{ "meta": { "templateCredsSetupCompleted": true }, "nodes": [ { "id": "688b53dc-149a-41b6-8447-cde64f7a5c5c", "name": "Basic LLM Chain", "type": "@n8n/n8n-nodes-langchain.chainLlm", "position": [ -700, -120 ], "parameters": { "text": "=You are an AI rewards assistant for a hotel CRM. \n\nWhen given the guest\u2019s spend summary in optional paid amenities:\n- {{ $json.Total_Room_Service_Spend__c }}\n- {{ $json.Total_Minibar_Spend__c }}\n- {{ $json.Total_Laundry_Spend__c }}\n- {{ $json.Total_Late_Checkout_Fees__c }}\n- {{ $json.Total_Extra_Bed_Fees__c }}\n- {{ $json.Total_Airport_Transfer_Spend__c }}\n\n**Task:**\n- STRICTLY CHOOSE THE OPTION BETWEEN THESE 5 REWARDS: ROOM SERVICE, MINI BAR, LAUNDARY, LATE CHECKOUT, EXTRA BED, AIRPORT TRANSFER\n- Look at which services the guest *already paid for* (any spend > $0)\n- Randomly pick **one** of the *other* services they haven\u2019t used (spend = 0). If used everything then look for where the user spend the most on... and then give the offer on which he spent less on.\n- Offer a realistic, valuable, **one-time free perk** on that unused service\n- The offer should be concrete, short, and sound like a real hotel promotion (e.g., \"One free airport drop on your next stay\", \"Free late checkout on your next booking\")\n\n**Output:**\nAlways return JSON with this structure:\n{\n \"suggested_offer\": \"...\"\n}\n\nDo not include anything else.\n", "batching": {}, "promptType": "define", "hasOutputParser": true }, "typeVersion": 1.7 }, { "id": "01bd11e8-4a82-4906-91e6-8e26976f0bbf", "name": "Structured Output Parser", "type": "@n8n/n8n-nodes-langchain.outputParserStructured", "position": [ -540, 100 ], "parameters": { "jsonSchemaExample": "{\n \"suggested_offer\": \"...\"\n}" }, "typeVersion": 1.3 }, { "id": "a741b4b3-0cec-4c40-aa3d-517d7d1ea315", "name": "Check for any latest checkouts", "type": "n8n-nodes-base.salesforceTrigger", "position": [ -1620, -100 ], "parameters": { "pollTimes": { "item": [ { "mode": "everyWeek" } ] }, "triggerOn": "customObjectUpdated", "customObject": "Guest__c" }, "credentials": { "salesforceOAuth2Api": { "name": "" } }, "typeVersion": 1 }, { "id": "ae15b8b0-3f66-434d-8730-5baf744984e7", "name": "Extract Checkout information", "type": "n8n-nodes-base.salesforce", "position": [ -1400, -100 ], "parameters": { "options": { "fields": [ "Name", "guest_id__c", "phone__c", "Total_Room_Service_Spend__c", "Total_Minibar_Spend__c", "Total_Laundry_Spend__c", "Total_Late_Checkout_Fees__c", "Total_Extra_Bed_Fees__c", "Total_Airport_Transfer_Spend__c", "Email__c" ] }, "resource": "customObject", "operation": "getAll", "returnAll": true, "customObject": "Guest__c" }, "credentials": { "salesforceOAuth2Api": { "name": "" } }, "typeVersion": 1 }, { "id": "75fc4e53-a12d-4dfe-85cf-f1ceb4a80c19", "name": "Look For VIP Clients", "type": "n8n-nodes-base.code", "position": [ -1180, -100 ], "parameters": { "jsCode": "return $input.all().map(item => {\n const data = item.json;\n\n const total = \n (data.Total_Room_Service_Spend__c || 0) +\n (data.Total_Minibar_Spend__c || 0) +\n (data.Total_Laundry_Spend__c || 0) +\n (data.Total_Late_Checkout_Fees__c || 0) +\n (data.Total_Airport_Transfer_Spend__c || 0);\n\n return {\n json: {\n ...data,\n total\n }\n };\n});\n" }, "typeVersion": 2 }, { "id": "26bd7967-8885-474e-bac2-c1aabb7bcf2f", "name": "Check the threshold exceedings", "type": "n8n-nodes-base.if", "position": [ -960, -100 ], "parameters": { "options": {}, "conditions": { "options": { "version": 2, "leftValue": "", "caseSensitive": true, "typeValidation": "strict" }, "combinator": "and", "conditions": [ { "id": "e8573d13-cdb4-48b7-a52d-f923cf4f9ebb", "operator": { "type": "number", "operation": "gte" }, "leftValue": "={{ $json.total }}", "rightValue": 50 } ] } }, "typeVersion": 2.2 }, { "id": "e04b8b27-474b-498d-ba44-04110d993217", "name": "Give Away Personalised Offers", "type": "@n8n/n8n-nodes-langchain.lmChatGoogleVertex", "position": [ -700, 100 ], "parameters": { "options": {}, "modelName": "gemini-2.5-flash", "projectId": { "__rl": true, "mode": "list", "value": "slidingpuzzlepro1", "cachedResultName": "Slidingpuzzlepro" } }, "credentials": { "googleApi": { "name": "" } }, "typeVersion": 1 }, { "id": "6e1c0c4b-a767-4420-bf30-1a91c4d539e6", "name": "Send offer via email", "type": "n8n-nodes-base.sendInBlue", "position": [ -340, -120 ], "parameters": { "sender": "emailplaceholder", "subject": "={{ $('Check the threshold exceedings').item.json.Name }}, We Have Something Special for Your Next Stay", "sendHTML": true, "htmlContent": "= Exclusive Offer Just for You
A Special Thank You

We're delighted to share an exclusive offer with you:

{{ $json.output.suggested_offer }}

We look forward to welcoming you again soon!

© 2025 Hilton Garden Inn New York Times Square South. All rights reserved.
", "receipients": "={{ $('Check the threshold exceedings').item.json.Email__c }}", "requestOptions": {}, "additionalFields": {} }, "credentials": { "sendInBlueApi": { "name": "" } }, "typeVersion": 1 }, { "id": "9135c69d-9372-4f77-8fd9-b50e915daa64", "name": "Sticky Note", "type": "n8n-nodes-base.stickyNote", "position": [ -2446.94942319236, -420 ], "parameters": { "width": 760, "height": 1120, "content": "## \ud83d\udce9 **High-Spender Guest Reward Automation**\n\nThis workflow automatically detects high\u2011spending guests after checkout and sends them a personalized reward offer by email...\n\n---\n\n### **\ud83d\udd27 What it does**\n- Listens for updates on the `Guest__c` custom object in Salesforce.\n- Fetches guests\u2019 spend across optional paid amenities:\n - Room Service\n - Minibar\n - Laundry\n - Late Checkout\n - Extra Bed\n - Airport Transfer\n- Calculates total spend to find VIP guests (threshold \u2265 **$50**).\n- Uses an AI agent to:\n - Spot which services the guest hasn\u2019t used yet.\n - Randomly pick one unused service to offer as a free perk.\n - Generate a realistic, short promotional text.\n- Parses the AI output.\n- Sends a branded HTML email to the guest with the customized offer.\n\n---\n\n### **\ud83d\udce6 Key nodes**\n- `Salesforce Trigger` \u2192 watches for checkout updates.\n- `Salesforce` \u2192 pulls guest spend details.\n- `Function` \u2192 computes total amenity spend.\n- `IF` \u2192 filters guests who spent over the threshold.\n- `LangChain LLM + Google Vertex AI` \u2192 drafts the offer text.\n- `Structured Output Parser` \u2192 extracts clean JSON.\n- `Brevo` \u2192 delivers the email.\n\n---\n\n### **\ud83d\udcca Example output**\n> _Subject:_ `John, We Have Something Special for Your Next Stay` \n> _Offer in email:_ `Enjoy a complimentary minibar selection on your next stay.`\n\n---\n\n### **\u2728 Why**\nRewarding guests *who already show willingness to spend* increases loyalty and repeat bookings \u2014 without blanket discounts. The offer feels personal, relevant, and exclusive." }, "typeVersion": 1 } ], "connections": { "Basic LLM Chain": { "main": [ [ { "node": "Send offer via email", "type": "main", "index": 0 } ] ] }, "Look For VIP Clients": { "main": [ [ { "node": "Check the threshold exceedings", "type": "main", "index": 0 } ] ] }, "Structured Output Parser": { "ai_outputParser": [ [ { "node": "Basic LLM Chain", "type": "ai_outputParser", "index": 0 } ] ] }, "Extract Checkout information": { "main": [ [ { "node": "Look For VIP Clients", "type": "main", "index": 0 } ] ] }, "Give Away Personalised Offers": { "ai_languageModel": [ [ { "node": "Basic LLM Chain", "type": "ai_languageModel", "index": 0 } ] ] }, "Check for any latest checkouts": { "main": [ [ { "node": "Extract Checkout information", "type": "main", "index": 0 } ] ] }, "Check the threshold exceedings": { "main": [ [ { "node": "Basic LLM Chain", "type": "main", "index": 0 } ] ] } } }