shae This workflow corresponds to n8n.io template #7344 — we link there as the canonical source.
This workflow follows the Emailsend → 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 →
{
"nodes": [
{
"id": "12cc91e8-fd72-41ff-b6f6-843b39b202d2",
"name": "Overview & Purpose",
"type": "n8n-nodes-base.stickyNote",
"position": [
368,
96
],
"parameters": {
"width": 320,
"height": 200,
"content": "## \ud83d\udd2e AI Customer Success Risk Prediction Workflow\n\n**Purpose:** Predict customer churn risk 30-90 days early using AI analysis of multiple data signals, then automatically create personalized interventions\n\n**Why This is Revolutionary:**\n- Most CS teams are reactive - this is predictive\n- Combines product usage + support sentiment + billing patterns\n- AI generates personalized interventions based on specific risk factors\n- Reduces churn by 15-30% through early intervention\n\n**\ud83d\udd10 Security:** All API keys stored in environment variables and credentials"
},
"typeVersion": 1
},
{
"id": "365de93c-898a-476e-a3ee-d80875707f01",
"name": "Setup Instructions",
"type": "n8n-nodes-base.stickyNote",
"position": [
368,
368
],
"parameters": {
"width": 320,
"height": 220,
"content": "## \ud83c\udfaf Setup & Configuration\n\n**Required Environment Variables:**\n- `MIXPANEL_PROJECT_ID` (or analytics tool)\n- `HIGH_RISK_SLACK_CHANNEL`\n- `CS_TEAM_EMAIL`\n- `OPENAI_MODEL_VERSION`\n\n**Credentials Needed:**\n- OpenAI/Anthropic API (AI analysis)\n- Analytics Platform (Mixpanel/Amplitude)\n- Support System (Zendesk/Intercom)\n- Stripe/Billing System\n- HubSpot CRM\n- Slack API\n- SendGrid/Email Platform\n\n**Setup Time:** ~45 minutes"
},
"typeVersion": 1
},
{
"id": "fd8d7b52-6862-410f-bb02-f1bd234060ce",
"name": "AI Analysis Logic",
"type": "n8n-nodes-base.stickyNote",
"position": [
1328,
96
],
"parameters": {
"width": 320,
"height": 240,
"content": "## \ud83e\udde0 AI Risk Analysis Logic\n\n**Data Sources Combined:**\n- Product usage trends (logins, feature usage, session duration)\n- Support ticket sentiment & frequency\n- Payment patterns & billing issues\n- Email engagement rates\n- Contract renewal dates\n\n**AI Scoring Factors:**\n- Usage decline >30% = High risk\n- Negative support sentiment = Medium risk\n- Payment delays = High risk\n- Low email engagement = Low risk modifier\n- Multiple signals = Exponential risk increase\n\n**Risk Levels:** Critical (90+), High (70-89), Medium (40-69), Low (<40)"
},
"typeVersion": 1
},
{
"id": "b3b1cfa4-cb6f-423c-9cee-a8d2b860c8bb",
"name": "Daily Risk Analysis",
"type": "n8n-nodes-base.cron",
"position": [
736,
560
],
"parameters": {},
"typeVersion": 1
},
{
"id": "ba458527-4372-4674-a99a-f792fd52e343",
"name": "Fetch Product Usage Data",
"type": "n8n-nodes-base.httpRequest",
"position": [
960,
368
],
"parameters": {
"url": "={{ $env.ANALYTICS_API_URL }}/api/export",
"options": {},
"sendQuery": true,
"sendHeaders": true,
"authentication": "genericCredentialType",
"genericAuthType": "httpHeaderAuth",
"queryParameters": {
"parameters": [
{
"name": "from_date",
"value": "={{ $now.minus({days: 30}).toISODate() }}"
},
{
"name": "to_date",
"value": "={{ $now.toISODate() }}"
},
{
"name": "event",
"value": "[\"login\", \"feature_used\", \"session_end\"]"
}
]
},
"headerParameters": {
"parameters": [
{
"name": "Authorization",
"value": "Bearer {{ $env.ANALYTICS_API_KEY }}"
}
]
}
},
"typeVersion": 4.2
},
{
"id": "9d0fc88a-5df1-4436-8adc-7631918a5c53",
"name": "Fetch Support Tickets",
"type": "n8n-nodes-base.zendesk",
"position": [
960,
560
],
"parameters": {
"options": {},
"operation": "getAll"
},
"typeVersion": 1
},
{
"id": "84229a28-d043-4271-93cd-9c35caaa8a41",
"name": "Fetch Billing Data",
"type": "n8n-nodes-base.stripe",
"position": [
960,
752
],
"parameters": {
"resource": "customer",
"operation": "list"
},
"typeVersion": 1
},
{
"id": "674fb095-502e-45a4-8283-0f2d7f4b5646",
"name": "Merge All Data Sources",
"type": "n8n-nodes-base.merge",
"position": [
1184,
464
],
"parameters": {},
"typeVersion": 3
},
{
"id": "3a258f57-473d-4dba-b6c2-9323bad7fab4",
"name": "AI Risk Prediction",
"type": "n8n-nodes-base.aiTransform",
"position": [
1408,
464
],
"parameters": {},
"typeVersion": 1
},
{
"id": "8fb5b97d-94b3-4249-82b8-a75d87666578",
"name": "Route by Risk Level",
"type": "n8n-nodes-base.if",
"position": [
1632,
464
],
"parameters": {
"options": {},
"conditions": {
"options": {
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"id": "critical-risk",
"operator": {
"type": "number",
"operation": "gte"
},
"leftValue": "={{ $json.risk_score }}",
"rightValue": 90
}
]
}
},
"typeVersion": 2
},
{
"id": "0b044e5d-7071-4621-b25a-6c2a2d0b4ba7",
"name": "Check High Risk",
"type": "n8n-nodes-base.if",
"position": [
1856,
560
],
"parameters": {
"options": {},
"conditions": {
"options": {
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"id": "high-risk",
"operator": {
"type": "number",
"operation": "gte"
},
"leftValue": "={{ $json.risk_score }}",
"rightValue": 70
}
]
}
},
"typeVersion": 2
},
{
"id": "45dcc6f5-ad2b-4e11-829b-bd6393e20062",
"name": "Generate Critical Intervention",
"type": "n8n-nodes-base.aiTransform",
"position": [
2080,
208
],
"parameters": {},
"typeVersion": 1
},
{
"id": "ba69f187-e251-43b9-a575-40c452b565ac",
"name": "Critical Risk Slack Alert",
"type": "n8n-nodes-base.slack",
"position": [
1856,
352
],
"parameters": {
"text": "\ud83d\udea8 CRITICAL CUSTOMER RISK ALERT \ud83d\udea8",
"select": "channel",
"channelId": {
"__rl": true,
"mode": "id",
"value": "={{ $env.HIGH_RISK_SLACK_CHANNEL }}"
},
"otherOptions": {}
},
"typeVersion": 2.2
},
{
"id": "56ff908b-337f-4287-ac06-bebcbd467c44",
"name": "Generate High Risk Intervention",
"type": "n8n-nodes-base.aiTransform",
"position": [
2080,
512
],
"parameters": {},
"typeVersion": 1
},
{
"id": "4d065f87-c832-467b-9892-c78a612e1824",
"name": "Send Personalized Email",
"type": "n8n-nodes-base.emailSend",
"position": [
2304,
592
],
"parameters": {
"options": {},
"subject": "={{ $json.subject_line }}",
"toEmail": "={{ $json.customer_data.email }}",
"fromEmail": "={{ $env.CS_TEAM_EMAIL }}"
},
"typeVersion": 2
},
{
"id": "fb61c43d-f77c-4a02-9219-33d2784ad975",
"name": "Update CRM with Risk Data",
"type": "n8n-nodes-base.hubspot",
"position": [
2304,
368
],
"parameters": {
"operation": "update"
},
"typeVersion": 2
},
{
"id": "835e4d22-7d99-47d6-8275-461513310a94",
"name": "Generate Medium Risk Check-in",
"type": "n8n-nodes-base.aiTransform",
"position": [
2080,
752
],
"parameters": {},
"typeVersion": 1
}
],
"connections": {
"Check High Risk": {
"main": [
[
{
"node": "Generate High Risk Intervention",
"type": "main",
"index": 0
}
],
[
{
"node": "Generate Medium Risk Check-in",
"type": "main",
"index": 0
}
]
]
},
"AI Risk Prediction": {
"main": [
[
{
"node": "Route by Risk Level",
"type": "main",
"index": 0
}
]
]
},
"Daily Risk Analysis": {
"main": [
[
{
"node": "Fetch Product Usage Data",
"type": "main",
"index": 0
},
{
"node": "Fetch Support Tickets",
"type": "main",
"index": 0
},
{
"node": "Fetch Billing Data",
"type": "main",
"index": 0
}
]
]
},
"Route by Risk Level": {
"main": [
[
{
"node": "Generate Critical Intervention",
"type": "main",
"index": 0
},
{
"node": "Critical Risk Slack Alert",
"type": "main",
"index": 0
}
],
[
{
"node": "Check High Risk",
"type": "main",
"index": 0
}
]
]
},
"Fetch Support Tickets": {
"main": [
[
{
"node": "Merge All Data Sources",
"type": "main",
"index": 1
}
]
]
},
"Merge All Data Sources": {
"main": [
[
{
"node": "AI Risk Prediction",
"type": "main",
"index": 0
}
]
]
},
"Fetch Product Usage Data": {
"main": [
[
{
"node": "Merge All Data Sources",
"type": "main",
"index": 0
}
]
]
},
"Generate Medium Risk Check-in": {
"main": [
[
{
"node": "Send Personalized Email",
"type": "main",
"index": 0
},
{
"node": "Update CRM with Risk Data",
"type": "main",
"index": 0
}
]
]
},
"Generate Critical Intervention": {
"main": [
[
{
"node": "Send Personalized Email",
"type": "main",
"index": 0
},
{
"node": "Update CRM with Risk Data",
"type": "main",
"index": 0
}
]
]
},
"Generate High Risk Intervention": {
"main": [
[
{
"node": "Send Personalized Email",
"type": "main",
"index": 0
},
{
"node": "Update CRM with Risk Data",
"type": "main",
"index": 0
}
]
]
}
}
}
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About this workflow
How it works This AI Customer Success Risk Prediction workflow revolutionizes customer retention by predicting churn risk 30-90 days before it happens. Here's the high-level flow: Daily Data Collection → AI Multi-Signal Analysis → Risk Scoring & Prediction → Smart Risk Routing →…
Source: https://n8n.io/workflows/7344/ — original creator credit. Request a take-down →
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