Workflow Templates

Water Reminder Workflow This workflow demonstrates how to use n8n and Slack to build an intelligent water drinking reminder system, combined with Google Sheets for data recording and OpenAI for generating personalized reminder messages. Google Sheet Template The iOS shortcut template: The result in iOS health: The template demo in Youtube Key Features Scheduled Reminders: Automatically sends water reminders at random times every hour. Intelligent Scheduling: Delays the next reminder if you've recently had water. AI-Generated Messages: Uses OpenAI to generate friendly and non-repetitive reminder messages. Data Tracking: Records daily water intake and calculates percentage of goal achievement. Quick Response: Easily record water intake through Slack buttons. iOS Integration: Provides iOS shortcut links to sync data with the Health app. Pre-Configuration Requirements To use this workflow, you need to set up the following: Google Sheets: Create a Google spreadsheet with log and setting sheets The log sheet should include date, time, and value columns The setting sheet is used to store daily water intake goals Slack: Create a Slack app and obtain an API token Configure permissions for interactive buttons OpenAI: Obtain an OpenAI API key iOS Shortcut (optional): Create an iOS shortcut named darrell_water for recording health data Node Configurations 1. Scheduled Triggers and Data Collection 1.1. Schedule Trigger Purpose**: Triggers water reminders on schedule Configuration**: Cron Expression: 0 {{ Math.floor(Math.random() * 11) }} 8-23 * * * Triggers at a random minute every hour, only between 8 AM and 11 PM 1.2. Google Sheets - Get Target Purpose**: Retrieves daily water intake goal Configuration**: Document ID: Your Google spreadsheet ID Sheet Name: setting 1.3. Google Sheets - Get Log Purpose**: Retrieves today's water intake records Configuration**: Document ID: Your Google spreadsheet ID Sheet Name: log Filter Condition: date equals today's date {{ $now.format('yyyy-MM-dd') }} 1.4. Summarize Purpose**: Calculates total water intake for today Configuration**: Fields to Summarize: value (sum) 1.5. Limit Purpose**: Gets the most recent water intake record Configuration**: Keep: Last items 2. Intelligent Reminder Logic 2.1. Combine Data Purpose**: Merges target and actual water intake data Configuration**: Combine By: Combine by position Number of Inputs: 3 2.2. If Purpose**: Checks if water was consumed recently Configuration**: Condition: {{ DateTime.fromISO($json.date+"T"+$json.time).format('yyyy-MM-dd HH:mm:ss') }} is after {{ $now.minus(30, "minutes") }} 2.3. Wait Purpose**: Randomly delays the reminder if water was consumed recently Configuration**: Wait Time: {{ Math.floor(Math.random() * 1) + 1 }} minutes 3. AI Message Generation and Sending 3.1. OpenAI Purpose**: Generates personalized water reminder messages Configuration**: Model: gpt-4o-mini Messages: System prompt: Requests responses in Traditional Chinese and in JSON format User prompt: Includes information about last water time, current time, goal, and progress Temperature: 1 3.2. Slack Send Drink Notification Purpose**: Sends water reminders to Slack channel Configuration**: Channel: Your Slack channel ID Message Type: Block Block UI: Contains AI-generated reminder message and water amount buttons (100ml, 150ml, 200ml, 250ml, 300ml) 4. User Interaction and Data Recording 4.1. Slack Drink Webhook Purpose**: Receives user interactions when water buttons are clicked Configuration**: HTTP Method: POST Path: slack-water-webhook 4.2. Slack Action Payload Purpose**: Parses Slack interaction data Configuration**: Mode: Raw JSON Output: {{ $json.body.payload }} 4.3. Slack Action Drink Data Purpose**: Extracts water amount and message information Configuration**: Assignments: value: {{ $json.actions[0].value }} message_text: {{ $json.message.text }} shortcut_url: shortcuts://run-shortcut?name=darrell_water&input= shortcut_url_data: JSON containing water amount and time message_ts: {{ $json.container.message_ts }} 4.4. Google Sheets Purpose**: Records water intake data to spreadsheet Configuration**: Operation: Append Document ID: Your Google spreadsheet ID Sheet Name: log Column Mapping: date: {{ $now.format('yyyy-MM-dd') }} time: {{ $now.format('HH:mm:ss') }} value: {{ $json.value }} 4.5. Send to Slack with Confirm Purpose**: Sends confirmation message and provides iOS shortcut link Configuration**: Channel: Your Slack channel ID Message Type: Block Block UI: Contains confirmation message and iOS Health app button Reply Settings: Reply to the thread of the original message Author Information This workflow was created by darrell_tw_, an engineer focused on AI and Automation. Contact: X Threads Instagram Website

This workflow is for automating and centralizing your bookmarking process using AI-powered tagging and seamless integration between your Android device and a self-hosted Read Deck platform (https://readeck.org/en/). This workflow eliminates manual entry, organizes links with smart AI-generated tags, and ensures your bookmarks are always accessible, searchable, and secure. How It Works 📱 Android Shortcut Integration Use the HTTP Shortcuts app to create a 1-tap trigger that sends URLs and titles from your Android phone directly to n8n. 🤖 AI-Powered Tagging & Processing Leverage ChatGPT-4 to analyze content context and auto-generate relevant tags (e.g., “Tech Tutorials,” “Productivity Tools”). Extract clean titles and URLs from messy shared data (even from apps like Twitter or Reddit). 🔗 Readeck Integration Automatically save processed bookmarks to your self-hosted Readeck-like platform with structured metadata (title, URL, tags). ⚡ Silent Automation It runs in the background—no pop-ups or interruptions. 🔒 Pro Security Optional authentication (API tokens, headers) to protect your data. Use Case Perfect for researchers, content creators, or anyone drowning in tabs who wants to: Save articles, videos, or social posts in one click. Organize bookmarks with AI-generated tags. Build a personal knowledge base that’s always accessible. Tutorial 1️⃣ Set Up Android Shortcut Install "HTTP Shortcuts" and configure it to send data to your n8n webhook. Enable “Share Menu” to trigger bookmarks from any app. 2️⃣ Configure n8n Workflow Import the template and add your Read Deck API token (or similar service). 3️⃣ Test & Scale Share a link from your phone—watch it appear in Read Deck instantly! Add error handling or notifications for advanced use. Note: For self-hosted platforms, ensure your instance is publicly accessible (or use a VPN). Why Choose This Workflow? Zero Manual Entry: Save hours of copying/pasting. AI Organization: Say goodbye to chaotic bookmark folders. Privacy First: Host your data on your terms. Transform your bookmarking chaos into a streamlined system—try “Save: Bookmark” today! 🚀

🎯 Precision Prospecting: Automate LinkedIn Lead Gen with n8n & Bright Data 📝 Overview This workflow turns n8n into an AI-powered prospector, automatically searching Google for LinkedIn profiles, scraping profile data via Bright Data, and summarizing key details. Ideal for sales and recruitment teams seeking targeted lead lists without manual research. 🎥 Workflow in Action Want to see this workflow in action? You have a chat window output below: 🔑 Key Features AI Chat Trigger**: Start prospecting via conversational prompts. Contextual Memory**: Retains the last 20 messages for coherent dialogue. Automated Google Search**: Generates site-restricted queries and fetches the top result. Bright Data Scraping**: Synchronously scrapes LinkedIn profile details by URL. Intelligent Filtering**: Extracts only valid LinkedIn profile links. Limit Control**: Returns a single, most relevant profile per request. LLM Summary**: Uses GPT-4o-mini to interpret and present scraped data. 🚀 How It Works (Step-by-Step) Prerequisites: n8n ≥ v1.0 with community nodes: install n8n-nodes-brightdata (not verified community node). API credentials: OpenAI, Bright Data (web unlocker zone “web\_unlocker1”). Webhook endpoint for chat trigger. Node Configuration: When chat message received (chatTrigger): Fires on user prompt. Simple Memory1 (memoryBufferWindow): Stores the last 20 chat messages. AI Prospector Agent (agent): Orchestrates search logic. Get 1 Google Result (brightData): Performs a Google search with site:linkedin.com/in. Get Links from Body (html): Extracts all `` hrefs from the search result page. Extract Links (splitOut): Splits out individual link entries. Filter only LinkedIn Profiles (filter): Ensures the URL contains “linkedin.com/” and starts with “https\://”. Limit (limit): Restricts output to the first valid profile URL. Search LinkedIn URI (toolWorkflow): Passes the URL to a secondary workflow to fetch the first link. Get LinkedIn Profile Data (brightDataTool): Scrapes the profile JSON. OpenAI Chat Model (lmChatOpenAi): Summarizes and formats the scraped data. Workflow Logic: User asks for a person by company & name, company & position, or LinkedIn URL. Agent builds a Google query (e.g., site:linkedin.com/in bright data cmo) and calls “Get 1 Google Result.” Extracted links are filtered and limited to the top valid profile. If user provided a direct LinkedIn URL, Agent skips search and scrapes immediately. Scraped profile JSON is passed to GPT-4o-mini to generate a concise summary. Testing & Optimization: Trigger via Execute Workflow for dry runs. Inspect intermediate node outputs in n8n’s Execution panel. Adjust maxIterations or memory window length for performance. Tune Bright Data zone or country settings to optimize scraping speed. Deployment & Monitoring: Activate the workflow and expose its webhook URL. Use n8n’s built-in Alerts or external monitoring (e.g., Slack notifications) on failures. Rotate credentials via n8n’s Credential Vault when needed. Version-control workflow via duplicates or Git-backed n8n instances. ✅ Pre-requisites OpenAI Account**: API key for GPT-4o-mini. Bright Data Account**: Zone “web\_unlocker1” and dataset gd_l1viktl72bvl7bjuj0. n8n Version**: v1.0+ with community nodes installed. Permissions**: Webhook access, Credential Vault read/write. 👤 Who Is This For? Sales teams automating outbound LinkedIn prospecting. Recruiters sourcing candidates without manual scraping. Marketing ops looking to enrich CRM with accurate profile data. 📈 Benefits & Use Cases Efficiency**: Reduces hours of manual search and data entry to seconds. Accuracy**: Filters out non-LinkedIn links and ensures high-quality results. Scalability**: Handle multiple prospect requests concurrently via chat or API. Integration**: Easily hook into CRMs or email sequencers downstream. Workflow created and verified by Miquel Colomer https://www.linkedin.com/in/miquelcolomersalas/ and N8nHackers https://n8nhackers.com

Overview This workflow exposes an HTTP endpoint (webhook) that accepts a JSON definition of an n8n workflow, validates it, and—if everything is correct—dynamically creates that workflow in the n8n instance via its internal API. If any validation fails or the API call encounters an error, an explanatory message with details is returned. Workflow Diagram Webhook │ ▼ Validate JSON ── fails validation ──► Validation Error │ └─ passes ─► Validation Successful? │ ├─ true ─► Create Workflow ──► API Successful? ──► Success Response │ │ │ └─ false ─► API Error └─ false ─► Validation Error Step-by-Step Details 1. Webhook Type**: Webhook (POST) Path**: /webhook/create-workflow Purpose**: Expose a URL to receive a JSON definition of a workflow. Expected Input**: JSON containing the main workflow fields (name, nodes, connections, settings). 2. Validate JSON Type**: Code Node (JavaScript) Validations Performed**: Ensure that payload exists and contains both name and nodes. Verify that nodes is an array with at least one item. Check that each node includes the required fields: id, name, type, position. If missing, initialize connections, settings, parameters, and typeVersion. Output if Error**: { "success": false, "message": "<error description>" } Output if Valid**: { "success": true, "apiWorkflow": { "name": payload.name, "nodes": payload.nodes, "connections": payload.connections, "settings": payload.settings } } 3. Validation Successful? Type**: IF Node Condition**: $json.success === true Branches**: true: proceed to Create Workflow false: route to Validation Error 4. Create Workflow Type**: HTTP Request (POST) URL**: http://127.0.0.1:5678/api/v1/workflows Authentication**: Header Auth with internal credentials Body**: The apiWorkflow object generated earlier Options**: continueOnFail: true (to handle failures in the next IF) 5. API Successful? Type**: IF Node Condition**: $response.statusCode <= 299 Branches**: true: proceed to Success Response false: route to API Error 6. Success Response Type**: SET Node Output**: { "success": "true", "message": "Workflow created successfully", "workflowId": "{{ $json.data[0].id }}", "workflowName": "{{ $json.data[0].name }}", "createdAt": "{{ $json.data[0].createdAt }}", "url": "http://localhost:5678/workflow/{{ $json.data[0].id }}" } 7. API Error Type**: SET Node Output**: { "success": "false", "message": "Error creating workflow", "error": "{{ JSON.stringify($json) }}", "statusCode": "{{ $response.statusCode }}" } 8. Validation Error Type**: SET Node Output**: { "success": false, "message": "{{ $json.message }}" } Example Webhook Request curl --location --request POST 'http://localhost:5678/webhook/create-workflow' \ --header 'Content-Type: application/json' \ --data-raw '{ "name": "My Dynamic Workflow", "nodes": [ { "id": "start-node", "name": "Start", "type": "n8n-nodes-base.manualTrigger", "typeVersion": 1, "position": [100, 100], "parameters": {} }, { "id": "set-node", "name": "Set", "type": "n8n-nodes-base.set", "typeVersion": 1, "position": [300, 100], "parameters": { "values": { "string": [ { "name": "message", "value": "Hello from a webhook-created workflow!" } ] } } } ], "connections": { "Start": { "main": [ [ { "node": "Set", "type": "main", "index": 0 } ] ] } }, "settings": {} }' Expected Success Response { "success": "true", "message": "Workflow created successfully", "workflowId": "abcdef1234567890", "workflowName": "My Dynamic Workflow", "createdAt": "2025-05-31T12:34:56.789Z", "url": "http://localhost:5678/workflow/abcdef1234567890" } Validation Error Response { "success": false, "message": "The 'name' field is required in the workflow" } API Error Response { "success": "false", "message": "Error creating workflow", "error": "{ ...full API response details... }", "statusCode": 401 }

This n8n workflow serves as an incident response and notification system for handling potentially malicious emails flagged by Sublime Security. It begins with a Webhook trigger that Sublime Security uses to initiate the workflow by POSTing an alert. The workflow then extracts message details from Sublime Security using an HTTP Request node, based on the provided messageId, and subsequently splits into two parallel paths. In the first path, the workflow looks up a Slack user by email, aiming to find the recipient of the email that triggered the alert. If a user is found in Slack, a notification is sent to them, explaining that they have received a potentially malicious email that has been quarantined and is under investigation. This notification includes details such as the email's subject and sender. The second path checks whether the flagged email has been opened by inspecting the read_at value from Sublime Security. If the email was opened, the workflow prepares a table summarizing the flagged rules and creates a corresponding issue in Jira Software. The Jira issue contains information about the email, including its subject, sender, and recipient, along with the flagged rules. Issues that someone might encounter when setting up this workflow for the first time include potential problems with the Slack user lookup if the user information is not available or if Slack API integration is not configured correctly. Additionally, the issue creation in Jira Software may not work as expected, as indicated by the note that mentions a need for possible node replacement. Thorough testing and validation with sample data from Sublime Security alerts can help identify and resolve any potential issues during setup.

-- Disclaimer: This workflow uses a community node and therefore only works for self-hosted n8n users -- Transform YouTube videos into comprehensive summaries and structured analysis instantly. This n8n workflow automatically extracts, processes, and analyzes video transcripts to deliver clear, organized insights without watching the entire video. Time-Saving Features 🚀 Instant Processing Simply provide a YouTube URL and receive a structured summary within seconds, eliminating the need to watch lengthy videos. Perfect for research, learning, or content analysis. 🤖 AI-Powered Analysis Leverages GPT-4o-mini to analyze video transcripts, organizing key concepts and insights into a clear, hierarchical structure with main topics and essential points. Smart Processing Pipeline 📝 Automated Transcript Extraction Supports public YouTube video Handles multiple URL formats Extracts complete video transcripts automatically 🧠 Intelligent Content Organization Breaks down content into main topics Highlights key concepts and terminology Maintains technical accuracy while improving clarity Structures information logically with markdown formatting Perfect For 📚 Researchers & Students Quick comprehension of educational content and lectures without watching entire videos. 💼 Business Professionals Efficient analysis of industry talks, presentations, and training materials. 🎯 Content Creators Rapid research and competitive analysis of video content in your niche. Technical Implementation 🔄 Workflow Components Webhook endpoint for URL submission YouTube API integration for video details Transcript extraction system GPT-4 powered analysis engine Telegram notification system (optional) Transform your video content consumption with an intelligent system that delivers structured, comprehensive summaries while saving hours of viewing time.

This is the workflow powering the n8n demo shown at StrapiConf 2022. The workflow searches matching Tweets every 30 minutes using the Interval node and listens to Form submissions using the Webhook node. Sentiment analysis is handled by Google using the Google Cloud Natural Language node before the result is stored in Strapi using the Strapi node. (These were originally two separate workflows that have been combined into one to simplify sharing.)

Tags: Supply Chain, Logistics, Control Tower Context Hey! I’m Samir, a Supply Chain Engineer and Data Scientist from Paris, and the founder of LogiGreen Consulting. We design tools to help companies improve their logistics processes using data analytics, AI, and automation—to reduce costs and minimize environmental impact. > Let’s use N8N to build smarter and more sustainable supply chains! 📬 For business inquiries, you can add me on LinkedIn Who is this template for? This workflow template is designed for logistics operations that need a monitoring solution for their distribution chains. Connected to your Transportation Management Systems, this AI agent can answer any question about the shipments handled by your distribution teams. How does it work? The workflow is connected to a Google BigQuery table that stores outbound order data (customer deliveries). Here’s what the AI agent does: 🤔 Receives a user question via chat. 🧠 Understands the request and generates the correct SQL query. ✅ Executes the SQL query using a BigQuery node. 💬 Responds to the user in plain English. Thanks to the chat memory, users can ask follow-up questions to dive deeper into the data. What do I need to get started? This workflow requires no advanced programming skills. You’ll need: A Google BigQuery account with an SQL table storing transactional records. An OpenAI API key (GPT-4o) for the chat model. Next Steps Follow the sticky notes in the workflow to configure each node and start using AI to support your supply chain operations. 🎥 Watch My Tutorial 🚀 Curious how N8N can transform your logistics operations? Notes The chat trigger can easily be replaced with Teams, Telegram, or Slack for a better user experience. You can also connect this to a customer chat window using a webhook. This workflow was built using N8N version 1.82.1 Submitted: March 24, 2025

This workflow is part of an MSP collection, which is publicly available on GitHub. This workflow archives or unarchives a Clockify projects, depending on a Syncro status. Note that Syncro should be setup with a webhook via 'Notification Set for Ticket - Status was changed'. It doesn't handle merging of tickets, as Syncro doesn't support a 'Notification Set' for merged tickets, so you should change a ticket to 'Resolved' first before merging it. Prerequisites A Clockify account and credentials Nodes Webhook node triggers the workflow. IF node filters projects that don't have the status 'Resolved'. Clockify nodes get all projects that (don't) have the status 'Resolved', based on the IF route. HTTP Request nodes unarchives unresolved projects, and archives resolved projects, respectively.

📄 Convert Parquet, Feather, ORC & Avro Files with ParquetReader This workflow allows you to upload and inspect Parquet, Feather, ORC, or Avro files via the ParquetReader API. It instantly returns a structured JSON preview of your data — including rows, schema, and metadata — without needing to write any custom code. ✅ Perfect For Validating schema and structure before syncing or transformation Previewing raw columnar files on the fly Automating QA, ETL, or CI/CD workflows Converting Parquet, Avro, Feather, or ORC to JSON ⚙️ Use Cases Catch schema mismatches before pipeline runs Automate column audits in incoming data files Enrich metadata catalogs with real-time schema detection Integrate file validation into automated workflows 🚀 How to Use This Workflow 📥 Trigger via File Upload You can trigger this flow by sending a POST request with a file using curl, Postman, or from another n8n flow. 🔧 Example (via curl): curl -X POST http://localhost:5678/webhook-test/convert \ -F "file=@converted.parquet" > Replace converted.parquet with your local file path. You can also send Avro, ORC or Feather files. 🔁 Reuse from Other Flows You can reuse this flow by calling the webhook from another n8n workflow using an HTTP Request node. Make sure to send the file as form-data with the field name file. 🔍 What This Flow Does: Receives the uploaded file via webhook (file) Sends it to https://api.parquetreader.com/parquet as multipart/form-data (field name: file) Receives parsed data (rows), schema, and metadata in JSON format 🧪 Example JSON Response from this flow { "data": [ { "full_name": "Pamela Cabrera", "email": "bobbyharrison@example.net", "age": "24", "active": "True", "latitude": "-36.1577385", "longitude": "63.014954", "company": "Carter, Shaw and Parks", "country": "Honduras" } ], "meta_data": { "created_by": "pyarrow", "num_columns": 21, "num_rows": 10, "serialized_size": 7598, "format_version": "0.12" }, "schema": [ { "column_name": "full_name", "column_type": "string" }, { "column_name": "email", "column_type": "string" }, { "column_name": "age", "column_type": "int64" }, { "column_name": "active", "column_type": "bool" }, { "column_name": "latitude", "column_type": "double" }, { "column_name": "longitude", "column_type": "double" }, { "column_name": "company", "column_type": "string" }, { "column_name": "country", "column_type": "string" } ] } 🔐 API Info Authentication: None required Supported formats: .parquet, .avro, .orc, .feather Free usage: No signup needed; API is currently open to the public Limits: Usage and file size limits may apply in the future (TBD)

The workflow is well-designed for CRM analysis with a robust quality control mechanism. The dual-AI approach ensures reliable results, while the webhook integration makes it production-ready for real-time CRM data processing. Dual-AI Architecture: Uses DeepSeek Reasoner for analysis and DeepSeek Chat for verification. Flexible Input: Supports both manual testing and production webhook integration. Quality Assurance: Built-in verification system to ensure report accuracy. Comprehensive Analysis: Covers lead conversion, upsell metrics, agent ranking, and more. Professional Output: Generates structured markdown reports with actionable insights

Vacation Planning Agent - n8n Workflow Overview This n8n workflow creates an intelligent vacation planning chatbot that helps users find and book the perfect hotel accommodations. The agent acts as a professional travel consultant, systematically gathering travel requirements and providing personalized hotel recommendations through an interactive chat interface. Core Functionality The workflow provides a conversational AI agent that: Conducts structured information gathering** through natural conversation Automatically searches for hotels** using real-time data from Google Hotels Provides personalized recommendations** with detailed hotel information Maintains conversation context** throughout the planning process Delivers professional travel consultation** in a friendly, accessible format User Experience Flow Initial Interaction Users are greeted with a warm welcome message in German: "Hallo! Ich helfe dir, deinen perfekten Urlaub zu planen. Bitte beanworte mir die folgenden Fragen :)" Information Collection Process The agent systematically collects essential travel details: Destination - City and country/state Travel Dates - Check-in and check-out dates Guest Count - Number of travelers Room Requirements - Number of rooms needed Budget Preferences - Optional price range Automated Hotel Search Once core information is gathered, the agent automatically searches for available accommodations without requiring user permission. Recommendation Delivery Results are presented in a structured format including: Hotel names and star ratings Pricing information Location details Guest ratings and reviews Key amenities and highlights Direct booking links Required Integrations OpenAI API Purpose**: Powers the conversational AI agent Model**: GPT-4o-mini for cost-effective, intelligent responses Requirement**: Valid OpenAI API credentials SerpAPI (Google Hotels) Purpose**: Real-time hotel search and pricing data Service**: Google Hotels search engine integration Requirement**: Active SerpAPI account and API key Key Features Intelligent Conversation Management Maintains conversation context with 20-message memory buffer Handles edge cases like no available hotels or unclear responses Provides alternative suggestions when initial searches yield limited results Flexible Search Parameters Supports location-based searches worldwide Accommodates date range specifications Handles guest count and room quantity requirements Optional budget filtering (min/max price ranges) Currency customization support Professional Presentation Structured hotel recommendation format Clear pricing and availability information Contextual explanations for recommendations Additional destination insights when relevant Use Cases This workflow is ideal for: Travel websites** seeking to add AI-powered hotel booking assistance Travel agencies** wanting to automate initial consultation processes Hospitality businesses** providing customer service automation Personal travel planning** applications Customer support** for travel-related inquiries User Benefits Time-saving**: Eliminates manual hotel research Personalized results**: Tailored recommendations based on specific needs Real-time data**: Current pricing and availability information Professional guidance**: Expert-level travel consultation Seamless experience**: Natural conversation flow without complex forms Technical Requirements Essential Services n8n workflow automation platform OpenAI API access (GPT-4o-mini model) SerpAPI account with Google Hotels access Configuration Needs API credential setup for both OpenAI and SerpAPI Webhook endpoint configuration for chat trigger Memory buffer configuration for conversation context Optional Enhancements Custom branding for chat interface Additional language support beyond German greeting Integration with booking platforms for direct reservations Analytics tracking for usage insights