{"workflow":{"id":13726,"name":"Predict customer churn daily using ML or LLM models and notify via Slack/email","views":33,"recentViews":0,"totalViews":33,"createdAt":"2026-02-26T09:36:56.415Z","description":"This n8n workflow runs daily to analyze active customer behavior, engineers relevant features from usage and transaction data, applies a machine learning or AI-based model to predict churn probability, classifies risk levels, triggers retention actions for at-risk customers, stores predictions for tracking, and notifies relevant teams.\n\n### Key Insights\n- Prediction accuracy heavily depends on feature quality — ensure login frequency, spend trends, support interactions, and engagement metrics are consistently captured and up-to-date.\n- Start with simple rule-based scoring or AI prompting (e.g., OpenAI/Claude) before integrating full ML models for easier testing and faster value.\n- High-risk thresholds (e.g., >70%) should be tuned based on your actual churn data to avoid alert fatigue or missed opportunities.\n\n### Workflow Process\n1. Initiate the workflow with the **Daily Schedule Trigger** node (runs every day at 2 AM).\n2. Query the customer database to fetch active user profiles, recent activity logs, login history, transaction records, and support ticket data.\n3. Perform feature engineering: calculate metrics such as login frequency (daily/weekly), average spend, spend velocity, days since last activity, number of support tickets, NPS/sentiment if available, and other engagement signals.\n4. Feed engineered features into the prediction step: call an ML model endpoint, run a Python code node with a lightweight model, or use an AI agent/LLM to estimate churn probability (0–100%).\n5. Classify each customer into risk tiers: HIGH RISK, MEDIUM RISK, or LOW RISK based on configurable probability thresholds.\n6. For at-risk customers (especially HIGH), trigger retention actions: create personalized campaigns, add to nurture sequences, generate discount codes, or create tasks in CRM.\n7. Store predictions, risk scores, features, and actions taken in an analytics database for historical tracking and model improvement.\n8. Send summarized alerts (e.g., list of high-risk customers with scores and recommended actions) via Email and/or Slack to customer success or retention teams.\n\n### Usage Guide\n- Import the workflow into n8n and configure credentials for your customer database (PostgreSQL/MySQL), ML API (if external), analytics DB, Slack webhook, SMTP/email, and CRM/retention platform.\n- Define feature extraction queries and thresholds carefully in the relevant nodes — test with a small customer subset first.\n- If using AI/LLM for prediction, refine the prompt to include clear examples of churn signals.\n- Run manually via the **Execute workflow** button with sample data to validate data flow, scoring logic, and notifications.\n- Once confident, activate the daily schedule.\n\n### Prerequisites\n- Customer database with readable tables for users, activity logs, transactions, and support interactions\n- ML integration option: either an external ML API endpoint, Python code node with scikit-learn/simple model, or LLM node (OpenAI, Claude, etc.) for probabilistic scoring\n- Separate analytics database (or same DB) with a table ready for churn predictions (customer_id, date, churn_prob, risk_level, etc.)\n- SMTP credentials or email service for alerts\n- Slack webhook URL (optional but recommended for team notifications)\n- CRM or marketing automation API access (e.g., HubSpot, ActiveCampaign, Klaviyo) for creating retention campaigns/tasks\n\n### Customization Options\n- Adjust the daily trigger time or make it hourly for near real-time monitoring of high-value accounts.\n- Change risk classification thresholds or add more tiers in the scoring logic node.\n- Enhance the prediction step: switch from LLM-based to a trained ML model (via Hugging Face, custom endpoint, or Code node).\n- Personalize retention actions: use AI to generate custom email content/offers based on the customer's behavior profile.\n- Add filtering (e.g., only high-value customers > certain MRR) to focus retention efforts.\n- Extend notifications: integrate with Microsoft Teams, Discord, or create tickets in Zendesk/Jira for follow-up.\n- Build feedback loop: after actual churn occurs, update a training dataset or adjust weights/rules in future runs.\n","workflow":{"id":"9pmjBc7dqrEagOoX","meta":{"instanceId":"dd69efaf8212c74ad206700d104739d3329588a6f3f8381a46a481f34c9cc281","templateCredsSetupCompleted":true},"name":"AI Customer Churn Predictor","tags":[],"nodes":[{"id":"1f854286-b67a-42f9-9711-0056c93f9a0d","name":"Sticky Note","type":"n8n-nodes-base.stickyNote","position":[-32,-384],"parameters":{"width":900,"height":1504,"content":"## AI Customer Churn Predictor\n\nAnalyzes user behavior data and predicts churn probability using machine learning algorithms.\n\n## How it works\n\n1. **Trigger** — Runs daily at 2 AM to analyze active customers\n2. **Collect** — Fetches customer profiles, activity logs, and transaction history from database\n3. **Engineer** — Extracts features: login frequency, spend patterns, support tickets, engagement metrics\n4. **Predict** — Uses ML model to calculate churn probability (0-100%)\n5. **Score** — Classifies as HIGH RISK / MEDIUM RISK / LOW RISK\n6. **Remediate** — Creates retention campaigns for at-risk customers\n7. **Store** — Saves predictions to analytics database\n8. **Notify** — Sends alerts and dashboards via Email & Slack\n\n## Setup steps\n\n1. **Database** — Connect your customer database (PostgreSQL/MySQL)\n2. **ML Model** — Configure ML API endpoint or use Python ML node\n3. **Analytics DB** — Set up table for churn predictions\n4. **Slack/Email** — Add webhook URLs and SMTP credentials\n5. **Retention API** — Configure CRM/marketing platform for campaigns\n6. **Test** — Run manually first, then schedule daily execution\n\n**AI Customer Churn Predictor**\n\nThis n8n workflow runs daily to analyze active customer behavior, engineers relevant features from usage and transaction data, applies a machine learning or AI-based model to predict churn probability, classifies risk levels, triggers retention actions for at-risk customers, stores predictions for tracking, and notifies relevant teams.\n\n### Key Insights\n- Prediction accuracy heavily depends on feature quality — ensure login frequency, spend trends, support interactions, and engagement metrics are consistently captured and up-to-date.\n- Start with simple rule-based scoring or AI prompting (e.g., OpenAI/Claude) before integrating full ML models for easier testing and faster value.\n- High-risk thresholds (e.g., >70%) should be tuned based on your actual churn data to avoid alert fatigue or missed opportunities.\n\n### Usage Guide\n- Import the workflow into n8n and configure credentials for your customer database (PostgreSQL/MySQL), ML API (if external), analytics DB, Slack webhook, SMTP/email, and CRM/retention platform.\n- Define feature extraction queries and thresholds carefully in the relevant nodes — test with a small customer subset first.\n- If using AI/LLM for prediction, refine the prompt to include clear examples of churn signals.\n- Run manually via the **Execute workflow** button with sample data to validate data flow, scoring logic, and notifications.\n- Once confident, activate the daily schedule.\n\n### Prerequisites\n- Customer database with readable tables for users, activity logs, transactions, and support interactions\n- ML integration option: either an external ML API endpoint, Python code node with scikit-learn/simple model, or LLM node (OpenAI, Claude, etc.) for probabilistic scoring\n- Separate analytics database (or same DB) with a table ready for churn predictions (customer_id, date, churn_prob, risk_level, etc.)\n- SMTP credentials or email service for alerts\n- Slack webhook URL (optional but recommended for team notifications)\n- CRM or marketing automation API access (e.g., HubSpot, ActiveCampaign, Klaviyo) for creating retention campaigns/tasks\n\n### Customization Options\n- Adjust the daily trigger time or make it hourly for near real-time monitoring of high-value accounts.\n- Change risk classification thresholds or add more tiers in the scoring logic node.\n- Enhance the prediction step: switch from LLM-based to a trained ML model (via Hugging Face, custom endpoint, or Code node).\n- Personalize retention actions: use AI to generate custom email content/offers based on the customer's behavior profile.\n- Add filtering (e.g., only high-value customers > certain MRR) to focus retention efforts.\n- Extend notifications: integrate with Microsoft Teams, Discord, or create tickets in Zendesk/Jira for follow-up.\n- Build feedback loop: after actual churn occurs, update a training dataset or adjust weights/rules in future runs.\n"},"typeVersion":1},{"id":"2ae5a72b-8d07-4274-8d1d-233863fb9a08","name":"Sticky Note1","type":"n8n-nodes-base.stickyNote","position":[928,112],"parameters":{"color":3,"width":500,"height":768,"content":"## 1. Trigger & Collect Data\n\nDaily schedule to fetch customer profiles and behavioral data"},"typeVersion":1},{"id":"43bb8c82-df36-49f3-89fe-70d908cd5e4a","name":"Sticky Note2","type":"n8n-nodes-base.stickyNote","position":[1472,256],"parameters":{"color":5,"width":388,"height":448,"content":"## 2. Feature Engineering\n\nExtract and calculate behavioral features for ML model"},"typeVersion":1},{"id":"45eec6c4-bb3c-40ae-91fd-f3523ced9e7b","name":"Sticky Note3","type":"n8n-nodes-base.stickyNote","position":[1920,320],"parameters":{"color":4,"width":596,"height":408,"content":"## 3. ML Prediction\n\nRun churn probability predictions and score customers"},"typeVersion":1},{"id":"a0bd3b99-1e56-4d62-9abb-b60829950996","name":"Sticky Note4","type":"n8n-nodes-base.stickyNote","position":[2592,192],"parameters":{"color":5,"width":550,"height":612,"content":"## 4. Classify & Remediate\n\nRoute customers by risk level and create retention campaigns"},"typeVersion":1},{"id":"197eba11-4fc5-477a-9ec7-73b512f5351c","name":"Sticky Note5","type":"n8n-nodes-base.stickyNote","position":[3232,208],"parameters":{"color":3,"width":550,"height":580,"content":"## 5. Report & Notify\n\nStore results and deliver churn analytics to stakeholders"},"typeVersion":1},{"id":"e6ec0958-1d51-45be-a2d3-e9e2edf6b9da","name":"Daily churn analysis at 2 AM","type":"n8n-nodes-base.scheduleTrigger","position":[1056,496],"parameters":{"rule":{"interval":[{"field":"cronExpression","expression":"0 2 * * *"}]}},"typeVersion":1.2},{"id":"4c3ff3c6-e6a4-4a16-8a9d-ce732dbccdc3","name":"Fetch active customer profiles","type":"n8n-nodes-base.httpRequest","position":[1280,304],"parameters":{"url":"https://api.example.com/customers/active","options":{}},"typeVersion":4.2},{"id":"ca121dcf-1812-463d-8388-10266013e886","name":"Fetch customer activity logs (30 days)","type":"n8n-nodes-base.httpRequest","position":[1280,496],"parameters":{"url":"https://api.example.com/logs/activity?days=30","options":{}},"typeVersion":4.2},{"id":"ed96d0b9-b6e3-408d-b955-2c3cf2080712","name":"Fetch transaction history (90 days)","type":"n8n-nodes-base.httpRequest","position":[1280,688],"parameters":{"url":"https://api.example.com/transactions?days=90","options":{}},"typeVersion":4.2},{"id":"5ac08f4f-5321-40ad-81a1-f027fd635970","name":"Merge customer and activity data","type":"n8n-nodes-base.merge","position":[1504,496],"parameters":{"mode":"combine","options":{}},"typeVersion":3},{"id":"39030266-1388-4a8f-8b10-4bb70898b93e","name":"Engineer behavioral features","type":"n8n-nodes-base.code","position":[1728,496],"parameters":{"mode":"runOnceForEachItem","jsCode":"// Feature engineering for churn prediction\nconst customer = $input.item.json;\n\n// --- Login Frequency ---\nconst loginDays = customer.activity_logs?.filter(log => log.type === 'login').length || 0;\nconst daysActive = customer.days_since_signup || 1;\nconst loginFrequency = Math.round((loginDays / 30) * 100) / 100; // logins per month\nconst loginTrend = (customer.login_last_7_days || 0) - (customer.login_previous_7_days || 0);\n\n// --- Spend Patterns ---\nconst totalSpend = customer.transactions?.reduce((sum, t) => sum + (t.amount || 0), 0) || 0;\nconst avgTransactionValue = customer.transactions?.length > 0 ? totalSpend / customer.transactions.length : 0;\nconst spendTrend = ((customer.spend_last_30 || 0) - (customer.spend_previous_30 || 0)) / (customer.spend_previous_30 || 1);\nconst daysLastPurchase = customer.days_since_last_purchase || 999;\n\n// --- Engagement Metrics ---\nconst supportTickets = customer.support_tickets || 0;\nconst featureUsage = customer.features_used || 0;\nconst emailEngagement = customer.email_open_rate || 0; // percentage\nconst accountAge = customer.days_since_signup || 1;\n\n// --- Subscription Details ---\nconst planValue = customer.plan_value || 0;\nconst isFreeTier = customer.plan_type === 'free';\nconst daysUntilRenewal = customer.days_until_renewal || 365;\nconst hasCardOnFile = customer.has_payment_method || false;\n\n// --- Feature Adoption ---\nconst adoptionScore = (featureUsage / (customer.available_features || 1)) * 100;\nconst apiCalls = customer.api_usage || 0;\nconst storageUsage = customer.storage_used_percent || 0;\n\n// --- Anomaly Flags ---\nconst suspiciousLogin = loginTrend < -5; // drop in logins\nconst noPurchaseInDays = daysLastPurchase > 60;\nconst lowEngagement = loginFrequency < 2; // less than 2 logins/month\nconst highSupportVolume = supportTickets > 5;\nconst negativeSpendTrend = spendTrend < -0.3; // 30% drop in spend\n\n// --- Compile feature vector ---\nconst features = {\n customer_id: customer.id,\n email: customer.email,\n signup_date: customer.signup_date,\n plan_type: customer.plan_type,\n \n // Core features\n login_frequency: loginFrequency,\n login_trend: loginTrend,\n days_last_purchase: daysLastPurchase,\n total_spend: totalSpend,\n avg_transaction_value: avgTransactionValue,\n spend_trend: spendTrend,\n account_age_days: accountAge,\n \n // Engagement\n support_tickets: supportTickets,\n feature_usage_count: featureUsage,\n adoption_score: adoptionScore,\n email_engagement: emailEngagement,\n api_calls: apiCalls,\n storage_usage_percent: storageUsage,\n \n // Subscription\n plan_value: planValue,\n is_free_tier: isFreeTier,\n days_until_renewal: daysUntilRenewal,\n has_card_on_file: hasCardOnFile,\n \n // Anomalies\n flag_suspicious_login: suspiciousLogin,\n flag_no_purchase_60days: noPurchaseInDays,\n flag_low_engagement: lowEngagement,\n flag_high_support_volume: highSupportVolume,\n flag_negative_spend_trend: negativeSpendTrend,\n \n // Calculated risk indicators\n anomaly_count: [suspiciousLogin, noPurchaseInDays, lowEngagement, highSupportVolume, negativeSpendTrend].filter(Boolean).length\n};\n\nreturn {\n json: features\n};"},"typeVersion":2},{"id":"a6e2a4e3-05e5-426f-b6e6-8502ea446f3d","name":"Call ML churn prediction model","type":"n8n-nodes-base.httpRequest","position":[1952,496],"parameters":{"url":"https://ml-api.example.com/predict/churn","method":"POST","options":{},"sendBody":true,"bodyParameters":{"parameters":[{"name":"features","value":"={{ {login_frequency: $json.login_frequency, login_trend: $json.login_trend, days_last_purchase: $json.days_last_purchase, total_spend: $json.total_spend, spend_trend: $json.spend_trend, support_tickets: $json.support_tickets, adoption_score: $json.adoption_score, anomaly_count: $json.anomaly_count} }}"}]}},"typeVersion":4.2},{"id":"241db3b1-befb-4b8e-9758-f84ae5d600c8","name":"Score and classify churn risk","type":"n8n-nodes-base.code","position":[2176,496],"parameters":{"mode":"runOnceForEachItem","jsCode":"// Score churn risk and classify into risk tiers\nconst data = $input.item.json;\nconst mlPrediction = data.churn_probability || 0; // 0-1 scale\n\n// Convert to 0-100 scale\nconst churnScore = Math.round(mlPrediction * 100);\n\n// Classification logic\nlet riskLevel = 'LOW_RISK';\nlet actionRequired = false;\nlet campaignType = 'none';\nlet priority = 'low';\n\nif (churnScore >= 70) {\n riskLevel = 'HIGH_RISK';\n actionRequired = true;\n campaignType = 'aggressive_retention';\n priority = 'critical';\n} else if (churnScore >= 50) {\n riskLevel = 'MEDIUM_RISK';\n actionRequired = true;\n campaignType = 'engagement_boost';\n priority = 'high';\n} else if (churnScore >= 30) {\n riskLevel = 'EARLY_RISK';\n actionRequired = false;\n campaignType = 'preventive';\n priority = 'medium';\n}\n\n// Risk factors summary\nconst riskFactors = [];\nif (data.flag_no_purchase_60days) riskFactors.push('No purchase in 60+ days');\nif (data.flag_low_engagement) riskFactors.push('Low login frequency');\nif (data.flag_negative_spend_trend) riskFactors.push('Declining spend');\nif (data.flag_high_support_volume) riskFactors.push('High support tickets');\nif (data.flag_suspicious_login) riskFactors.push('Unusual login activity');\n\n// Retention strategy\nlet recommendedAction = '';\nif (churnScore >= 70) {\n recommendedAction = 'Immediate outreach: Offer discount, upgrade incentive, or VIP support';\n} else if (churnScore >= 50) {\n recommendedAction = 'Engagement campaign: Personalized tutorials, feature highlights';\n} else if (churnScore >= 30) {\n recommendedAction = 'Monitor closely: Nurture with educational content and updates';\n}\n\nconst timestamp = new Date().toISOString();\nconst predictionDate = new Date().toLocaleDateString('en-US', { year: 'numeric', month: 'long', day: 'numeric' });\n\nreturn {\n json: {\n // Customer & Prediction Identity\n customer_id: data.customer_id,\n email: data.email,\n plan_type: data.plan_type,\n \n // Churn Score & Classification\n churn_probability_score: churnScore,\n churn_probability_percentage: churnScore + '%',\n risk_level: riskLevel,\n risk_level_numeric: riskLevel === 'HIGH_RISK' ? 3 : riskLevel === 'MEDIUM_RISK' ? 2 : riskLevel === 'EARLY_RISK' ? 1 : 0,\n \n // Key Metrics\n login_frequency: data.login_frequency,\n days_since_last_purchase: data.days_last_purchase,\n total_lifetime_value: data.total_spend,\n spend_trend_percent: Math.round(data.spend_trend * 100),\n support_tickets_90days: data.support_tickets,\n feature_adoption_score: Math.round(data.adoption_score),\n account_age_days: data.account_age_days,\n \n // Risk Indicators\n risk_factors: riskFactors,\n risk_factor_count: riskFactors.length,\n anomaly_flags_count: data.anomaly_count,\n \n // Action & Campaign\n action_required: actionRequired,\n campaign_type: campaignType,\n priority: priority,\n recommended_action: recommendedAction,\n \n // Timestamp\n prediction_date: predictionDate,\n prediction_timestamp: timestamp,\n \n // Original features for reference\n features: data\n }\n};"},"typeVersion":2},{"id":"e37bc7ba-bd03-41d3-9be4-0744bbb5b2d7","name":"Route by risk level","type":"n8n-nodes-base.switch","position":[2400,480],"parameters":{"rules":{"values":[{"outputKey":"High Risk","conditions":{"options":{"leftValue":"","caseSensitive":true,"typeValidation":"strict"},"combinator":"and","conditions":[{"operator":{"type":"string","operation":"equals"},"leftValue":"={{ $json.risk_level }}","rightValue":"HIGH_RISK"}]},"renameOutput":true},{"outputKey":"Medium Risk","conditions":{"options":{"leftValue":"","caseSensitive":true,"typeValidation":"strict"},"combinator":"and","conditions":[{"operator":{"type":"string","operation":"equals"},"leftValue":"={{ $json.risk_level }}","rightValue":"MEDIUM_RISK"}]},"renameOutput":true},{"outputKey":"Low Risk","conditions":{"options":{"leftValue":"","caseSensitive":true,"typeValidation":"strict"},"combinator":"and","conditions":[{"operator":{"type":"string","operation":"equals"},"leftValue":"={{ $json.risk_level }}","rightValue":"EARLY_RISK"}]},"renameOutput":true}]},"options":{}},"typeVersion":3},{"id":"c276d68c-d114-4894-a2c1-7b7a2f620762","name":"Create retention campaign task","type":"n8n-nodes-base.httpRequest","position":[2624,400],"parameters":{"url":"https://api.example.com/campaigns/create","method":"POST","options":{},"sendBody":true,"bodyParameters":{"parameters":[{"name":"customer_id","value":"={{ $json.customer_id }}"},{"name":"campaign_type","value":"={{ $json.campaign_type }}"},{"name":"churn_score","value":"={{ $json.churn_probability_score }}"},{"name":"risk_level","value":"={{ $json.risk_level }}"},{"name":"recommended_action","value":"={{ $json.recommended_action }}"},{"name":"priority","value":"={{ $json.priority }}"}]}},"typeVersion":4.2},{"id":"ca4e1fe7-be3f-4551-88f2-321fee4b8318","name":"Store churn predictions in database","type":"n8n-nodes-base.postgres","position":[2624,592],"parameters":{"table":"churn_predictions","schema":{"__rl":true,"mode":"list","value":"public"},"columns":{"value":{},"schema":[],"mappingMode":"autoMapInputData","matchingColumns":[],"attemptToConvertTypes":false,"convertFieldsToString":false},"options":{}},"credentials":{"postgres":{"id":"credential-id","name":"Postgres-test"}},"typeVersion":2.5},{"id":"77b5a3e0-50ac-4a7a-90fd-5df6a4245a73","name":"Generate churn analytics report","type":"n8n-nodes-base.code","position":[2848,496],"parameters":{"mode":"runOnceForEachItem","jsCode":"// Generate HTML report for churn predictions\nconst data = $input.item.json;\n\nconst statusColor = data.risk_level === 'HIGH_RISK' ? '#ef4444' \n : data.risk_level === 'MEDIUM_RISK' ? '#f59e0b' \n : '#22c55e';\n\nconst statusIcon = data.risk_level === 'HIGH_RISK' ? '🚨' \n : data.risk_level === 'MEDIUM_RISK' ? '⚠️' \n : '✅';\n\nconst reportHtml = `\n\n\n\n \n Churn Prediction Report - ${data.customer_id}\n \n\n\n
\n

${statusIcon} Customer Churn Risk Assessment

\n

Customer: ${data.email} | Plan: ${data.plan_type} | Generated: ${data.prediction_date}

\n
${data.risk_level.replace(/_/g, ' ')}
\n
\n\n
\n
${data.churn_probability_score}%
Churn Risk Score
\n
\\$${Math.round(data.total_lifetime_value)}
Lifetime Value
\n
${data.login_frequency.toFixed(1)}
Logins/Month
\n
${data.adoption_score}%
Feature Adoption
\n
\n\n
\n

📊 Key Metrics

\n \n \n \n \n \n
Days Since Last Purchase${data.days_since_last_purchase}
Spend Trend (90 days)${data.spend_trend_percent > 0 ? '+' : ''}${data.spend_trend_percent}%
Support Tickets (90 days)${data.support_tickets_90days}
Account Age${data.account_age_days} days
\n
\n\n
\n

⚠️ Risk Factors (${data.risk_factor_count})

\n \n
\n\n
\n

🎯 Recommended Action

\n
\n ${data.recommended_action}\n

\n Campaign Type: ${data.campaign_type.replace(/_/g, ' ').toUpperCase()}\n
\n
\n\n
AI Customer Churn Predictor | ${data.prediction_timestamp}
\n\n`;\n\nconst notificationMessage = `${statusIcon} *CHURN RISK ALERT — ${data.email}*\\n\\n` +\n `*Risk Level:* ${data.risk_level.replace(/_/g, ' ')}\\n` +\n `*Churn Score:* ${data.churn_probability_score}%\\n` +\n `*Lifetime Value:* \\$${Math.round(data.total_lifetime_value)}\\n\\n` +\n `*Key Metrics:*\\n` +\n `• Logins/Month: ${data.login_frequency.toFixed(1)}\\n` +\n `• Days Since Purchase: ${data.days_since_last_purchase}\\n` +\n `• Spend Trend: ${data.spend_trend_percent > 0 ? '+' : ''}${data.spend_trend_percent}%\\n` +\n `• Feature Adoption: ${data.adoption_score}%\\n\\n` +\n `*Risk Factors:* ${data.risk_factors.join(', ')}\\n` +\n `*Action:* ${data.recommended_action}`;\n\nreturn {\n json: {\n ...data,\n reportHtml,\n notificationMessage,\n notificationTitle: `${statusIcon} [${data.customer_id}] ${data.risk_level.replace(/_/g, ' ')} - Churn Score: ${data.churn_probability_score}%`,\n shouldNotify: data.action_required,\n shouldNotifyAll: true\n }\n};"},"typeVersion":2},{"id":"749cee1c-71ed-4c46-acf5-2bffb16fd094","name":"Filter at-risk customers","type":"n8n-nodes-base.filter","position":[3072,496],"parameters":{"options":{},"conditions":{"options":{"leftValue":"","caseSensitive":false,"typeValidation":"strict"},"combinator":"and","conditions":[{"id":"notify-condition","operator":{"type":"boolean","operation":"true"},"leftValue":"={{ $json.action_required }}"}]}},"typeVersion":2},{"id":"859e2c70-dded-45ee-a8a0-118a16762657","name":"Post churn alert to Slack","type":"n8n-nodes-base.httpRequest","position":[3296,400],"parameters":{"url":"YOUR_SLACK_WEBHOOK_URL","method":"POST","options":{},"jsonBody":"={\n \"text\": \"{{ $json.notificationTitle }}\",\n \"blocks\": [\n {\n \"type\": \"section\",\n \"text\": {\n \"type\": \"mrkdwn\",\n \"text\": \"{{ $json.notificationMessage }}\"\n }\n }\n ]\n}","sendBody":true,"specifyBody":"json"},"typeVersion":4.2},{"id":"c871536b-fb9f-4b73-ba8e-9666959da6ab","name":"Email report to customer success team","type":"n8n-nodes-base.emailSend","position":[3296,592],"webhookId":"b4c1a117-f1bd-46e9-8bda-063bd135bebd","parameters":{"html":"={{ $json.reportHtml }}","options":{},"subject":"={{ $json.notificationTitle }}","toEmail":"user@example.com","fromEmail":"noreply@example.com"},"credentials":{"smtp":{"id":"credential-id","name":"SMTP -test"}},"typeVersion":2.1},{"id":"f6b94a2d-c388-4596-9438-f8b593307b22","name":"Log analysis completion","type":"n8n-nodes-base.code","position":[3520,496],"parameters":{"mode":"runOnceForEachItem","jsCode":"// Log churn analysis completion\nconst timestamp = new Date().toISOString();\nconst data = $input.item.json;\n\nconsole.log(`✅ CHURN ANALYSIS: Customer ${data.customer_id} | Risk: ${data.risk_level} | Score: ${data.churn_probability_score}% | Action: ${data.campaign_type} | Time: ${timestamp}`);\n\n// Aggregate statistics\nconst stats = $getWorkflowStaticData('global').stats || {\n totalAnalyzed: 0,\n highRiskCount: 0,\n mediumRiskCount: 0,\n lowRiskCount: 0,\n avgChurnScore: 0,\n lastUpdated: null\n};\n\nif (data.risk_level === 'HIGH_RISK') stats.highRiskCount++;\nelse if (data.risk_level === 'MEDIUM_RISK') stats.mediumRiskCount++;\nelse stats.lowRiskCount++;\n\nstats.totalAnalyzed++;\nstats.lastUpdated = timestamp;\n\n$getWorkflowStaticData('global').stats = stats;\n\nreturn {\n json: {\n success: true,\n customer_id: data.customer_id,\n risk_level: data.risk_level,\n 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