Sentiment = positive (92/100), Urgency = low, Categories: staff excellence, amenities. Automated Response: 7:24 AM: Thank you email sent with review request links (Google, TripAdvisor) Email includes 15% discount code for next stay (WELCOME-BACK-2025) Review links customized with pre-filled star ratings 11:30 AM: Guest posts 5-star Google review mentioning staff by name Result: Positive review attracts 12 new corporate bookings over next quarter. Guest becomes regular weekly visitor. Zero manual effort required. Budget Hotel Chain (150 Locations) Scenario: Guest complains about unclean bathroom, slow check-in, and uncomfortable bed. Rating: 1/5. Multiple critical issues. AI Analysis: Sentiment = negative (15/100), Urgency = CRITICAL, Impact = very high reputation risk. Categories: cleanliness, operations, room quality. Automated Response: Instant email to hotel manager + regional director Slack alert to operations-critical channel AI recommends: full refund + 2 free night voucher + immediate room change PMS ticket created for housekeeping inspection Guest receives apology within 10 minutes Manager calls guest personally within 20 minutes Room changed immediately, housekeeping staff retrained Result: Guest accepts recovery offer, doesn't post negative review. Systemic cleaning issue identified and corrected across all 150 locations, preventing 1,000+ potential complaints. Boutique B&B (12 Rooms) Scenario: Couple celebrating anniversary rates stay 4/5, mentions minor issue with breakfast timing but overall lovely experience. AI Analysis: Sentiment = positive (78/100), Urgency = low, Issue noted: breakfast service timing, Categories: dining, overall satisfaction. Automated Response: Thank you email with review requests AI suggests small gesture: complimentary breakfast on next visit Owner receives gentle notification about breakfast timing feedback (not urgent) Follow-up email includes personalized anniversary wishes Result: Couple posts glowing TripAdvisor review, becomes annual anniversary tradition. Breakfast timing adjusted based on feedback trend analysis. Personal touch strengthens brand loyalty. Resort During Peak Season Scenario: Family of 4 complains about overbooked pool area, long wait times at restaurant, stressed staff. Rating: 3/5. Peak season capacity issues. AI Analysis: Sentiment = neutral-negative (45/100), Urgency = medium, Categories: capacity management, staffing, amenities access. AI notes this is systemic, not individual service failure. Automated Response: Manager receives analysis highlighting capacity issues vs service quality AI recommends: restaurant priority reservations rest of stay + late checkout + $150 resort credit Recovery offer emphasizes "peak season challenges we're addressing" Guest receives empathetic communication acknowledging valid concerns Operations team receives alert about capacity strain for staffing adjustments Result: Family accepts offer, enjoys remaining days. Operations team adds staff for following weekend. Feedback trends identify need for reservation system improvements, implemented before next season. Pro Tips QR Code Distribution: Place QR codes linking to feedback form in every room, at checkout desk, and in common areas Timing Optimization: Send feedback requests on Day 2 of stay (not checkout) to enable real-time recovery Manager Training: Educate managers on interpreting AI sentiment scores and urgency classifications Recovery Budgets: Set department budgets for service recovery offers ($50-500 per incident) Review Monitoring: Cross-reference internal feedback with online reviews to measure prevention effectiveness Staff Recognition: Share positive feedback with staff members mentioned by name Trend Analysis: Weekly reviews of Google Sheets data to identify recurring issues Seasonal Patterns: Track sentiment scores across different seasons and events Competitor Benchmarking: Compare your sentiment scores to industry averages Follow-Up Surveys: Send 30-day post-stay surveys to guests who received service recovery Loyalty Integration: Higher-tier loyalty members receive premium recovery offers Language Customization: For international properties, adjust AI prompts for cultural norms Response Time Tracking: Monitor average time from feedback to resolution Cost-Benefit Analysis: Track recovery offer costs vs prevented negative review impact Success Metrics Dashboard: Create Google Data Studio dashboard from Sheets data Learning Resources This workflow demonstrates advanced automation: AI Agents with Multi-Dimensional Analysis: Sentiment scoring, urgency classification, impact assessment, and recovery recommendations Conditional Logic Routing: Different workflows for positive, negative, and critical feedback Real-Time Alerting: Multi-channel notifications (email + Slack) for urgent issues Dynamic Content Generation: AI creates personalized emails based on sentiment analysis API Integration Patterns: Property Management System ticket creation via HTTP requests Data Aggregation: Complete feedback logging for business intelligence and reporting Natural Language Processing: AI extracts key issues, categories, and emotional tone from free-text feedback Decision Support Systems: Provides managers with AI recommendations and key considerations Approval Workflows: Optional manager approval step for high-value recovery offers Guest Communication Templates: Professional, empathetic email templates for all scenarios Business Impact Metrics Review Management ROI: Compare cost of service recovery vs reputation management services (typically $500-2000/month) Guest Lifetime Value: Track repeat booking rates for guests who received service recovery vs those who didn't Online Reputation Score: Monitor aggregate rating improvements on Google, TripAdvisor, Booking.com Staff Efficiency: Calculate hours saved vs manual feedback monitoring and response Revenue Protection: Estimate revenue preserved by preventing negative reviews (avg negative review costs hotel $2,000-5,000 in lost bookings) Recovery Success Rate: Percentage of negative feedback resolved without resulting in online reviews Response Time: Average minutes from feedback submission to initial response First-Contact Resolution: Percentage of issues resolved without multiple interactions Training ROI: Reduction in recurring issues after staff training based on feedback trends Competitive Positioning: Sentiment score comparison vs competitor properties --- Ready to Transform Your Guest Experience? Import this template and turn guest feedback into your competitive advantage with AI-powered insights and automation! 🏨✨ Questions or customization? The workflow includes detailed sticky notes explaining each AI analysis component and decision logic. Template Compatibility ✅ n8n version 1.0+ ✅ Works with n8n Cloud and Self-Hosted ✅ No coding required for basic setup ✅ Fully customizable for advanced users
By Jitesh Dugar