Oneclick AI Squad This workflow corresponds to n8n.io template #15198 — we link there as the canonical source.
This workflow follows the Agent → HTTP Request recipe pattern — see all workflows that pair these two integrations.
The workflow JSON
Copy or download the full n8n JSON below. Paste it into a new n8n workflow, add your credentials, activate. Full import guide →
{
"id": "BKdO3J1idnWv0mFP",
"name": "Customer Journey Predictor & Drop-off Analyzer",
"tags": [],
"nodes": [
{
"id": "d512ed68-a791-45c6-9fb7-5526a9836062",
"name": "Sticky Note",
"type": "n8n-nodes-base.stickyNote",
"position": [
-288,
-112
],
"parameters": {
"width": 920,
"height": 848,
"content": "## Customer Journey Predictor & Drop-off Analyzer\n\nThis workflow automatically ingests real-time user behavior events, detects drop-off points across the customer journey, predicts churn risk using AI, and triggers targeted retention actions while logging everything for analysis.\n\n### Who\u2019s it for\n\u2022 Product teams managing high-churn SaaS products\n\u2022 E-commerce businesses with cart abandonment issues\n\u2022 Subscription services tracking user engagement\n\n### How it works / What it does\n1. Captures new user behavior events (webhook or scheduled poll)\n2. Analyzes session events, actions, and engagement metrics for drop-off signals\n3. Loads user profile, history, and preferences\n4. AI predicts real-time drop-off risk and generates personalized retention actions\n5. Sends automated re-engagement messages or campaign triggers\n6. Logs predictions, risk scores, and actions in Google Sheets\n\n### How to set up\n1. Import this workflow\n2. Set up credentials (Webhook events, Google Sheets, OpenAI/Anthropic)\n3. Update user profile defaults and retention endpoints\n4. Activate workflow\n\n### Requirements\n\u2022 Event webhook (Segment, Mixpanel, custom analytics)\n\u2022 Google Sheets\n\u2022 OpenAI / Anthropic / Grok API\n\u2022 User behavior event schema\n\n### How to customize the workflow\n\u2022 Change AI tone and action templates in the AI node\n\u2022 Modify Python detection logic\n\u2022 Update Google Sheet columns\n\u2022 Adjust retention messaging or campaign endpoints"
},
"typeVersion": 1
},
{
"id": "9682585f-c395-4a71-83ae-18e7e1453d0d",
"name": "Sticky Note1",
"type": "n8n-nodes-base.stickyNote",
"position": [
752,
144
],
"parameters": {
"color": 4,
"width": 732,
"height": 480,
"content": "## 1. Trigger & Intake"
},
"typeVersion": 1
},
{
"id": "d26bfc54-de30-4251-b509-437444caf129",
"name": "Sticky Note2",
"type": "n8n-nodes-base.stickyNote",
"position": [
1520,
48
],
"parameters": {
"color": 4,
"width": 780,
"height": 732,
"content": "## 2. Analysis & Prediction"
},
"typeVersion": 1
},
{
"id": "64fff04e-d20c-4ab0-8a5c-5c32be980afd",
"name": "Sticky Note3",
"type": "n8n-nodes-base.stickyNote",
"position": [
2320,
112
],
"parameters": {
"color": 4,
"width": 944,
"height": 580,
"content": "## 3. Action & Track"
},
"typeVersion": 1
},
{
"id": "85f8f508-82b8-4f66-9c30-1792769252f2",
"name": "Webhook - New User Behavior Event",
"type": "n8n-nodes-base.webhook",
"position": [
912,
272
],
"parameters": {
"path": "customer-journey-event",
"options": {},
"httpMethod": "POST",
"responseMode": "responseNode"
},
"typeVersion": 1.1
},
{
"id": "9d152fdb-ad64-4887-95b7-c14dd8e24e03",
"name": "Poll New Behavior Events",
"type": "n8n-nodes-base.scheduleTrigger",
"position": [
912,
464
],
"parameters": {
"rule": {
"interval": [
{
"field": "cronExpression",
"expression": "*/20 * * * *"
}
]
}
},
"typeVersion": 1.2
},
{
"id": "c32e9b4b-4056-4b98-80a4-23f4e5ff1b9c",
"name": "Prepare Event Context",
"type": "n8n-nodes-base.set",
"position": [
1136,
368
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"name": "userId",
"type": "string",
"value": "={{ $json.userId || $json.body?.userId }}"
},
{
"name": "eventType",
"type": "string",
"value": "={{ $json.eventType || $json.body?.eventType }}"
},
{
"name": "sessionData",
"type": "string",
"value": "={{ $json.sessionData || $json.body?.sessionData || $json.body }}"
},
{
"name": "metrics",
"type": "string",
"value": "={{ $json.metrics || $json.body?.metrics }}"
},
{
"name": "userEmail",
"type": "string",
"value": "={{ $json.userEmail || $json.body?.userEmail }}"
},
{
"name": "userProfile",
"type": "string",
"value": "={{ $json.userProfile || $json.body?.userProfile || 'High-value user with frequent logins but recent drop in engagement...' }}"
},
{
"name": "preferences",
"type": "string",
"value": "={{ $json.preferences || $json.body?.preferences || 'Prefers personalized discounts, email nudges, in-app messages, loyalty rewards' }}"
},
{
"name": "eventId",
"type": "string",
"value": "={{ $json.eventId || Date.now().toString() }}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "0dfb436a-e44e-4b11-8c51-b8e02bfdcff5",
"name": "Python - Detect Drop-off Signals",
"type": "n8n-nodes-base.code",
"position": [
1360,
368
],
"parameters": {
"mode": "runOnceForEachItem",
"language": "python",
"pythonCode": "newItems = []\nfor item in items:\n data = item['json'].copy()\n event_type = data.get('eventType', '')\n engagement_score = data.get('metrics', {}).get('engagementScore', 0) if isinstance(data.get('metrics'), dict) else 0\n is_dropoff_related = event_type in ['abandon_cart', 'page_exit', 'checkout_fail', 'session_timeout', 'low_engagement']\n risk_score = 0.9 if is_dropoff_related else max(0.2, 1 - (engagement_score / 100))\n dropoff_stage = 'checkout' if 'cart' in event_type.lower() else 'onboarding' if 'signup' in event_type.lower() else 'engagement' if engagement_score < 40 else 'unknown'\n data['isDropoffRelated'] = is_dropoff_related\n data['riskScore'] = round(risk_score, 2)\n data['detectedDropoffStage'] = dropoff_stage\n newItems.append({'json': data})\nreturn newItems"
},
"typeVersion": 2
},
{
"id": "34717108-ae7e-4396-96d8-e47f48942aab",
"name": "Filter High-Risk Drop-offs",
"type": "n8n-nodes-base.filter",
"position": [
1584,
368
],
"parameters": {
"options": {},
"conditions": {
"conditions": [
{
"operator": {
"type": "boolean",
"operation": "true"
},
"leftValue": "={{ $json.isDropoffRelated }}"
}
]
}
},
"typeVersion": 2.2
},
{
"id": "ed07a581-72b2-4939-be2d-0c9cad9dc0e9",
"name": "AI - Predict Drop-off Risk & Recommend Actions",
"type": "@n8n/n8n-nodes-langchain.agent",
"position": [
1792,
368
],
"parameters": {
"text": "=Act as an expert customer retention strategist and journey predictor. Analyze the user behavior data in real time and generate a targeted retention recommendation.\n\nUser Profile:\n{{ $json.userProfile }}\n\nPreferences:\n{{ $json.preferences }}\n\nBehavior Event:\nEvent Type: {{ $json.eventType }}\nMetrics: {{ $json.metrics }}\nSession Data: {{ $json.sessionData }}\n\nPredict the exact drop-off point, confirm/assign risk score (0.0-1.0), and recommend 1-2 immediate retention actions (personalized offer, in-app nudge, email campaign, or support outreach). Keep the full output concise, actionable, and under 170 words.",
"options": {},
"promptType": "define"
},
"typeVersion": 1.6
},
{
"id": "a7235764-2d49-48e7-82d4-be3d1eb245a6",
"name": "JS - Format Recommendation",
"type": "n8n-nodes-base.code",
"position": [
2592,
368
],
"parameters": {
"mode": "runOnceForEachItem",
"jsCode": "const item = $input.item.json;\nreturn {\n json: {\n ...item,\n recommendationText: item.response || item.text || 'Thank you for your recent activity. Here is a special offer to continue your journey!',\n status: 'ActionRecommended',\n analyzedDate: new Date().toISOString().split('T')[0],\n riskScore: item.riskScore || 0.5,\n dropoffPoint: item.detectedDropoffStage || 'unknown'\n }\n};"
},
"typeVersion": 2
},
{
"id": "fc159ca5-8ecb-4cca-a64a-12b7b77aeea7",
"name": "Send Personalized Retention Message",
"type": "n8n-nodes-base.httpRequest",
"position": [
3040,
272
],
"parameters": {
"url": "https://api.sendgrid.com/v3/mail/send",
"method": "POST",
"options": {},
"jsonBody": "={\n \"personalizations\": [{\"to\": [{\"email\": \"{{ $json.userEmail }}\"}]}],\n \"from\": {\"email\": \"retention@yourcompany.com\", \"name\": \"Customer Success Team\"},\n \"subject\": \"We noticed your recent activity \u2013 here's something just for you!\",\n \"content\": [{\"type\": \"text/plain\", \"value\": \"{{ $json.recommendationText }}\"}]\n}",
"sendBody": true,
"specifyBody": "json"
},
"typeVersion": 4.2
},
{
"id": "4d437d75-5e6f-4ab6-a5bd-a34c821ec9e2",
"name": "Update Retention Tracker",
"type": "n8n-nodes-base.httpRequest",
"position": [
3040,
464
],
"parameters": {
"url": "https://sheets.googleapis.com/v4/spreadsheets/YOUR_SHEET_ID/values/Sheet1!A1:append?valueInputOption=USER_ENTERED",
"method": "POST",
"options": {},
"jsonBody": "={\n \"values\": [[\"{{ $json.analyzedDate }}\", \"{{ $json.userId }}\", \"{{ $json.dropoffPoint }}\", \"{{ $json.riskScore }}\", \"{{ $json.recommendationText }}\", \"ActionRecommended\", \"{{ new Date().toISOString() }}\"]]\n}",
"sendBody": true,
"specifyBody": "json"
},
"typeVersion": 4.2
},
{
"id": "4601aca8-a13c-4688-95cf-9b25bc0bf09c",
"name": "OpenAI Chat Model",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"position": [
1904,
576
],
"parameters": {
"model": {
"__rl": true,
"mode": "list",
"value": "gpt-4o-mini"
},
"options": {},
"builtInTools": {}
},
"credentials": {
"openAiApi": {
"name": "<your credential>"
}
},
"typeVersion": 1.3
},
{
"id": "16f0694d-b092-4748-a79f-1af856b84949",
"name": "Wait For Reply",
"type": "n8n-nodes-base.wait",
"position": [
2144,
368
],
"parameters": {},
"typeVersion": 1.1
}
],
"active": false,
"settings": {
"executionOrder": "v1"
},
"versionId": "8fa0b4e8-2024-42bd-a06c-0fe6abef1495",
"connections": {
"Wait For Reply": {
"main": [
[
{
"node": "JS - Format Recommendation",
"type": "main",
"index": 0
}
]
]
},
"OpenAI Chat Model": {
"ai_languageModel": [
[
{
"node": "AI - Predict Drop-off Risk & Recommend Actions",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Prepare Event Context": {
"main": [
[
{
"node": "Python - Detect Drop-off Signals",
"type": "main",
"index": 0
}
]
]
},
"Poll New Behavior Events": {
"main": [
[
{
"node": "Prepare Event Context",
"type": "main",
"index": 0
}
]
]
},
"Filter High-Risk Drop-offs": {
"main": [
[
{
"node": "AI - Predict Drop-off Risk & Recommend Actions",
"type": "main",
"index": 0
}
]
]
},
"JS - Format Recommendation": {
"main": [
[
{
"node": "Send Personalized Retention Message",
"type": "main",
"index": 0
},
{
"node": "Update Retention Tracker",
"type": "main",
"index": 0
}
]
]
},
"Python - Detect Drop-off Signals": {
"main": [
[
{
"node": "Filter High-Risk Drop-offs",
"type": "main",
"index": 0
}
]
]
},
"Webhook - New User Behavior Event": {
"main": [
[
{
"node": "Prepare Event Context",
"type": "main",
"index": 0
}
]
]
},
"AI - Predict Drop-off Risk & Recommend Actions": {
"main": [
[
{
"node": "Wait For Reply",
"type": "main",
"index": 0
}
]
]
}
}
}
Credentials you'll need
Each integration node will prompt for credentials when you import. We strip credential IDs before publishing — you'll add your own.
openAiApi
Pro
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About this workflow
This workflow automatically ingests real-time user behavior events, detects drop-off points across the customer journey, predicts churn risk using AI, and triggers targeted retention actions while logging everything for analysis.
Source: https://n8n.io/workflows/15198/ — original creator credit. Request a take-down →
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